Conference Agenda

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.

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.

This research and collaboration project aims at the calibration and validation (Cal/Val) of the European Sentinel-3, Sentinel-6 and the Chinese HY-2B & HY-2C satellite altimeters using two permanent Cal/Val facilities: (1) the Permanent Facility for Altimetry Calibration established by ESA in Crete, Greece and (2) the National Altimetry Calibration Cooperation Plan of China. Other satellites, such as the Guanlan, CryoSat-2, CFOSAT, CRISTAL, etc., could certainly be supported by these Cal/Val infrastructures. Both facilities attain the strategy of Fiducial Reference Measurements (FRM), established by the European Space Agency for reporting calibration of satellite altimeters.

Calibration of satellite altimeters has been accomplished by examining actual satellite observations in open seas against reference ground measurements defined by Cal/Val infrastructures at specific locations in Europe and China.

During this third year of this Dragon-5 collaboration, the following tasks are being carried out:

• An exhaustive analysis of all sources of uncertainties (i.e., water level observations, satellite signal delays, reference surface models, etc.) which each makes a contribution to the final results of satellite altimeter calibrations;
• Reference measurements and all kind of observations on the ground at the Cal/Val infrastructures are connected to the International System of Units (SI) and absolute international standards of reference (speed of light, atomic time, etc.);
• Uncertainties for the results of satellite calibration are reported in a realistic, universal and objective way;
• Independent and diverse techniques for estimating altimeter biases are applied by the European and Chinese teams;
• Final Cal/Val results are reported and intercompared at the two Cal/Val facilities.

The main findings of this joint work are:

• Both European and Chinese Cal/Val sites are operational and implement independent calibrations for European, Chinese and international satellite altimeters (i.e., Sentinel-6A MF, Sentinel-3A/B, HY-2B/C, Jason-3, etc.);
• Operations and data processing are standardized at both Cal/Val reference infrastructures in Europe and China;
• Common procedures are implemented (i.e., tide gauge quality control, GNSS positioning, etc.) towards standardization and unification of operations and data handling and processing.
• The performance of satellite altimeters is continuously monitored by the two Cal/Val infrastructures in China and Europe.

The report we are presenting focuses on the objectives and schedule of our project, providing an update on the ongoing activities and results of Bufeng-1 data processing, calibration workflow, and validation of the calibrated results on hurricane winds, soil moisture, and sea level measurements. This presentation is divided into three parts. Firstly, we will provide a brief introduction about Bufeng-1 and recent Chinese GNSS-R missions, highlighting their significance in the field. Secondly, we will delve into the preliminary results obtained by utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), earth reflectivity, and range measurements. The preliminary results indicate that Bufeng-1 has a high agreement compared with other observations on severe sea surface winds, soil moisture, and sea level. We will align the measurements of Bufeng-1 with SFMR collected hurricanes, SMAP derived soil moisture, and DTU18 sea level models to provide a comprehensive analysis of the results. We will also analyze the accuracy and correlation coefficients to discuss the limitations and issues for future research. This will be crucial in improving the quality of data and enhancing the accuracy of future measurements. For the last part, we will give the outlook about our future works of the objectives and the future plan of Chinese GNSS-R missions. Our aim is to provide a comprehensive and detailed report that will assist researchers and stakeholders in the field of climate research, weather forecasting, and disaster management in making informed decisions.

This report provides an overview of the recent progress on the cross-calibration and validation of CSES/Swarm satellite magnetic field and plasma measurements.

(1)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.

