Spatiotemporal variation of snow cover from space in northern Xinjiang

Abstract

Snow is an appreciable fraction of soil water recharge in the middle and high latitude areas, and snow cover and snow depth estimation is extremely important for regional climate change studies, agriculture and water source management. Traditional in situ measurments provide critical snow depth observations in limited areas and for ground control validation of remotely sensed estimations. Satellite-based snow measurements have revolutionized the monitoring of spatiotemporal variation of snow cover and snow depth in complex natural conditions at regional and global scales. This chaper introduces the algorithm of optical satellite snow cover detection and MODIS standard snow cover products, sumarizes recent studies on mitigating the cloud-blockage issues in MODIS snow cover products and their applications, and finally illustrates the spatiotemporal variatons of snow cover in the Northern Xinjiang, China by using the cloud-removed MODIS snow cover product. Together, snow cover days (SCD), snow cover index (SCI), snow cover onset date (SCOD) and snow cover end date (SCED) provide important information on the snow cover conditions and can be applied in any region of interests. These information would be critical for local government, such as land use planning, agriculture, live stock, and water resource management, for example, to mitigate snow-caused disasters and to plan for agriculture and industry water use. Long term availability of MODIS type of snow cover data for producing such datasets is key to study the connection between snow cover variation and climate change.