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), 12g% lower root mean square error (RMSE; 0.0824 to 0.0727gcm3gcm-3), and 95g% less bias (-0.0042 to 0.0001gcm3gcm-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.
|Number of pages||14|
|Journal||Hydrology and Earth System Sciences|
|Publication status||Published - 2021 Dec 17|
Bibliographical noteFunding Information:
Financial support. This work was funded by the Korea Meteorological Administration Research and Development Program “Development of Global Seasonal Forecast System” (grant no. KMA2018-00322) and the Ministry of Science and ICT, South Korea (grant no. NRF2018R1A5A1024958).
© 2021 Chang-Hwan Park et al.
All Science Journal Classification (ASJC) codes
- Water Science and Technology
- Earth and Planetary Sciences (miscellaneous)