(2)The first detailed analysis about the spacecraft potential (Vs) variations of Swarm satellites are provided, which are flying at about 400-500 km. Different to previous studies that investigate the extremely charging events, usually with spacecraft potential as negative as -100 V, we focus on the variation of Swarm Vs varying within a few negative volts. The Swarm observations show that the spacecraft at low Earth orbit (LEO) altitudes are charged slightly negative, varying between -7 V and 0 V, and with the majority around -2 V. Interestingly, a second peak of Vs is found at -5.5 V, though the event number is less by an order than the first peak around -2 V. The two groups show different spatial and temporal distributions. For the slighter negative charged group, at low and middle latitudes the Vs shows relatively larger values above the magnetic equator, and with much more negative values on the dayside at low and middle latitudes; at high latitudes, the Vs shows relatively negative values during local summer. For the deeper negative charged group, Vs at equatorial and low latitudes is slightly lower at the SAA region; and at high latitudes, the valid Vs observations appear mainly during local winter. We found for the first group much negative Vs is observed at regions with higher background plasma density, while for the second group much negative Vs is observed at regions with lower background plasma density.

(3) The high-resolution magnetic field measurements from ESA’s Swarm satellite constellation provide a good opportunity for revisiting the mean properties of ionospheric currents. Among the Swarm Level 2 data products, provided by ESA, are field-aligned current (FAC) estimates based on single-spacecraft (single-SC) and dual-spacecraft (dual-SC) solutions. For the more reliable dual-SC approach only magnetic signatures from currents flowing through the integration loop are considered. In the case of single-SC FAC estimates the magnetic effects of all remote current systems contribute also to the results. A direct comparison between the two FAC products at auroral latitudes reveals that the single-SC estimates systematically overestimate the current density of region 2 (R2) FACs (~15%) while underestimates the region 1 (R1) FACs (~10%). The differences between the two FAC products appear closely related to the location and direction of the horizontal polar electrojet (PEJ) at auroral latitudes. A direct comparison of these two current systems suggests an influence of the PEJ induced magnetic field on the solutions from single-SC FACs.

An IFS125HR has been deployed in Xianghe Integrated Observatory. Long-term operations were carried out for accumulating high quality data, which is significant for validating the satellite greenhouse gases products and for finding the signal of climate change. Methane (CH₄) is the second most important greenhouse gas after carbon dioxide. Accurate monitoring and understanding of its spatiotemporal distribution are crucial for effective mitigation strategies. In this study, the spatiotemporal variations of CH₄ over China were investigated based on the CH₄ products from 4 well-known satellites (GOSAT, TROPOMI, AIRS, and IASI). As we know, the spectrometers on board the 4 satellites are quite different in specifications as well as in the inversion algorithms. GOSAT and TROPOMI CH₄ retrieval use shortwave infrared spectra, having a better sensitivity near the surface, while IASI and AIRS CH₄ retrieval use thermal infrared spectra, showing a good sensitivity in the mid-upper troposphere. Therefore, GOSAT and TROPOMI observed higher CH₄ concentrations in the east and south, and lower concentrations in the west and north, which is highly related to the CH₄ emissions. IASI and AIRS show a more uniform CH₄ distribution over China, which is dominated by the variation of CH₄ at a high altitude. However, a large discrepancy is found among these satellite data. AIRS CH₄ mole fraction is systematically lower than the other 3 satellites. Significant differences in seasonal variations of CH₄ are observed between IASI and AIRS across several regions in China. The highest concentration of CH₄ was observed by AIRS in Inner Mongolia, which is probably due to the dust inferences above the bare soil.

Keywords: CH₄, spatiotemporal variation, methane measuring satellites, intercomparison, FTIR

In 2022, this project continues to carry out research on space radiation benchmark transfer and calibration technology based on the RadCalNet site according to the plan. The accuracy of on-orbit satellite radiation calibration was improved, and the transfer calibration method based on RadCalNet TOA reflectivity products was improved by using a high accuracy and stability reference satellite. The TOA reflectance conversion model of Baotou site was optimized. The TOA reflectance model of the American site and Namibian site was constructed. Based on the above, an uncertain transfer link was constructed that connects a spatial radiation reference to each of the RadCalNet ground sites. This link can provide measurements of the contributions to total uncertainty caused by each factor.

Surface reflectance is the fundamental quantity required in the majority of optical Earth Observation analyses, and as an essential input to derive biophysical products. In addition to parameters such as the fraction of vegetation cover (FCOVER) and Canopy Chlorophyll Content (CCC), these products also include essential climate variables (ECVs) such as leaf area index (LAI). LAI is an integral plant canopy attribute and critical indicator of plant growth status. Currently several satellite derived LAI products exist, covering local to global scales with various spatial resolutions. In turn, they are crucial in understanding vegetation productivity/yield, biogeochemical cycles, and the weather and climate systems. Therefore, validation of such products is of great importance to ensure they meet the accuracy requirements for specific applications. However, ground measurements are not always match reflective of the spatial resolution of the satellite imagery, and contribute to uncertainty in the validation of LAI products. The key to reducing this source of uncertainty is upscaling from ground-measured LAI values to data representative of a satellite pixel.

In the study, high-spatial-resolution UAV remote sensing images were used as an intermediary for upscaling processing. We applied this approach to validate LAI retrievals based on Sentinel-2 and Gaofen-6 imagery (in which the Sentinel-2 Level-2 Prototype Processor (SL2P) was used to retrieve LAI from Sentinel-2, whilst a look-up-table (LUT) method was used to retrieve LAI from Gaofen-6). UAV images can well connect ground data and satellite data, thereby reducing the error caused by the mismatch of spatial resolution.

In the mid-term of the project, this study collects field LAI data and UAV images in Taizishan Forest Park (30.91-30.92°N, 112.87-112.88°E), China. Very high spatial resolution LAI reference maps were derived from the UAV imagery using four vegetation indices (VIs). In order to verify the LAI products retrieved by Sentinel-2 and Gaofen-6, we upscaled the UAV LAI map to 10m and 16m resolution. Finally, we compared the UAV-based upscaling approach to the direct comparison between LAI retrievals and ground measurements. Our results revealed improved correspondence between the satellite retrievals and UAV-based reference map when compared to direct comparison with the ground measurements (RMSE reduced from 1.02 to 0.59 for Sentinel-2 and 1.49 to 0.89 for Gaofen-6). Compared to SL2P, larger MAE(≥0.59) and RMSE(≥0.76) values were obtained for the Gaofen-6 LAI retrievals, indicating a need for further algorithm refinement.

In August 2018, ESA’s Earth Explorer mission Aeolus has been successfully launched to space. Since then Aeolus has been demonstrating its capability to accurately measure atmospheric wind profiles from the ground to the lower stratosphere on a global scale deploying the first ever satellite borne wind lidar system ALADIN (Atmospheric Laser Doppler Instrument).

In order to identify and correct the systematic error sources, guarantee and enhance the performance of ALADIN and the data quality of the wind products, several calibration and validation campaigns were implemented.

In the aspect of ALADIN calibration, the ALADIN laser frequency stability and its impact on wind measurement was assessed and the correction of wind bias for ALADIN using telescope temperatures was conducted. By monitoring the ALADIN laser frequency over more than 2 years in space, excellent frequency stability with pluse-to-pluse variations of about 10MHz (root mean square) is evident despite the permanent occurrence of short periods with significantly enhanced frequency noise (> 30 MHz). Another systematic error source is related to small fluctuations of the temperatures across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m s−1. To correct for this effect ECMWF model-equivalent winds are used as a reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %.

Shortly after the launch of Aeolus, co-located airborne wind lidar observations, which employed a prototype of the satellite instrument – the ALADIN (Atmospheric LAser Doppler INstrument) Airborne Demonstrator (A2D), were performed in central Europe, meanwhile ground-based coherent Doppler wind lidars (CDLs) net was established over China, to verify the wind observations from Aeolus. In the first airborne validation campaign after the launch and still during the commissioning phase of the mission, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. The statistical comparison of the two instruments shows a positive bias (of 2.6 m s−1) of the Aeolus Rayleigh winds (measured along its LOS*) with respect to the A2D Rayleigh winds as well as a standard deviation of 3.6 m s−1. In the validation campaign over China, by the simultaneous wind measurements with CDLs at 17 stations, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. From the comparison results of respective Baselines, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. The vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Aeolus has the capability to measure wind profiles and aerosol optical properties profiles synchronously, which provides the possibility for studying the wind-driven evolution of aerosol. Combining the measurements of ALADIN/Aeolus and the data from other spaceborne sensors, together with NWP models, wind-driven dust aerosol transport and marine aerosol production are discussed, respectively.

