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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #382471
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: Expanding the application of soil moisture monitoring systems through regression-based transformation

Author
item Crow, Wade
item REICHLE, R. - National Aeronautics And Space Administration (NASA) - Johnson Space Center
item DONG, J. - US Department Of Agriculture (USDA)

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/15/2021
Publication Date: 10/1/2021
Citation: Crow, W.T., Reichle, R., Dong, J. 2021. Expanding the application of soil moisture monitoring systems through regression-based transformation. Journal of Hydrometeorology. 22(10):2601-2615. https://doi.org/10.1175/JHM-D-21-0061.1.
DOI: https://doi.org/10.1175/JHM-D-21-0061.1

Interpretive Summary: Accurate estimates of soil moisture in the crop root zone are valuable for tracking agricultural drought and forecasting streamflow in agricultural basins. Typically, decision support systems are developed around a particular hydrological model. Unfortunately, soil moisture estimates tend to be highly model dependent – such that two models run in parallel, with the same rainfall forcing data, often produce systematically different soil moisture time series. This complicates the development of centralized sources of near-real-time soil moisture information. This paper proposes a simple, regression-based solution to this problem that provides a valuable bridge between state-of-the art, real-time soil moisture estimates - available from new remote sensing and data assimilation sources - and existing agricultural water resource applications (e.g., drought monitoring). Once applied, this technique will improve the USDA’s ability to track and forecast hydrological extremes.

Technical Abstract: Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSM) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM estimates. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression - utilizing independent variables based on multi-layer and temporally lagged SM estimates - can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to LSM and LDAS-to-LDAS transformation of multi-layer SM percentiles. Application of this approach will expand the utility of existing, high quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture products