Conference Agenda

See attached agenda

The atmosphere is an important factor that affects the accuracy of remote sensing radiation at night. Effective atmospheric correction for night-light satellite data is a prerequisite for realizing the quantitative application of night-light remote sensing. The atmospheric correction method based on the radiative transfer model is widely used during the day because of its clear physical meaning and high accuracy. The transmission mechanism of atmospheric radiation at night is the same as that under daytime conditions. The main difference between day transmission and night
transmission is that the radiation source at night is the moon. The brightness of the moon will change due to the changes of the moon phase angle and the moon-earth distances, which will affect the upward and downward radiation during transmission. Therefore, the accurate calculation of lunar radiation is the prerequisite for the use of atmospheric radiative transfer model to carry out high-precision atmospheric correction at night. MODTRAN (MODerate resolution TRANsmission) is designed with night radiance mode, which can simulate the radiative transmission of lunar light at night, but this mode has certain defects, mainly including that the model does not consider the changes of moon-earth distances, replacing sun-moon distance with sun-earth distance, and the moon phase function does not consider wavelength correlation, etc. This may introduce a certain error to the MODTRAN night radiance mode and reduce the accuracy of atmospheric correction. To solve the problem, this research couples the MODTRAN model and MT2009 to simulate radiative transmission at night at a high precision. Based on this model, this paper proposes an atmospheric correction method for night-light remote sensing data and demonstrates its application.

Quantification and control of NOx and CO2 emissions are essential across the world to limit adverse climate change and improve air quality. We present a new top-down method, an improved superposition column model to estimate day-to-day NOx and CO2 emissions from the large city of Wuhan, China, located in a polluted background. The latest released version 2.3.1 TROPOMI (TROPOspheric Monitoring Instrument) NO2 columns and version 10r of the Orbiting Carbon Observatory-2 (OCO-2)-observed CO2 mixing ratio are employed. We quantified daily NOx and CO2 emissions from Wuhan between September 2019 and October 2020 with an uncertainty of 31 % and 43 %, compared to 39 % and 49 % with the earlier v1.3 TROPOMI data, respectively. Our estimated NOx and CO2 emissions are verified against bottom-up inventories with minor deviations (<3 % for the 2019 mean, ranging from −20 % to 48 % on a daily basis). Based on the estimated CO2 emissions, we also predicted daily CO2 column mixing ratio enhancements, which match well with OCO-2 observations (<5 % bias, within ±0.3 ppm). We capture the day-to-day variation of NOx and CO2 emissions from Wuhan in 2019–2020, which does not reveal a substantial “weekend reduction” but does show a clear “holiday reduction” in the NOx and CO2 emissions. Our method also quantifies the abrupt decrease and slow NOx and CO2 emissions rebound due to the Wuhan lockdown in early 2020. This work demonstrates the improved superposition model to be a promising new tool for the quantification of city NOx and CO2 emissions, allowing policymakers to gain real-time information on spatial–temporal emission patterns and the effectiveness of carbon and nitrogen regulation in urban environments.

As the spatial resolution of space-borne imaging spectrometers is rapidly improving and moving towards sub-kilometre scale, three-dimensional (3D) cloud effects become more prominent in the retrieval of atmospheric trace gases. Currently in the Sentinel-5P (S5P) nitrogen dioxide (NO₂) product (3.6 km x 5.6 km resolution) the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm is used to retrieve a one dimensional (1D) horizontal homogeneous Lambertian cloud layer for cloud correction. However, in reality clouds are 3D objects, they are not spatially homogeneous in brightness, and they can have effects on neighbouring clear-sky pixels by casting shadows on lower clouds or on the ground surface or by scattering light into the pixels.

In the S5P NO₂ retrieval algorithm the retrieved slant column density is translated to a vertical column density (VCD) by correcting for the light path using pre-calculated air-mass factors (AMF) from a 1D radiative transfer model (DAK), using surface and cloud parameters as input. When a cloud shadow is cast over a clear-sky pixel the downward light intensity is reduced, altering the average observed light path. This lowers the sensitivity of the measurement for the lower atmospheric layers and thus influences the AMF and the resulting NO₂ VCD.

We attempt to observe such cloud shadow effects in the AMF and VCD fields in the S5P NO₂ data with focus on hot-spot areas during winter when generally more clouds are present, and the cast cloud shadow surface areas are relatively high due to higher solar zenith angles. SUOMI-NPP VIIRS data can be used to identify the pixels affected by cloud shadows.

In addition, we use a vectorised 3D Monte Carlo radiative transfer model (MONKI), developed at KNMI, to simulate different cloud scenarios and calculate the 3D AMF and NO₂ VCDs. The 3D cloud effects on the NO₂ retrieval are then investigated and quantified by comparing the 3D results to their 1D counterparts.

Current satellite-based methods for measuring aerosols require a homogeneous surface and pre-assumed surface albedo or reflectance, which is not suitable for urban areas with highly inhomogeneous surfaces. However, with the development of high-resolution satellites, building shadows can be clearly identified in satellite images. A new algorithm has been proposed to retrieve aerosol optical depth and surface albedo by using building shadows and adjacent sun-shined bright pixels. The algorithm was validated using GF-2 satellite images with a spatial resolution of 4 meters and successfully retrieved AOD and surface albedo values for locations near the Beijing Olympic Center. The AOD derived from the shadow method were found to be in close agreement with those obtained from ground-based CIMEL sun-photometer measurements, with differences of less than±0.03. The results indicate that the shadow method can accurately retrieve aerosol data over megacities at a finer spatial resolution.

The Greenhouse gases Monitoring Instrument (GMI) on Chinese hyperspectral satellite GF5-02 can provide more abundant observations of global atmospheric CO₂ which plays an important role in climate research. CO₂ retrieval precision is the key to determine the application value of the GMI. In order to reduce the influence of atmospheric scattering on retrieval, we combined the Directional Polarimetric Camera (DPC) data on the same satellite to improve the anti-interference ability of GMI's CO₂ retrieval and ensure its retrieval precision. To realize the reliability and feasibility of the collaborative use of GMI and DPC, this paper designs the pointing registration method of the GMI based on the coastline observations, the spatial resolution matching method and the collaborative cloud screening method of the GMI and DPC observations. With the combination of DPC which supplied the spectral data and aerosol product, the retrieval ability of the Coupled Bidirectional reflectance distribution function CO₂ Retrieval (CBCR) method developed for GMI CO₂ retrieval was improved as the retrieval efficiency of CO₂ products increased by 27% and the CO₂ retrieval precision increased from 3.3 ppm to 2.7 ppm. Meanwhile, the collaborative use not only guaranteed the GMI's ability to detect global and area CO₂ concentration distribution characteristics like the significant concentration differences between the northern and southern hemispheres in winter and high CO₂ concentration in urban agglomeration areas caused by human activities, but also extended GMI’s potential of monitoring anomalous events like Tonga volcanic eruption.

In the last years, several researchers demonstrated the capability of a Synthetic Aperture Radar (SAR) to estimate the so-called Atmospheric Phase Screen (APS) accurately. Amplitude images are loosely affected by atmospheric conditions in the path from the satellite to the ground. On the other hand, variations in the refractive index in the medium primarily affect the phase of a coherent radar system. In SAR Interferometry (InSAR), the atmosphere is seen as a disturbance for estimating ground deformation. Therefore, the APS is generally removed or mitigated using Numerical Weather Prediction Models (NWPM) or data-driven methods exploiting the spatiotemporal statistics of the atmospheric signal.

However, the definition of signal and noise depends on the application at hand. While geologists define the deformation as a signal and the APS as noise, it is the inverse for meteorologists. It has been proved that APS can be used as an input dataset to NWPM with measurements from radio-sonde, ground-based weather radars, Global Navigation Satellite Systems (GNSS), ground-based weather stations, and more. SAR data is beneficial when the other measurements are unavailable or unreliable to provide high-quality input to NWPM.

However, the APS estimated using a SAR system must be properly calibrated before the ingestion process into NWPM. In particular, one of the most dangerous aberrations is the one that springs from an error in the knowledge of the platform trajectory during image acquisition. Even a tiny deviation in the order of a few centimeters can generate large-scale trends in the derived APS. The trend can generally be modeled as a plane added to the true APS map, often called Orbital Phase Screen (OPS). Very low spatial frequency aberrations are the most dangerous in an NWPM. In fact, such systems are programmed to work on a continental scale at low resolutions, taking advantage of very large-scale signals that must be error-free. The further problem is that the filtering of OPS is not trivial at all. The atmospheric signal is often a large-scale trend, and when signal and noise share the same statistics, their separation is impossible. One could attempt to remove the OPS by fitting a 2D plane into the atmospheric map and remove it, with the risk of also removing part of the APS.

In this poster, we propose a solution to the problem encompassing the usage of a network of GNSS stations on the ground. The raw data from each station is processed to extract a GNSS-derived APS. Then, the SAR-derived APS is extracted on the spatial location of the GNSS stations. Such measurements are the sum of the true APS and the orbital error. We use the GNSS-derived APS as a ground truth, removing them from the SAR-derived estimates leading to a set of measurements of the pure orbital error. An inverse problem is solved, leading to two parameters characterizing the orbital error. The benefit of this inversion is double. First of all, the two estimated parameters can be used to provide a quality proxy for the trajectory. Second, the two parameters can be used to compute the forward model on the whole grid of the APS map (and not just on the set of GNSS stations as done before), leading to a calibration phase screen.

The procedure is tested using a dataset of more than 30 Sentinel-1 images and a network of GNSS stations in Sweden. The algorithm shows excellent performance. The validation process compares a set of independent GNSS stations with the SAR-derived APS before and after the calibration procedure. A second validation is carried out using a separate NWPM showing, once again, very good performances.

Multi-Platform NESZ Estimation over Land
Naomi Petrushevsky (1), Marco Manzoni (1), Andrea Virgilio Monti-Guarnieri (1), Stefano Tebaldini (1)
(1) Department of Electronics, Information and Bioengineering; 20133 - Politecnico di Milano, Milan, Italy.
As the space economy grows and new satellites are constantly launched, fast and robust performance measures are of great interest. For example, the expected launch of Sentinel-1C by the European Space Agency will require tools to validate the new sensor in short time. Noise Equivalent Sigma Zero (NESZ) is an important property of a Synthetic Aperture Radar (SAR) system related to its noise floor. It defines the equivalent backscatter coefficient which would produce the actual noise power in the focused data. Noise properties depend mainly on the sensor’s inner circuits and may change from Its nominal values. Thus, NESZ should be estimated directly from the data.
Standard techniques exploit the smoothness of water bodies, causing all the energy from the radar to deflect, allowing the measurement of noise levels directly. However, calm waters are very different from standard land scenarios in terms of received power and surface temperature, so the applicability of the estimate to the general case may be inaccurate. Also, the approach requires procuring special data, which is generally not of interest for land monitoring.
An alternative approach for NESZ estimation exploits an interferometric pair of images over land. Both images should be radiometrically calibrated beforehand, converting the received intensity to backscatter and correcting fluctuations due to the antenna pattern. The method is based on the inverse relation between coherence and the NESZ. From the 2D histogram w.r.t backscatter and coherence, it is possible to fit the model and obtain the noise level, but only if both master and slave images are acquired by the same satellite. If two different platforms are used, each may have different gains and thermal noise. In this case, we propose an extension to the method, given that one satellite is already in orbit for a long time, such that a stack of master images is available.
First, we use the stack to generate a set of interferograms with short temporal baselines. Each interferogram is used to measure the master’s NESZ, and all measures are averaged to improve robustness. After obtaining an accurate estimate for the master, we proceed to process the interferogram between the master and the new satellite. By repeating for each incidence angle, the NESZ profile of the new satellite can be characterized.
Estimating NESZ for a new sensor was validated and tested using a stack of Sentinel-1A (S1A) and Sentinel-1B (S1B) images. First, the noise level of S1B was obtained separately according to Equation (1). Then, S1B’s NESZ was estimated by the procedure described above, using the stack of S1A data. The comparison between the two results confirmed that the noise level of a new satellite could be characterized over land, with as little as one available product.

In this paper, we compare two techniques for estimating forest height and vertical structure using airborne synthetic aperture radar (SAR) data, namely SAR tomography (TomoSAR) and the phase histogram (PH) technique. Using multiple SAR images, TomoSAR allows for a direct imaging of the three-dimensional (3D) electromagnetic structure of the vegetation layer, from which biophysical parameters such as forest height and terrain topography can be extracted[1], [2]. The PH technique assigns each pixel in a SAR interferogram to a specific height bin based on the value of the interferometric phase, allowing for a local estimation of the vertical profile of forest scattering by accumulation of pixels fall within a given spatial window[3]–[5].

The aim of this paper is to study the connection between TomoSAR and the PH technique on an experimental ground by analyzing L-Band tomographic data from the ESA airborne campaign TomoSense, flown in 2020 at the Kermeter area in the Eifel Park, North-West Germany[6]. The data analyzed in this paper feature 30+30 overpasses acquired along two opposite flight headings, and provide a vertical resolution consistently better than 5 m on the whole area of interest.

Results indicate that the PH technique can only loosely approximate the vertical structure produced by SAR tomography, but it can be used to produce a fairly good estimate of forest height. In particular, TomoSAR and the PH technique are observed to produce an average root mean square error (RMSE) of 2.63 m and 4.35 m in NW flight data, and 1.84 m and 5.46m in SE flight data, respectively. The observed results are interpreted in light of a simple physical model to predict phase variations in the two cases where forest scattering is determined by the presence of a dominant scatterer at each resolution cell or by a multitude of elementary scatterers, leading to the conclusion that the PH technique is best fit for the case of high- or very high-resolution data at higher frequency bands. Overall, the analysis in this paper demonstrates, both theoretically and experimentally, that the PH technique cannot achieve the same performance as multi-baseline tomography when applied to lower frequency data at a resolution of few meters. Yet, even in these conditions we remark that the PH technique allows for the retrieval of forest height based on a single interferogram at a single polarization. This makes the PH technique extremely interesting in the context of spaceborne missions.

The introduction of SAR tomography has opened the way to a completely new approach to look at SAR data, providing evidence of the possibility to directly image the 3D structure of natural media such as forests, snow, and ice. As of today, the benefit of Tomographic imaging has been demonstrated experimentally based on airborne data in the context of different environmental applications, including estimation of forest height and Above Ground Biomass, retrieval of snowpack depth, density, and internal layering, and monitoring the internal structure of alpine glaciers and ice sheets. Despite the many successful experimental campaigns, spaceborne tomography is yet to come for what concerns of natural scenarios. This is largely due to the fact that that the vertical resolution provided by SAR tomography is inherently linked to the number of available viewpoints, which - for the case of a single satellite - corresponds to the number of orbits over a given area. It is then clear that the success of spaceborne TomoSAR is crucially linked to the possibility to fly multiple sensors at the same time. Concrete signs in this direction have appeared in recent years, since advances in electronics and antenna technologies have made SAR payload compatible with small satellites.

In this context, activities are being carried out at Politecnico di Milano to develop specific signal processing algorithms for the implementation of a tomographic demonstrator based on the use of a small fleet of Unmanned Aerial Vehicles (UAVs) carrying Radio-Frequency devices. Specifically, in this paper we introduce a novel approach to the problem of focusing SAR data in the presence of a poor knowledge of platform trajectories. This is especially the case of UAV-based systems, which often employ low-cost navigational units.

The proposed algorithm is a geometrical evolution of the well-known Phase Gradient Algorithm (PGA) [1]. PGA is an iterative algorithm that tries to estimate the gradient of the unknown phase error based on SAR data at selected points. Four processing steps are required to compensate for the phase errors, these are: circular shifting, Fourier Transform over a selected window, phase gradient estimation, iterative correction. The PGA approach is based on the intrinsic assumption that the same phase correction applies to any point in the imaged scene. While this hypothesis can be approximately retained for a spaceborne geometry, it is surely non valid for a low altitude platform, for which the variation of incidence and squint angle determines space-varying phase errors.

In our approach, the processing steps within the PGA are re-interpreted on a rigorous geometrical basis. Circular shifting and Fourier Transforming are replaced by a defocusing operator that allows to measure the phase history of selected points. Phase gradient estimation is replaced by a direct estimation of platform trajectory. Final image correction is then carried out by refocusing the data according to the estimated trajectory. In so-doing, the proposed algorithm is intended to achieve the accuracy and efficiency of the PGA, while granting a rigorous geometrical approach as in [2],[3].

The algorithm has been applied in two real-world cases: 1) bistatic L-Band data acquired by operating a fixed transmitted and flying a receiver onboard a UAV; 2) monostatic P-Band acquired during the ASI helicopter borne campaign in [4].

Results indicate that the proposed approach can successfully correct trajectory errors when present, while it does not produce further degradation in the case where navigational data are accurate.

[1] D.E. Wahl, P.H. Eichel, D.C. Ghiglia, and C.V. Jakowatz. Phase gradient autofocus a robust tool for high resolution sar phase correction. IEEE Transactions on Aerospace and Electronic Systems, 30(3):827–835, 1994

[2] Hubert M. J. Cantalloube and Carole E. Nahum. Multiscale local map drift driven multilateration sar autofocus using fast polar format image synthesis. In 8th European Conference on Synthetic Aperture Radar, pages 1–4, 2010.

[3] Jan Torgrimsson, Patrik Dammert, Hans Hellsten, and Lars M. H. Ulander. Sar processing without a motion measurement system. IEEE Transactions on Geoscience and Remote Sensing, 57(2):1025–1039, 2019.

[4] Stefano Perna et al. The asi integrated sounder-sar system operating in the uhf-vhf bands: First results of the 2018 helicopter-borne morocco desert campaign. Remote Sensing, 11(16), 2019.

The Paris Agreement, adopted in 2015, requires monitoring of anthropogenic greenhouse gas (GHG) emissions and assessment of collective climate mitigation efforts. Several space-based carbon dioxide (CO₂) monitoring measurement systems have become available since 2009, including Japan’s GOSAT and GOSAT-2 and NASA’s OCO-2 and OCO-3. China’s first CO₂ measurement satellite mission, TanSat, was launched in December 2016.

In this work, we analyze anthropogenic emissions signatures using TanSat as well as OCO-2/3 measuring systems. The space-based CO₂ observations are analyzed together with the European Copernicus Sentinel-5 Precursor (S5P) TROPOMI nitrogen dioxide (NO₂) measurements as nitrogen oxides are often co-emitted with CO₂. In the future, satellite constellation missions will focus on carbon dioxide released into the atmosphere specifically through human activity. The future missions include China’s TanSat-2, Japan’s GOSAT-GW and the Copernicus Carbon Dioxide Monitoring mission CO2M.

Greenhouse gas emissions from anthropogenic activities are the main driver of current global climate change. Emission monitoring is essential for the verification of emission reduction efforts and a feasible way for attaining global coverage are satellite observations. Recent developments in space-based hyperspectral cameras open up new possibilities for greenhouse gas emission monitoring also on a smaller scale.
Most of the anthropogenic greenhouse gas emissions originate from urban areas. Urban areas are also sources of co-emitted atmospheric aerosols, which decrease the local air quality and complicate the atmospheric radiative transfer. Even slight concentrations of atmospheric aerosols can cause considerable inaccuracies in space-based remote sensing observations of carbon dioxide (CO2).

In this work, we present a novel retrieval method for a co-emitted CO2 and aerosol emission plume content originating from a point source observed from a satellite. We plan to test the method for a joint CO2 and aerosol retrieval and emission rate estimation from satellite-based hyperspectral imaging data, such as imagery obtained using PRISMA or EMIT. The solar and viewing angle dependent radiative coupling of adjacent camera pixels and co-emission of aerosols are investigated as means to improve the CO2 retrieval process.

Additionally, the prospect of optimizing radiative transfer (RT) calculations by preliminary wavelength pruning is examined. The presented approach reduced the amount of needed wavelengths in the calculation by 15 – 45 % in the tested cases and generalizes to arbitrary spectral observations.

As part of this work, a space-based hyperspectral imaging simulator is developed. The GPU-based simulator outputs top-of-the-atmosphere radiances in near- to shortwave-infrared wavelengths and thus enables a rapid retrieval of atmospheric constituents in a 3D atmosphere.

In the summer of 2022, nearly half of mainland China experienced a heatwave with a severity not experienced since 1961, with temperatures reaching 45oC in some parts of the country. The accompanying widespread drought, the worst since 1954, caused some of China’s main rivers, including part of Yangtze river, to dry up. This led to reduced hydropower generation, interrupted shipping, reduced agriculture and factory outputs, and severely impacted drinking water supplies to millions of people, livestock and wildlife. This nationwide drought will have likely caused widespread disturbances to carbon balance. Reduced hydropower generation resulted in higher GHG emissions from thermal power plants to meet energy demands. Low soil moisture and heat stress will have impacted carbon sequestration from the land biosphere. These impacts have not yet been quantified but are of great interest to the wider public because they illustrate how Chinese GHG emissions might change in the future as extreme climate events becomes more frequent.

Satellites developed in the last decade, such as the Japanese GOSAT and the NASA OCO-2, provide continuous monitoring of atmospheric greenhouse gases at the global scale, with unprecedented precision. We interpret those data to infer geographical distributions of CO2 and methane fluxes over mainland China. We put the fluxes inferred for 2022 during the extreme drought into context of fluxes in recent years.

Maritime transport plays a vital role in national trade, and the improvement of ship transport capacity, while boosting China's economic development, has also exacerbated air pollution in ports, coastal, river and surrounding areas. Due to the large number of domestic inland river vessels, limited legislation for emission control and no monitoring infrastructure, information on inland river vessel emissions is very limited. Taking the Yangtze River in the region of Nanjing as research area, the STEAM algorithm is used to calculate the emissions of inland vessels in Nanjing area one by one according to the real-time information received by the Automatic Vessel Identification System (AIS), the relevant basic data of ships provided by the China Classification Society (CCS) database and the relevant data of field research. The temporal and spatial characteristics of inland ship emissions are analyzed. Combined with the hourly meteorological data of Nanjing meteorological station, the estimated ship emissions were compared with MAX-DOAS data to explore the contribution of inland river ship emissions to air pollution. Using this comparison, we analyzed the relative effects of ship emissions on densely populated areas around rivers.

During the Research on the Simulation and Mechanism of the impacts of Black Carbon on Climate and Environment atmospheric measurement campaign carried out near Nanjing, China in June 2018, a lightweight, accurate nitrogen dioxide (NO₂) sensor was attached to a quadcopter to measure vertical profiles of NO₂. Between 1 and 14 June 2018, ∼50 vertical NO₂ profiles were measured inside the planetary boundary layer up to an altitude of 900-1300 meters during 13 subsequent measurement days. Six NO₂ soundings were conducted on a daily basis at approximately 8 AM (morning), 12 & 4 PM (afternoon), 8 PM (evening) and 12 & 4 AM (night). The NO₂ measurements were calibrated using a scaling factor derived from a side-by-side inter comparison with a commercial NO₂ analyzer operated by NUIST prior to the start of the campaign. These measurements clearly demonstrate the diurnal cycle of NO₂, including the emergence of elevated concentrations close to the surface during the night and early morning and the mixing of the boundary layer from sunrise onward resulting in flat NO₂ vertical profile shapes with lower concentrations. The in-situ NO₂ vertical profile shapes were compared to NO₂ profile information retrieved from nearby MAX-DOAS observations as well as computed using the CHIMERE chemistry-transport model. This comparison demonstrates that in-situ quadcopter measurements could play an important role in the validation of future geostationary satellites since the diurnal cycle of NO₂ will have an impact on the accuracy of the satellite retrievals and is not always flawlessly captured by commonly used measurement techniques and models.

We analyzed a great deal of SAR images acquired over the German part of the Wadden Sea by the L-, C-, and X-band SARs aboard ALOS-2, Radarsat-2 and Sentinel-1, and TerraSAR-X, respectively. Using this wide range of multi-frequency / multi-polarization SAR data we investigated which combinations of radar band and polarization are best suited for a classification of different Wadden Sea surface types, including sandy and muddy sediments, sea grass meadows, and bivalve beds. New parameters, based on a decomposition of the complex SAR data, were used as input into a UNet-based semantic segmentation network with a texture-enhancement (TE) module to classify intertidal sediments and habitats. The experiment results verified the superiority of TE-UNet model compared with state-of-the-art semantic segmentation models.

Oceanic eddies are widely distributed in the global ocean, they play a crucial role in the global ocean energy cycle, the transport of heat and salt, and the distribution of nutrients. Synthetic Aperture Radar (SAR) is an ideal sensor for studying ocean eddies due to its high spatial resolution and independence of daytime and weather conditions. This paper proposes a method based on the deep learning networks of the YOLO family, named EOLO, to detect and extract geographic information of ocean eddies on C-band spaceborne SAR images. Several key enhancements were made to improve the performance of EOLO, including the introductions of spatial attention mechanism and new up-sampling operator, and improvements of the feature fusion method, loss function, and anchor box size, which contribute to an average precision of 91.5%. We also conducted experiments in the Baltic Sea, the Red Sea, and the Western Mediterranean Sea to verify the generalization of EOLO in different seas, reaching 95.7%, 96.8%, and 96.5% precision respectively. Furthermore, 8569 eddies were extracted by EOLO in the Western Mediterranean Sea in 2021, compared with the eddies from the altimeter data, the results show that the SAR eddies based on EOLO can detect at least 45% of the ocean eddies invisible to altimeters and are more realistic.

We introduce a neural network for the automatic detection of waterlines on Sentinel-1A/B SAR imagery of intertidal flats. The neural network is able to segregate water from intertidal flats for certain weather conditions in the German North Sea coast with high precision for calm weather conditions.
To simultaneously detect large structures and achieve a high resolution, the neural network consists of two stages of resolution. The neural network is structured as an image-to-image network and takes radar images and an ordinary land-water mask as input. The first stage produces a low resolution (640x640 m) allocation, whether a depicted area contains mostly land, mostly water or approximately equal parts. The allocation from the first stage is added to the input for the second stage, clarifying where islands and tideways are located. The second stage considers small parts of the radar image in a high resolution, accurately segregating water from intertidal flats at the resolution of the radar image (10x10m).
After re-merging the image, flood filling is performed to eliminate minor inaccuracies. However, relevant parts can be negated by this procedure. A more detailed land-water mask might mitigate that problem.

The tangential wind speed increases from the center to the eyewall of tropical cyclones (TC) along the radial direction and begins to decay when it extends outward. The tangential wind profile model is one of the most effective and widely used methods to reconstruct the TC radial wind speed. This paper proposes a parametric tangential wind profile (TWP) model based on high-spatial-resolution SAR imagery. The new model functions are piecewise with maximum tangential wind speed as a threshold, and all of them are designed as nonlinear. Notably, the derivative at the segmentation threshold is zero to ensure a smooth transition of the estimated wind speed profile. With the SAR-derived azimuth-averaged wind speed, we can determine the model parameters and get the tangential wind speed. The TWP model outperforms the commonly used SMRV model, as it better resolves the tangential wind profile shape as depicted by both SAR-derived winds and hurricane hunter Stepped-Frequency Microwave Radiometer (SFMR) derived winds. A comprehensive analysis of the TWP model parameters is carried out by fitting tangential winds for 620 hurricane hunter flights. Interestingly, the tangential wind profiles for major hurricanes show a similar shape. The proposed TWP model can be used for improved TC characterization and forecasting purposes.

