Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture

Abstract

This study focused on the utility of coarse surface soil moisture observations for applications that require high resolution surface soil moisture information. This was accomplished by quantifying the information content of average soil moisture for three different spatial scales of 81 km2, 790 km2, and 4,400 km2. In situ point observations of soil moisture from 31 stations in Iowa were used to develop a spatial stochastic model that assumes hillslope-scale model parameters are independent. Soil moisture dry-downs and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of dry-downs were parameterized using a power-law decay, and soil moisture increases due to rainfall were parameterized using a non-dimensional logistic curve that is a function of soil moisture deficit. The resulting stochastic model is used to quantify the magnitude of the standard deviation, σ(θ), and skewness G(θ) as a function of the areal average. We show that the greatest information content (small spatial standard deviation) of average observation corresponded to values near the minimum or the maximum soil moisture with σ(θ) < 5%, while average observations for intermediate soil moisture values had the lowest information content with σ(θ) > 20%. The differences in information content as a function of the areal average were consistent with the statistical nature of soil moisture that can be interpreted as small range bounded variable. However, this study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications.