MULTI-TEMPORAL SAR IMAGE DESPECKLING BASED A CONVOLUTIONAL NEURAL NETWORK

Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.

Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.

This study generated the first global 1-km continuous NDVI product (STFLNDVI) for 4-decades by fusing multi-source satellite products. Simulated and real-data assessments confirmed the satisfactory and stable accuracy of STFLNDVI regarding spatial details and temporal variations. STFLNDVI is an ideal solution to the trade-off between spatial resolution and time coverage in current NDVI products, which of great significance for long-term regional and global vegetation and climate change studies.

Acquired daily soil moisture products are always incomplete globally (just about 30 %–80 % coverage ratio) due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we generate long-term seamless global daily (SGD) AMSR2 soil moisture productions from 2013 to 2019. These productions are significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.

Estimating ambient PM_2.5 and PM₁₀ considering their precursors and chemical compositions instead of AOD products; Both remote sensing (Sentinel-5P) and assimilated data (GEOS-FP) are adopted; Sample-based Cross-Validation R²s and RMSEs are 0.93 (0.9) and 8.982 (17.604) μg/m³ for PM_2.5 (PM₁₀), respectively; Achieving better performance compared to the baseline (AOD-based) in different cases (e.g., overall and seasonal).

This paper is a review article on the cloud removal methods of MODIS snow cover products.