Although The SAR is unanimously recognized as a key operational remote sensing instrument for oil spill surveillance and damage assessment owing to its all-day and almost all-weather observation ability together with its fine spatial resolution, the estimation of ancillary information related, e.g., to oil’s thickness and fraction water content is still a challenging task. Crude oil slicks and their emulsions can form thick layers ranging from micrometer to millimeter and can even reach centimeter thickness in the case of fresh oil under low sea state conditions [5]. The oil thickness distribution is as important as the spatial distribution of oil slicks, which is beneficial for proper choice of response methods and spatial allocation of response resources, as well as for legal purposes for prosecution.

In this study, the potential of the electromagnetic scattering model to retrieve quantitatively parameters of oil spills on the sea surface is investigated using the Artificial Neural Network (ANN) technique. In the SAR image plane, oil slicks appear as dark patches compared with ambient clean sea surface. This is due to both geometrical and electrical effects of the oil slick which, one on side, damps the short-gravity and capillary sea waves resulting in a lower backscattered signal and, on the other side, if the slick is thick enough or emulsified it can also change dielectric properties of the upper sea layer. To extract information about the oil slick from SAR imagery while limiting the effects of sea state conditions and incidence angle, the damping ratio (DR), i.e., the slick-free to slick-covered backscattering ratio, has been widely adopted. The backscattering DR of the oil slick is predicted using the AIEM augmented with MLB, effective dielectric constant model, and the composite medium model to include the effect of an oil slick. The simulated DR and their corresponding oil parameters, namely the oil thickness and seawater volume fraction, are utilized to train and test the NN with a relatively simple four-layer structure. The rationale consists of overcoming the lack of ground information using a forward scattering model. The adequately trained NN is then applied to the L-band UAVSAR image collected during the DWH oil spill accident to retrieve the slick thickness and volumetric fraction of seawater in the oil layer. The inversion results show that the thick emulsions are in the middle portion of the slick with 2 – 4 mm thickness surrounded by thin films with thickness less than 1mm. And the water content of the DWH oil slick is about 20% - 30%. Results are contrasted with optical data and previous studies of DWH oil spill and show qualitatively good agreement.

This study is supported by the ESA-NRSCC Dragon-5 cooperation project “Monitoring harsh coastal environments and ocean surveillance using radar remote sensing sensors” (ID 57979).

Ocean surface current (OSC) is one of the key marine dynamic elements which dominates the global circulation of carbon and heat. Measurements of the OSC are of particular significance for the studies and applications of marine environment monitoring, global climate change forecasting, marine search and emergency response, etc. [1-3]. By modulating ocean surface topography and roughness, the ocean currents could be characterized on synthetic aperture radar (SAR) images [4]. By far, two main-stream technical routines for OSC measurement based on SAR have been proposed, i.e., along-track interferometric SAR (along-track INSAR, ATI) [5, 6] and Doppler centroid anomaly (DCA) technique [7, 8]. Essentially, these techniques utilize the surface Doppler information to retrieve the corresponding velocity which are confronted with the challenge of separating the contributions from waves and currents. This requires a physical modeling of radar scattering from ocean current surface. Thus, in this study, we aim to numerically investigate the polarimetric SAR imaging responses to two-dimensional ocean surfaces with currents and waves. The well-developed radar imaging model (RIM) proposed by Kudryavtsev et al. [4] is employed to conduct the numerical simulations under various frequencies, incidence angles, wind speeds and full polarizations. The current surface with a typical internal wave phenomenon generated by the MITgcm numerical mode is used, which has resolution of about 1/200° in longitude direction and 1/60° in latitude direction. The current modulation of wave spectrum is considered in the KHCC03 spectrum. Experimental results indicate that current modulation on ocean scattering is more significant at lower wind speeds. It is also noted that the current modulation of ocean scattering performs most remarkable at cross-polarization, while has least effects at VV-polarization. The current modulation effect is nonlinear to current velocity. At large current velocity, the current modulation effect could be saturated, particularly for co-polarizations. More detailed numerical results will be given in the presentation.

Sea surface wind field is a parameter of key importance for several applications that span from weather forecast up to recreation activities. In addition, it also plays a role in the context of climate change as one of the effects of global warming is the increasing occurrence of strong winds that threaten coasts and critical infrastructures.

The wind can be estimated using either in situ or remotely sensed measurements. In the latter case, the main satellite remote sensor is the scatterometer, i.e., a meso-scale radar that is specifically designed to make normalized radar cross-section (NRCS) measurements that can be converted into the wind field using tailored geophysical model functions (GMFs).

There is an increasing interest in the exploitation of finer-spatial resolution NRCS measurements acquired by the Synthetic Aperture Radar (SAR), i.e.; an imaging radar that can achieve meter or sub-meter spatial resolutions even when operated by satellite. The main challenge relies on that fact that the SAR is not meant to be operationally used for wind field estimation. In fact, it can only provide one NRCS measure for each resolution cell and this makes the inversion of the sea surface wind field an under-determined problem.

To cope with this problem, two strategies are possible that rely on a wind direction information that is introduced into the inversion problem from either external source (e.g., scatterometer, ECMWF) or directly estimated from the SAR scene. This latter option is very promising since it allows forcing the wind field estimation process with a wind direction input on the same spatial grid of the wind to be estimated.

SAR-based wind direction has been addressed using different methods: a) the analysis of the orientation of the wind-induced streaks, using Fourier transforms [1],[2],[3], the discrete wavelet transform [4], the continuous wavelet transform [5] and gradient methods local [6]. In [7] a deep residual network is used to estimate the wind direction from the SAR images even in small turbulent areas, in the absence of streaks on the SAR images and in the presence of ships.

In this study, a sensitivity study is carried out to analyze the performance of neural network (NN) wind direction retrieval on Cosmo-SkyMed X - band SAR imagery. The main objective is to estimate the wind direction from the COSMO-SkyMed SAR imagery with artificial intelligence techniques. This is physically possible by exploiting wind-generated patterns in the SAR image, therefore by estimating the orientation of these patterns.

Experiments are carried out using 19 Cosmo-SkyMed SAR scenes that are augmented with ancillary spatially and time-collocated scatterometer information acquired between February and august 2022 in the North Sea.

References

[1] Wackerman, C., C. Rufenach, R. Schuchman, J. Johannessen, and K. Davidson, 1996: Wind vector retrieval using ERS-1 synthetic aperture radar imagery. IEEE Trans. Geosci. Rem. Sens., 34, 1343-1352.

[2] S. Lehner, J. Horstmann, W. Koch, and W. Rosenthal, “Mesoscale wind measurements using recalibrated ERS SAR images,” J. Geophys. Res., vol. 103, no. C4, pp. 7847–7856, Apr. 1998.

[3] P. W. Vachon and F. W. Dobson, “Validation of wind vector retrieval from ERS-1 SAR images over the ocean,” Global Atmos. Ocean Syst., vol. 5, pp. 177–187, 1996.

[4] Yong Du, Paris W Vachon & John Wolfe (2002) Wind direction estimation from SAR images of the ocean using wavelet analysis, Canadian Journal of Remote Sensing, 28:3, 498-509, DOI: 10.5589/m02-029

[5] Zecchetto, S., De Biasio, F., Della Valle, A., Quattrocchi, G., Cadau, E., Cucco, A., 2016a. Wind fields from C and X band SAR images at VV polarization in coastal area (Gulf of Oristano, Italy). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (6).

[6] Koch, W., 2004. Directional analysis of SAR images aiming at wind direction. IEEE Trans. Geosci. Remote Sens. 42 (4), 702–710.

[7] A. Zanchetta, S. Zecchetto, “Wind direction retrieval from Sentinel-1 SAR images using ResNet,” Remote Sensing of Environment, Volume 253, 2021, 112178, 2021.

Coastal macroalgae blooms have a profound influence on marine ecosystem balance, tourism, and aquaculture. Since 2008, the western coasts of the Yellow Sea have been damaged every summer by a green tide caused by the overgrowth of ulva prolifera. Remote sensing has been the primary tool for monitoring this green tide. With the emergence of more free synthetic aperture radar (SAR) images with high resolution and the ability to image in cloudy conditions, SAR images play an increasingly important role in green tide monitoring. Deep learning is a powerful method in remote sensing images classification. However, current studies mainly focus on the image's backscattering coefficient, while ignoring the morphological characteristics. Moreover, the proportion of algae-pixels and seawater-pixels is significantly unbalanced, which will reduce the learning ability of the deep learning method. To address these issues, we propose a deep learning method to detect ulva prolifera. The proposed model is designed on four sides: 1) We add texture features to extract the morphological characteristics of green algae. 2) We design a new loss function to maintain the learning ability of the proposed deep learning method. 3）we build a texture-enhanced path called texture concatenation to help extract Ulva prolifera with fuzzy boundaries. 4）we embed the convolutional block attention module (CBAM) after each convolution layer. For texture features, we calculated four representative gray level co-occurrence matrix (GLCM) maps of the VV polarized images, i.e., ASM, entropy, correlation, mean. Thus, the input dataset includes two polarization channels and four texture channels. For the new loss function, we used the combination loss of binary crossentropy and focal loss via different weights. To construct the proposed model, we labeled 6317/2124 pairs of Sentinel-1 SAR image patches as the training/testing dataset. All of the used images were preprocessed through radiometric calibration, speckle filtering, terrain correction, and incident angle effect correction. Experiments show that when binary crossentropy is weighted for 0.70 and focal loss for 0.30, the model performs best, with a mean intersection over union (mIOU) of 86.31%, outperforming the well-known segmentation models using the identical dataset and hyperparameters. In addition, we also used the proposed model to analyze the interannual variation of green tide in 2019-2021, and found that 2019(2020) has the longest (shortest) bloom duration and biggest (smallest) coverage area; 2021(2020) has the biggest (smallest) nearshore damage to the southern coastlines of the Shandong Peninsula.

The detection and monitoring of ground surface changes using multi-temporal, remotely-sensed images represent the most important applications of remote sensing (RS) technologies. Various applications have extensively used optical RS sensors for change detection (CD). The CD process involves analyzing two or more images captured over the same geographical area at different times to identify significant land cover changes. While optical sensors have been widely used for CD, microwave RS images acquired by synthetic aperture radar (SAR) have been less exploited. However, SAR images in CD are attractive for operational purposes since SAR sensors are active instruments that can operate in any atmospheric and sunlight conditions. Remotely sensed data collected by several constellations of SAR sensors, such as the twin Sentinel-1A/B sensors of the European (EU) Copernicus, enable fast mapping of changes of Earth's surface and allow the time monitoring of areas prone to geohydrological disasters.

Coastal flood risk is a global challenge, as about 40 million people living in coastal port cities will likely be subject to one significant coastal flood event per century. SAR remote sensing is a valuable tool for detecting and monitoring flood phenomena and can differentiate between inundated and non-inundated areas. Flood risk increases due to urban growth, ground subsidence, and climate change. Identifying areas more prone to extreme floods helps optimize urban planners' civil protection actions and evaluate the damage. Recent advances in RS technology have allowed the generation of rapid damage prediction maps and associated models that are helpful in the occurrence of a flood event.

This work focuses on the impacts of floods and extreme weather events on coastal areas' cultural heritage preservation, particularly the case of the monumental city of Venice and the whole Venice Lagoon area. The Venice Lagoon represents Italy's most extensive lagoonal system, one of the largest in the Mediterranean Sea, and one of Italy's most important industrial areas. The lagoonal system includes Venice, an extraordinary archaeological, urban, architectural, artistic, and cultural heritage masterpiece. The Venice Lagoon ecosystem is subject to various drivers of change, such as land-based feeding activities, heavy metal extraction, ground-water extraction, etc., causing multiple environmental impacts on the Lagoon. The subsidence phenomenon of the terrain is one of the most critical drivers of change. A CD study was conducted to analyze the ground deformation (subsidence) that occurred in the Venice Lagoon in recent years using the multi-temporal interferometric Small Baseline Subset (SBAS) technique. The study examined the interlinked effects between the underlying subsidence in the area and the recent extreme flood events that occurred in November 2019. The time series of backscattered S-1 signals were analyzed to identify the extent of the flooded regions and the impact of the floods on the city. The study also leveraged the potential of a newly developed AI method based on Random Forest.

This methodology uses coherent/incoherent SAR change detection indices (CDIs) and their mutual interaction in a single corpus to rapidly map surface changes. The method has shown great success in quickly mapping land surface changes of areas affected by enormous wildfires in Sardinia and Sicily in the summer of 2021 and flooded areas in Houston and Galveston Bay due to Hurricane Harvey in 2017.

We conducted a detailed analysis using 180 Sentinel-1 images acquired from January 2017 to December 2021 to investigate ground deformation in the Venice Lagoon. We generated a stack of 1736 short baseline (SB) interferograms and computed the lagoon's time series of deformation and mean deformation velocity map. We also used a series of pre- and post-flood event acquisitions on 12 November 2019 to perform a change detection analysis of Venice using temporal multi-looked sigma nought maps and Coherence Changes Indexes (CCI) from an AI-based methodology.

For the change detection analysis, we selected a time series of acquisitions with a temporal baseline of ±6, ±12, ±18 days before (11 November, 5 November and 30 October), during (17 November), and after (23 and 29 November, and 5 December) the flood event. Each SAR image of the time series underwent post-processing using a de-speckling noise filtering algorithm and co-registration using Enhanced Spectral Diversity (ESD) to the 17 November acquisition. We computed sigma nought differences (), and we used triplet of SAR images with temporal baselines of ±6, ±12, ±18, ±30, ±36, and ±42 days with respect to the 12 November pre- and co-disaster InSAR () CCI to determine the coherence changes indexes.

The results of the interlinked analysis showed that only a tiny region of the emerged lands in the north and south of the Venice Lagoon is affected by subsidence. Flood events represent a severe threat to the integrity of these areas. The deformation analysis of the city of Venice showed no significant subsidence phenomena. Still, some spot regions over the low-lying lagoon terrains were affected by remarkable signals associated with sea level rise (SLR), which can seriously impact the hydrogeology of the area.

Coastal regions are with dense population, buildings and infrastructures, and vulnerable to natural disasters. Frequent natural disasters will cause huge economic losses and human casualties. Shanghai is a costal mega city and located in low-elevation coastal zones of the Yangtze River Delta. The city is frequently affected by typhoon and storm surge. Besides, The geological foundation of the city consists of soft alluvial deposits, including clay, silt, and sand. Due to its geological conditions, Shanghai is vulnerable to ground subsidence, flooding, and other geohazards.

In order to prevent, resist and reduce the impact of disasters, this study assesses the regional disaster reduction risk (DRR) capacity of a district of Shanghai with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and established a machine learning aided evaluation models. We also retrieved long-term and recent ground deformation of the coastal areas of Shanghai with Small Baseline Subset (SBAS) technology and multi-sensor Synthetic Aperture Radar image time-series. We also simulate the possible flood inundation extent under different scenarios based on LISFLOOD-FP simulation model in coastal regions. For towns with weak DRR capacity, we analyze sensitivity of evaluation indicators to explore key indicators which affect improvement of DRR capacity. Finally, we proposed optimal strategies which could improve DRR capacity based on the assessment results of DRR capacity and regional disaster characteristics.

Wetland is known as the "kidney of the earth", and its ecological function, biodiversity, and various values ​​are irreplaceable. However, due to the rapid urbanization process, wetland resources are decreasing day by day, so the investigation of wetland resources is very important. The Yangtze estuarine wetland provides various ecosystem services. However, affected by human activities (upstream sediment reduction) and natural background (sea-level rise, SLR), the saltmarsh of the estuarine wetland is undergoing dramatic changes in recent years.

As one of the important ways of wetland monitoring, remote sensing technology is widely used in wetland monitoring. Traditional wetland monitoring remote sensing technology only uses optical remote sensing data for information extraction, but optical remote sensing data has high requirements for weather and specific characteristics of land cover. For wetland areas with more shallow water and higher rainfall than other areas, it is difficult to effectively obtain information on land cover for long time series using optical images. Comparing with optical remote sensing systems, Synthetic Aperture Radar (SAR) has all-weather, synoptic views of large areas, and day-night imaging capability. Microwave electromagnetic energy can penetrate shallow water and vegetation. In recent years, scholars have conducted extensive research on the information extraction of wetland characteristics based on SAR images.

In this paper, Chongming Dongtan Wetland is taken as the study area, and the multi-temporal Rardarsat-2 full-polarization SAR data and Sentinel-2A medium-resolution optical data are used as the data source. According to the particularity of the estuary wetland in the study area, different characteristic parameters are calculated, including vegetation index, water body index, spectral feature, radar feature, texture feature, and time feature. Six multi-dimensional feature data sets containing different feature parameters have been formulated. We perform object-oriented multi-scale inheritance segmentation on six feature data sets and use segmentation parameter optimization tools to select the optimal segmentation parameters. Combined with field investigation and visual interpretation of high-resolution remote sensing images, we build a classification system of Chongming Dongtan Wetland Saltmarsh Vegetation, which mainly includes three types of wetland saltmarsh vegetation: Phragmites australis, Spartina alterniflora, and Scirpus mariqueter. Different vegetation training samples and verification samples are selected on the segmented images, and the information on saltmarsh vegetation is extracted based on the random forest machine learning algorithm. To further study the interannual variation characteristics and phenological characteristics of wetland saltmarsh vegetation, this research also uses Sentinel-2, Sentinel-1, and Landsat-8 fusion images based on Google Earth Engine (GEE) to construct a medium-resolution long-term median image dataset, to obtain the spatiotemporal distribution results of saltmarsh vegetation in Chongming Dongtan Wetland from 2017 to 2022 and the quantitative migration of saltmarsh vegetation.

According to the results of this research, wetland saltmarsh vegetation protection and tidal flat utilization of Chongming Dongtan Wetland can be scientifically supported for Outline Development Plan for the Chongming world-class ecological island.

Change detection (CD) is to quantitatively analyze and determine the characteristics and process of earth surface change based on remote sensing data in different periods. It is widely used in disaster dynamic detection, urban planning, and other fields. Compared with optical remote sensing images, Synthetic Aperture Radar (SAR) images have the unique advantage for CD. As active remote sensing systems, SAR has all-weather, day-night imaging capability, and permits synoptic views of large areas. The backscatter intensity information of SAR images in urban areas is easily affected by comprehensive factors such as building layout, orientation, and surface materials, resulting in overall low radar echo intensity in some building areas. The introduction of coherence maps into change detection can improve the recognition of changed areas because urban buildings have high phase stability and coherence maps provide reliable information for building change detection (BCD). In this study, we mainly focus on two problems in practical application. First, speckle noise as an inherent characteristic of SAR images greatly influences the performance of BCD. Second, how to fuse intensity information and coherence information effectively is of significance for extracting the spatial features of changed areas. Thus, this study proposes a deeply supervised pseudo-siamese attention-guided network (DSPANet) for BCD, in which convolutional blocks have a strong ability in noise reduction owing to the large receptive fields, and the adopted pseudo-siamese structure does well in extracting intensity information and coherence information with the same network branch but different weights.

This study uses high-resolution TerraSAR-X (TSX) images covering Shanghai from a descending pass track. A set of four TSX images is acquired between 16 June 2019 and 10 September 2021. Before training the network, we pre-process TSX images to get intensity information and coherence information. Intensity information is obtained by pre-processing single-look complex (SLC) images, including radiometric correction, multi-looking processing, and geocoding. Coherence information is obtained by performing interferometry and computing coherence values of interferograms. DSPANet model is utilized to distinguish between changed and unchanged areas. It mainly consists of an encoder and a decoder. In the encoder, the convolutional block attention module (CBAM) is included to assign accurate labels to each pixel through the attention mechanism, thus enhance the feature learning of the network. In the decoder, the multi-level feature fusion (MFF) blocks with residual structure are used to avoid the gradient disappearance and gradient explosion caused by the deepening of the network, and the output of the encoder and decoder is concatenated with the skip connection for retaining shallow features. Besides, considering unstable weight updates and unsatisfactory performance while increasing the depth of the network, the deep supervision idea is introduced in DSPANet. It adds some auxiliary classifiers to realize gradient back-propagation by assisting middle layers in extracting features.

We evaluate the accuracy of the DSPANet model using five different metrics. The results show that the method is reliable in BCD and performs better than other advanced change detection methods (such as U-Net, FC-EF, and FC-Siam-Diff). The Precision, Recall, F1, Kappa, and Accuracy reach 88.02%, 84.13%, 86.03%, 85.09%, and 98.24% respectively.

Keywords: building change detection; Synthetic Aperture Radar (SAR); deep learning; coherence information

Shanghai is located in the coastal area of the Yangtze River Delta in eastern China, facing threats from various disasters such as typhoons, storm surges, flooding and ground subsidence. As a mega-city with an area of 6,340 km² and a population of over 24.75 million, Shanghai is densely populated with disaster-bearing bodies such as buildings, roads, and metro, which are easily affected by natural disasters. Frequent natural disasters will cause significant population and economic losses. Therefore, the objective of this study is to build a disaster-bearing body Comprehensive risk assessment model based on natural disaster scenario data and disaster-bearing body attribute data. Through analyzing the distribution of the disaster-bearing bodies’ comprehensive risk level, we can detect hazards in advance and reduce the potential impact of natural disasters.

Firstly, we constructed a comprehensive risk assessment indicator system according to three dimensions of hazard, vulnerability and exposure, based on the location and attribute information of buildings, and extracted the calculation indexes. We also simulated hazard Scenarios related to buildings and roads, including ground subsidence, typhoons, floods, and storm surges. Ground subsidence information is retrieved with 2017-2021 Sentinel-1A data and Small Baseline Subset (SBAS) technology. The comprehensive risk assessment model of disaster bearing bodies was constructed by weighted calculation input indexes, and realized automatic comprehensive risk assessment of the disaster-bearing bodies in large-scale. The weights of input indexes are determined by summarizing historical disasters data and scoring by experts. We use this model to evaluate the comprehensive risk level of the disaster-bearing bodies under multiple disaster scenarios, and analyze the distribution of risk levels. Finally, the regional disaster reduction risk (DRR) capacity of Shanghai and the comprehensive risk level of the disaster-bearing bodies were combined by disaster matrix to determine which high-risk disaster-bearing bodies are located in areas with low DRR capacity.

Synthetic aperture radar (SAR) Doppler shift measurements consist geophysical and non-geophysical contributions. The former is composed of the sea state (wind waves and swell) and underlying surface current. The latter contains geometry and scalloping errors, antenna electronic miss-pointing and unknown biases. In this letter, for the first time, we attempt to retrieve ocean surface radial current velocities from Sentinel-1A SAR Doppler shift observations acquired over Hurricane Maria. Doppler shifts caused by scalloping error are first estimated using linear fitting method. Doppler shifts arising from electronic miss-pointing and unknown biases are then calculated from mean value of Dopper observations over the land. Finally, we compute sea-state-induced Doppler shifts based on our recent ocean surface Doppler velocity model (so-called DPDop). The retrieved ocean surface radial current velocities are compared with the collocated high-frequency radar measurements, showing a bias of 0.02 m/s and a root-mean-square error (RMSE) of 0.19 m/s. These results suggest that the Doppler velocity model has potential to correct wave bias and can be used to derive reasonable radial current velocities under high wind conditions.

Sea ice plays an important role in the Earth's climate system, accurately identifying and monitoring melt ponds provides important information for understanding the sea ice evolution process. This study aims to identify the melt ponds in the Canadian Arctic Archipelago and estimate theri depths. To achieve this, Landsat-8 TOA data and ICESat-2 data were used. A multi-layer neural network and a multi-layer perceptron were adopted to successfully achieve accurate classification and depth estimation of melt ponds based on Sentinel-2. Meanwhile, the spatiotemporal variations of melt pond coverage and depth in the Canadian Arctic Archipelago in the last nine years were analyzed.

The near-instantaneous radial velocity of a target can be obtained using the Doppler effect of SAR, which is widely used in ocean current retrieval. However, in sea ice drift velocity measurements, only a Doppler centroid estimation algorithm in frequency domain has been studied, so whether there is a better algorithm is worth exploring. In this study, based on Sentinel-1A IW data, three Doppler centroid estimation algorithms applied to ocean current retrieval are selected. Combined with the characteristics of the TOPS mode, made two applicability adjustments to each algorithm, and finally applied the three algorithms to sea ice radial surface velocity measurements. The first adjustment is to explore and determine the optimal parameters. The second adjustment is to use parallel computing to improve the efficiency, which is improved by an average of 43.55%. In addition, the deviation of Doppler centroid estimation bias correction is verified using rainforest data, and the deviation is controlled at approximately 3 Hz. Based on the three algorithms, five sets of experiments are carried out in this study. By analyzing and comparing the results of each algorithm, it is found that the results of the three algorithms are relatively consistent, among which the correlation Doppler estimation algorithm has the advantages of high efficiency and high precision and is the most suitable method for sea ice drift measurement among the three methods. However, for SAR images with abnormal speckles caused by human activities, the sign Doppler estimation algorithm can effectively remove abnormal speckles and ensure the smoothness of the image with better adaptability.

The China-France Oceanography Satellite SCATterometer (CSCAT) can observe radar backscatter values on the same sea surface at multiple incidence angles, and is often used to estimate the ocean near-surface wind. However, CSCAT utilizes a novel scanning mechanism and the wind vector cell has a spatial resolution is 25km or 12.5 km, which limit the study of high-resolution land and sea ice monitoring. To address this issue, this paper constructs a geometric model of the main lobe-to-ground projection relationship and generates the enhanced-resolution radar images. CSCAT data are applied to three main image reconstruction algorithms (SIR, AART, and MART), and experiments are performed in the Iceland and Hudson Bay, and verified by Sentinel-2 optical remote sensing data. The experiments show the geometric model for CSCAT improves the spatial resolution from traditional 25km to 5 km, and the SIR-reconstructed images are characterized by higher accuracy and better suppression of noise than are those obtained with the AART and MART methods. Therefore, this study extends the application of domestic remote sensors and provides data support for high-resolution applications, such as land and sea ice monitoring.

Images from satellite Synthetic Aperture Radar (SAR) systems are widely used for iceberg monitoring. Icebergs can be detected in SAR images if the difference (the “contrast”) between the backscattered radar intensities from an iceberg and from the surface around it is statistically significant. In our presentation, we focus on sea ice surfaces. For test sites from the Northern Hemisphere (Belgica Bank, Northeast Greenland) and the Southern Hemisphere (Prydz Bay), we manually identified icebergs and determined the backscattering coefficients averaged over the iceberg area and over an area of the sea ice around it. For Belgica Bank, we used ALOS-2 PALSAR-2 (L-band) ScanSAR, and Sentinel-1 (C-band) extra wide swath imagery. For Prydz Bay, we used ALOS PALSAR quad-polarimetric and Radarsat-2 dual-polarimetric SAR imagery. We found that the intensity contrast depends on the radar frequency, the incidence angle, and the sea ice surface characteristics. In our poster, we will present examples and summarize the results for each of our test sites. The findings are valuable for developing strategies and algorithms for automated iceberg detections as required by operational sea ice and iceberg monitoring services, considering the use and combination of recent and upcoming SAR satellite missions.

The Bohai Sea and its surrounding areas are rich in oil and natural gas, and play an important role in the industry, agriculture and economy. However, the Bohai Sea suffers severely from the sea ice in the winter. The Geostationary Ocean Color Imager (GOCI) is the first geostationary orbit ocean color satellite, providing high spatial and temporal resolution for extraction of sea ice parameters in the Bohai Sea. Based on GOCI data, a systematic and standardized method is developed for extracting sea ice parameters. This method can perform normalized preprocessing on the GOCI raw data, including atmospheric correction, relative radiation correction, and sea ice or cloud masking. Subsequently, it extracts relevant sea ice parameters, including sea ice concentration, sea ice thickness, and sea ice drift velocity. The unique advantage of GOCI is its geostationary orbit and short imaging interval (1 hour), which enables tracking the daily drift of the sea ice in the Bohai Sea. Using this method, sea ice parameters are retrieved in the Bohai Sea in winter from 2017 to 2021, and the retrieval accuracy meets the sea ice forecast demand. Finally, we extract a long time series dataset of sea ice parameters from 2011 to 2020 (December to March of the following year), and conduct a statistical analysis of the long-term sea ice changes in the Bohai Sea, which is consistent with the information formally released by the State Oceanic Administration. The sea ice extent and thickness in the Bohai Sea reached their maximum in 2012 and their minimum in 2019, respectively. The sea ice growth during each winter follows the same pattern: the sea ice forms in late December, reaches its maximum extent in January, begins to shrink in early February, and disappears completely by early March. The sea ice drift velocity is largely influenced by the wind and currents, without significant rules of inter-annual or annual changes. The extraction of these parameters will provide initial field data of the sea ice for sea ice forecasting in the Bohai Sea. Furthermore, it will provide valuable data support for sea ice monitoring and ocean environmental research, helping to better understand the trends in oceanic changes and ultimately contribute to the preservation of the health and stability of marine ecosystems.

Sea ice in the Yellow Sea and Bohai Sea affects maritime transportation and economic activities every winter. Hence, monitoring sea ice concentration and thickness, the key parameters, is vital. HY-1C and HY-1D are the ocean color satellite series that provide optical data in the morning and afternoon, respectively. In the afternoon, rising temperatures may cause slight melting on the sea ice surface, which may hamper optical detection. Therefore, HY-1C is more suitable for Bohai sea ice monitoring than HY-1D. Its onboard Chinese Ocean Color and Temperature Scanner (COCTS) has ten spectral bands for retrieving sea ice concentration and thickness. This study proposes a systematic and standardized method for extracting sea ice parameters based on HY-1C data. The raw COCTS data undergoes normative pre-processing, which includes geometric correction, atmospheric correction, radiometric calibration, and sea ice masking. Then, sea ice concentration and thickness are retrieved. For sea ice thickness, the linear correlation between MODIS shortwave broadband reflectance and HY-1C band reflectance is analyzed. Then, a linear regression equation is established between MODIS shortwave broadband reflectance and HY-1C band reflectance to obtain shortwave broadband reflectance from HY-1C data. Subsequently, based on the theoretical model of sea ice thickness and shortwave broadband reflectance, the Bohai Sea ice thickness is calculated. Sea ice concentration is extracted using the shortwave reflectances of sea ice and sea water. Three methods are used to calculate the shortwave reflectance of sea water: standard, mean, and direct assignment. Two methods are used to calculate the shortwave reflectance of the sea ice: the standard method and mean method. Six sea ice concentration results from these method combinations are obtained and compared. The comparison shows that using the direct assignment method for sea water shortwave reflectance and the standard method for sea ice shortwave reflectance yields the most accurate results relative to the original image. Hence, this approach is adopted for sea ice concentration extraction. Using these methods, we have monitored sea ice in the Yellow Sea and Bohai Sea from 2021 to 2023. This project provides a systematic and standardized method for inverting sea ice thickness and concentration based on HY-1C data. It provides initial fields of sea ice parameters for sea ice forecasting in the Yellow Sea and Bohai Sea, which is vital for shipping, transportation, and resource development.

Lakes comprise approximately 1.8% of the Earth's surface, and up to 40%-50% in certain areas of the Arctic and subarctic regions. Frozen lakes represent approximately 59.55% of the total lake area in the Northern Hemisphere, as determined by the January 0°C isotherm. Lake ice serves as a key Environmental Climate Variable (ECV) in the Global Climate Observing System (GCOS), where ice extent, phenology, thickness, and type are crucial indicators for assessing climate change and ecological research. However, global warming is causing a decline in ice coverage, with delayed freeze-up dates and earlier ice breakup dates. The reduction of lake ice has significant implications for lake ecosystems, including biodiversity, biogeochemical processes, and greenhouse gas emissions.

Satellite remote sensing has become a widely employed tool for lake ice monitoring owing to its broad spatial coverage, frequent observation cycles, and high precision. The optical remote sensing data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) features high spatial resolution and enables bi-daily monitoring. In contrast, passive microwave data remains unaffected by weather and cloud cover, and both data types present unique advantages for large-scale lake ice monitoring. Accordingly, we leveraged both MODIS optical remote sensing and passive microwave data to investigate lake ice in the Northern Hemisphere.

We utilized MODIS NDSI data to monitor lake ice for over 23,000 lakes across Eurasia.We applied a series of cloud removal methods to process the data and effectively reduce the cloud cover. Using the cloud-free MODIS data, we extracted the long time series of lake ice coverage data from 2002 to 2022. The dataset was verified to have high accuracy and can effectively monitor the changing trends of lake ice.To classify lakes with different lake ice cover trends, we have developed a convolutional neural network-based method for time series classification of lake ice cover. This method effectively categorizes lakes into four different types.

Based on passive microwave data, we employed the nearest neighbor algorithm to reduce the impact of mixed pixels and extracted brightness temperature data for 753 lakes in the Northern Hemisphere from 1978 to 2020. We then derived corresponding lake ice phenological parameters and verified the results to have a high degree of accuracy.Based on the analysis of the dataset, the following results were obtained: lakes freeze earlier, melt later, and have longer ice periods as latitude increases. Lakes located between 28°N and 40°N have longer ice cover durations compared to those located north of 40°N, mainly due to the prevalence of lakes at low latitudes on the Qinghai-Tibet Plateau. Above 45°N, at the same latitude, the average ice cover duration of North American lakes is longer than that of Eurasian lakes.

Meanwhile, we analyzed the changes in lake ice phenology of lakes in Northern Europe, Qinghai-Tibet Plateau, and Mongolian Plateau using passive microwave data, and investigated their associations with climate.The results showed that the lake ice changes in the three regions were significantly correlated with the corresponding regional temperature changes. Among them, in Northern Europe, the temperature change had a more sensitive impact on the lake ice phenology. There were still other factors that influenced the lake ice changes. However, in the northern region of the Tibetan Plateau, the ice period of many lakes has increased since 2000. There are many factors contributing to this phenomenon, such as the decrease of Kara Sea ice ，the winter North Atlantic Oscillation (NAO) and early spring Antarctic Oscillation (AAO) anomalies.

Glacier surface albedo is one of the most important parameters to determine the net shortwave radiation and therefore affect glacier energy and mass balance. Glaciers in the High Mountain Asia (HMA) are the main water resource for local ecology and local people (~one billion), and have accelerated mass loss in the past 20-years. Due to a good sensitivity to climate, better understanding of the spatiotemporal variability of glacier albedo can help us to understand the mass balance and the response of glacier to climate in this region. With our retrieval method developed for Sentinel, Landsat and MODIS data, we firstly generated half-monthly glacier albedo by MODIS surface reflectance data, and then analyzed its change from 2001 to 2020. The results show that the glacier albedo experienced a decline over the entire region, but with a distinct spatial and seasonal differences. In the westerly-dominated regions, glacier albedo shows a slight decrease even increase in the Hindu Kush and West Himalaya, while in the monsoon-dominated and transition regions, it shows large decrease with the rapidest change in the Inner Tibetan Plateau. Autumn albedo shows the quickest decrease, while the lowest is observed in spring. Good correlation between albedo and mass balance indicates that decreasing albedo is indeed a key driver of mass loss in this region.

Mountains act as water towers, supplying crucial freshwater to downstream areas and affecting large populations particularly in High Mountain Asia. Yet, the propagation of water from HMA headwaters to downstream areas is not fully understood, as interactions in the mountain water cycle between the hydrosphere and biosphere remain elusive. Understanding how green water processes affect the availability of blue water from glaciers, snow and precipitation in High Mountain Asia is a pressing but challenging research question. Cyrosphere and biosphere dynamics are manifest in distinct manners across the extreme elevation range of catchments in High Mountain Asia due to the intra-annual variability of climate and associated ecosystems. It is therefore imperative to couple our understanding of blue and green water fluxes, which traditionally have been studied in an isolated way, and to examine these fluxes sub-seasonally and with elevation.

Land surface models are numerical models that account for these blue-green fluxes in a complete manner by solving the coupled fluxes of water, energy, and carbon between the land surface and atmosphere. However, most land surface models focus at the global or regional scale with horizontal grid dimensions of 0.25–1° (equivalent to about 25–100 km at mid-latitudes), and are thus not able to resolve land-surface energy and water fluxes at sufficient spatial detail to capture local topographic and microclimatic effects or lateral flows of water, as found in complex mountainous topographies such as glacierized watersheds in HMA. Due to the lack of observations or computational constraints, land surface models usually focus on specific processes and neglect the links between the cryosphere, hydrosphere and biosphere, or represent them in a simplistic way. This is problematic, for example because plant transpiration forms a major part of the green water flux even in high mountain areas with scarce vegetation, but the large variability in water-use strategies between different plants hinders a quantification based on simple parameterizations. Similarly, mountain glaciers, let alone debris-covered glaciers which are common in HMA, have been neglected in land surface models so far.

High resolution meteorological forcing information is usually less robust for mountain regions as station data are available at a few research sites only. Downscaling methods have seen improvements but either suffer from the lack of station data needed for statistical downscaling or from computational resources needed for dynamical downscaling.

Given the dramatic lack of high-elevation in-situ data in HMA, and the general difficulty of capturing land-atmosphere interactions in complex topographies well with field measurements, remotely sensed data of high spatiotemporal resolution offer a great opportunity to develop, calibrate and test land surface models, while reducing uncertainties in model initialization, simulation, and validation.

The increasing resolution and accuracy of remote sensing data, and a new generation of models representing the cryosphere, hydrosphere and biosphere within one modeling system and with the highest degree possible of physical representation, bring the possibility of a paradigm shift in the simulation of blue-green water interactions in high mountain catchments. As an example for an integrated approach to reveal blue-green water fluxes in a high mountain region we show how we apply a state-of-the-art land surface model (Tethys & Chloris) to the glacierized Langtang catchment in the Nepalese Himalayas and explain the use of high resolution earth observation data (e.g. glacier thinning and surface motion; glacier albedo; snow cover) to constrain the meteorological uncertainty and validate our model results.

High Mountain Asia (HMA) hosts the largest mass of ice outside the Polar Regions and provides water to large downstream communities. Glacier change has been highly diverse across the region over the last decades, with glaciers in the Pamirs experiencing near-neutral mass balance while fast rates of mass loss are observed in the Southeastern Tibetan Plateau (SETP). In a previous modeling study in the SETP, we found that precipitation phase changes associated with climate warming were a major accelerator of glacier losses, but this mechanism of mass loss acceleration has yet to be explored across the rest of HMA. Additionally, snow sublimation and gravitational redistribution are two processes known to influence glacier mass supply, but their relevance has not been systematically investigated at the catchment scale at distinct locations across HMA.

Here we apply a mechanistic land-surface model at high spatial and temporal resolution (100m, hourly) at three glacierized catchments covering distinctive climates in HMA (Kyzylsu in the Northern Pamirs, Trakarding-Trambau in the Nepalese Himalayas, and Parlung No.4 in the SETP). We force the model with ERA5-Land reanalysis which was downscaled and bias-corrected with locally available meteorological observation. We constrain and evaluate our model with independent in-situ observations (ablation stakes, snow depth measurement) and remote-sensing observations (snow cover, surface elevation changes, glacier surface mass balance and albedo). Our goal is to quantify the importance of solid precipitation, snow sublimation, and gravitational snow redistribution on the glacier mass balance and in the catchment water balance. Our first modeling results highlight the challenges but also the added value of applying such sophisticated models in these remote areas characterized by extreme topography and scarce or altitudinally-biased local observations. We show how the choice of the precipitation phase scheme influences the seasonality of the simulated snowfall amounts and the overall glacier mass balance. We discuss the limitations associated with the use of reanalysis datasets and ways forward to better account for the spatial variability of key meteorological variables.

This work paves the way towards a better understanding on how snow accumulation processes will be affected by climate change and what the implications will be for glacier future evolution and high-elevation catchment hydrology.

Mountain glaciers are sensitive to climate variability and can be of great importance for downstream residents due to their hydrological significance. Synthetic Aperture Radar images are often used to monitor glaciers based on the backscatter coefficient, but the influence of satellite orbit and polarization when collecting images for wide regions were not well considered. We study the changes of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band data acquired in the summer 2019 and winter 2019/20. We found that there is a clear threshold for the backscattering coefficient in the glacier area after using the maximum likelihood classification, and using this threshold allows monitoring of both wet snow and firn. Images from ascending and descending may differ greatly in summer for wet snow detection. This effect can be related to the orbit and therefore the different acquisition time and different air temperature in the morning and afternoon. Using the proposed method, we show that West Kunlun Shan has lower wet- snow-area ratio, but higher firn-area ratio than the Tibet Interior Mountains. In general, orbital produce greater identification differences than polarization.

In recent decades, the most significant mass losses in the Antarctic Ice Sheet (AIS) have been found to be driven by ocean-forced basal melting reducing the buttressing ability of ice shelves. The Ross Ice Shelf (RIS) is the largest cold water ice shelf on the AIS and buttresses both the West and East Antarctic Ice Sheet. Understanding the current dynamics of the RIS in a warming world is important as the ice shelf has a large control over the mass balance of the AIS. The RIS has been suggested to be in steady state but recently seasonal changes in sea ice cover have been found to elevate basal melt rates at the calving front of the RIS (Stewart et al., 2019). Understanding of the influence that short-term environmental variability, such as seasonal basal melt rates, have on the RIS dynamics and mass loss is not yet fully understood. In this project, further understanding is achieved through observing the RIS flow rates over seasonal and annual timescales using GNSS and satellite methods at different locations on the ice shelf. Quantifying the variability of the RIS flow rates provides critical information on the ice dynamics and how these could change in a warming world. Sensitivity experiments are also carried out using the Ice-sheet and Sea-level System Model (ISSM) to understand which short-term environmental forcings may be driving the observed velocity variations, and how these may impact the mass loss of the RIS.

China is the main contributor to global vegetation greening, and large-scale greening has been proven to be related to afforestation. However, with the rise in global temperatures, climate change has become an undeniable factor affecting regional vegetation changes. It is necessary to quantitatively evaluate the relative contributions of climate change and afforestation to China’s vegetation greening, and evaluating the vegetation recovery in forestry projects is conducive to future policy formulation and response to climate change. This study is based on meteorological observation data and satellite remote sensing data. Firstly, the greening of vegetation in China and eight forestry projects from 1982 to 2020 was monitored and evaluated. Then, the relative contributions of climate and afforestation initiatives to vegetation greening were quantitatively evaluated, and the future vegetation greenness change was predicted on this basis. The main research results are as follows:

During the study period, vegetation in China has significantly increased. Pixels with increasing trends accounted for 57% of the region, pixels with stable or unchanged trends accounted for 27% of the region, and pixels with decreasing trends accounted for 16% of the region. The pixels with a significant increase trend are mainly distributed in the Loess Plateau, Northeast Plain, and South China region, while the pixels with a significant decrease trend are mainly distributed in the Qinghai-Tibet Plateau and Northeast region. Due to differences in land use types, climate conditions, and topographic conditions in different regions, there are differences in the ecological implementation effects of the eight forestry project areas, and vegetation degradation is still relatively obvious in some forestry engineering areas.

Climate change is the main factor affecting the recovery of vegetation in China. The contribution rates of climate change and human activities to vegetation recovery are 72.34% and 27.66%, respectively. In arid and semi-arid areas such as the Mongolian Plateau, Qinghai-Tibet Plateau, and Loess Plateau, precipitation is crucial for vegetation growth. Temperature has a significant promoting effect on vegetation growth in the southeast region because the region has abundant precipitation resources and higher temperatures are conducive to regional vegetation growth.

Only 14% of the regions with continuous NDVI growth are expected to continue to grow in the future, and the remaining regions show obvious anti-continuity (59% from increase to decrease, 22% from decrease to increase). The risk of vegetation degradation in the future is high. The impact of climate factors on vegetation is gradually weakening, while the impact of human activities on vegetation changes will become more complex. Although ecological engineering has played a positive role in the restoration of vegetation ecosystems, vegetation degradation in the Three North Shelterbelt Program (TNSF), Coastal Shelterbelt Program (CSP), and Shelterbelt Program for Liaohe River (SPLR) are still relatively obvious. This is related to the fragile regional ecological environment and the destruction of vegetation by agriculture, animal husbandry, and urbanization. Therefore, it is necessary to further strengthen the construction of ecological engineering to better maintain the effectiveness of these projects.

Lakes in the basin of the Yangtze River, play a fundamental role in regional bio-geochemical cycles and provide major services to the communities, provisioning services (drinking water, fishing) and biodiversity keeping. However, the extreme temporal and spatial variability of these massive but extremely shallow ecosystems prevents a reliable quantification of their dynamics with respect to changes in climate and land use. The final aim is to model, map and explain the distribution of biodiversity and their associated habitats, explaining spatio-temporal changes in biodiversity caused by biotic and abiotic factors. Within this dragon 5 project ID 58815, sensitive areas having a rich biodiversity including the East Dongting lake, Hunan Porvince (Xiaoxi, Daxi, Caisang,) and the disconnected lakes of the Anhui Province (Wuchang, Shengjin and Baiding Lakes) are considered.

For the epoch 2019-2023, by exploiting our house tool ExtractEO which is a software implementing automated end-to-end chains, water surfaces were detected over Sentinel-2 data using a multilayer perceptron algorithm and integrating the Global Surface Water database for sampling. Sentinel-2 water extents were generated from the Sentinel-2 time series and then densified by exploiting RADARSAT-2 and ICEYE SAR imagery. Validation of the processing chain was done by comparing water surface derived from S2 with the one obtained from a Pléiades NEO imagery with a resolution of 30 cm. Water levels were also monitored by exploiting Sentinel-3 altimetric data and validated by comparison with ICESat-2 known for its high precision.

Results obtained over the sensitive Anhui and Hunan province lakes, will be presented and discussed. Based on these preliminary results, guidelines for further investigation particularly for SWOT data exploitation will be presented.

During November 2022, Poyang Lake suffered from a severe drought disaster, and the water level at Xingzi Station receded to 6.48 meter, which set a new record low water level. In order to explore the impact of this extreme drought event on the hydrological patterns of Poyang Lake, we constructed a dataset of the water area in different periods by utilizing Sentinel-1 Synthetic Aperture Radar (SAR) images, with the advantages of high spatial–temporal resolution and all-day and all-weather working capacity. The relationship model between lake area and water level was constructed based on the data from hydrological stations in Poyang Lake. We found that the water level and water area showed strong correlation in recent years, especially at Xingzi station (R²=0.88). Therefore, we can make an early warning of the overall drought condition of Poyang Lake through the real-time water level of Xingzi Station, especially the change of food and environment of migratory birds' habitats. For purpose of assessing the drought disaster in Poyang Lake more accurately, we carried out the research on the precise classification of land cover. Afterwards, the algorithm was applied to estimate the yield of oilseed rape in Poyang Lake. Our research results can provide decision support for the relevant management departments for disaster early warning and assessment of Poyang Lake.

This work aims to assess the effects of various acquisition geometries devoted to the crop’s studies using the PRISMA ("PRecursore IperSpettrale della Missione Applicativa") hyperspectral satellite data.

PRISMA is a mission of the Italian Space Agency Agenzia Spaziale Italiana (ASI) aiming at qualifying space-based hyperspectral technology and providing imaging spectroscopy data to promote a variety of resource management and environmental monitoring applications. The satellite's payload instruments include a VNIR-SWIR imaging spectrometer and a high-resolution panchromatic camera (PAN). The satellite was launched on 22 March 2019, with an expected operational mission lifetime is 5 years. PRISMA is in a Sun-Synchronous Low Earth Orbit flying at an altitude of 615 km with an inclination of 97.85°and local time of equator crossing on Descending Node (LTDN) of 10:30, with a re-look capacity of 7 days and off-nadir observation, the nominal orbit revisit time is 29 days (from nadir). Off-nadir observations (±21°) are performed through platform roll manoeuvres (across-track or along track). Typical image size is of 30 x 30 km with a Ground Sampling Distance (GSD) of 30 m for (VNIR-SWIR) and 5 m for (PAN).

Variations in the geometry of the sun and the view can lead to unwelcome brightness gradients throughout an image. Image brightness gradients can seriously impact on the analysis in research where reflectances from many images will be compared. These effects related to the bidirectional reflectance distribution function (BRDF) are also impacting on imaging spectroscopy data (Gu et al. 2021; Moriya, Imai, and Tommaselli 2018; Zhang et al. 2021). The BRDF describes the reflectance of a surface by considering the incoming and outgoing light direction. The function is parameterized by the zenith and azimuth angles of the incoming (solar) and outgoing (sensor) directions, in total 4 parameters. The BRDF effects in imagery result from different sunlit/shaded portions of the same surface target seen by the sensor under different solar and view geometries (Roujean, Leroy, and Deschanps 1992; Queally et al. 2022). BRDF effects are most evident when using wide field of view sensors such as MODIS (Roujean, Leroy, and Deschanps 1992; Queally et al. 2022). Time series of sensor data characterized by a large range of view or sun angles show the same effect. BRDF correction aims to minimize such effect by normalizing the reflectance to the same solar and view geometry, this same solar and view geometry are defined by a constant view zenith angle (θ_v) and solar zenith angle (θ_s).

This study was motivated by the observation that, even though the brightness gradients for PRISMA hyperspectral imaging inside an image don't change greatly due to its small FOV (2.77°), the various acquisition geometry across images may produce unfavourable artifacts.

This study intends to investigate the impact of the BRDF effect on PRISMA images when utilized to retrieve biophysical parameters of different crops such as cereals and sugarcane. This study aims to assess the effect on the retrieval of crops biophysical variable like Leaf Area Index (LAI) and Chlorophyll retrieved by hybrid procedures utilizing the PROSAIL radiative transfer model. Two BRDF models are considered in this work: i) a simple kernel multiplicative correction, in which surface reflectance is viewed as a combination of two different components, diffuse reflection and volume scattering and ii) the Flexible BRDF correction (FlexBRDF) (Queally et al. 2022), in which the image pixel is pre-classified using the Normalized Difference Vegetation Index (NDVI).

The current study area location is the Maccarese farm (Rome, Italy), while other sites will be selected during the coming days on the base of the results of the ongoing PRISMA and the contemporary field collection in China.

Gu, Lingxiao, Yanmin Shuai, Congying Shao, Donghui Xie, Qingling Zhang, Yaoming Li, and Jian Yang. 2021. ‘Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring’. Remote Sensing 13 (9). https://doi.org/10.3390/rs13091699.

Moriya, Erika, Nilton Imai, and Antonio Tommaselli. 2018. ‘A Study on the Effects of Viewing Angle Variation in Sugarcane Radiometric Measures’. Boletim de Ciências Geodésicas 24 (March): 85–97. https://doi.org/10.1590/s1982-21702018000100007.

Queally, Natalie, Zhiwei Ye, Ting Zheng, Adam Chlus, Fabian D Schneider, Ryan Pavlick, and Philip Townsend. 2022. ‘FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines’. Journal of Geophysical Research: Biogeosciences 127 (April). https://doi.org/10.1029/2021JG006622.

Roujean, Jean-Louis, Marc Leroy, and Pierre-Yves Deschanps. 1992. ‘A Bidirectional Reflectance Model of the Earth’s Surface for the Correction of Remote Sensing Data’. Journal of Geophysical Research 972 (April): 20455–68. https://doi.org/10.1029/92JD01411.

Zhang, Xiaoning, Ziti Jiao, Changsen Zhao, Siyang Yin, Lei Cui, Yadong Dong, Hu Zhang, et al. 2021. ‘Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data’. Remote Sensing 13 (23). https://doi.org/10.3390/rs13234911.

Pizhou city is one of the main production areas of garlic in China, and accurate and fast access to spatial distribution information on garlic plays a very important role in predicting garlic production and daily prices. In this paper, using Pizhou city as the study area, based on Google Earth Engine (GEE) cloud platform and Sentinel-2 data, training samples were determined by visual interpretation and fieldwork, and three classification methods were used to classify typical crops in the study area through the construction of spectral features and index features. After comparing three classification algorithms, random forest classification, classification regression tree, and support vector machine, to evaluate the classification performance of different algorithms and to verify the accuracy, among them, the random forest algorithm has obvious advantages over other algorithms. By analyzing and comparing the values of nine types of vegetation indices, combining the 12-month physical characteristics, the confusion matrix of kappa coefficients and overall accuracy is derived after mathematical operations such as difference or ratio, and the time combination with the best extraction effect is analytically preferred. The normalized garlic indices based on the phenological characteristics were constructed.

Remotely-sensed Evapotranspiration (ET) estimates can effectively contribute to agricultural water stress detection. Fully operational, high temporal, and moderate spatial resolution ET products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG) satellites make it a suitable candidate for water stress monitoring. However, dedicated efforts are still required to evaluate the accuracy of SEVIRI observations and develop simple workflows, preferably executable on cloud-based platforms, to exploit its information content for water stress monitoring at larger scales. In this study, an extensive assessment of actual and reference ET (SEVIRI-ET_a and SEVIRI-‎ET₀) observations were conducted against in situ measurements collected at 54 Eddy Covariance (EC) sites across Europe ‎distributed in various terrestrial ecosystems, ecoregions, and climatological zones between 2011-2018. The evaluated SEVIRI-ET products were then utilized mainly for two purposes: i) providing inputs to run a proposed water stress detection workflow, based on monthly evaporative stress index (ESI) anomaly, and implemented in cloud-based Virtual Earth Laboratory (VLab) platform to monitor one decade (2011 to 2020) of spatio-temporal water stress variations for entire Europe, and ii) investigating the mean terrestrial ecosystems response to water stress.

