Conference Agenda

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sea ice is a highly sensitive indicator of past and present climate change. The demand for getting comprehensive, continuous, and reliable sea ice information from multi-source satellite data is growing as a result of climate change and its impact on environment and regional weather conditions, and on human activities such as operations in ice-covered ocean regions. This paper provides an overview of the Dragon 5 project dealing with synergistic monitoring of sea ice in the Arctic by multi-source remote sensing data.

For sea ice classification, the multi-frequency polarimetric backscatter behavior of sea ice during the melt period was investigated. Multi-frequency (L-, S-, C-, X- and Ku-band) airborne SAR scenes were recorded in the Bohai Sea with air temperatures varying around 0℃. In this work, we quantified the redundancy and relevance of polarimetric features for identifying ice types during the melting period, and assess the discrimination ability of melting sea ice types at the different radar frequencies. Considering the needs of operational Ice Services responsible for producing sea ice maps, another study dealt with a comparison of ice type separation in satellite C- and L-band SAR images as stand-alone and in combination. Since L- and C-band SAR systems have to be operated from different satellite platforms, an optimal data acquisition strategy has also to be developed. For sea ice thickness, we analyzed the feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter. To this end, we used the HY-2B radar altimeter to retrieve the Arctic radar freeboard and sea ice thickness, and compared the results with the co-incident CryoSat-2 products by AWI. By comparing with the OIB and IceSAT-2 data, we found that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice are larger than those over first-year ice. For iceberg detection by SAR data, the variations of signature contrast between icebergs and sea ice dependent on ice conditions and radar parameters was investigated. We found that the intensity contrast depends on the radar frequency, the incidence angle and the sea ice surface characteristics. The latter study will be presented by our young investigators.

Sea ice drift and thickness retrieval methods that are specifically designed for the FY-3D radiometer were proposed. For sea ice drift in the Arctic we used a continuous maximum correlation (CMCC) approach. To address the challenge of retrieving Arctic sea ice thickness, a FY-3D specific method was developed that relies on different parameters derived from the brightness temperature data (i.e. polarization ratio and gradient ratio). Besides estimating sea ice thickness with radiometer data we also investigated detection of thin ice (<20 cm) in the Arctic using AMSR2 and FY-3C radiometer data. The thin ice detection is based on the classification of the 36 GHz polarization ratio and H-polarization 89-36 GHz gradient ratio (GR) with linear discrimination analysis, and thick ice restoration with GR3610H. An integral part of the thin ice detection is the atmospheric correction of the brightness temperature data, following an EUMETSAT OSI SAF correction scheme. The thin ice detection algorithm was developed using MODIS ice thickness charts over the Barents and Kara Seas. The AMSR2 and FY-3C daily thin ice charts are calculated for one winter season, and their statistical similarities and differences are investigated. They are also compared against the SMOS ice thickness data. The AMSR2 and MWRI daily thin ice charts are targeted to be used together with SAR imagery for sea ice classification.

