The recent developments in cloud removal approaches of MODIS snow cover product

Li, Xinghua; Jing, Yinghong; Shen, Huanfeng; Zhang, Liangpei

The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.



Li, Xinghua / Jing, Yinghong / Shen, Huanfeng / et al: The recent developments in cloud removal approaches of MODIS snow cover product. 2019. Copernicus Publications.


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