Preprints
https://doi.org/10.5194/hess-2021-233
https://doi.org/10.5194/hess-2021-233

  31 May 2021

31 May 2021

Review status: this preprint is currently under review for the journal HESS.

An inverse dielectric mixing model at 50 MHz that considers soil organic carbon

Chang-Hwan Park1, Aaron Berg2, Michael H. Cosh3, Andreas Colliander4, Andreas Behrendt5, Hida Manns2, Jinkyu Hong6, Johan Lee1, and Volker Wulfmeyer5 Chang-Hwan Park et al.
  • 1National Institute of Meteorological Sciences, Earth System Research Division, Korea Meteorological Administration
  • 2Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada
  • 3United States Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
  • 5Institute of Physics and Meteorology, University of Hohenheim, Stuttgart 70599, Germany
  • 6Ecosystem-Atmosphere Process Lab., Dep. of Atmospheric Science, Yonsei Univ., Seoul, 03722 Republic of Korea

Abstract. The prevalent soil moisture probe algorithms are based on a polynomial function that does not account for the variability in soil organic matter. Users are expected to choose a model before application: either a model for mineral soil or a model for organic soil. Both approaches inevitably suffer from limitations with respect to estimating the volumetric soil water content in soils having a wide range of organic matter content. 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 in the in-situ soil moisture data obtained using soil probe instruments. To test this theory, we derived a multiphase inversion algorithm from a physically based dielectric mixing model capable of using the SOM amount, performed a selection process from the multiphase model outcomes, and tested whether this new approach improves the accuracy of soil moisture (SM) data probes. The validation of the proposed new soil probe algorithm was performed using 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 a slightly improved correlation (0.824 0.848), 12 % lower root mean square error (RMSE; 0.0824 0.0725 cm3·cm−3), and 90 % less bias (−0.0042 0.0004 cm3·cm−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.

Chang-Hwan Park et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-233', Anonymous Referee #1, 26 Jun 2021
    • AC1: 'Reply on RC1', Chang-Hwan Park, 13 Aug 2021
  • RC2: 'Comment on hess-2021-233', Anonymous Referee #2, 05 Jul 2021
    • AC2: 'Reply on RC2', Chang-Hwan Park, 14 Aug 2021

Chang-Hwan Park et al.

Chang-Hwan Park et al.

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Short summary
In this study, we proposed an inversion of the dielectric mixing model for a 50-MHz soil sensor for agricultural organic soil. This model can reflect the variability of soil organic matter (SOM) in wilting point and porosity, which plays a critical role in improving the accuracy of SM estimation, using a dielectric-based soil sensor. The results of statistical analyses demonstrated a higher performance of the new model than the factory setting probe algorithm.