The direct comparison of in situ ET with their corresponding SEVIRI-ET products resulted in a fair agreement in various ecosystems, ecoregions and climate zones albeit with expected inter-site variability. Considering SEVIRI-‎ETa, the highest (lowest) accuracy was obtained in peatland (forest) ecosystem, Carpathian montane coniferous forests (Iberian sclerophyllous and semi-deciduous forest) ecoregion, and the warm temperate fully humid warm summer (warm temperate steppe hot summer) climate zone with KGE values of 0.82 (0.67), 0.85 (0.48) and 0.88 (0.47), respectively. Regarding SEVIRI-ET₀, the highest (lowest) accuracy was obtained in grassland (forest) ecosystems, Baltic mixed forest (Iberian sclerophyllous and semi-deciduous forest) ecoregion, and the alpine polar tundra (warm temperate, steppe, hot summer) climate zone with KGE values of 0.83 (0.76), 0.9 (0.6) and 0.88 (0.61), respectively. The SEVIRI-ESI-based monthly water stress workflow implemented on the online VLab platform provides spatio-temporal variations of water stress in Europe for the last decade (i.e., 2011 – 2020) that can be further utilized in scientific research and terrestrial applications. The analysis of various ecosystems' responses to water stress revealed that general water stress effects on vegetated ecosystems are “visible” in the SEVIRI-ESI-based water stress values and anomalies. The results from this study highlight the value, support the potentials, and unlock the full capacity of SEVIRI-ET products and the VLab platform for agricultural water stress detection at larger domains.

Net primary productivity (NPP) of vegetation is an important indicator for evaluating the quality of terrestrial ecosystems and characterizing the carbon balance of ecosystems. In this paper, we analyzed the spatiotemporal distribution pattern and sustainability of NPP in African terrestrial vegetation based on NPP long-term data from 1981 to 2018, and explored the response relationship between NPP and various driving factors. The results of trend analysis show that NPP in the Sahara arid region in northern Africa and the arid region in South Africa shows an extremely significant reduced trend; Most of the NPP in the tropical rainforests in central Africa and the deciduous broadleaved forests and deciduous needleleaved forests on the north and south sides of the tropical rainforests increased significantly; Congo Basin, Gabon, Cameroon, Ghana, Nigeria, Tanzania and other regions are affected by human activities, while NPP shows an extremely significant reduced trend. Anomaly analysis shows that NPP in Africa generally showed a slow upward trend during 1981–2018, and the trend was basically consistent in different seasons, which can be divided into three stages: 1) a stage of descent from 1981 to 1992, with NPP was below the average value for most years; 2) a stage of steady growth from 1993 to 2000, and reached the peak in 2000; 3) a stage of fluctuations from 2001 to 2018, and the NPP value was above the average value in all years except 2015 and 2016, when the NPP value was low due to abnormal high temperature and drought. Sustainable analysis shows that the reverse characteristics of NPP changes in Africa are much stronger than the same direction characteristics. The results of the structural equation model show that cumulative precipitation and average temperature changes have the greatest impact on NPP changes, while human activities and terrain changes have the smallest impact on NPP changes. Among human activity factors, population density changes can better measure the impact of human activity changes on NPP changes, while in terrain factors, elevation changes can better measure the impact of terrain changes on NPP changes. The results of this study can provide scientific basis for the sustainable development of Africa's ecological environment, agricultural production and social economy.

With the rapid development of remote sensing technology, remarkable progress has been made in the monitoring of surface ecological environment quality based on remote sensing, which contribute to improve the regional environmental quality to meet sustainable development goals. However, few studies have reported investigations on ecological monitoring for mountain-basin transition zone. Use of single surface element, such as vegetation or hydrology, may not be enough to reflect the ecological environment status of a region. Therefore, a comprehensive ecological index is needed, in association with the multi-scale and multi-temporal characteristics of remote sensing observation capabilities. In this study, based on the Landsat 5 TM and Landsat 8 satellite data collected in 2000, 2007, 2011, 2017 and 2021, the remote sensing ecological index (RSEI) was used to evaluate the ecological environment quality of Guangyuan City located in the mountain-basin transition zone over the past 22 years. The results are: (1) temporally, the RSEI were as 0.603, 0.821, 0.548, 0.565 and 0.595 in 2000, 2007, 2011, 2017 and 2021, respectively, which show a trend of upward-downward-upward, with an overall decreasing trend; (2) spatially, the study area was dominated by good grade in 2000, 2011 and 2017; excellent grade in 2007; and medium grade in 2021. The spatial and temporal distribution characteristics of RSEI are closely related to local climate, urbanization process and vegetation cover dynamics.

Taking Xuzhou City as the research area, this paper analyzes the land use changes from 2000 to 2020, and uses the PLUS model to predict the future spatial distribution pattern of land use under three scenarios of natural growth, urban development and ecological protection in 2030.Combined with the InVEST model, the carbon storage from 2000 to 2020 and the carbon storage in 2030 under three different scenarios were estimated and analyzed.Using the land use data in 2000 and 2010 and 13 influencing factors such as precipitation, temperature and elevation, the accuracy of the land use data in 2020 was 93.76%, and the Kappa coefficient was 87.21%, which verified the strong reliability of the PLUS model.In 2000, 2010 and 2020, the carbon storage was 1085.11×10⁵Mg, 1066.32×10⁵Mg, 1061.42×10⁵Mg, respectively.The simulated carbon storage under natural development, urban development scenarios and ecological protection in 2030 was 1056.84×10⁵ Mg, 1055.4×10⁵Mg and 1059.26×10⁵Mg, respectively.

Keyword:Land use/cover change(LUCC);PLUS model;InVEST model;Carbon stocks

The topography can be very important for passive microwave remote sensing of soil moisture due to its complex influence on the emitted brightness temperature observed by a satellite microwave radiometer. In this study, a methodology of using the first brightness Stokes parameter (i.e., the sum of vertical and horizontal polarization brightness temperature) observed by the Soil Moisture and Ocean Salinity (SMOS) was proposed to improve the soil moisture retrieval under complex topographic conditions. The applicability of the proposed method is validated using in-situ soil moisture measurements collected at four networks (Pali, Naqu, Maqu and Wudaoliang) on the Qinghai-Tibetan Plateau. The results over Pali, which is a typical mountainous area, showed that soil moisture retrievals using the first brightness Stokes parameter are in better agreement with the in-situ measurements (the correlation coefficient R >0.75 and unbiased root mean square error < 0.04 m³/m³) compared with that using the single-polarization brightness temperature. At the other three networks with relatively flatter terrains, soil moisture retrievals using the first brightness Stokes parameter are found to be comparable to the single-polarization retrievals. On the contrary, the maximum bias of the retrieved soil moisture caused by topographic effects exceeds 0.1 m³/m³ when using vertical or horizontal polarization alone, which is far beyond the expected accuracy (0.04 m³/m³) of SMOS satellite. In the regions on the Qinghai-Tibetan Plateau where the vegetation effect can be ignored, soil moisture retrieved using horizontal polarization brightness temperature is generally underestimated, overestimated when using vertical polarization brightness temperature. It is reasonable due to the polarization rotation effect (depolarization) caused by the topographic effects. It is concluded that the proposed method for soil moisture retrieval using the first brightness Stokes parameter has a great potential in reducing the influence of topographic effects.

The Sahel region is considered as one of the most vulnerable zones to climate and environmental changes, specifically in terms of water resources. Thus, the investigation of the hydrological responses to land use/land cover (LULC) change and climate variability is essential for understanding catchment hydrology. Hence, our study contributed to separating and assessing the impacts of LULC change and climate variability on water balance components in the Sahel at the basin and sub-basin levels. In order to realize this contribution, three basins have been selected as study cases due to their importance in terms of catchment area (i.e. Senegal river, Niger river and Lake Chad basins). In this work, we have applied Soil and Water Assessment Tool (SWAT) model coupled with remote sensing retrievals of actual evapotranspiration (ETa) and surface soil moisture (SSM). To separate the impacts of the two aforementioned factors, two numerical experiments were designed: (i) climate variability effects by applying frozen LULC while changing the climate; (ii) LULC change impacts by applying frozen climate while changing LULC. The results revealed that, overall in the 2010s compared to the 1990s, the combined impact of LULC change and climate variability as well as separate effect of climate showed an increase in surface runoff, groundwater recharge and return flow in Senegal river and Lake Chad basins, while in Niger river basin most of all water balance components were declined. Frozen climate and change in LULC showed that spreading of natural vegetation at the expense of bare land led to an increase in actual ET and a decrease in surface runoff in the three watersheds, while in Senegal river basin it shows a slight increase in groundwater recharge and return flow. At sub-basin level, the analysis of LULC change showed that the gain in cropland and urban areas at the expense of the forest in some sub-basins, led to a local increase in surface runoff. This implies a better redistribution of water downstream and compensates the deficit in surface runoff caused by natural vegetation at the expense of bare land in some other catchments, i.e. a beneficial increase in fresh water availability. These changes at the same time with high intensity and long duration precipitation, this is likely to be a source of inundation and soil erosion in some small catchments in Niger river basin. Globally, the climate variability had a dominant impact on increasing water balance components resulting an increase in fresh water availability, with an extension and recovery of lake area in Lake Chad, which also increased groundwater return flow to rivers and water recycling within Senegal river and Lake Chad basins. In contrast, the LULC change was the major driver of decreasing the surface runoff, which could be a reason for lake area depletion in Lake Chad. At the same time, the two factors led to increasing water scarcity in Niger river basin. These outcomes emphasize the crucial role of water recycling which is the amount of water transferred from a sub-basin upstream to the next downstream within the watershed as well as give a good hydrological insight about water and land management in the study area. These findings are relevant to water resource management and to advance towards water-related Sustainable Development Goals (SDGs).

Keywords: African Sahel, SWAT model, ETMonitor, remote sensing soil moisture, LULC change, climate variability.

Water accounting is an important process to enhance water management and support sustainable water use, which involves all components of the natural water cycle and is closely related to human activities on the water cycle. The blue-green water concept are introduced in the water accounting, which can expand the scope of traditional water resources and provide a more comprehensive and realistic understanding of water resources. According to the difference of water sources, the actual evapotranspiration (ET) could be partitioned into green water ET (GWET, from green water) and blue water ET (BWET, from blue water), which are key parameters in water accounting. However, current ET remote sensing products generally only provide total ET and lack GWET and BWET information, which limits their application in water accounting. In this study, three methods were used to partition GWET and BWET based on ETMonitor, CHIRPS and land use/cover data of the Heihe River Basin in the arid-semi-arid region. The three partitioned ET methods include the precipitation deficit method (i.e., precipitation minus evapotranspiration (P-ET) method, or PD), water balance method (WB) and Budyko method (BD). The results showed that the GWET estimated by the WB and the BD were similar, while the GWET estimated by the PD was higher than the other two methods. Compared with the observation and simulation data of field experiments, the GWET estimated by the three methods is overestimated in the Heihe River Basin, among which the PD has the largest deviation, while the WB has the best results, followed by the BD. The irrigated districts in the middle reaches of the Heihe River, BWET (average 357.5 mm) was much larger than GWET (average 141.4 mm), and the average of its three method results accounted for 71.65% of the total ET. Moreover, BWET was larger than precipitation (178.3 mm), which indicats that irrigation plays an important role in maintaining agroecosystems in this region. This study can help improve the comprehensive water resources and land use management capabilities of the basin.

Accurately measuring the amount of water (e.g., evapotranspiration—ET) and energy (e.g., of latent and sensible heat) that are exchanged at the Earth's surface is crucial for various applications in fields such as meteorology, climatology, hydrology, and agronomy. Having reliable estimations of these fluxes, particularly of ET, is considered essential for effective natural resource management. The distributed ET models are important tool for policy planning and decision-making in terms of calculating the water productivity in agricultural crops. However, the model calibration and validation present a crucial challenging task. The Sentinel-2 and Sentinel-3 satellite constellation contains most of the spatial, temporal and spectral characteristics required for accurate, field-scale actual evapotranspiration (ET) estimation. The one remaining major challenge is the spatial scale mismatch between the thermal-infrared observations acquired by the Sentinel-3 satellites at around 1 km resolution and the multispectral shortwave observations acquired by the Sentinel-2 satellite at around 20 m resolution. The Sen-ET SNAP Plugin bridges this gap by improving the spatial resolution of the thermal images. We have implemented the model for Purvomaj municipality study area in Bulgaria.

Accurate estimation of the leaf area index (LAI) of crops is essential for effective agricultural monitoring. However, the currently most efficient remote sensing systems rely on optical imagery, which makes them less dependable in regions of the world that experience frequent cloud cover. The use of synthetic aperture radar (SAR) data presents a promising alternative to them, offering the potential for reliable LAI estimation at the parcel-level and at large scale even under cloud-covered conditions.
The main objective of this study is to develop an operational framework that enables SAR-to-optical LAI estimation in maize crops, eliminating the need for extensive ground truth measurements of crop and soil bio-geophysical variables.
To validate the retrieval performance of the method, time series of maize LAI will be collected in the field during the 2023 growing season in the Shanxi province of China, as well as derived from Sentinel-2 optical imagery both in China and in a second, geographically distinct region in Belgium.
The anticipated outcomes of this study include the development of a reliable LAI retrieval method, leveraging dual-pol SAR data, and the assessment of its transferability across diverse geographic regions. These advancements have the potential to enhance agricultural monitoring capabilities, particularly in cloud-prone areas, contributing to improved decision-making and resource management in the agricultural sector.

The rice-crop intensity, defined as the number of rice growth cycles per year, is crucial for estimating national rice production. Observing rice-crop intensity using optical data can be challenging due to frequent cloud and foggy weather in Southern China, while Synthetic Aperture Radar (SAR) data can provide a reliable alternative. However, national-scale monitoring faces several challenges, including the diversity of rice backscatter patterns resulting from complex cultivation practices, the inefficiency of time-series denoising and feature extraction, the unavailability of prior knowledge on asynchronous rice phenology, and the overestimation of rice-crop intensity caused by backscatter variations from non-rice land processes. Here, we systematically studied the rice backscatter variations derived from Sentinle-1 under varying local and regional conditions throughout each growth cycle. Then, harmonic analysis was conducted to explore the periodic characteristics of the time-series VH backscatter. A simple profile and trough detection method was proposed to effectively recognize fields’ annual backscatter patterns. Time series temperature data derived from ERA5-Land product were used to parse the potential rice phenology, effectively distinguishing rice growth cycles from non-rice processes. Moreover, overestimations were identified and corrected according to the spatiotemporal temperature suitability for multiple rice-crop intensities. Then, the single (135,537 km²), double (19,036 km²), and triple (259 km²) rice-crop intensities, covering the entire Southern China, were mapped with the Google Earth Engine and achieved an overall accuracy rate of 81.64% at a 10×10 m spatial resolution. The method is expected to support Asian or global rice-crop intensity mapping further. This work is supported by the Dragon project [Granted Number 58944].

Remote sensing technology is widely used in monitoring the ecological environment and crop growth in farmland. Through remote sensing technology, we can monitor and investigate farmland macroscopically, timely and dynamically, which enables us to obtain more comprehensive, accurate and real-time data. With the development of deep learning, deep learning has achieved satisfactory results in agricultural planting area extraction. However, there are still challenges in processing multisource multispectral data. Therefore, using LANDSAT 8 and Sentinel-2 as data sources, central Guangxi and a county in Hunan province were selected as study areas, and the following algorithms were proposed for crop extraction from multispectral data:

(1) Two improved U-Net remote sensing classification algorithms, namely the multi-feature fusion perception based improved U-Net algorithm and the fused attention and multi-scale features based improved U-Net algorithm were developed for central Guangxi using Landsat 8 data. Firstly, both algorithms used U-Net as the base network, utilized multi-scale feature fusion to enhance the expression ability of features, and fused spatial and semantic information using attention mechanism to enable the encoder to recover more spatial information. Secondly, the proposed methods were used to classify land cover in the study area from Landsat images in 2015, 2017, 2019 and 2021, and to monitor dynamic changes in the four periods for dynamic monitoring of crop planting areas.

(2) A pixel-level multispectral image classification algorithm combining Transformer and CNN was developed for Huarong County in Yueyang City, Hunan Province using Sentinel-2 data. Firstly, the features of pixel sequences were extracted using Transformer and CNN, and then fused through a feature fusion module before classification. Secondly, the proposed method was used to classify land cover in the study area from Sentinel-2 images in 2015, 2017, 2019 and 2021, and to monitor dynamic changes in the four periods.

In recent years, with the continuous development of precision agriculture, fine classification of crops is an important way to achieve precision agriculture. The identification accuracy of crop information extraction using mid-to-high resolution remote sensing images that only contain visible light and near-infrared spectra is limited, and it is difficult to achieve accurate identification of crops. In order to improve the classification accuracy of crop information extraction in farming areas, this paper takes the Taiyuan Basin in Shanxi Province as the research area, uses high spatial resolution Sentinel-2 multispectral image data, combined with digital elevation model (DEM) data to construct four types of feature variables: spectral features, texture features, remote sensing index features, and terrain features, and ranks the importance of features for the above feature variables to filter the optimal features. Combining the phenological information of crops, a variety of feature schemes are combined, which are based on spectral features, based on spectral features + remote sensing index features, based on spectral features + texture features, based on spectral features + terrain features, based on spectral features + remote sensing index features + texture features, based on spectral features + remote sensing index features + terrain features, based on spectral features + remote sensing index features + texture features + terrain features. The random forest algorithm is used to finely extract the typical crops in the study area, and the classification accuracy of different feature schemes is compared and verified. Discuss the influence of different feature combinations on the classification accuracy of crops, and provide theoretical basis and technical support for accurate and fast extraction of crop information. Analyze the changes of arable land in the study area to provide a scientific basis for the development and utilization of reserve resources of arable land and rural revitalization.

Traditional climate risk and impact assessments typically consider a single extreme event, a fact that leads to the underestimation of risks, as such events are often interdependent. The principal aim of this study is to evaluate the current state of the climate in Beijing, China, and Athens, Greece in terms of droughts and heatwaves, focusing on their compound effects (CDHW) and examining their association with urban form and fabric factors. The term compound events describe the combined effect of multiple climate factors (processes, variables, phenomena including feedback mechanisms) or climate hazards. In urban areas, these compound events can lead to other challenges, such as increased energy demand for cooling, higher air pollution levels, and impacts on critical infrastructure which can be associated with urban morphology. The determination of the CDHW climatology is carried out through the joint use of an Excess Heat Factor (EHF) and a Standardized Precipitation Index (SPI), according to the general definition of CDHW events (heat waves occurring during the period of drought events), using the high-resolution state-of-the-art ERA5-Land reanalysis product along with ground-based climate data, while Earth Observation (EO) imagery is used to extract land cover information from visible and near-infrared sensors. The study addresses the challenges of CDHW in cities and a range of strategies is proposed that include climate-resilient infrastructure, nature-based solutions, and heat warning systems.

While more and more people migrate to cities, uncontrolled urban growth poses pressing threats such as poverty and environmental degradation. Although sustainable urban planning can mitigate these threats, the lack of timely information on the sprawl of settlements hampers ongoing sustainability efforts. Multi-modal deep learning offers new opportunities for timely and accurate urban mapping and change detection by exploiting the complementary information acquired by Synthetic Aperture Radar (SAR) and optical sensors. In particular, the Copernicus Program's Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument (MSI) missions play a crucial role in multi-modal remote sensing research. For example, our previous work demonstrated that the complementary information in Sentinel-1 SAR and Sentinel-2 MSI data can be utilized to improve the transferability of deep learning models for urban extraction at a global scale (Hafner et al., 2022). However, the optical modality may not always be available due to cloud cover or other atmospheric conditions, which is particularly relevant for multi-temporal urban mapping and change detection. Although a limited number of studies have addressed this so-called missing modality problem (e.g., Zheng et al., 2021, Saha et al., 2022, and Li et al., 2022), multi-modal methods that are robust to a missing modality are still under-researched in remote sensing. Here, we propose a novel multi-temporal urban mapping approach that uses multi-modal satellite data from the Sentinel-1 SAR and Sentinel-2 MSI missions. In particular, our approach focuses on the problem of a partly missing optical modality due to clouds. The proposed model utilizes two networks to extract features from each modality separately. In addition, a reconstruction network is utilized to approximate the optical features based on the SAR data in case of a missing optical modality. Our experiments on a multi-temporal urban mapping dataset with Sentinel-1 SAR and Sentinel-2 MSI data demonstrate that the proposed method outperforms a multi-modal approach that uses zero values as a replacement for missing optical data, as well as a uni-modal SAR-based approach. Therefore, the proposed method effectively exploits multi-modal data, if available, but it also retains its effectiveness when the optical modality is missing.

Self-supervised learning methods are gaining popularity in remote sensing community due to their ability to utilize unlabeled data for representation learning. These representations can then be adapted to downstream tasks through pre-training and fine-tuning. Masked Autoencoder (MAE) is a concise self-supervised learning method that learns better semantic representations by masking most of the content in the input image. However, MAE was originally designed for natural images and may not be the best choice for remote sensing images. We propose a masking method to enhance correlation feature extraction capability. Our proposed model surpasses state-of-the-art contrastive learning and MAE-based models on land-cover classification tasks and reduces input data volume, achieving a more efficient model. Additional experiments demonstrate that the proposed model has good generalization performance and maintains good representation learning capabilities on small-scale data.

Artificial Intelligence (AI) is currently studied mainly for optical imagery, i.e. photography. Earth Observation (EO) images are basically different and much more complex. AI for EO requires specific methods for the full information extraction from spatial, temporal or spectral information at global scale. This involves new paradigms to analyze jointly multimodal sensor records as the EO multi-sensor data optical, IR or microwaves. EO records data of high complexity, physically-based, dynamic, non-linear coupled Earth System. We need to develop new AI paradigms with integrated physical principles into the learning mechanism. These are well beyond and do not emerge form the present cats and dogs recognition techniques. Thus, there is a huge motivation in developing AI for EO methods and exploiting the results.

Eddy interactions, including typical splitting and merging processes, are a popular research focus in oceanography. Automatic splitting and merging identification algorithms are crucial for global eddy interaction research. This study proposes an algorithm for identifying and tracking global mesoscale eddy splitting and merging events based on sea level anomaly (SLA) data. For identification, we present a multilevel eddy detection method that introduces eddygroups and eddytrees to describe the complicated spatial and topological relationships between different levels of closed SLA contours. For tracking, we define an eddy segment, eddy branch and eddy directed acyclic graph (eddy-DAG) to describe the complex topological trajectory of eddies that include at least one splitting or merging event. Only eddies contained within a common eddygroup and with the same polarity can be tracked as sources for merging events or sinks during splitting events. The Global Eddy Graph dataset (DOI: 10.12237/casearth.63369940819aec34df2674d8) extracted 1,905,742 splitting events as well as 1,790,266 merging events from CMEMS’s SLA data (1993-2020). Based on the typical events extracted from the Global Eddy Graph, the normalized results of different remotely sensed sea surface parameters (SSTA, SSSA) or in situ data (drifters) verify the reliability of the dataset and the effect of the interaction between eddies on marine material distribution.

As an important place for shipbuilding enterprises to manufacture and repair ships, docks and berths are the most critical components of shipbuilding enterprises.In the shipyard scene, the dock and berth are closely related to the production status of the shipyard. They are the core land types in the shipyard production status monitoring. Therefore, the production status of the shipyard can be inferred by monitoring the dock and berth in the satellite remote sensing image.In this paper, based on the characteristics that shipyards with different production states differ greatly in remote sensing images, five deep learning networks ( GoogLeNet, integrated network, Xception, VGG and Alexnet ) are used to train and predict the dock data set, and the accuracy and effect of the evaluation model are compared. Then, combined with the shipyard vector data, the production state activity of the shipyard 3km along the coastline is counted. The experiment adopts cross-time series statistics, and selects the areas with different production state activity across time series as the research area ( the research area chooses to avoid factories and many housing construction areas ). Finally, the Sentinel-2A image data of the selected study area in the cross-temporal period was obtained, and the water body was extracted by MNDWI. The water color index (FUI), turbid water index (TWI), cyanobacteria and macrophytes Index (CMI), river pollution index (RPI) were calculated to evaluate the water pollution situation, and the correlation analysis between the activity of the shipyard and the water pollution situation was established.

Landslides are one of the most serious geological disasters in the world, which seriously damage people's property and safety. In this paper, an object-oriented segmentation method is proposed, which combines spectral, terrain and texture features. The Lengqu basin, a tributary of the Nujiang River on the south side of the Tanggula Mountains in China, and parts of the Hunza basin in Pakistan were selected as the study areas. Landslides in the study area were identified using 12.5 m elevation data and Sentinel-2 data. The identification results were validated against images on Google Earth and collected landslide data. The results show that the object-oriented method can extract the landslide boundary accurately. The research results have great scientific significance for disaster prevention and mitigation, line planning and site selection and follow-up maintenance of the Sichuan-Tibet transportation corridor and the Karakorum line.

Remote sensing techniques are widely used for identification of ancient landslides and monitoring their activity In the present study, we used the Hunza valley basin in Pakistan as the study area, and enhanced the DEM (Digital elevation model) based on RRIM (Red relief image map) to identify the ancient landslides the SBAS InSAR (Small baseline subset synthetic aperture radar) technique was also used to monitor the surface deformation rate in the study area from 2004 to 2022 and then the histograms of the radar line of sight deformation rate results were used to categorize the deformation rate results In this research, a total of 157 ancient landslides with activity characteristics were identified It is found that the RRIM method supplemented with InSAR technology can effectively monitor the ancient landslides and avoid the risk by monitoring the hidden ancient landslides in a long time series.

The Karakoram region is located on the tectonic belt and is also a high-risk area for geological disasters. Due to the complex terrain, high mountains and deep valleys, geological disasters such as landslides are prone to occur, and traditional monitoring is extremely difficult to carry out, which hinders the understanding of landslides in the region and leads to a lack of disaster prevention and reduction measures for local landslide disasters. This study is based on the 2021 Sentinel-1A data along the Karakoram Highway, and starts from the identification results of Stacking InSAR technology, focusing on analyzing typical landslides along the Karakoram Highway. Utilizing Small Baseline Subset Synthetic Aperture Interferometric Radar (SBAS InSAR) technology to monitor the displacement characteristics of landslides, and analyzing the causes of landslides in conjunction with the environment in which they occur. The research results are as follows: (1) Based on Stacking InSAR technology, 7 potential landslides along the Karakoram Highway were obtained, all of which are in an unstable state. (2) In 2021, landslides occurred frequently along the Karakoram Highway, and the displacement data of the landslide line of sight showed significant deformation of the Mostag landslide, with a maximum deformation rate of 94 mm/a. The research results are of great significance to the prevention and control of geological disasters along the Karakoram Highway and to serving the national "the Belt and Road" strategy.

