The effect of spatial variability on the sensitivity of passive microwave measurements to snow water equivalent

Abstract Passive microwave (PM) remote sensing measurements are routinely utilized to estimate snow depth and water equivalent (SWE). Both vegetation and physical snowpack variables including snowpack grain size, snow depth, and stratigraphy influence the observed brightness temperature. The natural heterogeneity of snowpack and vegetation states within the microwave footprint occurs at spatial scales shorter than PM observation scales. In this study, we analyze the relationship between PM brightness temperature measurements and the heterogeneity of snowpack and vegetation. Specifically, we explore the question of whether PM observations are sensitive to changes in snow depth even given sub-pixel variability in snow and vegetation. To examine this question, densely sampled, spatially distributed in situ snow properties from multiple study areas made during the NASA Cold Land Processes Experiment (CLPX) are employed in a forward modeling scheme to study the effect of highly variable snow and vegetation properties on the observed PM measurement. In all test cases, this study finds that there exists sensitivity of microwave brightness temperature ( T b ) to total snow depth contained within the measurement footprint, regardless of the heterogeneous nature of snow pack properties. Across three study areas, T b decreases by 23–35 K as depth increased up to the signal saturation depth, which ranged from 70 to 120 cm. With regard to vegetation sensitivity, forest fractions (F) as little as 0.2 can modify the PM measurement by up to 10 K, and F greater than 0.6 mask virtually all of the microwave signal attributable to snow. Finally, with respect to the measurement scale, our results indicate that the scale at which the PM measurement is made does not affect the sensitivity of the T b to mean snow depth, to the scale (1 km) examined herein.