Abstract
Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R2 values ranging from 0.46 to 0.80.
Highlights
The extent and variability of snow cover highly influences Earth’s water cycle, surface energy balance, climate, and weather
The Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow cover (FSC) maps served as complementary information for evaluating the FSC using Landsat-8 Operational Land Imager (OLI) data and for demonstrating the cloud removal capabilities of the FY-2E/F Visible and Infrared Spin Scan Radiometer (VISSR)
The performance of the FY-2E/F VISSR FSC over the Tibetan Plateau (TP) was demonstrated through a validation against the Landsat-8 OLI FSC
Summary
The extent and variability of snow cover highly influences Earth’s water cycle, surface energy balance, climate, and weather. The accumulation and depletion of seasonal snow cover, which possesses a substantial capacity for water storage, has profound effects on hydrological processes [1,2]. Hemisphere [3], high albedo and low thermal diffusivity values make snow cover an important factor in the surface energy balance [4,5]. The sensitivity of snow cover to temperature makes it a high-profile indicator of climate change [6,7]. Satellite remote sensing provides a unique ability to monitor snow cover and its dynamics at large scales that sparse meteorological observation networks cannot accomplish. Snow can be reliably distinguished from other surface features (e.g., soil, rock, vegetation, and water bodies)
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