The contribution of meltwater from the snowpack and glaciers in High Mountain Asia (HMA) is rather well documented, as are changes in glacier extent and volume. Less explored are the overall dynamics of the high mountain water cycle, and the interactions of snow and ice dynamics with those of vegetation to shape HMA catchments response to weather and climate and their water yield .
This is the goal of our project and we made progress on several aspects, linking progresses in remote sensing and advanced land surface modelling to advance simulations of HMA water cycle. Inter- and intra-annual elevation changes of glaciers in the HMA region in 2003–2020 were studied using Ice, Cloud and land Elevation Satellite (ICESat) data and Shuttle Radar Terrain Mission (SRTM) digital elevation model (DEM) data. The inter-annual change of glacier elevation in 2003–2020 had large spatial heterogeneity. Glacier elevation reduction mainly occurred in the marginal region of the HMA with the maximum decline in the Nyainqentanglha region, while glacier elevation increased in the West Kunlun of inner HMA regions in 2003–2020. The intra-annual change of HMA glacier elevation in 2019 and 2020 showed a clear spatiotemporal heterogeneity, and the glacier thickening period was gradually delayed from the marginal area to the inner area of the HMA.
The inter- and interannual variability in snow cover during the period 2000 – 2020 was studied in the Tarim Basin to understand changes in glacier extent in relation with snow accumulation. We evaluated how observed trends in glacier area related to snow cover area in five subregions in the Tarim Basin. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. The analysis of the cross- correlation functions of glacier and snow cover areas showed that the glacier area responds to temperature, precipitation and snow cover within the same year.
Glacier surface albedo is one of the most important parameters to determine the net shortwave radiation and therefore affect glacier energy and mass balance, which in turn affect glacier surface flow, especially its spatial and temporal variability. The results show that the glacier albedo declined over the entire HMA, but with distinct spatial and seasonal differences. In the westerly-dominated regions, glacier albedo decreased slightly and even increased in the Hindu Kush and West Himalaya, while in the monsoon-dominated and transition regions, it showed a large decrease with the fastest change in the Inner Tibetan Plateau.
Patterns of glacier surface velocity and its seasonal and interannual variability in the temperate glaciers of the Parlung Zangbo Basin (PZB) are still uncertain. On the basis of satellite images acquired from 2013 to 2020, we produced a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. There was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue.
The generation of water in HMA headwaters and their downstream flow is not fully understood, as interactions in the mountain water cycle between the hydrosphere and biosphere remain elusive. Understanding how blue meltwater from snow-pack and glaciers contribute to total water vapour flux from vegetation, both is high elevation catchments and further downstream is a pressing and challenging research question. We applied a state-of-the-art land surface model (Tethys & Chloris) to the glacierized Langtang catchment in the Nepalese Himalayas to demonstrate the advantage of using high resolution earth observation data on e.g. glacier thinning and surface flow, glacier albedo and snow cover to constrain the meteorological uncertainty and validate our model results.

Part 1, Glacier velocity estimation based on Sentinel-2 observations at Karakoram.

The Sentinel-2A/B Twin satellites provide 5-day repeat observation to the Earth and capable of deriving glacier velocity with high-temporal resolution. In this study, the ‘Karakoram-Pamir anomaly’ region was taken as the study site and a data processing procedure was proposed to derive quasi-monthly glacier flow velocity fields. Each acquisition is performed offset-tracking to its next three almost cloud-free acquisitions to increase number of redundant observations. The detector mosaicking errors are eliminated if offset-tracking is performed between two different Sentinel-2 satellites. Flow speed and direction referenced method is taken to remove the wrong matching of offset-tracking. Then an iterative SVD method solves the glacier velocity and removes the observation with large residual. According to the glacier flow velocity time series between Oct 2017 and Sep 2021, it captures plenty of surged glaciers start and/or end their surging phases across this region. Two types of surging glaciers are identified according to the shape of their high temporal resolution flow rates time series. The first types’ surging phase last for only a few years, and shows no seasonal variation. Rimo’s southern tributary is an example of this type, it experienced a full surging phase during our study period and last for about two years, the maximum speed exceeded 10 m/day. Another type behaves similar to a normal type glacier but with glacier front advancing and much higher summer speed than their stagnation phase, such as Gando at Pamir. Normal type glaciers also presented annually speed up and slow down, with acceleration started usually in late April or earth May, and ends before September.

Part 2, Greenland ice sheet melting and re-freezing status monitoring with Sentinel-1 imagery

First this study introduced a method of incidence angle normalization to the backscatter coefficient of dual-polarized Sentinel-1 images. A multiple linear regression model is trained using the ratio between backscatter coefficient differences and incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include geographical position and elevation. The precision evaluation of the ascending and descending images suggests better normalization results than the widely-used cosine-square correction method for HH images and little improvement for the HV images.

Referring to the 2m air temperature data of AWS, we find that the daily average 2m air temperature higher than 0℃ cannot accurately indicate if the ice sheet melted. The daily maximum 2m air temperature on two consecutive days higher than 0°C and the daily average 2m air temperature exceeds -1°C on the SAR acquisition day that recorded by the AWS find good agreements with the -3dB decrease of the backscatter coefficients. The overall agreement and Kappa coefficients are mostly better than 0.85 and 0.70, respectively. However, at the ablation zone, although backscatter coefficient drops when the melting begins, but it also increases during the melting status, resulting a lower estimation of the melting duration.

