Publications

Huang, JY; Jiang, LM; Pan, FB; Zhong, B; Wu, SL; Cui, HZ (2025). Estimation and Evaluation of the FY-3D/MERSI-II Fractional Snow Cover in China. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 18, 2497-2511.

Abstract
The frequent occurrence of intense snowfall events and extreme temperature fluctuations in China significantly hampers the precise monitoring of snow-covered areas. The provision of near-real-time monitoring of fractional snow cover (FSC) in China is essential to bolster disaster management initiatives. The medium-resolution spectral imager (MERSI-II), aboard the FengYun-3D satellite, presents significant capabilities for effective snow monitoring. The aim of this article was to enhance the multiple endmember spectral mixture algorithm based on automatic extraction of endmembers for estimating FY-3D/MERSI-II FSC in China. The accuracy of these estimations was corroborated using 30-m FSC maps derived from Landsat-8/Operational Land Imager (OLI) images. This validation process resulted in RMSE and R-2 values of 0.13 and 0.88, respectively, across 253 OLI-based FSC scenes. The article examined and analyzed the effects of diverse land cover types and complex terrain on the FSC estimates, focusing specifically on three major snow-covered areas in China. In comparison to areas with cultivated land and sparse vegetation, bare surfaces yielded the most accurate results, whereas forest regions showed the lowest accuracy. The comparative analysis revealed that the Tibetan Plateau and Northern Xinjiang achieved high accuracy in mapping snow cover. The maps of Northeast China were less precise, largely due to the prevalence of forests. Additionally, while the MERSI-II FSC is effective in monitoring snow cover on flat plains, its performance is less reliable in steep terrain. Moreover, the comparison between MERSI-II and moderate-resolution imaging spectroradiometer FSC indicated that MERSI-II has a marked advantage in areas with substantial snow cover.

DOI:
10.1109/JSTARS.2024.3517845

ISSN:
2151-1535