Abstract
Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.
Highlights
Snow cover has a high albedo, high emissivity, and an important impact on regional climate and energy variation [1,2,3]
Snow cover days (SCD) were calculated through the generated cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover extent (SCE) products, and the snow cover days (SCD) is defined as the total number of days on which each pixel has snow in a given year
Based on the gap-filling method proposed in this paper, daily cloud-free MODIS SCE products for 2001–2018 in northern Xinjiang, China can be produced
Summary
Snow cover has a high albedo, high emissivity, and an important impact on regional climate and energy variation [1,2,3]. Snow cover is a critical water source and significantly influences the hydrological and biochemical processes in surrounding lowland areas [9,10,11]. Snow is a main water resource in northern Xinjiang, China, and there is long-term snow cover in winter [4]. Due to the influence of topography and geomorphology, the distribution of snow cover is uneven in different regions and seasons. It is necessary to generate cloud-free SCE products and analyze the spatiotemporal change in snow cover in northern Xinjiang, China
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