Preprints
https://doi.org/10.5194/essd-2022-134
https://doi.org/10.5194/essd-2022-134
 
05 May 2022
05 May 2022
Status: this preprint is currently under review for the journal ESSD.

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

Yan Huang1,2,, Jiahui Xu1,2,, Jingyi Xu1,2, Yelei Zhao1,2, Bailang Yu1,2, Hongxing Liu3, Shujie Wang4, Wanjia Xu1,2, Jianping Wu1,2, and Zhaojun Zheng5 Yan Huang et al.
  • 1Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
  • 2School of Geographic Sciences, East China Normal University, Shanghai 200241, China
  • 3Department of Geography, the University of Alabama, Tuscaloosa, AL 35487, USA
  • 4Department of Geography, Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA 16802, USA
  • 5National Satellite Meteorological Center, Beijing 100081, China
  • These authors contributed equally to this work.

Abstract. 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 98.31 % in comparison with in situ observations and 92.44 % in comparison with high-resolution snow maps derived from Landsat-8 imagery. Our evaluation also suggests that the incorporation of spatiotemporal solar radiation as the environmental contextual information in HMRF modelling, instead of the simple use of surface elevation as the environmental contextual information, results in the accuracy of the snow products increase by 2.72 % and the omission error decrease by 4.59 %. The accuracy of our snow products is especially improved during snow transitional period and over complex terrains with high elevation as well as 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, analysing 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).

Yan Huang et al.

Status: open (until 30 Jun 2022)

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Yan Huang et al.

Yan Huang et al.

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Short summary
Reliable snow cover information is very important for understating climate change and the hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the Hidden Markov Random Field model. The accuracy is 92.44 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.