Abstract
A study was conducted to assess the potential of C-band synthetic aperture radar (SAR) data to determine the snow water equivalent (SWE). A multitemporal (three winters) SAR data set was obtained using the Convair-580 from the Canada Centre for Remote Sensing (CCRS) over a watershed in the Appalachian Mountains in Southern Quebec, Canada. The SAR data were relatively calibrated using extended targets (coniferous stands). Extensive ground measurements were done simultaneously to each of the seven flights, in order to measure the snow cover characteristics (depth, density, SWE, liquid water content, temperature, and dielectric profiles) as well as the soil characteristics (moisture, temperature). To estimate the SWE of a given snowpack, a model which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. The model is based on the ratio of the scattering coefficient of a field covered by snow to the scattering coefficient of a field without snow. The analysis has revealed that volume scattering from a shallow dry snow cover (SWE<20 cm) is undetectable. The backscattering power is dominated by soil surface scattering, the latter varying with the decrease of liquid water content in the surface layer with decreasing soil temperature below 0/spl deg/C. Then, the scattering ratio decreases proportionally to the dielectric constant of the soil in winter. Furthermore, a unique relationship for three acquisition dates has been found between the thermal resistance, R, of the snow pack and the backscattering power ratio. Then, the spatial distribution of the power ratio should depict the spatial distribution of R, given spatially uniform climatological conditions over the study area. Since linear relationships between SWE and R have been observed, it should be possible to estimate the SWE of shallow dry snow cover with C-band SAR data using few ground truthing data in an open area when the soil is frozen.
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More From: IEEE Transactions on Geoscience and Remote Sensing
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