Data Sheet 1_Advances and prospects in reconstruction approaches for snow cover mapping using polar-orbiting satellites.docx
<p>Snow cover is recognized as one of the most variable land cover parameters and plays a critical role in the global energy balance, climate change, and hydrological processes. Polar-orbiting satellites serve as the primary data source for monitoring both polar and global snow cover, providin...
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2025
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| Summary: | <p>Snow cover is recognized as one of the most variable land cover parameters and plays a critical role in the global energy balance, climate change, and hydrological processes. Polar-orbiting satellites serve as the primary data source for monitoring both polar and global snow cover, providing wide coverage and high spatial resolution products. However, the utility of these snow cover products is significantly limited by data gaps caused by unfavorable observation conditions, such as cloud cover. Various reconstruction approaches are required to fill these gaps, depending on the snow cover product type (binary snow cover (BSC), normalized difference snow index (NDSI), or fractional snow cover (FSC)), snow characteristics, and availability of auxiliary datasets. This paper categorizes current reconstruction approaches into eight types: temporal filters, spatial filters, multisensor fusion, and the hidden Markov random field (HMRF) model for BSC mapping, as well as temporal and spatial interpolation methods, spatiotemporal reconstruction algorithms, machine learning-based reconstruction techniques, and data assimilation methods for NDSI or FSC mapping. This paper provides a comprehensive review of the principles, advantages, and limitations of these approaches and offers recommendations for their appropriate application. The discussion highlights that future improvements in snow cover reconstruction can be achieved through three key approaches. First, enhancing snow cover recognition algorithms will increase the accuracy of the original snow cover products, providing more reliable prior information for reconstruction. Second, careful consideration of spatiotemporal environmental factors, such as terrain, temperature, precipitation, solar radiation, and forest cover, along with the development of corresponding multisource data processing and fusion techniques, is essential. Third, further exploration of the synergy between machine learning and data assimilation could leverage their strengths in multisource data processing scenarios, offering novel insights for conducting snow monitoring and forecasting in complex environments. This review contributes to snow cover mapping and related research by offering a comprehensive analysis and guidelines for generating gap-filled snow cover products across a variety of spatiotemporal scales.</p> |
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