A good-quality landslide inventory map is necessary for assessing landslide hazard. However, it remains difficult and time-consuming to produce and update landslide inventories in most regions of the world, especially in mountainous areas with high extension and poor accessibility. Moreover, the inventoried landslides are usually the most morphologically visible on the landscape, while other typologies of large dimensions and more diffuse boundaries are often overlooked. Therefore, new technologies such as satellite remote sensing or advanced landscape analysis are gaining prominence to optimise landslide mapping at regional scale, in terms of time-consuming and cost-effectiveness. In this study, we performed a combination of two well-implemented techniques to improve landslide detection in a mountainous area. These techniques are Differential Interferometric Synthetic Aperture Radar (DInSAR) and Landscape Analysis through the double normalised channel steepness (k_sn) geomorphic index. The southwestern sector of Sierra Nevada mountain range (Granada, Southern Spain) was selected as the case study.

We derived DInSAR mean displacement or velocity maps from Sentinel-1 images through the P-SBAS automated and un-supervised processing chain, that is implemented on the European Space Agency (ESA)’s Geohazard Exploitation Platform (GEP) (https://geohazards-tep.eu/#!). Ascending and descending orbit data was obtained with spanning times from September 2016 to March 2020 and December 2014 to March 2020, respectively, with temporal sampling up to 6 days. The k_sn index was computed through the open Python library ‘landspy’ (https://github.com/geolovic/landspy). The only needed input was a 10 m resolution Digital Elevation Model to extract the drainage network and the k_sn index from rivers.

We identified the unstable areas from the DInSAR ground displacement maps and the k_sn anomalous values from the k_sn map to associate them with large landslides. To delimit the landslides’ boundaries as accurately as possible, it was essential an exhaustive examination of morphologies in the field, as well as the examination of products derived from high-resolution Digital Elevation Models (e.g. hillshade, slope, aspect, rugosity). This work conducted us to provide an updated inventory of 28 landslides, what implies the 33.5% of the analysed area. Most of the identified landslides are large Deep-Seated Gravitational Slope Deformations (DGSDs), that have not been discovered in the Sierra Nevada until this study. This new inventory has relevant implications as landslides are larger and more abundant than previously considered. Our work also emerges the potential of integrating data from DInSAR techniques and Landscape Analysis to detect large landslides and provide updated inventories in mountainous areas. Moreover, we proved that some limitations of both techniques could be well-compensated.

Monitoring mining subsidence dynamically offers valuable opportunities for exploring and examining the directional changes in surface displacement resulting from underground resource extraction. These changes can be significantly influenced by both natural geological environmental factors and human activities. LuTan-1(LT-1) mission is the first L-band bistatic spaceborne SAR mission for civil application in China which provides continuous DInSAR ground deformation results. Although orbital determination accuracy of LT-1 is 5 cm, we conducted a linear fitting and removed the orbital-induced phase ramp by means of Kriging’s interpolation method in this work. The subsidence bowl results derived from LT-1 show good agreement with the results derived from Sentinel-1 between March and April 2022. Furthermore, due to the scarcity of GNSS points and the irregular mining deformation, it is difficult to obtain high precision 3D deformation through GNSS and InSAR. Therefore, we projected continuous 3D GNSS to LOS direction to validate the DInSAR results derived from LT-1 and Sentinel, respectively. Finally, we observed the dynamic process associated to mining activities in this area by using four DInSAR results from different dates. InSAR results revealed obvious directional changes of the spatial location of ground surface displacements, with maximum horizontal displacement of the subsidence bowl of about 1.26 km during the observation time lag of approximate one year. This approach opens the door to the dynamic analysis of mining subsidence by DInSAR method.

This study investigated the use of C- and X-band SAR data for estimation of forest changes (height, biomass and biomass change) in a boreal forest in Sweden. Field plot data (10 m plots) from 2016 and 2021, and lidar data were used as references. Plots with substantial decreases of biomass (due to clear-cuts and thinning) could be detected using Radarsat-2 normalized backscatter images (C-band) while the use of interferometry (InSAR) of TanDEM-X images allowed both accurate biomass and biomass change estimation, and mapping of smaller forest height changes (increase). We conclude that both C- and X-band SAR (Radarsat-2 and TanDEM-X) are useful for estimation of forest decline, while the use of TanDEM-X InSAR provides added value in terms of height information and therefore more accurate estimates of biomass and biomass change.

With the aggravation of global warming, the outbreaks of forest pests happen more frequently and damage huge amounts of forests. Detecting and removing trunk-boring infestations from the forest at an early stage (green attack) is important to avoid spreading. By using remote sensing techniques, forest mortality can be efficiently detected and mapped; however, achieving early detection of the infestation is still challenging because the spectral changes are subtle.

This study assessed the detectability of the green attacks by the European spruce bark beetle (Ips typographus, L.) using multispectral drone images. The scientific questions and objectives are (1) testing whether the infestations showed detectable vulnerability before attacks, (2) quantifying the detectability of the attacked trees with different duration of infestations, (3) testing the detection performance using a single vegetation index (VI) in comparison with machine learning models with multiple variables, and testing their performance when applied on untrained areas, and (4) testing the performance of the MR_DSWI2 index we proposed in a previous study.

The study used multispectral drone images covering 24 plots from 6 forest stands in southern Sweden, acquired in May (before attacks), June (green attack), August (green and yellow attack), and October 2021 (red attack). Weekly field inventory was conducted on 997 spruce trees and the starting weeks of the infestations were recorded for 208 attacked trees. Drone images of individual-tree crowns were segmented using marker-controlled watershed segmentation, and 10 VIs [5] were calculated for every single tree. Trees with the same duration of infestation were grouped for the analysis. Random Forest Classification (RF) and linear discriminant analysis (LDA) models were built using (1) all bands, (2) all VIs, and (3) the four bands used for MR_DSWI2. Three LDA models were also built using MR_DSWI2, NDRE2, and NGRDI indices, respectively. To test the potential overfitting and test the applicability of the models on mapping untrained areas, two ways of separating training and testing data were used and compared. Method A trained the model using trees from 5 stands and tested on the trees from the remaining stand. This design tested the performance of the models in untrained areas, and overfitted models would yield low accuracy. Method B did not separate trees from different stands, but randomly assigned 90% of all trees for training and the other 10% for testing. Method B is commonly used for separating training and testing dataset, but it could not show the potential performance when applied to untrained areas. When comparing the results from Method A and B, a potential overfitting could be exhibited. The classification accuracy was presented using the Kappa Coefficient.

The results and conclusions are: (1) When using models with more dimensions and higher complexity (i.e., the RF models), the detection had high accuracy in the trained areas but low accuracy in untrained areas. Overfitting was more prominent with those models, and Method B, i.e., testing models with data from the same areas as the training data, could not indicate the applicability outside the study area. Thus, testing the model performance using Method A was proposed and results were further discussed. (2) When testing on untrained areas (Method A, Figure 1), no model successfully classified healthy and attacked trees before the attacks. We conclude that the attacked trees showed no vulnerability before the attacks. (3) When testing on untrained areas (Method A, Figure 1), no model successfully separated healthy and attacked trees when infested for less than 5 weeks. The green attacks during 1-5 weeks of infestation were almost undetectable. During 10–13 weeks of infestation, the detectability increased, with 0.67–0.75 of the median kappa coefficients using the best classification model (LDA by MR_DWSI2, Figure 1). Trees infested for 19–22 weeks (red attack) showed high detectability. (4) Among all classification models, using LDA on the MR_DWSI2 index achieved the highest and most stable accuracy at various infestation stages, followed by LDA using the four bands and LDA on NGRDI.

【Objective】Using the five bands (P/L/S/C/X) of quad-pol SAR data acquired by the High-resolution Airborne System, this study analyzed the response law and sensitivity of different band signals to forest stem volume. Moreover, the capability of estimating stem volume based on both single-frequency and multi-frequency PolSAR data was evaluated. 【Method】Combined with field measurements and airborne LiDAR data, forest stem volume was scaled-up to the entire study area, and a total of 196 samples were obtained through stratified sampling. Based on the samples, the forest stem volume estimation capability of multi-band polarimetric SAR was evaluated. The Water Cloud Model was utilized to analyze the response law of SAR backscattered intensity to stem volume across different bands, and the dynamic range and saturation point were quantified. Furthermore, multiple polarimetric decomposition components were extracted and their sensitivity to stem volume was analyzed using correlation coefficients. On this basis, random forest and support vector regression algorithms were used to perform feature selection and regression modeling. 【Result】The Water Cloud Model analysis revealed that the dynamic range of the longer wavelength (P/L) is higher compared to the shorter wavelength (S/C/X). In particular, the saturation point for the P band exceeds 160 m³/ha, whereas it does not surpass 100 m³/ha for the other bands.The correlation analysis results indicated that the correlation between the P band, L/S bands, and C/X bands and stem volume decreases in order, with values above 0.6, between 0.3-0.4, and below 0.3, respectively. When estimating stem volume using a single band, the accuracy of the P band was 73.79%, while other bands did not exceed 60%. When using multi-band joint estimation, the combination of L/S and P bands improved the estimation accuracy by approximately 2% compared to using the P band alone. The contribution of adding the C/X band to the accuracy improvement was minimal. The best estimation result was obtained by combining all the bands, achieving an accuracy of 77.25%. 【Conclusion】Taking into account factors such as signal dynamic range, saturation point and correlation, the P band is the most sensitive to forest stem volume, which is significantly better than the other bands. The L/S bands are second in sensitivity, while the C/X bands are the least sensitive. When estimating forest stem volume using PolSAR data, the P band should be the first choice. Additionally, when using multi-band joint estimation, the combination of P and L/S bands should be preferred.

Cold-temperate Forest (CTF) is a vital carbon sink and source for the world economy but has been significantly disturbed by intensified human activities and climate change such as deforestation, fires, pests, diseases, etc., resulting in a significant degradation trend, which has an impact on the regional and global carbon budget process and its assessment. However, the pattern of forest disturbance in CTF in northern China is not well known. In this paper, Genhe forest area, a typical CTF region located in Inner Mongolia Autonomous Region, Northeast China (about 2.001×10⁴ km²), was selected as the study area. Based on the Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the Continuous Change Detection and Classification (CCDC) algorithm incorporating spectral indices and seasonal characteristics to detect forest disturbances in nearly 30 years. First, we created six (2 seasons × 3 indices) interannual time-series seasonal vegetation index datasets to map the forest coverage using the between-class variance algorithm (OTSU). Second, we improved the CCDC algorithm incorporating with vegetation index and seasonal characteristics to extract the disturbance information. Finally, we evaluated how the disruption relates to the climate and human activity. The results showed that the disturbance map produced by using summer (June-August) imagery and the EVI vegetation index had the highest overall accuracy of 84.15%. Forests have been disturbed to the extent of 12.65% (2137.31km²) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. But there was an unusual increase in the disturbed area in 2002 and 2003 due to large fires. Monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrates that CTF disturbance can be robustly mapped using CCDC algorithm based on Landsat time-series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

The multi-temporal polarimetric SAR data provides the difference of scattering characteristics in time dimension for scene observation, hence it could reflect the time-variant characteristics of the same scene. Based on this advantage, classification is one of the important applications of multi-temporal polarimetric SAR data. However, the features of time and polarization dimension used for classification basically are from the data at each certain time, which lack the interpretation of the scattering variant characteristics between multi-temporal data. To solve the problem, based on the specific data representation models for multi temporal polarimetric SAR data, this paper extracts new time-variant scattering features, including the change type as well as the change direction of scattering, which the previous static temporal/polarimetric features cannot provide.

Time series Radarsat-2 data is used for experiments. It includes 8 Fully PolSAR images from April 14,2009 to September 29,2009. The data interval of each scene is 24 days. The image size is 5300*3100 pixels. It contains 22 categories. Based on the difference model T_m=T_i-r_mT_j（T_i,T_j are polarization coherence matrices of two times polarimetric SAR data, and r_m is the smallest eigenvalue of T_i^-1T_j, it can make sure that T_m is always a positive semidefinite Hermitian matrix）, we extract a series of time-variant scattering features, using the components of H/A/α decomposition and Freeman decomposition to classify. Through analysis the classification results of transformer classifier, compared with traditional static features, using the proposed time-variant scattering features to classify can effectively improve the classification accuracy.

As a kind of active microwave remote sensing, SAR (Synthetic Aperture Radar) is very suitable for the resolution of different features with its all-weather and all-day characteristics. In order to improve the resolution of different features and make in-depth analysis and research on different feature types, it is necessary to use the multi-band and multi-polarization information of SAR. The transmission characteristics and the backscattering characteristics of target echoes are different for SAR of different bands, and the fusion of SAR images of different bands can better integrate the information of SAR images of different bands.

In this paper, the experimental data were selected from the airborne data acquired by two Xinzhou 60 remote sensing aircraft modified by the Air and Space Academy of Chinese Academy of Sciences under the support of the Chinese Aeronautic Remote Sensing System (CARSS) construction project. Fully PolSAR data including C and S bands contains five types such as paddy fields, forested lands, dry lands, artificial buildings and water. And it is classified using scattering features such as H/A/α and Freeman decomposition components. In order to make full use of the advantages of multi-band, a multi-band fully PolSAR image fusion method based on wavelet transform is also proposed in the paper, which takes advantage of the wavelet transform for multi-resolution fusion and combines the variation of scattering features of different feature types by different bands, and is applied to airborne C-band and S-band SAR images to realize the classification of different types of features in the same area, which can effectively improve the classification effect and increase the classification accuracy.

Polarized synthetic aperture radar (PolSAR) can obtain rich feature information by receiving electromagnetic waves from different features at different polarization combinations, and is widely used in fields such as feature classification. The coherence of multi-temporal polarization SAR data is a useful supplement to polarization SAR data, which contains information that is not available in single phase polarization data. This paper aims to introduce temporal coherence analysis into dual polarization data information and coherently process SAR images acquired at different times in the same region, so as to effectively combine the information of both time dimension and polarization dimension and improve the accuracy of crop classification of Sentinel data. In order to fully extract the changes of feature in the time dimension, this paper obtain the distinction and connection of each category in temporal features by using the multi-temporal feature information of SBAS. Based on multi-temporal polarization SAR coherence optimization, Sentinel-1 data is used for experimental analysis and verification. Data were collected from the time series dual-polarization data of Yucatan Lake area from April to September 2019. The effects of different polarization states on the characteristics of crop species is explored and the classification effects of the optimized values of coherence features is analyzed.

With the BIOMASS mission, developed by the European Space Agency, P-band polarimetric interferometric synthetic aperture radar (PolInSAR) is expected to provide a fresh perspective on the estimation of above-ground biomass in global forests. However, in sparse boreal forest areas, the strong penetration of P-band interferometric SAR (InSAR) can lead to a loss of forest canopy information, resulting in a biased estimation of forest AGB. In contrast, X-band InSAR signals are sensitive to forest canopy information. By combining the two type approaches, forest parameters such as height, density, and AGB can be effectively extracted. Furthermore, there are a number of X-band InSAR satellite systems which is expected to be launched or already in operation, such as Tandem-X. The data from those systems has great potential to collaborate with BIOMASS data to estimate forest AGB. In summary, a novel forest AGB estimation algorithm based on dual-frequency InSAR data has been proposed for accurate estimation of above-ground biomass in boreal forests. This approach utilizes the difference in penetration ability of P-band and X-band InSAR in forest areas to extract forest height without bias. Based on this, a multi-dimensional feature set has been constructed, including direct information such as forest height and density, as well as indirect information such as backscatter intensity, polarization features, and image texture, to achieve the estimation of forest AGB. To verify the method, experiments were conducted based on airborne P-band PolInSAR data and spaceborne X-band InSAR data.

Synthetic aperture radar tomography (TomoSAR) is a three-dimensional imaging technique developed on the basis of multi-baseline SAR interferometry (MB-InSAR). In forest scenarios, TomoSAR can achieve vertical characterization of forest scatterers, which is an effective method for extracting structural parameters such as forest height and above-ground biomass (AGB). However, due to factors such as baseline errors and changing atmospheric conditions, phase errors between MB-InSAR data cause severe sidelobe effects or even complete defocusing in tomography imaging.

In the study, we compared the calibration methods based on polynomial fitting (PF) and entropy minimization (EM), as well as proposed a novel approach combine the two methods. All three methods are tested based on airborne P-band MB-InSAR data, and the accuracy of the forest height extracted based on proposed method is verified based on airborne light detection and ranging (LiDAR) data. he results indicate that the proposed approach outperforms the other two methods in term of tomography imaging and can accurately determine forest height with an accuracy of over 80%.

Grassland is an important component of terrestrial ecosystems, but due to human activities and natural changes, the productivity and ecological service capacity of grassland ecosystems have declined. Ecological environmental problems such as land desertification and grassland degradation have become hot topics of global concern. Therefore, timely and accurate monitoring of changes in grassland type distribution, vegetation utilization, and intensity is of irreplaceable importance for protecting the ecological environment. High- and medium-resolution optical imagery is the most commonly used data source for grassland remote sensing monitoring. However, due to limitations in data acquisition capabilities, it is not possible to obtain time-continuous data using a single data source, which affects the precise monitoring of grassland distribution, utilization, and intensity. With the increasing availability of different high- and medium-resolution remote sensing data, the fusion of multiple data sources to generate high spatiotemporal resolution data for grassland monitoring has been widely applied. However, there is currently limited research on grassland monitoring that considers the fusion of China's high-resolution data with other medium- to high-resolution data without introducing low spatial resolution data information. To address this issue, this study aims to use a combination of China's satellite products and other high- to medium-resolution optical remote sensing data to generate daily high spatiotemporal resolution surface reflectance data for grassland monitoring. This study was conducted in a 50×50 km study area in Hulunbuir grassland. First, based on the coordinated satellite products Landsat, Sentinel-2 (HLS) data, and GF-6 WFV image data, the spectral reflectance conversion equation between the two was constructed using the least squares method to transform the reflectance of GF-6 WFV. Second, based on the acquired real image data set, the local linear interpolation method was used to fill in the missing images to generate a daily data set. The Savitzky-Golay spatial filtering algorithm was used to smooth and denoise the time series data set to construct the daily high spatiotemporal resolution surface reflectance data set (HLSG). Finally, based on the time series vegetation index information, the grassland utilization intensity index was proposed. The study showed that time series reconstruction based on GF-6 WFV and HLS data can solve the problems of data missing, quality, and accuracy, thereby improving the reliability and accuracy of medium spatial resolution time series data. Moreover, the grassland utilization intensity estimation method based on the daily NDVI time series data set can well reflect the differences in grassland utilization intensity and has important significance for monitoring the utilization status of grasslands.

Vegetation is an important material foundation for the ecological function of grassland ecosystem, and the long-term changes in vegetation status can provide important information reference for the study on evolution laws of various ecosystems. Therefore, based on the normalized vegetation index product (NDVI, MOD13A2) of long time series (2000-2020) MODIS data, an improved directional pixel binary model was used to construct the annual vegetation coverage dataset of the Beijing-Tianjin Sandstorm Source Area (BTSSA) from 2001 to 2021. Research methods such as Sen-Mann Kendall time series trend significance test and spatial statistical analysis were used to analyze the spatiotemporal changes in vegetation coverage in the region over the past 20 years. The results showed that in the past 20 years, 50.94% of the study area had significantly decreased vegetation coverage, while less than 2% had significantly increased vegetation coverage. This indicates that due to urban development and climate change, the vegetation situation in the BTSSA has caused some damage to a certain extent. These research results, combined with climate and underlying surface change data, can provide important information support for subsequent research on grassland ecosystem.

In this paper, continuously altimetric satellite sea surface height measurements from Cryo-TEMPO between 2011 and 2020 are used to illustrate that the NPO plays a significant role in connecting the Beaufort Sea level to the Pacific Ocean. It is found that summertime NPO has a significant negative connection with sea surface heights in the Beaufort Sea. A negative NPO phase tends to be associated to an intensified Beaufort High paired with anomalous anticyclonic circulations over the Arctic, contributing to positive SSH anomalies locally because of increasing more freshwater entering the Beaufort Sea from the Chukchi Sean through Bering Strait. CESM2-LE is used to examine the connection between North Pacific teleconnection and the Beaufort Sea level change for longer time spans. It is suggested that the remarkable relationship between SSH in the Beaufort Sea and NPO is reproduced during 2011–2020, 2000–2020 and 1990–2020. In addition, the pre-winter SST may be a predictor for SSH in the Beaufort Sea. These findings highlight that the impacts of the teleconnection and SST anomalies in North Pacific on the Arctic sea level are of great importance and need to be taken into consideration when evaluating future climate predictions and projections.

The increasing influence of the Atlantic Water (AW) in the Arctic, known as “Atlantification”, has been an important topic of scientific interest for several years. Recent studies reiterated the need to have a better understanding of AW transformation in Nordic Seas (NS) to understand and predict the ocean’s role in ongoing and future Arctic climate change (Asbjørnsen et al., 2020). A “missing puzzle” yet to be studied in detail is the role of mesoscale eddies on the Atlantification. Eddies generated from instabilities of the mean-flow (Stammer and Wunsch, 1999) are ubiquitous features in the NS (e.g., Raj et al., 2016) whereby mean kinetic energy is transformed to eddy kinetic energy with subsequent reduction in the mean northward flow of the AW. Eddies can also capture and trap heat and salt from the mean AW flow (Bolenenko et al., 2020), thereby cooling the AW poleward heat transport (Isachsen et al., 2012). In regards to the Atlantification in the Arctic Ocean, the question is therefore related to the occurrences of eddies in the NS over the last decades; has the number of eddies changed or is it stable?

In this poster we present the results of two distinct analysis of 11 years (2011-2022) of satellite sensed data (interpolated to 25 km spatial resolution at monthly to seasonal timescales) combined with mesoscale eddy tracking to advance the insight of mesoscale eddy activity and upper ocean circulation in the NS. First, the Multiparameter Eddy Significance Index (MESI) proposed by Roman-Stork et al.(2023) is estimated. The index combines sea level anomaly, sea surface temperature and salinity fields, chlorophyll distribution and eddy kinetic energy for all the eddies in the NS. Second, climatologies of the singularity exponents for the satellite-based sea surface temperature and salinity values are provided. The singularity exponent is expected to reveal mixture of horizontal transport and dispersion processes of the upper ocean circulation with particular focus on the impact of mesoscale eddies.

In this presentation we will highlight the findings and results in relation to: (i) observed changes in the annual number of eddies in the NS from altimetry; (ii) assessment of the number of eddies based on the MESI approach; and (iii) consistency between the climatology of singularity exponents and MESI. The relevance of the results, in turn, will also be discussed in relation to the Atlantification of the Arctic Ocean.

High-resolution soil moisture data are essential for studying the complex interactions between the water, energy, and carbon cycles from local to global scales. For agricultural and hydrological applications, a 1 km global-scale soil moisture product is of great interest to the community. In this study, we propose a new dual-polarization algorithm (DPA) for soil moisture retrieval using C-band synthetic aperture radar (SAR) observations. Based on this algorithm, a Sentinel-1-based global-scale soil moisture dataset with a spatial resolution of 1 km (S1-DPA) was generated. Specifically, using optical data as a proxy of vegetation water content, a semi-empirical forward model from soil moisture to backscattering was constructed and calibrated based on the relationship between Sentinel-1 SAR backscatter and SMAP (Soil Moisture Active and Passive) soil moisture product under different vegetation and soil texture conditions. With the calibrated forward model, soil moisture was estimated using the backscatter coefficients on VV and VH polarizations observed by Sentinel-1 C-band SAR in ascending and descending orbits. The S1-DPA soil moisture data product has the same temporal resolution as Sentinel-1, of 3-6 days for Europe and 6-12 days for other regions. It covers the global land surface and spans the period from 2016 to 2020, utilizing both daily ascending and descending data. The S1-DPA product was validated using ground measurements from the International Soil Moisture Network (ISMN). The results show that the S1-DPA product captures the spatial and temporal characteristics of in-situ soil moisture reasonably, with an overall median Pearson correlation of 0.372, bias of -0.003 m³/m³, RMSD (root mean squared difference with respect to in-situ measurements) of 0.105 m³/m³, and ubRMSD (unbiased root mean squared difference) of 0.076 m³/m³. The generated global 1 km soil moisture product has the potential to promote the application of high-resolution soil moisture data in the fields of hydrology, ecology, and meteorology.

The Tibetan Plateau is a climate change sensitive and ecologically fragile area. Global climate change is prone to have an higher impact on the region's local climate than other regions, and therefore have a potential that impose a significant influence on local ecological environment. Therefore, this study aims at evaluating the climate change in the Tibetan Plateau and its impact on vegetation in the plateau in the past up to the end of 21th century in the future. First of all, this study uses CN05.1 meteorological data to first conduct trend analysis, mutation analysis, and periodic analysis on precipitation and temperature in the Tibetan Plateau region over the past 40 years (1979-2017). Then, combined with 11 GCM model data and CN05.1 data from the CMIP6, the ability of a single climate model, a full model set (MME) and a better model set (BMME) to simulate precipitation and temperature in the Tibetan Plateau was evaluated using Taylor chart, interannual variability assessment index and rank scoring method (RS method). A set of optimal models with good simulation capabilities of precipitation and temperature under three future climate scenarios (SSP126, SSP245, and SSP585) was selected, and the Delta method was used for bias correction. After that, by utilizing CN05.1 data, GIMMS NDVI data, and 1:1000000 vegetation distribution map data in China, methods such as linear regression analysis, Sen's slope, Hurst index, partial correlation coefficient, and residual analysis were applied to explore the dynamic changes of existing vegetation in the Tibetan Plateau and its response to climate factors. Finally, based on the corrected CMIP6 climate model data, the CSCS model and land use transfer matrix were used to analyze the potential vegetation distribution and changes in the Tibetan Plateau under three different climate change scenarios in the early 21st century (2021-2040), mid 21st century (2041-2060), and late 21st century (2081-2100).

