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), 12 % lower root mean square error (RMSE; 0.0824 to 0.0727 cm3 cm−3), and 95 % less bias (−0.0042 to 0.0001 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.