Part 3, Glacier velocity estimation based on both Sentinel-1 and -2 observation at Greenland Ice Sheet

Two different methods are designed for deriving glacier velocity fields for Greenland Ice Sheet. The first is designed for area where Sentinel-1 6 or 12 days interferogram show certain level coherence. To overcome the high gradient of phase, a method of re-differential interferometry that employs the result of offset-tracking is designed. The maximum capacity of detecting deformation is ~3.6m for 6-day interferogram than conventional D-InSAR. The second method is designed for quick flowing area, where no coherence can be found for the Sentinel-1 interferogram. This method introduces a small baseline offset-tracking to Sentinel-1 and -2 images, then a least square method based on connective component is applied. SAR images can hardly give acceptable result at wet-snow zone during the melting seasons, while optical images are not obtained from Oct to next Mar. Error propagation theory is employed for precision analysis. Then a weighted least square method based on connective component method combines the time series derived from Sentinel-1 and -2. This method can provide a full time series of glacier velocity fields. The errors of Sentinel-1 images offset-tracking are ~0.4 m while ~2.5 m for Sentinel-2.

Glaciers are crucial sources of freshwater in particular for the arid lowlands surrounding High Mountain Asia. In order to better constrain glacio-hydrological models, annual, or even better, seasonal information about glacier mass changes is highly beneficial. In this study, we test the suitability of very high-resolution Pleiades DEMs to measure glacier-wide mass balance at annual and seasonal scales in two regions of High Mountain Asia (Muztagh Ata in Eastern Pamir and parts of Western Nyainqêntanglha, South-central Tibetan Plateau), where recent estimates have shown contrasting glacier behavior. We find that the average annual mass balance in Muztagh Ata between 2020 and 2022 was -0.11 ±0.21 m w.e. a^-1, suggesting the continuation of a recent phase of slight mass loss following a prolonged period of balanced mass budgets previously observed. The mean annual mass balance in Western Nyainqêntanglha for the same period was highly negative (-0.60 ±0.15 m w.e. a^-1 on average), suggesting increased mass loss rates. The 2022 winter (+0.21 ±0.24 m w.e.) and summer (-0.31 ±0.15 m w.e.) mass budgets in Muztag Ata and Western Nyainqêntanglha (-0.04 ±0.27 m w.e. [winter]; -0.66 ±0.07 m w.e. [summer]) suggest winter and summer accumulation-type regimes, respectively. We support our findings by implementing a Sentinel-1–based Glacier Index to identify the firn and wet snow areas on glaciers and characterize accumulation type. The good match between the geodetic and Glacier Index results demonstrates the potential of very high-resolution Pleiades data to monitor mass balance at short time scales and improves our understanding of glacier accumulation regimes across High Mountain Asia.

The monitoring and forecasting of global water cycle under climate changes indeed require enhancement of satellite remote sensing products in both of spatial resolution and accuracy and to seek for new opportunities of satellite missions. We have developed new soil moisture and vegetation optical depth datasets from current sensors including the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Soil Moisture and Ocean Salinity (SMOS), AMSR2, and Soil Moisture Active Passive (SMAP). we applied the Multi-Channel Collaborative Algorithm (MCCA) to those microwave sensors operating at different frequencies possess differentiated vegetation penetration capabilities and might provide significant information of the Soil-Plant-Atmosphere-Continuum (SPAC) system.

The SMAP MCCA retrievals are inter-compared with other SSM and VOD products (MT-DCA version 5, and DCA, SCA-H, SCA-V from SMAP Level-3 products version 8, and SMAP-IB), showing an analogous spatial pattern. The MCCA derived SSM had the lowest unbiased root mean square error ubRMSE of 0.055 m3/m3 followed by SMAP-IB and DCA (0.061 m3/m3), and an overall Pearson’s correlation coefficient of 0.744 (SMAP-IB performed best with R=0.764) when evaluated against in situ observations from the International Soil Moisture Network (ISMN). Comparable accuracy also found in widely used validation spare network SCAN. The MCCA generates VOD at both vertical and horizontal polarization. While the magnitude of the polarized VODs is lower than other products. MCCA polarized VODs were found to have a good linearity with live biomass and canopy height, though partial saturation exists in the relationship with live biomass of tropical forests but not canopy height. The polarization difference of L-band VODs is mainly located at densely vegetated and arid areas.

