Abstract
With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.
Highlights
Snow cover is an important component of land cover and plays a key role in balancing global energy and water resources because of its high albedo and thermal storage properties [1,2,3]
Because optical sensors are strongly affected by clouds, an effective statistical analysis of fractional snow cover (FSC) cannot be realized based on snow cover products from optical sensors
Combining these data with passive microwave data, which are unaffected by clouds, is an effective way to improve the snow-covered area monitoring accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) data
Summary
Snow cover is an important component of land cover and plays a key role in balancing global energy and water resources because of its high albedo and thermal storage properties [1,2,3]. Precisely obtaining snow-covered area information via remote sensing is vitally important in understanding climate variations, performing water circulation and water resource investigations, and predicting and preventing snow-related disasters in China. Since the launch of television infrared observation satellites (TIROS)-1 in 1960, with the capability to monitor snow cover, dozens of satellites have been used to monitor snow cover and have played an important role in it. Such snow cover products include Landsat and SPOT [10], AVHRR [11], VEGETATION [12], MODIS [13], SMMR, SSM/I [14,15] and AMSR-E [16]. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and
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