Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #363402
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

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

Author
item JADIDOLESLAM, N. - University Of Iowa
item MANTILLA, R. - University Of Iowa
item KRAJEWSKI, W. - University Of Iowa
item Cosh, Michael

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2019
Publication Date: 9/1/2019
Citation: Jadidoleslam, N., Mantilla, R., Krajewski, W., Cosh, M.H. 2019. Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture. Journal of Hydrology. 576(9):85–97. https://doi.org/10.1016/J.JHYDROL.2019.06.026.
DOI: https://doi.org/10.1016/J.JHYDROL.2019.06.026

Interpretive Summary: Many agricultural and flood forecasting applications require information about the statistical variability of the input parameters. Soil moisture estimates are now becoming widely available with in situ and satellite estimates coming online. The study of soil moisture variability is now advancing as well, because of the amount of information now available. This study looks at the linkages between insitu and satellite variability within Iowa and theorizes how the distributions of this parameter may be generalized for model parameterization. This information is useful for hydrologic modelers, meteorologists, and flood forecasters.

Technical 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 square km, 790 square km, and 4,400 square km. 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 drydowns and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of drydowns were parameterized using power-law, and of soil moisture increases due to rainfall 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, and skewness 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 theta less than 5%, while average observations for intermediate soil moisture values had the lowest information content with theta greater than 20%. The differences in information content as a function of the areal average were consistent with the practical 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.