Conference 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.