Abstract
High-resolution snow distributions are essential for studying cold regions. However, the temporal and spatial resolutions of current remote sensing snow maps remain limited. Remotely sensed snow cover fraction (SCF) data only provide quantitative descriptions of snow area proportions and do not provide information on subgrid-scale snow locations. We present a downscaling method based on simulated inhomogeneous snow ablation capacities that are driven by air temperature and solar radiation data. This method employs a single parameter to adjust potential snow ablation capacities. Using this method, SCF data with a resolution of 500 m are downscaled to a resolution of 30 m. Then, 18 remotely sensed TM, CHRIS and EO-1 snow maps are used to verify the downscaled results. The mean overall accuracy is 0.69, the average root-mean-square error (RMSE) of snow-covered slopes between the downscaled snow map and the real snow map is 3.9°, and the average RMSE of the sine of the snow covered aspects between the downscaled snow map and the real snow map is 0.34, which is equivalent to 19.9°. This method can be applied to high-resolution snow mapping in similar mountainous regions.
Highlights
Accurate snow spatial heterogeneity descriptions are essential to scientific studies in cold regions [1].Snow distributions present high degrees of heterogeneity under complex terrain conditions [2,3]
Because this paper focuses on the downscaling method, we only analyze the influence of errors from MODIS standard products (MOD10A1) in the downscaling accuracy and do not involve other snow cover fraction (SCF) products using different retrieval methods
A simple and reliable method is used to downscale snow cover fraction data in a mountainous region
Summary
Accurate snow spatial heterogeneity descriptions are essential to scientific studies in cold regions [1].Snow distributions present high degrees of heterogeneity under complex terrain conditions [2,3]. Accurate snow spatial heterogeneity descriptions are essential to scientific studies in cold regions [1]. High-spatial-resolution snow maps can be used to accurately characterize the patterns of snow distribution and melting in such regions. The limited available studies of the spatial heterogeneity of subgrid-scale snow cover have indicated that using different grid sizes while modeling distributed snowmelt can result in considerable variability in modeling accuracy [4]. Accurate descriptions of the snow’s spatial heterogeneity are essential to estimating solar radiation in snow-covered mountainous areas [6]. These works further underscore the importance of obtaining accurate, high-resolution snow distributions
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