HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model

• Published in: Earth system science data Vol. 14; no. 9; pp. 4445 - 4462
• Main Authors: Huang, Yan, Xu, Jiahui, Xu, Jingyi, Zhao, Yelei, Yu, Bailang, Liu, Hongxing, Wang, Shujie, Xu, Wanjia, Wu, Jianping, Zheng, Zhaojun
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 29.09.2022; Copernicus Publications
• Subjects: Accuracy; Climate change; Climatic changes; Data centers; Datasets; Elevation; Elevation angle; Environmental information; Fields (mathematics); Hydrologic cycle; Hydrological cycle; Hydrology; Impact analysis; Landsat; Management; MODIS; New products; Plateaus; Precipitation; Radiation; Remote sensing; Resolution; Resource management; Satellite imagery; Snow; Snow accumulation; Snow cover; Snow cover distribution; Solar radiation; Spectroradiometers; Topography; Water; Water resources; Water resources management; Tibetan Plateau; China
• ISSN: 1866-3516; 1866-3508; 1866-3516
• DOI: 10.5194/essd-14-4445-2022

Snow cover plays an essential role in climate change and the hydrological cycle of the Tibetan Plateau. The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) snow products have two major issues: massive data gaps due to frequent clouds and relatively low estimate accuracy of snow cover due to complex terrain in this region. Here we generate long-term daily gap-free snow cover products over the Tibetan Plateau at 500 m resolution by applying a hidden Markov random field (HMRF) technique to the original MODIS snow products over the past two decades. The data gaps of the original MODIS snow products were fully filled by optimally integrating spectral, spatiotemporal, and environmental information within HMRF framework. The snow cover estimate accuracy was greatly increased by incorporating the spatiotemporal variations of solar radiation due to surface topography and sun elevation angle as the environmental contextual information in HMRF-based snow cover estimation. We evaluated our snow products, and the accuracy is 98.29 % in comparison with in situ observations, and 91.36 % in comparison with high-resolution snow maps derived from Landsat images. Our evaluation also suggests that the incorporation of spatiotemporal solar radiation as the environmental contextual information in HMRF modeling, instead of the simple use of surface elevation as the environmental contextual information, results in the accuracy of the snow products increases by 2.71 % and the omission error decreases by 3.59 %. The accuracy of our snow products is especially improved during snow transitional period, and over complex terrains with high elevation and sunny slopes. The new products can provide long-term and spatiotemporally continuous information of snow cover distribution, which is critical for understanding the processes of snow accumulation and melting, analyzing its impact on climate change, and facilitating water resource management in Tibetan Plateau. This dataset can be freely accessed from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272204 (Huang and Xu, 2022).