积雪
融雪
雪
环境科学
遥感
数字高程模型
构造盆地
流域
大洪水
卫星
地表径流
仰角(弹道)
水文学(农业)
自然地理学
气候学
气象学
地理
地质学
地图学
地貌学
几何学
生物
工程类
航空航天工程
考古
岩土工程
数学
生态学
作者
Boris Snapir,Andrea Momblanch,Sanjay K. Jain,Toby W. Waine,Ian P. Holman
出处
期刊:International journal of applied earth observation and geoinformation
日期:2019-02-01
卷期号:74: 222-230
被引量:37
标识
DOI:10.1016/j.jag.2018.09.011
摘要
Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.
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