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Comment on hess-2021-233

Park, Chang-HwanDepartment of Civil Systems Engineering, Ajou University, Tashkent, UzbekistanBerg, AaronDepartment of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, CanadaCosh, Michael H.United States Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USAColliander, AndreasJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USABehrendt, AndreasInstitute of Physics and Meteorology, University of Hohenheim, Stuttgart 70599, GermanyManns, HidaDepartment of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, CanadaHong, JinkyuEcosystem-Atmosphere Process Lab., Department of Atmospheric Science, Yonsei University, Seoul, 03722 Republic of KoreaLee, JohanNational Institute of Meteorological Sciences, Earth System Research Division, Korea Meteorological Administration, Jeju, Republic of KoreaZhang, RunzeDepartment of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USAWulfmeyer, VolkerInstitute of Physics and Meteorology, University of Hohenheim, Stuttgart 70599, Germany

2021en

ABI

Аннотация

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 with 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 to 0.848), 12â% lower root mean square error (RMSE; 0.0824 to 0.0727âcm3âcmâ3), and 95â% less bias (â0.0042 to 0.0001âcm3âcmâ3). 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.

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Темы

Soil Moisture and Remote Sensing Soil and Unsaturated Flow Soil Geostatistics and Mapping

Идентификаторы

DOI: 10.5194/hess-2021-233-rc2

Цитирования и источники

Цитирований: 0Использованных источников: 59

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