Quantifying Spatiotemporal Variability of Controls on Microwave Emission From Snow-Covered Mountainous Regions

Abstract

Using passive microwave (PM) remote sensing to estimate snow water equivalent (SWE) in mountainous areas is challenging, because the interactions between the mountain environmental variables and microwave emission are very complex; a better understanding of these interactions will be helpful to ultimately improve the estimation of mountain SWE with PM. In this study, we performed an analysis of the 36.5-GHz vertical polarization (V-Pol) microwave radiance over the Upper Kern basin in the southern Sierra Nevada that was simulated for a previous study, to interpret the spatiotemporal radiance variability and its connection with the mountain environment. The modeled radiance is close to the advanced microwave scanning radiometer for EOS (AMSR-E) observed radiance; the RMSE of the modeled dry season basin-scale brightness temperature ( ${{\mathbf{T}}_{\mathbf{b}}}$ ) is 3.1 K. The modeling was conducted at a high spatial resolution (90 m) to better characterize the significant radiance variability as a result of the complex mountainous terrain. A set of environmental variables has been explicitly parameterized into each modeling pixel; these environmental variables include elevation, fractional vegetation coverage, SWE, snow grain size, aspect, and slope. We correlated these environmental variables with the modeled microwave radiance to explore the effects of each environmental control, and to quantify each variable’s explanatory power in terms of the radiance variability. In this study, all the correlation calculations are statistically significant with confidence level higher than 95%. We found that the controlling power of each environmental factor varies over a snow season, and the dominating environmental factors change with respect to increase in snow accumulation. Vegetation and elevation, and SWE and grain size are two sets of dominating factors for the radiance: when SWE is less than 0.01 m, vegetation and elevation explain 55% of the radiance variability, while SWE and grain size explain 29% of the spatial variability. When SWE exceeds 0.01 m, SWE and grain size take over the dominance and explain 56% of the radiance variability, while vegetation and elevation explain 25%. The distribution of snow grain size over the Upper Kern exhibits large spatial variability. Snowpack temperature gradient, snow age, and wet metamorphism are significant factors related to snow grain growth. Snow age and temperature gradient are equally dominant for spatial patterns of grain size and ${{\mathbf{T}}_{\mathbf{b}}}$ in the areas with deep and dry snow cover, while snow age and wet metamorphism dominate the grain size and ${{\mathbf{T}}_{\mathbf{b}}}$ in the low elevation areas with shallow snow.