Precipitation is an important component of water circulation and an essential input for various hydrological models. A high quality, high spatial resolution, and long-term precipitation dataset would benefit hydrological investigations, particularly for regions having insufficient precipitation records. The upper Huaihe River Basin (UHRB) was selected as the research location in this study, and the accuracies of three precipitation products (PPs: a high-resolution daily gridded precipitation dataset for China (HRLT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Global (CPC) precipitation dataset) were assessed at multiple spatio-temporal scales comparing with the gauge precipitation (GP) for 2000–2019. Subsequently, the applicability of the three PPs on streamflow (Q) and sediment yield (SY) simulations, as well as the impact on parameter sensitivity, were compared using the Soil and Water Assessment Tool (SWAT) model. The results showed that the accuracy of the three PPs were ranked as CPC > HRLT > PERSIANN-CDR on the watershed average scale, HRLT would underestimate the extreme precipitation; and PERSIANN-CDR would overestimate the annual precipitation. On the grid-to-point scale, PERSIANN-CDR was found to be the most stable with high accuracy, followed by CPC and HRLT on all temporal scales. The ability of these PPs to detect rainfall events was ranked as CPC > HELT > PERSIANN-CDR. The sensitivity of the Q parameters changed with the variation in the precipitation input. The sensitive parameters for GP were distributed on average for almost all processes, while the sensitive parameters for PPs mainly controlled the groundwater and evapotranspiration processes. Among all the PPs, the performance of CPC in the Q and SY simulations was found to be the best, followed by HRLT and PERSIANN-CDR, and all the PPs could simulate SY better than Q in spatial distribution. HRLT has the potential to be used in long-term hydrological simulations in ungauged or small watersheds based on its high spatial resolution compared to other products.

Accurate calculation of evapotranspiration at the basin scale can provide the information for dynamic analysis of the hydrological cycle within the basin. In this study, The Qinghai-Tibet Plateau (TP) , consisting of 12 watersheds, was used as the study area. The process of realization of the medium-scale evapotranspiration calculation by the REOF-3T model can be generalized as follows. Each watershed was divided into several subregions based on the analysis results of the rotated empirical orthogonal function (REOF) method for 10a downward shortwave radiation. The modified 3T model was used to calculate the evaporation in the subregions, thus realizing the distributed calculation of the 3T model. To validate the accuracy of the model, site observations and other remote sensing products were compared to the calculated ET series. The results showed that the REOF-3T model has a significant correlation with the average ET in 8 days of six eddy covariance flux stations over the TP. The Pearson’s correlation coefficient (R) of EC observed sites ranged from 0.6 to 0.78 (P<0.01), the root-mean square error (RMSE) ranged from 1.006 mm/d to 1.408 mm/d. The estimated ET (REOF-3T model) also displayed a good consistency with the observed ET (water evaporation) in 93 meteorological stations during 2008 – 2018. More than 93% of sites have R-values over 0.6. The average annual R in 93 stations exceeded 0.9, except for 2008, 2016, and 2018. There is an increasing trend of ET in the southwestern of TP, especially in the upper Yangtze River basin. While the north and northwest are on a downward trend.

Although global land-atmosphere energy and carbon fluxes is a key driver of Earth’s climate system, global continuous high resolution fluxes datasets are still limited. In this study, we used the STEMMUS-SCOPE simulations at 170 FLUXNET sites as the training dataset, enabling the physics-informed Machine Learning (PIML) to generate a global, long-term, spatially continuous high resolution dataset of fluxes. STEMMUS-SCOPE model is a process-based model simulating water, carbon, and energy fluxes, along with predicting leaf to canopy photosynthesis, reflectance and fluorescence spectra, as well as subsoil moisture and temperature dynamics. Results show that PIML can estimate fluxes with Pearson Correlation Coefficient score (r score) 0.99 for latent heat (LE), and 0.99 for sensible heat (H), and the root mean square error (RMSE score) are 12.89 W/m² and 18.6 W/m² respectively. It can also predict net radiation (Rn) with r score 0.99 and RMSE 7.54 W/m², and root zone soil moisture (RZSM) with r score 0.99 and RMSE 0.0045 cm³/cm³. With solar induced chlorophyll fluorescence (SIF), the r score is 0.99 and RMSE lower than 0.03 W/m²/μm/sr. Incoming shortwave radiation, surface soil moisture, and air temperature are the main predictor variables that determine the prediction performance, followed by incoming longwave radiation and wind speed etc.

Soil moisture is an essential variable in the hydrological cycle and exhibits a strong connection to weather and climate change. The comprehensive understanding of the physical mechanism underlying brightness temperature enables more accurate estimation of soil moisture. The integration of process-based understanding into machine learning models has the potential to leverage the advantages of both methods. This research aims to develop an emulator using machine learning algorithms to conduct a forward simulation of ELBARA-III brightness temperature at L-band. A combination of meteorological data, in-situ soil moisture and soil temperature data and vegetation parameters was used for training. A total of four years’ data, encompassing various combinations, is employed for training purposes, resulting in the construction of 64 models each for horizontal and vertical polarizations. The best-performing model exhibits a correlation coefficient of R = 0.995 for horizontal polarization and R = 0.998 for vertical polarization. Notably, there was a significant enhancement in performance after incorporating the observed data for model training. The primary objective of this research is to investigate the underlying physical mechanisms involved in the emission process and explore the potential of employing machine learning algorithms for simulating microwave signals across extensive spatial and temporal domains. These findings suggest that while random forest regression and support vector regression can capture the general variation trend observed in brightness temperature, some challenges remain. During specific time periods, such as the transition season of October and November, the models' predictions appear smoother and fail to fully capture all signal fluctuations.

Synthetic aperture radar (SAR) is broadly known for its high-resolution imaging of the ocean surface under all weather conditions during day and night. The Doppler centroid anomaly (DCA) derived from SAR imagettes has been evidenced to well capture the line-of-sight component of ocean current velocity. Several studies have reported the analytical basis of DCA method and the monitoring of major current systems. Its applicability under tropical cyclone (TC) events is not yet examined. In this study, we focus on demonstrating the spatial features of DCA obtained over TC Maria (2017) and Cimaron (2018) as well as it relation to the surface winds. We found that the Doppler velocity provides a promising spatial representation of the surface flow under tropical cyclone wind forcing. The Doppler velocity exhibits an asymmetric feature similar to the radial wind speed with larger velocity-to-winds ratio in the front than in the rear quadrant. The combined Doppler velocity resulted from a tropical cyclone and the Kuroshio Current is distinct, particularly over the regions of encountering flow. The results shall shed light on the SAR observational capability of TC-induced surface velocity and extends our understanding of how the winds and current are coupled under TC.

Due to signal degradation or saturation within tropical cyclones, accurate estimation of inner-core high winds using satellite remote sensing is still challenging. To address this, we propose a deep learning-based approach that leverages generative adversarial networks (GANs) to reconstruct the inner-core high winds from satellite remote sensing data. Our deep learning-based model integrates dilated convolution and attention mechanisms to improve this underestimation issue of synthetic-aperture radar (SAR) data in tropical cyclones. We also tackle the scarcity of SAR data by developing a GAN model that uses Hurricane Weather Research and Forecasting (HWRF) data as a proxy to simulate missing SAR data via transfer learning. To improve the transfer learning process, we explore different pre-trained models and expand the HWRF dataset used for training the deep learning-based models. Additionally, we aim to investigate other machine learning algorithms to enhance the accuracy of scatterometer wind products (e.g. Chinese Haiyang-2 and CFOSAT). We also employ machine learning approach to fuse multi-source data for synergistic monitoring of ocean dynamic environment, specifically in the context of tropical cyclones. In summary, our study demonstrates the potential of deep learning technology for tropical cyclone reconstruction and monitoring using satellite remote sensing data，which can contribute to improving the accuracy of wind products in the context of tropical cyclones.

The CFOSAT (Chinese-French Oceanic SATellite), carrying the first Ku-band scatteromenter (SCAT) with rotating fan beam, was successfully launched in October 2018. The preliminary quality assessment of CFOSAT SCAT wind data is carried out through the comparison for the period from Jan 2019 to Jun 2021 operationally released products with in situ measurements. The reference winds include in situ measurements from offshore (> 50 km) meteorological buoys of the National Data Buoy Center (NDBC) and serval research vessels. All in situ winds were converted to the 10 m equivalent neutral winds using the coupled ocean atmosphere response experiment (COARE) bulk algorithm. The temporal and spatial differences between the CFOSAT SCAT and the in situ observations were limited to less than 30 min and 12.5 km. For CFOSAT SCAT wind speed products, the comparison and analysis using the NDBC buoys yield a bias of 0.34 m/s, a root mean square error (RMSE) of 1.24 m/s. Although less accurate of CFOSAT SCAT wind direction at low winds, the RMSE of 19.76 deg with a bias of 1.13 deg is found for wind speeds higher than 4 m/s. Moreover, CFOSAT SCAT winds were evaluated against anemometers in situ onboard R/Vs , whose cruise were distributed globally. The comparison results against R/V winds are found consistent with those by the widely used NDBC buoys. The encouraging assessment results show that wind products from CFOSAT SCAT satisfy the mission specification and will be useful for scientific community.

The Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT) can provide wave spectra using its off-nadir beams. Although SWIM shows a reasonable performance for capturing spectral peak, the accuracy of mean wave periods (MWPs) computed directly from the SWIM spectra is not satisfying due to the high noise level of the spectra. SWIM can also provide good-quality simultaneous wind speed (U10) and significant wave height (SWH) like an altimeter. The MWP can also be estimated using a U10-SWH look-up table presented in previous studies. However, the accuracy of this method is also limited as the U10-SWH look-up table is only applicable for wind-sea-dominated conditions. The two MWP retrieval methods are independent of each other, and their error properties are complementary to each other. Therefore, this study further presents a merged MWP retrieval model combining the nadir U10-SWH and the MWP from the off-nadir spectrum of SWIM using a simple artificial neural network. After training against some buoy data, the model reaches unprecedented accuracy for MWP retrievals (RMSEs of ~0.36 s for zero up-crossing periods, ~0.41 s for mean periods, and ~0.60 s for energy periods), demonstrating the usefulness of SWIM in the studies of ocean waves.

Satellite Radar Altimetry (RA) missions are one of the important means of global ocean observation and global and regional sea level changes monitoring. Satellite radar altimetry technology can provide continuous, all-weather, nearly whole coverage observations of global ocean. Atmospheric Wet Tropospheric Delay (WTD) is one of the error sources in satellite altimetry. The WTD with the range of 0~50cm is related to the variabilities of tropospheric water vapor and cloud liquid water in the radar signal propagation path, and varies spatially and temporally. The HY-2C satellite is the third China's marine dynamic environment monitoring satellite, which carries Radar Altimeter (RA), Scanning Microwave Radiometer, Microwave Scatterometer and Calibrated Microwave Radiometer (CMR). The CMR can provide WTD data for the correction of RA sea surface height. Due to the pollutions of coastal land, sea ice, rainfall and anomalies of instrument, CMR wet tropospheric delay sometimes has large errors or even is missing. In order to solve the problem of missing or low accuracy of CMR WTD data, the Wet Tropospheric Correction (WTC) of HY-2C RA is carried out by GNSS data, reanalysis data and other microwave radiometer data in this study. Taking the WTD correction data obtained by ERA5 reanalysis data as the background field, and combining the effective CMR WTD data along the ground track of the HY-2C altimeter, the nearshore WTD data obtained by the GNSS data and the WTD data obtained by other satellite microwave radiometers, multi-source data fusion was carried out on the basis of retaining the effective CMR WTD data by using spatiotemporal matching and objective analysis methods. Eventually, the missing CMR WTD data are filled and the accuracy of sea surface height measurement of HY-2C RA is improved to meet the growing data demand.

Internal tides are internal waves with tidal frequency those occur in the stratified ocean and are generated by barotropic tides flowing through steep topography. The internal tide is an important body of energy transfer, propagation and dissipation of barotropic tide in the ocean, and is also one important factor of driving the vertical transport of ocean nutrients and influencing ocean structure and ocean circulation. The study of the propagation and dissipation of internal tides at different spatial and temporal scales has been one of the important directions in marine science. Although both the theory and numerical models of internal tides indicate that they have obvious seasonal variations, the multiscale temporal modulation of internal tides is still poorly understood. This is very important to study the dissipation mechanism of internal tides and their interaction with other oceanic dynamic processes. With the development of satellite altimetry technology and the update of internal tide information extraction technology, satellite altimetry can obtain the characteristics of internal tide with large spatial and temporal coverage compared to in situ measurement. The Luzon Strait is one of the important generation sources of internal tides in the global ocean due to its steep ridges and strong barotropic tides. In this study, the seasonal variations of two major internal tides in the Luzon Strait region, M2 and K1, are analyzed based on multi-source altimeter data. Using the multi-satellite altimetry data from 1992 to 2020, a two-dimensional plane wave fitting method is used to extract the internal tide information and establish the internal tide model for different seasons. Furthermore, the origin, propagation direction, integrated energy flux and seasonal variation of the internal tide are analyzed, which is important for the parameterization of internal tide mixing in the numerical simulation.

Ocean waves are seawater fluctuation phenomena that occur on the ocean surface and are closely related to human beings. The study of ocean waves has great significance in shipping, offshore platform construction, national defense and military affairs. Though the traditional means of ocean wave observation have accurate results, the costs are high and the spatial-temporal coverage is sparse. Satellite remote sensing such as altimeter and SAR provide a new way for ocean wave observation, but their observation coverage is also sparse and large-area synchronous observation data of ocean wave cannot be obtained. The scatterometer can obtain large-area synchronous sea surface wind field data, and there is a nonlinear relationship between the sea surface wind field and ocean wave. HY-2C is the third marine dynamic environment monitoring satellite of China, and radar altimeter (RA) and microwave scatterometer are equipped which can simultaneously obtain ocean wave height and sea surface wind field. In order to obtain more ocean wave observation data with large-area synchronous spatial-temporal coverage, the wide swath significant wave height from HY-2C scatterometer is retrieved by the deep learning method in this study. The significant wave height data of HY-2C, sea surface wind field data of HY-2C and sea surface temperature data are used as the training set. For high sea state situations with relatively small data volume, the training data set is expanded by data augmentation methods. The HY-2C wide swath significant wave height is intelligently extracted by using the recurrent neural network, and the retrieval accuracy of the HY-2C wide swath significant wave height is evaluated by the data of buoys and other satellite altimeters.

Oceanic mesoscale eddy is a kind of vortex-current motion that is approximately in geostrophic balance, which are characterized by dynamic height anomalies in the surface. Vigorous mesoscale eddies are broadly distributed in the global ocean and can be readily observable from sea surface height anomaly (SSHA) field. The vertical temperature and salinity information of the subsurface ocean can be obtained from Argo floats. Combining sea surface observation (SSHA field) provided by satellites and the vertical temperature/salinity profiles provided by Argo floats, the vertical temperature and salinity structure of mesoscale eddies in the global ocean are analyzed. The result shows that cyclone eddies generally dominate the negative temperature anomalies, and anticyclonic eddies generally dominate the positive temperature anomalies in the global ocean. In the regions with strong current variation, eddy activities are vigorous, the temperature anomalies within eddies are more significant. The global distribution of vertical salinity anomalies of eddies is more complicated than that of temperature anomalies. Both the cyclonic eddies and anticyclonic eddies show positive salinity anomalies and negative salinity anomalies. Research suggests that the differences in the temperature and salt structure of mesoscale eddies in different regions of the global ocean are mainly caused by the differences in the temperature and salt characteristics of local water masses.

Laser altimetry has been shown to be able to provide high-resolution observations of ocean properties, which are crucial for global oceanographic monitoring. The Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) has demonstrated the ability to distinguish between individual ocean surface waves. This provides physical observations far from coastal regions, where most in-situ gauges are located. The CRYO2ICE campaign, which started in the summer of 2020, has provided periodic coincident orbits between ICESat-2 and CryoSat-2, allowing for the validation of ICESat-2 observations with radar altimetry. However, the data available was restricted to the northern hemisphere. Since the summer of 2022, the CRYO2ICE campaign has performed a realignment, to get coincident orbits in the southern hemisphere, enabling ocean observations in a far larger area, as well as observing the overall higher significant wave height (SWH) in this region of the oceans. This is an opportunity to extend our dataset of observed sea states and get a better understanding of the performance from ICESat-2 at extreme wave heights. In this study, the validation of ICESat-2 thereby includes data from the southern hemisphere, where we are validating two methods: individual wave observations and a statistical model. The former uses the high-resolution data from ICESat-2 to directly observe the individual surface waves and estimate the SWH. The statistical model leans closer to the conventional estimate for the SWH, which observes the general surface variance, and uses this empirical relationship to estimate the SWH. By analyzing this new data, we seek to gain insight into ICESat-2's performance at these extreme wave heights, as well as improve the accuracy of our models.

In the waters west of the Luzon Strait, a phenomenon of alternating Type A and Type B waves sometimes occurs. Previous explanations for Type A and Type B waves mostly relied on in-situ measurements at a daily or weekly scale, with fixed-point observations, short observation times, and considerable randomness due to factors such as sea conditions. Satellite SAR remote sensing, with its all-day, all-weather, large-scale observations, and relatively low-cost data acquisition, has become an important data source for ocean internal wave research.

This study uses Envisat, ERS, and Sentinel 1A/B satellite SAR remote sensing images as in-situ data and employs the MITgcm model to investigate the generation mechanisms of internal waves in the South China Sea. The model utilizes GEBCO real depth measurements as topographic input, employs the TPXO forecast model for tidal currents at the eastern ridge of the Luzon Strait and applies least squares method to extrapolate these currents to the model boundary as boundary conditions, and uses WOA13 (World Ocean Atlas 13) data as stratification input. The research results show that the moment when the model-generated internal waves reach the specified location is consistent with the moment when the internal waves captured in SAR images arrive at the same location. The numerical model results are satisfactory when verified with in-situ measurements, providing favorable conditions for investigating the generation of internal waves in the South China Sea. Furthermore, the MITgcm results are processed, and combined with internal tidal beam theory to explore the generation and evolution of Type A and Type B waves in the time series. By utilizing methods such as Hovmuller diagrams, the study traces back the timing of Type A and Type B wave generation and offers reasonable explanations.

In the previous research, we proposed a satellite SAR internal wave amplitude inversion algorithm based on Euler numerical simulation. This method simulates a steady internal solitary wave by giving an initial flow field, and then obtains the corresponding amplitude and surface flow field. The algorithm constantly modifies the initial flow field to calculate the correlation coefficient between SAR internal wave profile curve and surface flow field gradient. When the correlation coefficient reaches the maximum, the internal wave amplitude is the amplitude of inversion.

However, the initial flow field simulated in previous studies is set by the initial solution of the KdV equation. Since the internal wave theory of KdV is only valid under the condition of weak nonlinear and weak dispersion, there is a large gap between the internal solitary wave flow field given by the KdV equation and the actual internal solitary wave flow field under the condition of strong nonlinear and large amplitude, so when it is used as the initial flow field, it takes a long time for the numerical model to get a stable internal solitary wave solution.

However, the MCC internal wave theory contains high-order nonlinear terms, and the internal solitary wave solution of MCC is closer to the actual internal solitary wave in the case of large amplitude, so it can be used as the numerical model of the initial flow field to obtain the steady internal solitary wave faster. Therefore, the MCC internal wave theory is adopted to set the initial flow field, and the steady-state internal solitary waves under different stratification conditions are simulated by numerical models. The simulation results of MCC and KdV are compared.

The results show that the numerical simulation results using MCC are basically the same as those using KdV, but in the case of strong nonlinear large amplitude, the time of the numerical simulation using MCC is shorter.

The maximum wave height (Hmax) is an extremely important factor that has a significant impact on the safety of maritime activities. However, the Hmax is much less investigated than significant wave height (SWH) in the wave remote sensing. Nowadays, radar altimeters and CFOSAT provide the SWH operational but without Hmax products. A method of obtaining the Hmax from CFOSAT SWIM Level 2 parameters is presented. The buoys are the most reliable way to observe the Hmax, but the collocations between buoys and CFOSAT tracks are too few to perform the supervised learning training. The ERA5 wave reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) is one of the most accurate datasets. However, the obvious bias and scatter index of Hmax are found from the comparison between ERA5 and buoys located west of France. A machine learning model is firstly built to reduce the error of ERA5 Hmax. Then the corrected ERA5 Hmax is collocated with CFOSAT observations and used for the training target of SWIM Hmax retrieval. The SWIM parameters both from SWIM nadir and boxes, including the SWH, wavelength and wave partition information, are used to obtain the Hmax based on machine learning. The CFOSAT data in 2021 are used to train the Hmax machine learning model while the data in 2020 are used to perform the independent validation. The bias, RMSE and scatter index of CFOSAT Hmax are 0.01m, 0.51m, 16%, while 0.77m, 1.09m, 19% are for the ERA5. Therefore, this study provides a perspective to obtain the Hmax from satellite remote sensing for further applications such as marine forecasts.

Rivers provide key ecosystem services that are inherently engineered and optimised to meet the strategic and economic needs of countries around the world. However, limited water quality records of a full river continuum hindered the understanding of how river systems response to the multiple stressors acting on them. This study highlights the use of Sentinel-2 MSI data to monitor changes in water colour in two optically complex river systems: the Yangtze and Danube using the Forel-Ule Index (FUI). FUI divides water colour into 21 classes from dark blue to yellowish brown stemming from the historical Forel-Ule water colour scale, and has been promoted as a useful indicator showing water turbidity variations of water bodies. The results revealed contrasting water colour patterns in the two rivers on both the spatial and seasonal scales. Spatially, the FUI of the Yangtze River gradually increased from the upper reaches to the lower reaches, while the FUI of Danube River declined in the lower reaches, which is possibly due to the sediment sink effect of the Iron Gate Dams. The regional FUI peaks and valleys observed in the two river systems have also shown to be related to the dams and hydropower stations along them. Seasonally, the variations of FUI in both systems can be attributed to the climate seasonality, especially precipitation in the basin and the water level. Moreover, land cover within the river basin was possibly a significant determinant of water colour, as higher levels of vegetation in the Danube basin were associated with lower FUI values (7.9±0.6), whereas higher FUI values (9.3±1.5) and lower levels of vegetation were observed in the Yangtze system. This study furthers our knowledge of using Sentinel-2 MSI to monitor and understand the spatial-temporal variations of river systems and highlights the capabilities of the FUI in the optically complex environment.

Harmful algal blooms (HABs) occur due to a proliferation of phytoplankton within a body of water, resulting in deterioration of the aquatic environment which affects human and animal health. HABs are now a global issue with their frequency and severity increasing significantly due to anthropogenic activity and climate change. As with any natural or anthropogenic induced hazard, it is vital that efficient and effective monitoring strategies are put in place. Ocean colour satellites are effective in monitoring HABs around the world over long-term scales, however, species identification in dynamic coastal waters is challenging. Phytoplankton size class (PSC) is suggested to be a good indicator of cell size and considered to reflect the ecological and biogeochemical functional role of the phytoplankton present in the water column. Thus, it is important to be able to monitor PSCs, particularly in dynamic coastal waters where there are frequent changes in nutrients and phytoplankton community structure.

Our research draws on Sentinel-2 MSI and Sentinel-3 OLCI which differ in spatial, spectral, and temporal resolution. The objectives of this study are to develop and validate HAB detection and PSC algorithms for near-shore and coastal waters, with better generalisation capability and lower computational overload that could improve the identification of the optical characteristics directly associated with phytoplankton properties.

The study will be focused on four optically diverse regions of interest; The Danube Delta and Black Sea Coastline (Romania), Galician Coast (NW Spain), Shandong Peninsula Coast (China) and the Northern-South China Sea (China). Here, we will present results from the Galician coast. We used in-situ data such as hyperspectral Remote Sensing Reflectance, Chlorophyll-a concentration, phytoplankton abundance and taxonomy, along with fractionated chlorophyll-a and particle absorption properties to develop and test the algorithms. We focus on the detection of Alexandrium minutum from Sentinel-2 MSI and Sentinel-3 OLCI data, through the characterisation of the spectral properties directly associated with the bloom. Alexandrium minutum exhibit a range of spectral signatures depending on which optically active constituents are present in the water. We use an unsupervised Random Forest classification algorithm to develop clusters of similar reflectance spectra and propose a new indicator which can detect Alexandrium minutum from diatom dominated waters. Existing PSC retrieval algorithms based on pigment cover, chlorophyll-a abundance, and phytoplankton absorption for coastal and transitional waters will be tested in dynamic coastal waters. In addition, atmospheric correction models such as Polymer, Acolite and C2RCC were tested against in-situ hyperspectral data and their performance was evaluated.

We will present results on the optical characteristics of Alexandrium minutum and the potential of MSI and OLCI for their remote detection. We will discuss our plans for the development of Super Learners for HAB indicators and PSC and the evaluation of the PSC algorithms.

Glacier velocity fields are typically derived through offset tracking techniques applied to optical and/or SAR remote sensing images. This is mainly because offset tracking is highly effective at detecting small changes in image features caused by glacier motion, which often results in strong decorrelation. Correlation algorithms extract the pixel-level offset, which can then be refined to a sub-pixel level using various interpolation techniques. However, the accuracy of these interpolation algorithms incorporated in different offset tracking software has rarely been evaluated or compared. In addition, the lack of in-situ observations to confirm the sub-pixel precision of derived offset can cause uncertainties. For above reason, a digital image processing method was used to evaluate the precision of various software and algorithms. The study aimed to assess the sub-pixel precision of derived offset and suggested an algorithm to correct possible offset tracking bias. This will ultimately help improve the accuracy of glacier velocity fields, which is crucial for climate change research and hazard assessment.

This study focused on the two largest glaciers in Greenland, Petermann Glacier and Kangerlussuaq Glacier, which account for roughly 4% each of the entire ice sheet's glacier mass loss and flow in northwestern and southeastern directions, respectively. To evaluate the precision of different algorithms, six pairs of Sentinel-2 images were used.The study combined the offset tracking results obtained from different algorithms, including COSI-Corr, autoRIFT, and ImGRAFT (CCF-O and NCC), and treated them as pre-set offset fields. Using the Sinc interpolation, which is an optimal interpolation method according to the sampling theory, simulated offset images are generated using the pre-set offset fields and pre-event images. The mentioned software and algorithms were then used to obtain offset tracking results based on the pre-event images and simulated offset images. Precision was assessed and possible bias inspected at the sub-pixel level only, as all algorithms first established a dependable offset value at the pixel level and then interpolated to the sub-pixel level. The displacement results and the pre-set offset fields were wrapped to a range of [-0.5, 0.5], designated as y and x, respectively. A cubic function, y=ax+4(1-a)x^3 (where a is the correction parameter), was chosen for the regression. The precision was exhibited by the fitting's RMSE, while parameter a indicated the presence of bias. if a equals 1, then no bias exists, but if not, there is a bias. Finally, the inverse function of the fitting can rectify potential systematic errors at the sub-pixel level.

