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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 #424755
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: Simultaneous estimation of soil moisture and soil organic matter from in situ dielectric measurements - Part 1: Optimal estimation strategy

Author
item PARK, C - Ajou University Of Korea
item DESAI, A - University Of Wisconsin
item HUANG, J - University Of Wisconsin
item LAKSHMI, V - University Of Virginia
item KIM, H - Gwangju Institute Of Science And Technology
item JAGDHUBER, T - German Aerospace Center
item Cosh, Michael
item WIGNERON, J - French National Institute For Agricultural Research

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/14/2026
Publication Date: 5/11/2026
Citation: Park, C.H., Desai, A.R., Huang, J., Lakshmi, V., Kim, H., Jagdhuber, T., Cosh, M.H., Wigneron, J. 2026. Simultaneous estimation of soil moisture and soil organic matter from in situ dielectric measurements - Part 1: Optimal estimation strategy. Agricultural and Forest Meteorology. 386. Article e111196. https://doi.org/10.1016/j.agrformet.2026.111196.
DOI: https://doi.org/10.1016/j.agrformet.2026.111196

Interpretive Summary: Monitoring soil moisture is important for many agricultural purposes, including irrigation and nutrient management. Current commercial technologies are able to automate monitoring with good accuracy for most soils, but soil organic matter can cause errors which impact agricultural management. So it is advantageous to improve monitoring technologies so that organic matter can be accounted for in the estimation. An improved calibration equation is proposed to a traditional soil moisture sensor that accounts for soil organic matter with minimal additional input into the calculation. This work will improve the capability of monitoring networks to provide accurate agricultural soil moisture information for management and decision making.

Technical Abstract: Simultaneously estimating soil moisture (SM) and soil organic matter (OM) from microwave dielectric measurements has substantial value for sustainable agriculture and environmental monitoring, as both crop health and carbon sequestration depend heavily on these soil properties. However, existing dielectric mixing models often treat SM alone, neglecting the influence of OM and introducing discontinuities that complicate dual-parameter optimization. Here, we propose a refined optimal estimation (OE) approach that seamlessly incorporates OM into dielectric modeling. Including OM in the dielectric modeling framework not only refines SM estimation but also provides a unique pathway for leveraging soil moisture sensors to assess soil carbon content. By decomposing multiphase dielectric mixing models into continuous segments and carefully managing SM priors that contain OM-related uncertainties, the ambiguity typically associated with jointly estimating SM and OM is significantly reduced. Field data from the SMAP Validation Experiment 2012 (SMAPVEX12) strongly agree with the simultaneously estimated SM (R = 0.805, RMSE = 0.086 cm3cm-3), as well as OM and laboratory-derived soil organic carbon (R = 0.850, RMSE = 0.059 cm3cm-3). These improvements have direct implications for practical agricultural water management and ecological stewardship, especially when optimizing irrigation strategies or tracking carbon stocks. By enabling more accurate, spatially explicit, and temporally dynamic estimates of SM and OM, this method broadens the capabilities of remote sensing tools, ultimately aiding both farmers seeking to enhance soil health and environmental managers charged with carbon accounting.