The AMSR-E/2 MCCA retrievals are inter-compared with other SSM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) at ISMN soil moisture networks. Although the R-value of MCCA (0.709) was slightly lower than that of LPRM-X (0.735), MCCA achieved the best scores in terms of RMSE=0.074 m3/m3, ubRMSE=0.073 m3/m3 and bias=0.007 m3/m3. For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions, especially for the H-polarized VOD. MCCA-derived VODs can physically present reasonable variations across the microwave spectrum, which is superior to the LPRM and VODCA.

Overall, MCCA products developed in this study showed good performance on both SSM and VOD. It is crucial for studies that consider the effects of paired SSM and VOD simultaneously, such as water fluxes in the SPAC system. In addition, the retrieval is implemented on snapshot observations, and MCCA can provide continuous daily data once the daily Tb is updated. It is expected that the MCCA algorithm can be extended to the observations of the upcoming Copernicus Imaging Microwave Radiometer (CMIR) mission.

The main objective of the project is to develop prototypes of real-time remote sensing (RS) land data assimilation systems (LDAS) for monitoring the water cycle in the silk road endorheic river basins and EUROCORDEX-domain. This will provide a synergic and innovative way to integrate RS data from NRSCC and ESA into terrestrial system models for better quantifying the water cycle at the watershed/regional scale. The objective will be achieved through the following sub-objectives: i) Retrieval of key water cycle variables from multi-source RS data (WP1); ii) Development of real time RS LDAS to integrate RS data into terrestrial system models (WP2); iii) Calibration/validation of terrestrial system models using RS retrievals of key water cycle variables (WP3); iv) Parameter estimations for terrestrial system models based on the LDAS (WP3); v) Closing and quantifying the water cycle at the watershed/regional scale based on the LDAS (WP4).

Two LDAS will be developed in the project, one for the silk road endorheic river basins (LDAS_Silk) and one for EUROCORDEX-domain (LDAS_EU). LDAS_Silk will be based on the recently developed watershed system model and a common software for nonlinear and non-Gaussian land data assimilation (ComDA). LDAS_EU will be based on the recently developed Terrestrial System Modeling Platform (TSMP) and Parallel Data Assimilation Framework (PDAF). Multi-source RS data, from visible to thermal infrared and microwave, will be used to retrieve key ecohydrological variables, such as evapotranspiration (ET), snow coverage area (SCA), snow water equivalent (SWE), snow depth (SD), soil moisture (SM), lake and glacier extents, irrigation, and vegetation density and structure. These data will be used as forcing data, calibration and validation data, and for assimilation into the two LDAS.

In this presentation, the progress of the project in the past three years will be reported.

This report will present the recent developments and accuracy validation of our project's snow remote sensing data products. In terms of snow cover area, we focused on analyzing the consistency between two snow products, VIRSS and MODIS. We found that although the NDSI obtained by the two sensors were very consistent, there were significant differences between the final snow cover area products due to differences in cloud identification algorithms. This study suggests that we should develop a cloud identification algorithm that can be applied to both VIRSS and MODIS to ensure the consistency of snow cover area data products and provide reliable data for further research on snow changes and related studies. In terms of snow depth remote sensing, we developed a machine learning-based fusion method for snow depth in the northern hemisphere. This method combined six existing snow depth remote sensing and reanalysis data, and through learning observations from nearly 20,000 stations in the northern hemisphere, obtained the fused snow depth data, which is much more accurate than existing data.

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The 2030 SDGs identify water (SDG 6), as well as vegetation/land use (SDG15) as keys parameters for providing the economic, social, and environmental well-being of the present and future generations. Remote sensing can be a powerful tool to reach these objectives and support SDG indicator analysis at different scales.