The regression results showed that the sub-pixel systematic error of COSI-Corr is negligible and could be disregarded, whereas autoRIFT and ImGRAFT (CCF-O or NCC) displayed a certain degree of systematic errors in their offset results. Specifically, the values of a were 1.008, 0.778, 0.915, and 0.886 for COSI-Corr, autoRIFT, ImGRAFT (CCF-O), and ImGRAFT (NCC), respectively. In COSI-Corr, the Sinc function was used to interpolate the correlation coefficient matrix, while ImGRAFT applied bicubic interpolation regardless of the correlation algorithm being CCF-O or NCC. autoRIFT utilized a rapid Gaussian pyramid upsampling algorithm for estimating the sub-pixel displacement with a precision of 1/64 pixel. The results suggest that the use of COSI-Corr may be more reliable regarding the interpolation technique for obtaining sub-pixel precision in offset tracking.

Sub-pixel systematic error correction yielded the most significant improvement in the autoRIFT algorithm, reducing RMSE by an average of 0.0054 pixels in a single direction and increasing precision by 11%. This demonstrates the significance of performing this type of correction. ImGRAFT's RMSE also decreased slightly: ImGRAFT (CCF-O) and ImGRAFT (NCC) decreased by an average of 0.0014 and 0.0012 pixels in a single direction, respectively. However, whether to apply this correction to ImGRAFT depends on the desired level of precision as it only resulted in a 1.5% increase. Furthermore, as no noticeable systematic sub-pixel errors were detected in COSI-Corr, this correction is unnecessary. The similar regression results across different study sites and deformation directions indicate that sub-pixel systematic error is dependent on interpolation algorithm. After systematic correction, all algorithms showed reliable results. For instance, COSI-Corr and autoRIFT showed higher precision than ImGRAFT, with RMSEs of 0.04~0.14 pixels at Kangerlussuaq. In contrast, ImGRAFT had slightly lower precision with RMSEs of 0.08-0.10 pixels at Petermann, and 0.09~0.13 pixels at Kangerlussuaq. ImGRAFT (CCF-O) shows slightly better precision than ImGRAFT (NCC). Finally, it is worth noting that autoRIFT has much higher computational efficiency than the other algorithms, this study recommends combining it with a post-correction step for systematic error.

Synthetic Aperture Radar (SAR) images are commonly used to monitor glacier flow velocity at Greenland Ice Sheet (GrIS). However, in summer, offset-tracking with SAR imagery in summer usually show poor quality because the rapid ice surface freezing-melting cycles contaminating the surface backscattering characteristic. Optical images are less sensitive to this phenomenon. In this study, we combine Sentinel-1 and -2 images to create the glacier velocity time series for the Petermann glacier, located in the northern GrIS. Firstly, the offset-tracking technique is employed to acquire the initial deformation fields with SAR and optical sensors separately, each SAR and/or optical acquisition is tracked with its closest next three acquisitions. Next, after removing the outliers the least squares method based on connected components is employed to calculate the time series of glacier velocity for Sentinel-1 and -2, separately. Finally, these two kinds of derived time series are integrated with a weighted least squares method, where weights are evaluated according to the estimated RMSEs in the last step. Error propagation analysis suggests RMSEs of the single pair of Sentinel-1 and -2 images offset-tracking are ~0.22 m and ~2.5 m for Petermann glaciers. Standard deviation of the difference between Sentinel-1 and Sentinel-2 measured velocity are ~0.25 m/day. Compared with 6-day velocity fields product, NSIDC (National Snow and Ice Data Center) -0766, which is only derived with Sentinel-1observations, our results show good agreement and less defects in summer. The differences are ~0.20 m/day in non-melting seasons and ~0.34 m/day in summer. Longitudinal velocity differences growing in 2019 and 2020 at ~20 Km up to the terminus are consistency with the crevasse expansion, indicating another calving event is approaching. This research finds that the fusion of Sentinel-1 and -2 offset-tracking results improves the completeness of the ice movement time series for polar glaciers.

The validation of satellite soil moisture products has been serving as an active research topic for the application of the products and improvement of the retrieval algorithms, attracting extensive attention. Nevertheless, most existing validation activities focus on the validation of coarse-resolution soil moisture products at regional or global scales, seldom on the validation of high-resolution SM products at the fine scale. To this end, the State Administration of Science, Technology, and Industry for the National Defense of China initiated a research project entitled "Key Technology Research and Standard Specifications for the Validation of High-Resolution Remote Sensing Products" in 2020. Under the framework of the project, a soil moisture experiment was conducted in the middle stream of the Heihe River Basin in northwestern China in the summer of 2021, aiming to validate high-resolution satellite remote sensing products of soil moisture. The paper introduces the design, composite, and preliminary results of the experiment. A ground soil moisture observation network was established, and several synchronized campaigns were conducted. Simultaneously, several satellite remote sensing observations and soil moisture products were collected and validated against the ground measurements. A preliminary analysis shows that the experimental datasets can support the validation of satellite soil moisture products, as well as the monitoring of irritation at the agricultural field scale. Overall, the experiment provides fruitful methodologies and datasets for the validation of high-resolution remote sensing products, benefiting the development and improvement of soil moisture retrieval algorithms and products to support irrigation scheduling and management at a precision agricultural scale in the future.

Monitoring the evolution of of glacier is essential to understanding glacier reaction to climate change. To better track the long-term changes in glacier mass balance, some archived historical images has been widely used, but a knowledge gap of how glaciers evolve across the whole High Asia Mountain remains to be addressed. To this end, we reprocessed all KH-9 stereo images covering glacierized areas of the HMA, and combined them with NASADEM and Copernicus DEM to estimate glacier mass changes over two periods (i.e., 1975-2000 and 2000-2015). The results show that the eastern part of the HMA experienced a sustained acceleration of glacier mass loss, with relatively significant acceleration in the Nyainqentanglha and Hengduan mountains. In contrast, the glacier mass loss rate slowed down in the western part of the HMA, especially Pamir Alay and Eastern Pamir. In addition, there is no significant change in the rate of mass loss in the Gangdise, Karakoram and Hindu Kush mountains.

The terrestrial water cycle is affected by climate change, through changing evaporation and precipitation patterns. Reanalysis products play an important role in monitoring the changing climate, where the past weather and climatological conditions are estimated based on assimilating historical observational data into numerical models. Reanalysis products in the past largely focused on the estimation of atmospheric variables. While some reanalysis products included the usage of land surface models, the hydrological component in these models is often rudimentary or lacking. Furthermore, reanalysis of land surface variables is often done in an offline approach, where the atmospheric forcing is prescribed using already existing datasets: feedback from the land to the atmosphere is not taken into account.

To overcome these limitations and gain a deeper understanding of the terrestrial water cycle, we introduce a novel weakly coupled reanalysis framework. This framework addresses the shortcomings by incorporating a comprehensive representation of land surface processes and a three-dimensional model for subsurface and surface flow. Our study focuses on Europe from 2000 to 2020, utilizing a horizontal spatial resolution of approximately 11km.

The Community Land Model (CLM3.5) is employed to capture crucial land surface processes such as evaporation, transpiration, and infiltration, accounting for land cover and vegetation types across Europe. We used CLM3.5 coupled with ParFlow, a hydrological model that simulates subsurface and surface flow. Initially, we use ERA5 data for atmospheric forcing, but later replace it with the Icosahedral Nonhydrostatic (ICON) model to achieve a fully coupled terrestrial framework. All components of our model are integrated using the Parallel Data Assimilation Framework (PDAF).

To estimate both uncertain state variables (e.g., soil moisture) and uncertain parameters (e.g., hydraulic conductivities) for the land surface and subsurface, we explore the application of Ensemble Kalman Filters and iterative Ensemble Kalman Smoothers. We present preliminary results where Soil Moisture Passive Active (SMAP) data have been assimilated.

These results are part of ongoing work, exploring the added benefit of a fully coupled reanalysis framework. In the weakly coupled reanalysis framework presented here, only state variables and parameters related the the land surface and subsurface are updated in the data assimilation cycle. In the fully coupled reanalysis framework this will be done for all three model components simultaneously.

Soil moisture plays a critical role in governing water and energy exchanges in the land-atmosphere continuum. Accurate knowledge of soil moisture is essential for water resources management, agricultural production, and weather prediction. The assimilation of remotely sensed soil moisture data into land surface models (LSMs) has demonstrated potential in improving land surface states and fluxes. However, the relative value of assimilating microwave soil moisture observations acquired at different frequencies remains uncertain. Limited studies have examined the impact of applying different merging algorithms to generate a merged soil moisture product prior to data assimilation (DA). This study focuses on assimilating soil moisture data obtained from L-band (Soil Moisture Active Passive Mission- SMAP and Soil Moisture and Ocean Salinity Mission - SMOS), C-band (Advanced SCATterometer - ASCAT), and X-band (Advanced Microwave Scanning Radiometer 2 - AMSR2) into the land surface model (CLM, Community Land Model) using the Ensemble Kalman Filter (EnKF) approach. This is done for the North-Rhine-Westphalia region in Germany, for the years 2017 and 2018. Initially, each remotely sensed soil moisture product is assimilated individually. Subsequently, both a conventional linear combination method and a novel Long Short-Term Memory (LSTM) approach are employed to calculate weights for the different remotely sensed soil moisture products. These weights are determined with in situ soil moisture measurements acquired through Cosmic Ray Neutron Sensors (CRNS). The two merged products are then assimilated into the Community Land Model (CLM), a land surface model. The simulated soil moisture time series are evaluated against independent point measurements. The study shows that joint assimilation of merged retrievals can offer improved characterization of soil moisture compared to assimilating each remote sensing product individually. In addition, by analyzing multiple data assimilation results, we are able to assess the variations and similarities in assimilating retrievals from different microwave bands. This analysis allows us to evaluate the impact on data assimilation performance, particularly in situations involving mission or sensor transitions or discontinuities. Furthermore, given that the behavior of different retrieval schemes is influenced by surface characteristics and spatial heterogeneity, this study also examines the spatial patterns of soil moisture and explores the potential for capturing and propagating spatial information of remotely sensed soil moisture in the land surface model.

Snow density varies spatially, temporally, and vertically within the snowpack and is the key to converting snow depth to snow water equivalent. While previous studies have demonstrated the feasibility of retrieving snow density using a multiple-angle L-band radiometer in theory and in ground-based radiometer experiments, this technique has not yet been applied to satellites. In this study, the snow density was retrieved using the Soil Moisture Ocean Salinity (SMOS) satellite radiometer observations at 43 stations in Quebec, Canada. We used a one-layer snow radiative transfer model and added a vegetation model over the snow to consider the forest influence. We developed an objective method to estimate the forest parameters (tau, omega) and soil roughness (SD) from SMOS measurements during the snow-free period and applied them to estimate snow density. Prior knowledge of soil permittivity was used in the entire process, which was calculated from the Global Land Data Assimilation System (GLDAS) soil simulations using a frozen soil dielectric model. Results showed that the retrieved snow density had an overall root-mean-squared error (RMSE) of 83 kg/m³ for all stations, with a mean bias of 9.4 kg/m³. The RMSE can be further reduced if an artificial tuning of three predetermined parameters (tau, omega, and SD) is allowed to reduce systematic biases at some stations. The remote sensing retrieved snow density outperforms the reanalysis snow density from GLDAS in terms of bias and temporal variation characteristics.

The two vegetation transfer parameters of tau (Vegetation Optical Depth, VOD) and Omega (Single Scattering Albedo) could vary significantly across microwave channels in terms of frequencies, polarizations, and incidence angles, and their characteristics of channel dependence have not yet been fully investigated. In this study, we investigate the channel dependence of vegetation effects on microwave emissions from soils using a higher-order vegetation radiative transfer model. Corn was chosen as the research object, and a corn growth model was developed using the multifrequency and multiangle ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River (SMELR). After establishing the corresponding database of corn radiation characteristics, the effective scattering albedo under various channels was calculated using the higher-order radiation transfer model. The channel dependence analysis of the vegetation optical depth and effective scattering albedo in the database was performed. The results show that the channel dependence of vegetation optical depth can be described as the polarization dependence parameter (C_P ) and the frequency dependence parameter (C_f ). According to these two parameters, the vegetation optical depth can be calculated at any channel under three adjacent frequencies (L band, C band and X band). The effective scattering albedo has no obvious dependence on the angle, so the effective scattering albedo based on the higher-order radiation transfer model under three adjacent frequencies with different polarizations is obtained. This study is helpful for understanding the differences in vegetation radiation characteristics in different channels, thereby promoting the development of large-scale soil moisture retrieval accuracies in vegetated areas.

Surface soil moisture (SSM) is an important variable in drought monitoring, floods predicting, weather forecasting, etc. and plays a critical role in water and heat exchanges between land and atmosphere. SSM products from L-band observations, such as the Soil Moisture and Ocean Salinity (SMOS) mission and the Soil Moisture Active Passive (SMAP) mission, have proven to be optimal global estimations. Although X-band has a lower sensitivity to soil moisture than that of L-band, Chinese FengYun-3 series satellites (FY-3A/B/C/D) have provided sustainable and daily multiple SSM products from X-band since 2008. This research developed a new global SSM product (NNsm-FY) from FY-3B MWRI from 2010 to 2019, transferred high accuracy of SMAP L-band to FY-3B X-band. The NNsm-FY shows good agreement with in-situ observations and SMAP product and has a higher accuracy than that of official FY-3B product. With this new dataset, Chinese FY-3 satellites may play a larger role and provide opportunities of sustainable and longer-term soil moisture data record for hydrological study.

Spatio-temporal patterns of Suspended Particulate Matter (SPM) and Chlorophyll-a (Chla) concentrations, have been assessed from the Ocean and Land Color Instrument (OLCI) over the whole European coastal waters from 2016 to 2023. The semi-analytical algorithm of Han et al. 2016 has been used for SPM estimation, while Chla has been computed based on an optical classification approach proposed by Tran et al. 2023, that combines several Chla algorithms. The generated products have been validated using an extensive dataset of in-situ measurements. Chla and SPM climatologies have been generated at the scale of Europe, and the temporal patterns (seasonal variability, long term trend, and irregular component) have been described using the Census-X-11 time series decomposition method.

By utilizing Level 2A products (particle optical properties and numerical weather prediction data) and Level 2C products (numerical weather prediction wind vector assimilated with observed wind component) provided by the Atmospheric Laser Doppler Instrument (ALADIN) onboard the Aeolus mission, and Level 2 vertical feature mask (VFM) products provided by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, three remote ocean areas are selected and the optical properties at 355 nm of marine aerosol are derived. The combined analysis of marine aerosol optical properties at 355 nm and instantaneous co-located wind speeds above the remote ocean areas are conducted. Eventually their relationships are explored and discussed at two sperate vertical atmospheric layers (0-1 km and 1-2 km, correspond to the heights within and above marine atmospheric boundary layer (MABL)), revealing the marine aerosol related atmospheric background states. Pure marine aerosol optical properties at 355 nm are obtained after quality control, cloud screening and backscatter coefficient correction from the ALADIN observations. The spatial distributions of marine aerosol optical properties and wind speed above the study areas are presented and analysed, respectively, at two vertical layers. The statistical results of the marine aerosol optical properties along with the wind speed grids at two vertical layers together with the corresponding regression curves fitted by power law functions are acquired and analysed, for each remote ocean area. The optical properties present increasing trends with wind speed in all cases, implying that the atmosphere of the two vertical layers will both receive the marine aerosol input produced and transported by the wind and the turbulence. The marine aerosol enhancement caused by the wind speed at the lower layer is more intensive than at the higher layer. As derived data from ALADIN, the averaged marine aerosol optical depth and the averaged marine aerosol lidar ratio at 355 nm are acquired and discussed along the wind speed range. The marine aerosol optical properties distributions, wind speed bins, and the marine aerosol variation tendencies along wind speed above the individual study areas are not totally similar, implying that the development and evolution of the marine aerosol above the ocean might not only be dominated by the drive of the wind, but also be impacted by other meteorological and environmental factors, e.g., atmospheric stability, sea and air temperature, or relative humidity. Combined analysis on the aerosol optical properties and wind with additional atmospheric parameters above the ocean might be capable to provide more detailed information of marine aerosol production, entrainment, transport and removal.

The main objective of the Dragon V project (ID 59198) is to standardize procedures for calibrating European and Chinese satellite altimeters. Calibration and Validation (Cal/Val) actions should follow the guidelines prescribed by the Fiducial Reference Measurements for Altimetry strategy, developed by the European Space Agency for standardizing procedures and results. One of the fundamental quantities that needs to be calibrated in satellite altimetry is the backscatter coefficient (sigma-naught). This is a satellite measurement related to wind observations at sea and constitutes an important and indispensable parameter for climate change models. At the moment, there is no European or Chinese Cal/Val facility dedicated to sigma-naught calibration.

This work presents the progress made in the design, analysis and validation of corner reflectors for the absolute and direct calibration of the backscatter coefficient in satellite altimeters. Requirements and specifications (i.e., material, dimensions, etc.) for manufacturing such corner reflectors have been defined. These are tailored for calibrating Ku and Ka-band satellite altimeters. Finally, the ground location where these corner reflectors are to be installed has been selected because of its low clutter level and capability of calibrating multiple satellites.

High precision magnetometer (HPM) has worked successfully more than 5 years to provide continuous magnetic field measurement since the launch of CSES. After rechecking these years data, it is necessary to make an improvement for fluxgate magnetometer (FGM) orthogonal calibration (to estimate offsets, scale values and non-othogonalities) and alignment (to estimate three Euler angles). The following efforts are made to achieve this goal: For orthogonal calibration, we further considered the FGM sensor temperature correction on offsets and scale values to remove the seasonal effect. Based on these results, Euler angles are estimated along with global geomagnetic field modeling and then the latitudinal effect for east component is improved. After considering above improvement, we can prolong the updating period of all calibration parameters from daily to 10 days, without the separation of dayside and nightside data. These algorithms will be helpful to improve HPM routine data processing efficiency and data quality to support more scientific studies.

We propose a GNSS-R soil moisture (SM) retrieval algorithm based on land surface clustering using the twin satellites A/B of BuFeng-1 (BF-1). Similar to other semi-empirical algorithms, this algorithm incorporates vegetation and roughness parameters. However, it introduces empirical clustering as an alternative to quantitative calculations. Vegetation and roughness, recognized as significant factors influencing GNSS scatter signals, are utilized to categorize the land surface into distinct classes. The opportunity observations of spaceborne GNSS-R presents a challenge in obtaining a sufficient number of valid observations within a grid cell at the theoretical spatial resolution of approximately 3.5 km to 20 km over land. This limitation hampers the establishment of robust empirical relationships. Consequently, our algorithm avoids pixel-by-pixel fitting and instead establishes empirical relationships between SM and GNSS-R observations within each class. A global comparison between the algorithm's results and the 36-km soil moisture product from the Soil Moisture Active Passive (SMAP) mission reveals a correlation coefficient (R) of 0.82 and an unbiased root mean square error (ubRMSE) of 0.070 cm³·cm⁻³.

Quantitative estimation of the Leaf Area Index (LAI) from remote sensing imagery is crucial for monitoring vegetation growth and assessing the ecological environment. Gaofen-6, a high-resolution remote sensing satellite launched by China, offers a valuable tool for vegetation monitoring due to its high spatial resolution and spectral coverage. Accurate LAI estimation from Gaofen-6 imagery can provide essential information for crop management, land use planning, and climate modelling. A number of studies have explored the LAI estimation from Gaofen-6 using machine learning algorithms and have achieved nice results. However, these studies are non-torableable to different locations and hardly applicable to complex vegetation structures. The physically-based Look-up Table (LUT) approach is relay on radiative transfer models (RTMs), it takes into account the fundamental principles of light interaction with vegetation, which can result in more accurate and reliable LAI as well as other vegetation biophysical parameters estimations. So far, to our knowledge, there's no research that has attempted to use the LUT method to invert LAI on Gaofen-6 imagery. Therefore, in this study, we explore the LUT for LAI retrieval on Gaofen-6 and validate the LAI with both in-situ measurements and Drone-based LAI estimations.

In this study, one wide-field view (WFV) scene of Gaofen-6 over Taizishan Forest Park (30.91-30.92°N, 112.87-112.88°E), China is used. The LUT method was implemented in R and applied to the subset of the Gaofen-6. The Gaofen-6-based LAI inversion result achieved a comparable result with Sentinel-2 LAI inversion. The latter has RMSE of 1.02 and 0.59 when evaluated with UAV-based LAI reference map and in-situ measurements, while Gaofen-6 achieved RMSE of 1.49 and 0.89. The estimated LAI value ranges between 0 and 5 in the study area, which is consistent with our prior knowledge and ground measurements.

Overall, our study is one of the few that have implemented a LUT-based inversion approach on Gaofen-6 data. However, due to the lack of ground information, there is a certain gap between the Gaofen-6 LAI map and the UAV-based LAI estimation. In the future, we will continue to supplement the measurement of ground information and seek a method to invert a higher-precision Gaofen-6 LAI map over other study areas like Whythum forest in the UK.

Since a significant scale difference exists between the field of view of ground station sensors and satellite sensors, the validation of satellite-retrieved land surface parameters is usually performed over homogeneous surfaces. However, due to typically inhomogeneous natural surfaces and the urgent need to evaluate satellite-retrieved land surface parameters over a broad range of representative land cover types, it is crucial to be able to evaluate those parameters over inhomogeneous surfaces. In an attempt to address this issue, a temporal variation method for evaluating the spatial representativeness of ground stations was proposed for kilometer-scale LST validation (Ma et al., 2021). In this method, a station’s spatial representativeness indicator (SRI) is defined as the LST difference between the ground radiometer’s FOV and the corresponding satellite pixel. In order to estimate the SRI, which is effectively the temperature difference due to spatial scale, a temporal variation model of SRI is established, which combines the temporal variation of LST and its main influence factors. Meanwhile, according to its definition, SRI can be used as a bridge to convert in-situ LST to the corresponding pixel scale. Therefore, the SRI allows to validate satellite LST against in-situ LST at the same spatial scale.

The method was applied in the validation of MODIS and AATSR LST. Based on Landsat TM/ETM+, the LST within the ground radiometer’s FOV and the corresponding MODIS and AATSR pixel were simulated at 16 Chinese stations. Then the annual variation of LST at the two spatial scales was modeled using the annual cycle model (ATC), from which SRI’s variation tendency ∆ATC was obtained. Using the random forest method, a temporal variation model was constructed for the fluctuation term (∆USC) around ∆ATC, which was based on surface condition parameters and instantaneous meteorological parameters. Results show that when the spatial representativeness of the ground station is ignored, the systematic bias is between -4.05 K and 5.08 K, and the standard deviation of the bias is between 1.11 K and 6.95 K, for MODIS daytime LST. After considering the stations’ spatial representativeness, the systematic bias is between -4.35 K and 1.17 K, and the standard deviation of the bias is between 0.61 K and 6.01 K. Here, the systematic deviation and the corresponding standard deviation are 1.43~5.34 K and 0.35~3.39 K, respectively, due the ground station’s spatial representativeness. For the AATSR daytime LST, when the spatial representativeness of the ground station is ignored, the systematic bias is between -3.57 K and 7.28 K, and the standard deviation of the bias is between 1.26 K and 6.35 K. After considering the stations’ spatial representativeness, the systematic bias is between -2.63 K and 4.36 K, and the standard deviation of the bias is between 0.28 K and 5.07 K. Here, the systematic deviation and the corresponding standard deviation are -1.95~5.6 K and 0.07~3.72 K.

It can be concluded that large systematic deviations and random errors can result from a lack of spatial representativeness of a ground station, which considerably reduces the meaningfulness of the validation results obtained on the satellite pixel scale. Therefore, it is recommended to always analyze and account for the spatial representativeness of ground stations at the satellite pixel scale, e.g. by using the proposed or another established method for validating LST.

Ma, J., Zhou, J., Liu, S., Frank-Michael Göttsche, Zhang, X., Wang, S., Li, M., 2021. Continuous evaluation of the spatial representativeness of land surface temperature validation sites. Remote Sensing of Environment 265, 112669. https://doi.org/10.1016/j.rse.2021.112669

Land surface temperature (LST) is a key variable in a wide variety of studies directly linked to land–atmosphere energy transfer and flux balances, as well as in a broad range of applications such evaporation monitoring, estimates of fire size, detection of volcanic activity, permafrost detection or monitoring of vegetation health. Furthermore, LST is considered by World Meteorological Organization (WMO) and the Global Climate Observing System (GCOS) as one of the essential climate variables (ECVs) for climate change monitoring. However, satellite LST acquisitions are often limited due to cloudy skies. Several methods have been proposed in the literature to estimate the under-cloud LST from thermal and passive microwave data: these are known as all-weather LST products. Thus, all-weather LST products are required for an accurate analysis on climate studies at global and local scale and climate change monitoring.

In this study we investigate the accuracy of an all-weather LST products produced within the Dragon 5 project ’All-weather land surface temperature at high spatial resolution: validation and applications’. The investigated LST product merges clear-sky MSG/SEVIRI LST at a spatial resolution of 5 km with the surface temperature of a Soil-Vegetation-Atmosphere (SVAT) model (Martins et al., 2019). This product is validated over KIT’s permanent validation site on the gravel plains at Gobabeb (Namibia) for years 2010 to 2012. This period includes the largest rainfall at Gobabeb in recorded history, which makes the product retrievals challenging due to the extreme atmospheric conditions but also due to the changes on biophysical surface properties, which are linked to surface emissivity and LST. Thus, the results will provide a comprehensive analysis of the all-weather LST product performance over a broader range of atmospheric and surface conditions.

Opencast mining, which involves huge quantities of overburden removal, dumping and backfilling in excavated areas, is a classical operation mode of large coal mines worldwide. With the continuous expansion of open pit mining areas, the mining angle has also increased sharply, resulting in frequent landslide disasters and significant safety threats to mining production operations. Therefore, it is of vital significance for the safety of personnel, mining operation equipment and infrastructures to perform continuous displacement monitoring of opencast mines and their surroundings. In recent decades, with the continuous enrichment of satellite Synthetic Aperture Radar (SAR) data resources, Multi-temporal SAR Interferometry (MT-InSAR) technique has become a fundamental tool to estimate surface displacements with high spatial resolution, short temporal revisit interval, wide coverage and millimeter accuracy.

In this paper, multi-temporal InSAR technology is adopted to monitor the line of sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in Northeast China using Sentinel-1 SAR images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross-wavelet transform with monthly precipitation data are also conducted to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with the settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding from 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in the vicinity of the Liushan Paleochannel. The results of this paper have fully taken into account the complications of large topographic relief, geological conditions, spatial distribution, and temporal evolution characteristics of surface displacements in opencast mining areas. The wide range and long time series dynamic monitoring of opencast mines is of great significance to ensure mine safety, production, and geological disaster prevention in the investigated mining area.

