Estimating snow depth or snow water equivalent from space

Satellite remote sensing is widely used to estimate snow depth and snow water equivalent (SWE) which are two key parameters in global and regional climatic and hydrological systems. Remote sensing techniques for snow depth mainly include passive microwave remote sensing, Synthetic Aperture Radar ( SAR), Interferometric SAR (InSAR) and Lidar. Among them, passive microwave remote sensing is the most efficient way to estimate large scale snow depth due to its long time series data and high temporal frequency. Passive microwave remote sensing was utilized to monitor snow depth starting in 1978 when Nimbus-7 satellite with Scanning Multichannel Microwave Radiometer (SMMR) freely provided multi-frequency passive microwave data. SAR was found to have ability to detecting snow depth in 1980s, but was not used for satellite active microwave remote sensing until 2000. Satellite Lidar was utilized to detect snow depth since the later period of 2000s. The estimation of snow depth from space has experienced significant progress during the last 40 years. However, challenges or uncertainties still exist for snow depth estimation from space. In this study, we review the main space remote sensing techniques of snow depth retrieval. Typical algorithms and their principles are described, and problems or disadvantages of these algorithms are discussed. It was found that snow depth retrieval in mountainous area is a big challenge for satellite remote sensing due to complicated topography. With increasing number of freely available SAR data, future new methods combing passive and active microwave remote sensing are needed for improving the retrieval accuracy of snow depth in mountainous areas.