Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

Abstract

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

A Combination of PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas

“…Although there have been various spaceborne multispectral sensor-based SCE products, such as the Moderate Resolution Imaging Spectroradiometer (MODIS)-based snow cover products [12] and the European Space Agency (ESA)'s GlobSnow product [13], these are still fundamentally constrained by cloud coverage, polar darkness, and the confusion between snow and ice clouds [14,15]. Thus, the utilization of active synthetic aperture radar (SAR) has been explored in the recent three decades (for a comprehensive review we refer to Tsai et al (2019) [16]). Thanks to its longer wavelength, SAR can sense ground features under all weather and solar illumination conditions.…”

Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements

“…Furthermore, local meteorological [3] and topographical [4] conditions control distribution of snow accumulation and spatial variability in snowpack parameters, for example and hence considers the local topography variation as required in the mountainous regions [13]. However, POLSAR data decomposition methods are independent of the frequency and incident angle, and they are then more suitable for snow-cover studies in mountainous terrains [14,17]. In this paper, we are proposing POLSAR-based snow depth estimation by utilizing co-polarization coherence and physical model-based scattering power decomposition.…”

“…A frequent GPR survey (ground or air platform based) will not only be costly but also limited in spatial and temporal coverage. Conversely, remote sensing techniques using satellites is A cost-effective monitoring system that can be used frequently to retrieve snowpack accumulation (depth) variability in time and space [13][14][15][16][17][18][19].Passive microwave radiometers are used to generate global snow water equivalent (SWE) and snow depth (SD) at coarse resolutions [20][21][22], but fails to capture spatial variability at the catchment and glacier-system levels. Retrieving the SD is one of the critical problems faced by the global scientific community, and many efforts have been taken in the past to develop several SD retrieval algorithms using high-resolution spaceborne active microwave synthetic aperture radar (SAR) sensor measurements.…”

“…The extinction coefficient calculated from the imaginary part of the dielectric constant enables the SD estimation from the X-band full polarization SAR data. However, selection of the correct model to relate the dielectric constant to wetness to retrieve the imaginary part of dielectric constant is difficult.The retrieval of snow wetness and density based on physical approaches using fully polarimetric SAR (POLSAR) data are well established [13][14][15][16][17][18][19]23,24]. However, there is no operational SD estimation algorithm available based on POLSAR data.…”

“…A frequent GPR survey (ground or air platform based) will not only be costly but also limited in spatial and temporal coverage. Conversely, remote sensing techniques using satellites is A cost-effective monitoring system that can be used frequently to retrieve snowpack accumulation (depth) variability in time and space [13][14][15][16][17][18][19].…”