Comparison of the Fractional Snow Cover Retrieval Capabilities of China's New Generation Geostationary Meteorological Satellites, FY-4A and FY-4B

• Published in: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 121 - 124
• Main Authors: Pan, Fangbo, Jiang, Lingmei, Wang, Gongxue
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.07.2024
• Subjects: Accuracy; Asia Water Tower; Distance measurement; Earth; Fractional snow cover; FY-4A; FY-4B; Geometry; Imaging; Poles and towers; Snow
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS53475.2024.10641862

One of the main features of the Asian Water Tower imbalance is the massive melting of snow, necessitating enhanced snow monitoring. However, the sensors aboard polar-orbiting satellites, such as MODIS, yield only one to two valid observations daily. This, coupled with the extensive cloud cover and prolonged duration over the Asian Water Tower, results in a significant number of data gaps. China's new generation of geostationary satellites FY-4A and FY-4B have high frequency observations, making it possible to monitor snow with high precision. In this study, we systematically analyze the image pixel size stretching of FY-4A and FY-4B in the Asian Water Tower region, based on their imaging geometry. This analysis offers a theoretical foundation for fractional snow cover retrieval in the subsequent integration of these two satellites. Concurrently, this study conducts fractional snow cover (FSC) retrieval for FY-4A and FY-4B, utilizing the multiple endmember spectral mixture analysis algorithm with automatic endmember extraction (MESMA-AGE). High spatial resolution Landsat-8 imagery serves as reference data for accuracy assessment. The results indicated that FY-4A's retrieval accuracy remained unaffected by pixel size stretching, achieving an Overall Accuracy (OA) of up to 0.97 and a Root Mean Square Error (RMSE) of less than 0.13. For FY-4B, the retrieval accuracy demonstrated higher snow identification precision, with an OA of up to 0.95. However, the RMSE varied significantly due to pixel size stretching, ranging from 0.12 to 0.21. FY-4A and FY-4B fusion enables high precision and near-real-time snow monitoring.