Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

Journal Article · 25 October 2023 · Remote Sensing

DOI: https://doi.org/10.3390/rs15215120 · OSTI ID:2221783

Tobin, Kenneth ^[1]; Sanchez, Aaron ^[1]; Esparza, Daniela ^[1]; Garcia, Miguel ^[1]; Ganta, Deepak  ^[1]; Bennett, Marvin ^[1]

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1. Texas A & M International University, Laredo, TX (United States)

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field scale domain (100 to 3000 m). Study area was northern Oklahoma and southern Kansas. The coarse resolution of SMERGE (0.125 degree) limits this product’s utility. To validate downscaled results in situ data from four sources were used that included: United States Department of Energy Atmospheric Radiation Measurement (ARM) observatory, United States Climate Reference Network (USCRN), Soil Climate Analysis Network (SCAN), and Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE). In addition, RZSM retrievals from NASA’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) campaign provided a nearly spatially continuous comparison. Three periods were examined: era 1 (2016 to 2019), era 2 (2012 to 2015), and era 3 (2003 to 2007). During eras 1 and 2, RF outperformed XGBoost and GBoost, whereas during era 3 no model dominated. Performance was better during eras 1 and 2 as opposed to the pre-L band era 3. Improvements across all eras, regions, and models realized from downscaling included an increase in correlation from 0.03 to 0.42 and a decrease in ubRMSE from -0.0005 to -0.0118 m³/m³. This study demonstrates the feasibility of SMERGE downscaling opening the prospect for the development of a long-term RZSM dataset at a more desirable field-scale resolution with the potential to support diverse hydrometeorological and agricultural applications.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
• Contributing Organization: Pacific Northwest National Laboratory (PNNL); Brookhaven National Laboratory (BNL); Argonne National Laboratory (ANL); Oak Ridge National Laboratory (ORNL)
• Grant/Contract Number: SC0023067
• OSTI ID: 2221783
• Alternate ID(s): OSTI ID: 2242395
• Journal Information: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 21 Vol. 15; ISSN 2072-4292
• Publisher: MDPICopyright Statement
• Country of Publication: United States
• Language: English