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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 #384218
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: An inverse dielectric mixing model at 50MHz for soil water characterization of agricultural soil carbon

Author
item PARK, C. - Korea Meteorological Administration
item BERG, A. - University Of Guelph
item Cosh, Michael
item COLLIANDER, A. - Jet Propulsion Laboratory
item BEHRENDT, A. - University Of Hohenheim
item MANNS, H. - University Of Guelph
item HONG, J. - Yonsei University
item LEE, J. - Korea Meteorological Administration
item WULFMEYER, V. - University Of Hohenheim

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/12/2021
Publication Date: 12/17/2021
Citation: Park, C.H., Berg, A., Cosh, M.H., Colliander, A., Behrendt, A., Manns, H., Hong, J., Lee, J., Wulfmeyer, V. 2021. An inverse dielectric mixing model at 50MHz for soil water characterization of agricultural soil carbon. Hydrology and Earth System Sciences. 25(12):6407-6420. https://doi.org/10.5194/hess-25-6407-2021.
DOI: https://doi.org/10.5194/hess-25-6407-2021

Interpretive Summary: Soil moisture estimation from handheld probes use simple algorithms for mineral soils. Soil organic matter is not considered in the algorithm, though for many soils, organic matter can be significant. Therefore, a new algorithm was developed and tested to determine if there is an improvement in the accuracy of the soil moisture estimation. A previous study in the southern portion of Manitoba, Canada was used as there were significant amounts of organic matter in the soils. The new algorithm showed improvement in the estimation of soil moisture as well as improving the correlation and lowering the bias of the estimation as well. This is of special value to agricultural scientists who need to know soil moisture status in often organic soils for crop production.

Technical Abstract: The prevalent soil moisture probe algorithms are based on a polynomial function which does not account for the variability of soil organic matter. Users are expected to choose a model before the application; either model for mineral soil or model for organic soil. Both approaches will inevitably suffer the limitation in the estimation of an accurate volumetric soil water content from the probe in soils in a wide range of organic matter contents. In this study, we propose a new algorithm based on the idea that the amount of soil organic matter (SOM) is related to major uncertainties of the in-situ soil moisture data obtained with soil probe instruments. To test this idea, we derived a multiphase inversion algorithm from a physically-based dielectric mixing capable of using the SOM amount, perform a selection process from the multiphase model outcomes and test whether this new approach improves the accuracy of SM data probes. The validation of the proposed new soil probe algorithm is performed with both gravimetric and dielectric data from the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12). The new algorithm is more accurate than the previous soil-probe algorithm resulting in slightly improved correlation (0.824 0.848), 12% lower root mean square error (0.0824 0.0725 cm3cm-3) and 90% less bias (-0.0042 0.0004 cm3cm 3). These results suggest that applying the new dielectric mixing model together with global SOM estimates will result in more reliable soil moisture reference data for weather and climate models and satellite validation.