In recent years，thermal anomalies prior to large and hazardous earthquakes have been extensively detected by microwave remote sensing techniques. In order to effectively detect microwave brightness temperature (MBT) anomalies caused by seismic factors, a wavelet-based two-step difference (WTSD) method is proposed in this paper. In the WTSD method, the radiation received by the microwave sensor comprises of two components if no earthquakes happen, which are stable radiation and random radiation respectively. Since the radiation caused by topography, surface coverage and seasonal change has strong regularity and varies little over the years, it is therefore considered as stable contribution to the microwave radiation. On the other hand, radiation caused by meteorological conditions (e.g., precipitation and temperature change, etc.) frequently changes within several days, which has no regularities, and it is therefore considered as random contribution to the microwave radiation. The stable components and random components are removed step by step in the WTSD method. The key steps prior to difference calculation rely on reliable retrieval of the stable component (which is the background MBT), and on successful elimination of the random component (which is the meteorological factor) as well, which is realized by adopting the hierarchical clustering and wavelet analysis. Then, the proposed WTSD method was used to detect seismic MBT anomalies prior to three strong earthquakes happened in the Central and Eastern Qinghai-Tibet Plateau, including the Ms 7.1 earthquake in Yushu in 2010, the Ms 5.5 earthquake in Dingqing in 2016 and the Ms 7.4 earthquake in Maduo in 2021. Surprisingly, the MBT anomalies prior to the three earthquakes are generally similar in terms of location, shape and evolution characteristics. Preliminary mechanistic analysis suggests that the pre-earthquake MBT anomalies are consistent with spatial distribution of the NE-oriented normal faults and geothermal activities in this region. The pre-earthquake thermal anomalies may becaused by intensified extrusion of the Indian plate to the Eurasian plate and the increased crustal stress in this area

Assessing the impact of the February 6^th earthquake, which occurred in South-eastern Turkey near the Turkey-Syria border, on cultural heritage sites is crucial to ascertain the cultural and historical cost of the disaster. These twin quakes, which had a magnitude of 7.8 and an after-shock magnitude of 6.7, resulted in widespread damages with the official death toll figure rising to 55,000+ and over 107,000 injured across the eleven cities most affected. The zone of occurrence of this earthquake is a hotbed for seismic activity because of the complicated network of plate boundaries underlying the area. This zone is under-laid by three major plate boundaries namely the Anatolian plate, the Arabian plate and the African plate. It is characterized by series of lateral strike-slip fault movement which ultimately results in series of frequent earthquakes of varying magnitude.

The aim of this study is to detect damaged cultural heritage sites in the earthquake zone in Turkey, by using SAR (Synthetic-Aperture Radar) images. The affected cities are home to some of Turkey’s most iconic heritage sites. In this study, TerraSAR-X high-resolution X-band data and open access Sentinel-1 data was used. At some locations we also used Google Earth images as a reference images.

To detect damages on cultural heritage sites, two methods were adopted. First, since TerraSAR-X have high resolution spotlight mode, we tried to ‘visually recognize’ damages on historical buildings by comparing SAR images with terrestrial and UAV photos from the area taken by locals, archaeologists, and reporters. Second, we processed open access Sentinel-1 data of different dates, before and after the earthquake using ‘coherence change detection’ to detect the changes in specific structures in the city. The research will focus to a large extent the cultural and historical cost of the impact of earthquake and also highlight the further impacts of after-shock on damaged cultural heritage sites through time series analysis of images. We realized that some damaged buildings continued to collapse several days later as a result of subsequent aftershocks which shows the need to initiate mitigative measures as fast as possible to save what is left of the important monuments.

Looting is an ongoing global threat to cultural heritage. Detecting looting activities is therefore of the utmost importance. Remote sensing offers a possibility to detect looting in remote and inaccessible areas. The all-weather and continuous observation capabilities of SAR would be extremely beneficial for any practical implementation. However, SAR data is difficult to interpret and suffers from speckle noise, making the detection of small changes challenging.

The detectability of large-scale looting activity in high-resolution SAR images, for example in the context of the Syrian civil war, has been shown before. Many other looting activities are rather small scale and do not reach the almost industrialized looting activities witnessed in this conflict.

Therefore, the detectability of small-scale looting will be analyzed in this work. Based on an experimental setup with two different sized artificial looting holes, we analyze the detectability of these activities in SAR images of different resolution, polarization, looking angle, orbit, etc. Detectability in amplitude and coherence are being analyzed.

The results will provide deeper insight into the requirements in terms of resolution and other imaging parameters for looting detection.

Regular and continuous monitoring of surface deformation and structural instability is crucial for cultural heritage protection. Increasing urbanization and development are one of the causes of ground subsidence. In the last decade, the city of Wuhan (China) has experienced major threats in urban areas due to rapid expansion and ground deformation, as revealed by recent studies published prior to the Dragon-5 SARrchaeology project by the ASI, Wuhan University and CNR-ISAC team in the framework of the WUHAN-CSK project (Jiang et al., 2021, Tapete et al., 2021) and the follow-on research within SARrchaeology (Jiang et al., 2023). While the whole InSAR literature on Wuhan so far has focused on the relationships between urbanization and land subsidence, as well as on impacts on modern structures and infrastructures, no studies have been undertaken to assess the effect on the conservation of heritage buildings spread across the city.

To fill this gap, high-resolution COSMO-SkyMed and TerraSAR-X satellite imagery is used in this work for assessing potential deformation of cultural heritage in Wuhan using the PSInSAR technique, which allows object subsidence monitoring up to millimeter-level accuracy. However, for long-term observations of a highly dynamic urban environment, such as Wuhan, several assumptions of PSInSAR, like PS stability over the acquisition period or linear deformation, are unsuitable. Changes to the processing framework are therefore necessary and are tested in this work.

In the final paper, we will demonstrate the effectiveness of applying a modified PSInSAR technique for the analysis of high-resolution SAR images for long-term monitoring of subsidence. The effect and potential damage to different cultural heritage sites will be discussed.

References

Jiang H., Balz T., Cigna F., Tapete D. (2021) Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data. Remote Sens., 13, 1256. https://doi.org/10.3390/rs13071256

Jiang H., Balz T., Cigna F., Tapete D., Li J., Y. Han (2023). Multi-Sensor InSAR Time Series Fusion for Long-term Land Subsidence Monitoring. Geo-spatial Information Science. https://doi.org/10.1080/10095020.2023.2178337

Tapete D., Cigna F., Balz T., Tanveer H., Wang J., Jiang H. (2021) Multi-Temporal InSAR and Target Detection with COSMO-SkyMed SAR Big Data to Monitor Urban Dynamics in Wuhan (China). 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 3793-3796, doi: 10.1109/IGARSS47720.2021.9554360

Groundwater overexploitation and its resulting surface subsidence have been critical issues in the North China Plain (NCP) for the last half-century. This problem, however, is being alleviated by the implementation of the South-to-North Water Diversion (SNWD) Project since 2015. Here, we monitor surface deformation and investigate aquifer physical properties in NCP by combining Interferometric Synthetic Aperture Radar (InSAR), Global Positioning System (GPS), and hydraulic head data observed during 2015-2019.

We process data from the ascending track 142 of the Sentinel-1A/1B satellites, with a total of 92 acquisitions among 5 consecutive frames during 4 years. The InSAR time series are generated using the StaMPS software package, and all of the interferograms are formed with respect to one reference image. By dividing the study area into overlapping patches, we use parallel computing algorithms and cluster job management system to reduce the computational overburden. With this method, we effectively reduce computation time and successfully obtain the InSAR time series in NCP with full resolution for the first time. The atmospheric phase screen (APS) is estimated and reduced using a combined method, in which the first-order APS is estimated using the ERA5 global atmosphere model, and the residual APS is estimated using the Common Scene Stacking method.

Geodetic observations reveal widespread and remarkable subsidence in the NCP, with an average rate of ~30 mm/yr, and ~100 mm/yr for the maximum. We successfully extract seasonal and long-term deformation components caused by different hydrogeological processes. By joint analysis of the seasonal deformation and hydraulic head changes, we estimate the storativity of 0.07~12.04*10^-3 and the thickness of clay lenses of 0.08~2.00 m for the confined aquifer system, and attribute their spatial distribution patterns to the alluvial and lacustrine sediments of the subsystem layers. Our study also reveals fulfilment of the SNWD Project in alleviating the groundwater shortage. About 57% of the NCP is found to have experienced subsidence deacceleration, mostly along the SNWD aqueduct lines, by a total of 37.0 mm on average during 2015-2019. The subsidence was reduced by 4.1 mm on average for the entire NCP, suggesting that although subsidence was still ongoing, the trend was reversed, particularly for some major cities along the routes of the SNWD Project. A distinct difference in subsidence rates is found across the borderline between the Hebei and Shandong Provinces, resulting from differences in groundwater use management. Our study demonstrates that the integration of geodetic and hydrological data can be effectively used for the assessment of groundwater circulation and to assist groundwater management and policy formulation.

With fast shale gas exploitation in Sichuan basin in China in recent years, numerous micro-seismicities and even some medium-sized earthquakes occurred. Some studies show that shale gas exploitations can generate detectable surface deformation. We used ALOS-2 InSAR data to measure the surface deformation over the Changning shale gas block and find significant ground deformation that may be caused by massive shale gas production. Meanwhile, we also did time-series analysis of Sentinel-1 satellite radar data to measure the surface deformation of the Sichuan basin during the active periods of shale gas exploitation, which shows strong correlations between the surface deformation and three major shale gas blocks, namely the Changning, Weiyuan, and Fulin blocks. So the observed InSAR deformation in the tectonic-stable Sichuan basin is probably caused by hydraulic fracturing for shale gas production.

Some speculations on deformation sources could be made based on such deformation patterns. Firstly, the surface deformation could be caused by long-term fluid injection or pumping which lasted several months in a poroelasticity medium. Secondly, such deformation may be due to multiple induced seismicities or fault creeping caused by pore pressure diffusion or fluid migration to vulnerable faults. Thirdly, the long-term shale gas development in the Sichuan basin could change the underground fluid mass. Injection or pumping of fluids into the crust would change upper crustal gravity and produce the elastic response of the crust, called the mass loading effect. We test these hypotheses based on numerical analysis of surface deformation patterns from InSAR data.

To quantitatively interpret the surface deformation with shale gas production, we model the deformation sources as multiple fluid injection and pumping processes in a poroelasticity layer by spatiotemporal Green’s function method, rather than the simple elastic volcanic-like sources, which may misinterpret the physical parameters of the shale gas production. Then we invert for the production parameters in a least-squares solution and compare our results with limited open production data as a verification. The details will be reported in the meeting.

In 2013, the European Space Agency (ESA) launched a constellation of three satellites: known collectively as the SWARM satellites. Their mission is to monitor variations in the Earth’s magnetic field. It has long been theorised that anomalous fluctuations in the Earth’s ionosphere could herald the beginnings of major earthquakes. However, the ability to accurately capture the frequency and extent of these anomalies has proven to be a persistent challenge to the scientific community. Anomalies are defined as data points which lie outside of the scope of normal data. High-intensity anomalies are comparatively easy to detect, but it is difficult to distinguish low-intensity anomalies from normal data, using purely mathematical or statistical means. The aim of this research is to apply Q-Learning and Deep Q-Networks to SWARM (and possibly CSES) satellite data and solve this problem. The proposed method uses kNN machine learning algorithms; a modified version of Matrix Profiles and planar wave functions to construct a Q-Learning Table for our agent. Double Deep Q Networks could also be trained using the kNN and modified Matrix Profiles. This method eliminates the need for Active Learning (or human feedback) when training such algorithms. Reinforcement Learning could be the key to unlocking the Earth’s magnetic field, predicting earthquakes and saving countless lives.

The Tianshan orogenic belt (TSOB) is one of the most active regions in Eurasia. The far-range effect of the collision between the Indian and the Eurasian plates in the late Cenozoic led to the reactivation of the TSOB and the occurrence of intracontinental orogeny. At the same time, the TSOB expanded to the foreland basins on its both sides, forming multiple rows of décollement- and fault-related fold belts in the basin-mountain boundary zone. Global Positioning System (GPS) observations show that the shortening rate in the north-south direction across the TSOB gradually decreases from ~ 20 mm/yr in the west to ~ 8 mm/yr in the east. However, how the deformation is distributed inside the TSOB is controversial. Here, we determine the present-day kinematics of the major structural belts based on the Interferometric Synthetic Aperture Radar (InSAR) data of the Sentinel-1 satellites.

We process Synthetic Aperture Radar (SAR) data from 5 ascending tracks (T27;T129;T56;T158;T85) and 4 descending tracks (T107;T34;T136;T63) of the Sentinel-1A/1B satellites recorded between November 2014 and December 2020. We constructed a total of 1074 single-reference single-look interferometric pairs based on Gamma software covering a 790-km-length and 520-km-width area of the TSOB. Finally, the InSAR time series are processed using the StaMPS software package. The long-wavelength and elevation-dependent atmospheric errors from each date are mitigated using the TRAIN package and ECWMF ERA5 models.

Combining InSAR and GPS measurements, we show that the tectonic deformation is not evenly distributed in the TSOB. The convergence across the Tianshan ranges is approximately 15–24 mm/yr; the deformation gradient in the junction area between South Tianshan and Pamir is the largest and adjusts ∼68% of the total convergence deformation. South Tianshan is relatively stable without sharp gradients, and the remaining deformation is distributed in the intermontane faults and basin systems in the north of South Tianshan. We also find that the Kashi fold-thrust belt is the most active unit in this area, and the deformation is mainly concentrated on a series of folds: the Mushi, Kashi, and Atushi folds, and the faults between the folds, such as the Kashi, Atushi, and Toth Goubaz faults. As the boundary fault between the South Tianshan and the Tarim basin, the Maidan fault shows a clear deformation gradient. In the Keping nappe, the deformation is mainly concentrated on the Keping hill and Kepingtag fault in the front of the nappe. There are several remarkable deformation zones in the Kuche foreland. The deformation in the north of South Tianshan is dispersed in a series of intermountain active structures and the depression basins, unlike in the south side, where the deformation is mainly concentrated on the thrust folds. Furthermore, our study can provide constraints for deformation and slip partitioning patterns associated with the ongoing India-Eurasia collision in the TOSB.

In this report we will present the latest development of anomaly detection algorithms underpinned with Deep Neural Networks (DNN), which focuses on predicting and generating electromagnetic data from the Swarm historic data. We report our investigation into the two architectures of Recurrent Neural Networks (RNN) and generative adversarial network (GAN), particularly illustrating the development of Long-Short Term Memory (LSTM) based architectures and a flow-based generative model. The first RNN architecture is modelling with a stacked LSTM layers. There are several variations of this architecture, however our empirical analysis that the best result achieved is three LSTM layers structure. The second architecture is an architecture on three RNN models, called Encoder-Predictor-Decoder that is inspired by the work of Multi-head CNN–RNN for multi-time series anomaly detection. We will present the design of the architectures and their implementation, and compare the predicted and generated results of applying these approaches to the Swarm historic data. Based on the predicted and generated results, we will describe error metrics that can be used to measure the accuracy of reconstructed Swarm data reconstruction. Finally we will present our methods of detecting anomalies in the synthesized and true Swarm data along with possible applications in detecting seismic precursors from the synthesized and true Swarm data.

Building damage assessment is one of the most important parts of the earthquake damage assessment, the rapid and accurate damage assessment can help to reduce the disaster loss. A method built on using SAR images for building damage assessment is independent of weather conditions, and also using one post-earthquake SAR image to assess building damage is much quicker and more convenient than using the multi-source or multi-temporal data. PolSAR (fully-polarimetric SAR) data contain much more information than single- or dual-polarization SAR data, and the texture features extracted are very useful for recognizing ground objects in SAR image. However with PolSAR images, building damage recognition results directly generated by a polarimetric decomposition method always give rise to excessive assessment of damaged buildings. To overcome this deficit and improve the identification accuracy of building earthquake damage, we developed the two new texture feature parameters CV_AFI in the frequency domain and MSD in the spatial domain.
In SAR imagery, the scattering intensity of collapsed buildings is weaker than that of standing buildings, as the dihedral structures in collapsed buildings are destroyed. The standing oriented buildings (whose orientation is not parallel to the flight direction) have strong depolarization effect. Therefore, both collapsed buildings and oriented buildings are dominated by volume scattering, and they are easily misclassified. The oriented buildings always show banded textures with consistent arrangement, but collapsed buildings often show more random textures with a disordered distribution. The spatial frequency of SAR images can be clearly rendered in the frequency domain. For comparing the classification performance of texture features in the frequency domain and the spatial domain, we proposed the variable coefficient of angle domains based on the Fourier amplitude spectrum parameter (CV_AFI) and the mean standard deviation (MSD) parameter based on the statistical characteristics to discriminate oriented buildings and collapsed buildings.
We used CV_AFI and MSD, and combined the Yamaguchi four-component polarimetric decomposition method to extract building earthquake damage information, respectively. The double-bounce scattering components generated from Yamaguchi four-component decomposition are directly regarded as the intact buildings which is called DB intact buildings for short. The total power image as the intensity image of PolSAR data is used to compute CV_AFI and MSD. According to the threshold values of CV_AFI and MSD, the volume scattering components generated from the Yamaguchi four-component decomposition are classified into two categories of the collapsed buildings and the oriented buildings. The volume-dominated buildings corresponding to the CV_AFI or MSD values which are bigger than the thresholds are classified as the collapsed buildings, and the remaining volume-dominated buildings are classified as the oriented buildings. Finally, the oriented buildings are incorporated into the intact buildings.
The experimental results show that the overall correct building damage recognition accuracies of CV_AFI and MSD are 84.45% and 80.65%, respectively. The correct identification rates of CV_AFI and MSD for the collapsed buildings are 82.95% and 82.43%, respectively. The undamaged building correct identification accuracies of CV_AFI and MSD are 85.20% and 80.30%, respectively. The correct recognition accuracies of collapsed buildings and undamaged buildings and the building damage recognition overall accuracy with CV_AFI are higher than those of MSD. Especially, more oriented buildings are correctly detected using CV_AFI, that is, less oriented buildings are misclassified as collapsed buildings. Thus the identification accuracy of intact buildings is higher than that of collapsed buildings. This result well confirmed that the texture features in the frequency domain can better reflect the difference in the spatial distribution between oriented buildings and collapsed buildings. Therefore, the texture features in frequency domain are more effective for building damage recognition, and they should be given more consideration when developing earthquake damage assessment methods, in addition to applying the texture features in spatial domain.

In the context of climate change UK forests are increasingly susceptible to drought, leading to reduced productivity and increased mortality, thus reducing the carbon sink. Understanding how species and genotypes respond to drought can inform the transition to more drought tolerant forests in the future. Remote sensing provides tools to non-destructively monitor plant health at multiple spatial scales. Here, Sitka spruce clones are exposed to an experimental drought and monitored over eight weeks. The spruce express stress pigments and lose water content as the drought progresses The stress response differs between clones suggesting intraspecific drought tolerance detectable by remote sensing. This work can inform future spruce breeding programmes and contribute to national forest health monitoring.

I will present some of my work on developing methodologies to detect, quantify and better understand climatic and structural forest stress in the United Kingdom with satellite remote sensing data. In particular, I will describe the utility of the time series analysis of vegetation indexes for stress detection, with a focus on effective models for removing the systematic noise and phenological signals in the data. Additionally, I will describe experiments with regression models for predicting the health of a region of forest in a given year, using climate, geographical and contextual information as predictors.

Quantitative assessment of forest carbon sequestration capacity is of great significance for maintaining sustainable forest development, improving resource utilization efficiency, and mitigating environmental degradation. This research takes Pu'er City, Yunnan Province as the research area to explore the potential of using LiDAR biomass index (LBI) for precise carbon sequestration measurement of Simao pine species. Firstly, airborne laser scanning (ALS) data of the research area in 2018 and 2023 were obtained, and accurate matching of multiple data periods was achieved. Secondly, based on the 2018 ALS data, we selected the measured individual trees of different diameter classes to calibrate the individual tree level biomass model based on the LBI. Thirdly, the individual tree segmentation of ALS data in 2018 and 2023 were completed using the same segmentation algorithm, respectively. The AGB_LBI model was applied to the segmented laser point cloud data of the two periods to realize biomass estimation. To verify the accuracy of the model, we obtained a certain amount of individual trees with precise positions and forest sample plots from the flight area of the two periods airborne LiDAR. The reference biomass was calculated using the existing allometric equation, and then used to evaluate the applicability accuracy of the 2018 biomass estimation model for the sample plots obtained in 2018 and 2023, respectively. The results indicate that high-precision AGB_LBI model (R²=0.83, RMSE=15.68 kg) was constructed using 57 individual trees, and high accuracy was obtained when using the model to calculate the biomass of the same year's sample plots (R²=0.78, RMSE=26.49 t/ha). Meanwhile, when using this model to calculate the biomass of the sample plots obtained in 2023, R² of 0.72 and RMSE of 33.11 t/ha were obtained. Therefore, LBI has great potential for measuring carbon sinks in forest stands or even on a larger scale.

Quantitative ground validation of satellite remote sensing reflectance data is of great research significance to determine whether the data can support quantitative remote sensing applications, especially accurate extraction of forest parameters in complex terrain forest areas. In this paper, Pu'er City, Yunnan Province was selected as the research area to explore the method of using airborne hyperspectral reflectance data based on the Bidirectional Reflectance Distribution Function (BRDF) model to validate satellite reflectance data. Firstly, the airborne hyperspectral image acquired on December 13, 2020 (local imaging time: 13:47) was radiometrically calibrated, atmospherically corrected, and geometrically corrected to obtain the airborne hyperspectral reflectance image. Secondly, the GF-6 satellite image on December 14, 2020 (local imaging time: 12:26) was radiometrically calibrated, atmospherically corrected and geometrically corrected to obtain the surface reflectance image. Thirdly, based on the spectral response function of both images, the narrow band reflectance of the airborne hyperspectral image was converted to the broad band reflectance of the GF-6 satellite image. Finally, the BRDF model was used to model the airborne reflectance image (corresponding to the GF-6 bands), and the surface reflectance at specific time (i.e., specific solar-observation geometry) was generated based on this. This paper compared two validation methods, direct use of airborne data to validate satellite data and reconstruction of airborne data at different times (i.e., local time 10:30, 11:30, 12:26, 13:30, 14:30, 15:30) to validate satellite data. The results showed that the reconstructed airborne reflectance data based on the BRDF model (corresponding to the satellite imaging time of 12:26) were more effective in validating satellite reflectance images in complex terrain forest areas.

Validation of high spatial-temporal resolution gross primary production (GPP) is not only important in monitoring vegetation, but also crucial in calibrating remote sensing models for vegetation productivity. Flux tower measurements provide an effective way to validate GPP derived from satellite observations. Studies have demonstrated that footprint variations can affect the feasibility to match flux tower GPP with satellite GPP in any type of ecosystem due to spatial heterogeneity within the flux tower footprint. Besides, the average daily GPP could not fully reflect the photosynthesis of the satellite overpassing time as GPP varied a lot in a day due to the changes of solar radiation and meteorological condition. But few studies focused on the spatial and temporal scale to validate the high resolution GPP. In this condition, optimizing the strategy to validate high resolution GPP could help to improve their agreement between high resolution satellite GPP and flux tower GPP in photosynthesis estimates.

In this study, based on the flux tower measurement from Puer site (101°5′24″E, 22°24′59″N) in Yunnan Province and Baotianman site (111°56′10″ E, 33°29′59″ N) in Henan Province in China, an optimized method to validate Sentinel-2 GPP was proposed. Firstly, high resolution GPP was estimated by using a Light Use Efficiency (LUE) model and Sentinel-2 images in 2019 and 2020. Then the Footprint Source Area Model (FSAM) was adopted to obtain the real time and near real time (1 min, 5min, 10min, 30 min, 1 h, 2 h, 3h, 4 h) footprint when the Sentinel-2 overpassing time. And the weighted GPP in the footprint was used to validate the Sentinel-2 GPP. Results of this study demonstrated that better linear relationships could be achieved between the satellite derived GPP and ground observed GPP when taking into account the footprint of flux data. And better correlation could be observed between Sentinel-2 GPP and flux tower GPP derived in 30min~2h of the satellite overpassing time. Results of this study may provide some new theory for the validation of satellite derived GPP with high resolution.

The forest fuel load influences the spreading rate and fire intensity during a forest fire. However, the mechanism of environmental filtering and dispersal limitation that affects the spatial distribution of the forest fuel load remains unclear. In this study, live (tree, herbaceous, and shrub) and dead fuel loads (litter and humus) were estimated based on the plot investigation results of four typical stands (Pinus massoniana, Platycladus orientalis, Ficus microcarpa, and Cinnamomum camphora) in Chongqing, China. The results demonstrated that the tree, shrub, herbaceous, litter, and humus fuel loads of the four typical stands were 66.92–118.54 Mg/ha, 2.93–4.04 Mg/ha, 0.77–1.01 Mg/ha, 0.90–1.39 Mg/ha, and 1.49-1.98 Mg/ha, respectively. The forest fuel load varied significantly among the different stands. The Mantel test revealed that the forest fuel load had significantly positive correlations with the geospatial distance and stand environment but no significant correlation with the topographic factor. Additionally, the redundancy analysis demonstrated that the stand factors, canopy density and average canopy height, and the topographic factor, altitude, had significant impacts on the forest fuel load. The variance partitioning analysis revealed that the spatial heterogeneity of the forest fuel load was mainly attributed to the co-variation of environmental and spatial factors (29.55%). Moreover, the geospatial distance was a dominant independent factor for the fuel distribution (14.66%), followed by the stand environment (9.51%), and topographic factor (0.35%). In summary, the spatial distribution of the forest fuel load was dependent on niche-based and random processes, and dispersal limitation was the dominant factor.

Land-use change is the second largest source of carbon emissions and directly affects the balance and structural function of carbon storage in terrestrial ecosystems. Analyzing the driving mechanisms of regional land-use change on carbon storage and exploring a sustainable land-use approach is of great practical significance for guiding future urban planning and development. In this study, we used Beijing as an example and based on Landsat MSS, TM/ETM and Landsat 8 land-use/cover data, we applied the PLUS-InVEST model to analyze the correlation between the growth of various land types and multiple driving factors using the random forest classification algorithm. We also established a multi-sensor remote sensing and non-remote sensing data-driven system to analyze the characteristics and driving mechanisms of land-use change in Beijing from 2000 to 2020. Based on this, we predicted the spatial pattern of land-use and the spatiotemporal differences in carbon storage in Beijing in 2030 under natural evolution and ecological protection scenarios, providing theoretical support for optimizing land-use structure and achieving carbon neutrality in Beijing in the future. The results show that annual average temperature is the largest driving factors affecting arable land expansion, respectively. DEM and distance from municipal government are key driving factors for forest expansion, while population density is the main driving factor for construction land expansion. Under the natural evolution scenario, by 2030, forest, grassland, and water area will increase by 161.59 km², 142.23 km², and 100.06 km² respectively, with a carbon storage of 2.03×10⁸ t. Under the ecological protection scenario, forest, grassland, and water area will increase by 168.11 km², 148.85 km², and 56.13 km² respectively, with a carbon storage of 2.10×10⁸ t.