Based on the observation of ALADIN, combined with the data of CALIOP, AIRS, ECMWF and HYSPLIT, a long-term large-scale Saharan dust transport event which occurred between 14 and 27 June 2020 is tracked and the possibility of calculating the dust mass advection is explored. The dust event's emission phase, development phase, transport phase, descent phase and deposition phase on 15, 16, 19, 24 and 27 June are captured by the quasi-synchronization observations of ALADIN and CALIOP, which is verified with the AIRS Dust Score Index data and the HYSPLIT trajectories. The dust mass advection of each transport phase is calculated.

Based on the observation of ALADIN and CALIOP, combined with the data from ECMWF, three remote ocean areas are selected and the optical properties at 355 nm of pure marine aerosol are derived. Then the optical properties are analyzed and discussed combined with the wind speeds. Eventually, the relationships between the marine aerosol optical properties and the wind speeds are explored at two sperate vertical atmospheric layers (0-1 km and 1-2 km, correspond to the heights within and above marine atmospheric boundary layer), revealing the marine aerosol related atmospheric background states. The optical properties present increasing trends and fitted by power law function 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. As derived data, the averaged marine aerosol optical depth and the averaged lidar ratio are acquired and discussed along wind speed bins.

Global observations of column carbon dioxide concentrations and aerosol optical properties profiles are important for climate study and environment monitoring which is why China decided to implement the lidar mission ACDL (Aerosol and Carbon dioxide Detection Lidar) to measure CO2 and aerosol from space – has been launched to space successfully on 16 April 2022. The commissioning phase of ACDL is scheduled to be 6 months, during which the calibration and validation campaigns are implemented and the retrieval algorithms of column carbon dioxide concentration and aerosol optical properties profiles are improved. It is expected that with the calibrations and validations of ACDL and the updates of retrieval algorithms, the products of ACDL will be accurate and robust for science applications.

Remote sensing of ocean color over coastal waters is challenging and these difficulties can be placed in 3 categories: i) adverse atmospheric conditions associated with the presence of thin clouds or thick aerosol plumes (sometimes biomass burning), ii) challenging environments found over or around the water target (boundary conditions); iii) extreme conditions associated with the water content in optically active constituents (high concentrations of sediments). Evaluation and improvements of the estimation of bio-optical and biogeochemical parameters is an indispensable task for accurately monitoring the dynamics and the quality of coastal waters through the use of ocean color remote sensing. Especially, with the improvement of sensor ability and the advent of novel retrieval algorithms/models, ocean color is playing a more and more important role in understanding the utilization, the protection and the management of coastal environments. Ocean color data can thus provide biogeochemical data with known uncertainty, which is of great importance for quantitatively characterizing variation of key elements in coastal ecosystem and is required for input in modelling. Sentinel 3A/3B is new generation ocean color missions in Copernicus program in Europe, while HY-1C/1D is the first operational ocean color satellites in China. Their optical sensors (OLCI for Sentinel 3 and COCTS for HY-1) provide invaluable knowledge of ocean ecosystems due to their large swath and frequent coverage.

This project aims at tackling those issues over European (mainly French) and Chinese coastal waters. The main scientific objectives concern the monitoring of the quality of the French and Chinese coastal waters using OLCI and COCTS/CZI space-borne sensors. The project is divided into different tasks: (1) Characterization of uncertainty of OLCI and COCTS/CZI ocean color products in coastal waters; (2) Development of novel regional EO datasets in coastal waters. The first task aims at evaluating the atmospheric correction and bio-optical algorithms of OLCI and COCTS/CZI in our two areas of interest using in-situ measurements collected by both teams and the second task aims at developing regional bio-optical algorithms for the Chinese/French coastal waters according to specific spectral configuration of COCTS and OLCI.