At regional and local scale, the consortium has is exploring tools to identify changes in sensitive ecosystems of the Yangtze River basin and surrounding regions. These include new approaches to study Poyang and Dongting lakes, as well as the Anhui’s small lakes. These have taken advantage of ICEYE and Radarsat data in synergy with Sentinel2 to ensure the monitoring of water extent, while IceSat and Sentinel3, Sentinel6 altimetric data have been exploited to monitor water bodies’ altitude. This acquired knowledge is crucial for the exploitation of SWOT products which first delivery will occur in summer 2023. Over Poyang, an increase in earlier draw off of the water since 2000 has an important effect on the mudflat evolution. Based on MR EO imagery, the mudflat has been increasing and the main distribution area is shifted from North to South. In the Northern area affected by sand mining, the water surface rate of the inlet channel has increased, and the overall outer edge of the mudflat is more fragmented than before. In the South side of the Ganjiang River, the delta area on the is affected by the water and sand entering the lake and is growing steadily, with the front edge of the delta extending outward for about 1.84 km.

Water quality assessment, a pilar for SDG6, requests to develop and validate processing protocols for multiple sensor systems. New advances have been done for the cCO2 estimation based on model using Sentinel-3-derived lake environmental variables and field data. Works done over sixteen lakes on the middle and lower reaches of the Yangtze basins shown that CO2 concentrations were low in the summer and autumn but high in the winter and spring with dramatic variations. The annual mean CO2 concentrations of lakes revealed that about 28% of the lakes acted as weak atmospheric CO2 sinks while the rest were sources. CO2 concentrations decreased with increasing eutrophication and decreasing lake size. With this problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it’s not clear how lake transitions occur at regional and global scales. To answer this point, a long-term monitoring of 22 lakes of the Yangtze watershed have been carried out, exploiting a novel innovative and efficient three steps algorithm that can distinguish, aquatic vegetation, floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). The AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the middle Yangtze watershed. In addition, over large lakes (>500 km2), including the Taihu and Chaohu, daily MR satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics. Chaohu and Taihu lakes are presenting perennial blooms with an increasing trend. Climate factors were found to be linked to changes in annual initial bloom time; while an increase in human activities was associated to bloom duration, area and frequency.

At a larger scale, an important work has been done on the distribution and dynamics of vegetation related to vegetation growth and carbon cycling, with an analysis of the impact of global climate change, changes in temperature and precipitation. Based on meteorological and remote sensing data; critical soil moisture (CSM) was used as a proxy of the land-atmosphere coupling to study the interaction process between land and atmosphere and its impact on land vegetation. Then, single models of CMIP6 were optimized through machine learning methods to develop future climate and Gross Primary Production (GPP) datasets and analyze the temporal and spatial changes of climate and GPP.

The vegetation in China has significantly increase over the last four decades, 1982-2020, with 72.34% of the regional greening. At regional scale, precipitation is appearing as a key factor affecting vegetation growth in arid and semi-arid areas such as the Mongolian Plateau, the Qinghai-Tibet Plateau, and the Loess Plateau. Precipitation resources are abundant in southeast China, and temperature is the dominant factor of regional vegetation growth. In another hand, it must be noticed that drought stress is also an important factor affecting vegetation growth. Short-term cumulative drought promotes regional vegetation greening (1-4 months), while medium-term and long-term cumulative drought inhibits vegetation greening (5-12 months). The carbon sequestration function of vegetation in southwestern and southeastern China will be affected under continuous drought conditions.

Drought conditions were analyzed through an innovative approach based on the differential correlation measure (∆Corr) that characterizes the strength of water or energy limitations. The ∆Corr detecting the response of surface water and energy to short-term surface processes. Based on climate and vegetation characteristics, sufficient variation was found in both global and grid unit CSM content. CSM content is wetter in areas with less annual rainfall, shorter root systems, and lower vegetation coverage. It was found that under three SPP scenarios in the future (2021-2100), the high-latitude regions of the Northern Hemisphere will warm significantly, while the spatiotemporal distribution pattern of precipitation shows no significant difference. Under different scenario models, GPP changes show obvious spatiotemporal heterogeneity.

Obtained results shown how EO data exploitation can provide important insight into the knowledge and understanding of keys parameters such as water resources and quality, eutrophication purposes and this from local to global scale.