Effect of spatial variability of wet snow on modeled and observed microwave emissions
Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the TB response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50 m) energy balance snow model over a 34 × 34 km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the TB signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data.
Earth Systems Research Center
Remote Sensing of Environment
Digital Object Identifier (DOI)
Vuyovich, C., J.M. Jacobs, C.A. Hiemstra, and E.J. Deeb. 2017. Effect of spatial variability of wet snow on modeled and observed microwave satellite observations. Remote Sensing of Environment. 198. pp. 310-320.
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