During the symposium, we will present the validation results of OLCI and COCTS L2 products over different coastal waters across Europe and China. In this report, reference data including both aerosol and sea-water reflectance are acquired by an automatic photometer (CE318-TV12-OC, also called SeaPRISM) manufactured by CIMEL corporation (France). It measures the sun, sky and sea surface periodically, from which aerosol optical thickness (AOT) and Remote-sensing reflectance (Rrs) can be derived. This instrument has already been deployed in AERONET-OC network. Four CE318 are selected across Europe and China, two in Europe and two in China. They are all deployed on offshore platforms where sea water demonstrates different optical signatures. With temporal coverage spanning between January 2020 and December 2022, validation results show that (1) OLCI can provide AOT generally in agreement with in-situ data but tends to over-estimate AOT in both European and Chinese waters. Such over-estimation is more notable in Europe. (2) Irrespective of water types, AOT from COCTS shows no obvious over-/under-estimation in general, but demonstrates significant uncertainty (i.e., big dispersion). (3) Rrs from OLCI agrees very well with in-situ measurements in most visible-infrared bands. (4) COCTS tends to under-estimate Rrs across various waters. Furthermore, validation results for NASA L2 ocean color products (e.g., MODIS/AQUA) with same CE318 dataset will also be presented for inter-comparison. Also, consistency will be checked among ocean color products.

Finally, spatial-temporal analysis of the variability of the concentration of chlorophyll-a concentration is analyzed for the OLCI sensor over European coastal waters. This analysis is part of the PhD thesis of a young scientist. The trend, seasonal and intra-seasonal patterns are analyzed between 2016 and 2023. Future work plan and young scientist training will also be presented.

Land Surface Temperature (LST) is one of the main quantities governing the energy exchange between the surface and atmosphere. This abstract provides a summary of the latest progress of Dragon-5 project (59318), including (1) all-weather LST generating methods and its implementation; (2) all-weather LST downscaling methods; and (3) satellite LST validation against in-situ LST.

To investigate the temporal and spatial variations of LST in China, long-term, high-quality, and all-weather LST datasets are urgently needed. However, the publicly reported all-weather LSTs are not available during the temporal gaps of MODIS between 2000 and 2002. Therefore, the enhanced RTM (E-RTM) method was proposed to produce a daily 1-km all-weather LST dataset for the Chinese landmass and surrounding areas, i.e. TRIMS LST (Tang et al., 2023). Validation against in-situ LST shows a MBE range of -2.26~1.73 K and a RMSE range of 0.80~3.68 K, with slightly better accuracy than the MODIS. The TRIMS LST has already been used by scientific communities in various applications, e.g. evapotranspiration estimation, and urban heat island (UHI) modelling.

A method integrating reanalysis data and TIR data from geostationary satellites (RTG) was proposed for reconstructing hourly all-weather LST (Ding et al., 2022). The method was implemented over Tibetan Plateau with the Chinese Fengyun-4A (FY-4A) TIR LST and China Land Surface Data Assimilation System (CLDAS) data. Validation against in-situ LST shows that the accuracy of the all-weather LST is better than FY-4A LST and CLDAS LST. The mean RMSE is better than 3.94 K for all conditions, respectively. The reconstructed all-weather LST also has good image quality and provides reliable spatial patterns.

To obtain high spatial resolution all-weather LST, two downscaling methods were developed. The first is downscaling TRIMS LST using LightGBM from 1 km to 250 m over Southeast Tibet (Huang et al., 2021). Validation against in-situ temperature shows a MBE of 0.74 K (-0.01 K) and RMSE of 2.25 K (2.15 K) for daytime and nighttime, respectively. The second is a method, which is proposed based on the geographically weighted regression (GWR) and random forest (RF), and considering the weights of LST descriptors (Ding et al., 2023). The method was tested to downscale 1000-m aggregated ASTER LST to 100 m. Validation against in-situ LST shows that the MBE and RMSE can be reduced more than 0.22 K and 0.1 K in Beijing and Zhangye. The explorations in downscaling provide the basis for obtaining high-resolution all-weather LST.

