Abstract
Spatial variations of snowpack properties are an essential component in flood predictions and water resource management. Satellite microwave remote sensing has shown great potential in retrieving snowpack properties such as: snow depth, snow grain size, and snow density. In this research, we investigate the potential of microwave emissivity which is highly influenced by snowpack properties. Brightness temperature and emissivity data generated from HUT (Helsinki University of Technology) microwave emission of snow model were evaluated with satellite microwave measurements. The comparison of the real measurements (in-situ and satellite) with the modeled results shows that the scattering signature (19GHz-37GHz and 19GHz-85GHz) shows better results in emissivities rather than brightness temperature data. Furthermore, the over the deep snow (>30cm), the emissivities scattering signature of (19GHz- 37GHz) has best performance while over shallow snow (<30cm) the emissivities scattering signature of (19GHz- 85GHz) performs superior. The results indicate the validity of grain growth assumption to some extent but it fails to address it quantitatively as a function of time.
Highlights
According to the Federal Emergency Management Agency (FEMA), floods are one of the most common hazards in the United States
In the initial stage of this study, we investigated the sensitivity of brightness temperature and emissivity of different Sensor Microwave Imager (SSM/I) frequencies to snow parameters (19, 37, 85GHz) and their vertical and horizontal polarized scattering signatures
The grain size was assumed a constant in the density range (0.01-0.41 g/cm3) and was used in the model to show the variation of density versus snow depth and emissivity/brightness temperature
Summary
According to the Federal Emergency Management Agency (FEMA), floods are one of the most common hazards in the United States. A reanalysis of the National Weather Service (NWS) estimates of flood damage in the United States, showed that flood damage continues to be a concern despite local and federal efforts to mitigate floods [1]. Snow Water Equivalent (SWE), the volume of liquid water present in the snowpack is a function of snow depth and snow density used in hydrological modeling. When rain accompanies melting snow, the melting process is accelerated due to warm temperature, causing difficulty in quantify snow-melt water from snow, results in unpredicted flooding [2]. An adequate knowledge of snowpack properties is necessary for use in hydrological, meteorological, and hydroclimatological models for flood analysis, weather forecasting, and water resource management [2,3]
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