To validate the satellite-retrieved LST at the kilometer scale, we proposed a temporal variation method for evaluating the ground station’s spatial representativeness (Ma et al., 2021). The spatial representativeness indicator is defined as the LST difference between the in-situ radiometer’s FOV and satellite pixel scale and extended in temporal with the temporal variation of LST and related parameters. Then, the in-situ LST was convert to pixel scale to validate the MODIS and AATSR LST. Results show that, among the selected stations, a systematic bias of -1.95~5.60 K and a random error of 0.07~3.72 K can be found for the validation results if the station’s spatial representativeness is ignored. Therefore, it is suggested that the ground station’s spatial representativeness over inhomogeneous surfaces should be considered in LST validation, as well as other related parameters. In further research, the spatial representativeness of KIT’s station and HiWATER station will be evaluated and then used in all-weather LST validation.

Since 2008 KIT operates a permanent LST validation station near Gobabeb, Namibia (Göttsche et al., 2022). In the rainy season of 2010/2011 the largest amount of rainfall in recorded history was measured at Gobabeb’s meteorogical station (Eckhardt et al., 2013), which resulted in an exceptionally strong growth of grass over large parts of the gravel plains. Due to the extreme atmospheric conditions and the changes in biophysical surface properties, LST retrievals for this period can provide interesting insights into the performance of LST products. Here, two all-weather LST products are compared over the gravel plains: 1) all-weather LST obtained with Reanalysis and Thermal infrared remote sensing Merging (RTM) (Zhang et al., 2021), which uses reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data to estimate LSTs at cloudy MODIS overpasses, and 2) the operational all-weather LST product of the Land Surface Analysis (LSA) Satellite Application Facility (SAF), which merges clear-sky MSG/SEVIRI LST with surface temperatures from a Soil-Vegetation-Atmosphere (SVAT) model driven by LSA SAF satellite products (Martins et al., 2019). The two all-weather LST products are validated with in-situ LST and their spatial variation over the gravel plains is investigated, thereby providing a comprehensive analysis of their performance over a broad range of atmospheric and surface conditions encountered at Gobabeb.

Eckardt, F.D., Soderberg, K., Coop, L.J., et al., 2013. The nature of moisture at Gobabeb, in the central Namib Desert. J ARID ENVIRON, 93, 7–19.

Ding, L., Zhou, J., Li, Z.-L., et al., 2022. Reconstruction of Hourly All-Weather Land Surface Temperature by Integrating Reanalysis Data and Thermal Infrared Data From Geostationary Satellites (RTG). IEEE Trans. Geosci. Remote Sensing, 60, 1–17.

Ding, L., Zhou, J., Ma, J., et al., 2023. A Spatial Downscaling Approach for Land Surface Temperature by Considering Descriptor Weight. IEEE Geosci. Remote Sensing Lett., 20, 1–5.

Göttsche, F.-M., Cermak, J., Marais, E., et al., 2022. Validation of Satellite-Retrieved Land SurfaceTemperature (LST) Products at Gobabeb, Namibia. Journal Namibia Scientific Society, 69, 43–61.

Huang Z., Zhou J, Ding L., et al., 2021. Toward the method for generating 250-m all-weather land surface temperature for glacier regions in Southeast Tibet. Journal of Remote Sensing, 25, 1873–1888.

Ma, J., Zhou, J., Liu, S., et al., 2021. Continuous evaluation of the spatial representativeness of land surface temperature validation sites. Remote Sensing of Environment, 265, 112669.

Martins, J. P. A., Trigo, I. F., Ghilain, N., et al., 2019. An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations. Remote Sensing, 11(24), 3044.

Tang, W., Zhou, J., Ma, J., et al., 2023. TRIMS LST: A daily 1-km all-weather land surface temperature dataset for the Chinese landmass and surrounding areas (2000–2021), Earth Syst. Sci. Data Discuss. in review.

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