Retrieving the Soil Moisture in Bare Farmland Areas Using a Modified Dubois Model

Abstract

Soil moisture is an important parameter for global soil moisture transport, environmental evaluation, and precision agricultural research. The accurate retrieval of soil moisture in farmland areas using Synthetic Aperture Radar (SAR) depends on the accurate description of surface and SAR parameters. In these parameters, surface roughness and incidence angle are the key factors that affect the accuracy of the soil moisture retrieval model. This article proposes a modified Dubois model to retrieve soil moisture suitable for the bare surface of farmland area. The model eliminates the incidence angle parameters and uses polarization parameters to depict the surface roughness parameters in the Dubois model. To eliminate the incidence angle, the backscattering coefficients gamma0, which eliminates the effect of the incidence angles, are used to replace the sigma0. Under rain and no rain condition, the trend of backscattering coefficients (VH and VV) and cross-polarization ratio (VH-VV) of different soil texture with the soil moisture are compared. Then, the polarization parameter based on VH backscattering coefficients is used to describe surface roughness. The model is evaluated with time-series soil moisture observation data in situ of the study area. The results indicate that the modified model can retrieve soil moisture with high accuracy, and the total RMSE can reach 0.064 cm3cm−3 while the Dubois model is 0.124 cm3cm−3. Under rain and no rain condition, the retrieval accuracy of the modified model is 0.066 cm3cm−3 and 0.063 cm3cm−3. The retrieval accuracy is 0.060 cm3cm−3 and 0.067 cm3cm−3 under high and low incidence angles conditions, respectively. These results indicate that the modified Dubois model can retrieve soil moisture with high accuracy under different conditions.

A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

“…These methods provide reasonably accurate estimates of soil moisture, however, they are sensitive to site-specific parameters such as soil roughness (Mattia et al, 1997), and are only capable of estimating the soil moisture on bareground soils (Verhoest et al, 2008) or soils with low vegetation content. These models have been extensively tested and evaluated (Choker et al, 2017;Ma et al, 2021;MirMazloumi & Sahebi, 2016;Panciera et al, 2014), showing that they are generally not reliable when scaled globally.…”

Modeling shallow soil moisture dynamics in mountainous landslide active regions

“…The soil water accounts for merely 0.1% of the total water on the Earth (Strobbia and Cassiani, 2007;Ma et al, 2021). Lying in the pathway of the surface-and subsurfacehydrological processes, the soil water plays a decisive role in the transformation of water and energy falling on the Earth's surface to regulate the level of surface runoff and groundwater recharge (Ma et al, 2021).…”

“…The soil water accounts for merely 0.1% of the total water on the Earth (Strobbia and Cassiani, 2007;Ma et al, 2021). Lying in the pathway of the surface-and subsurfacehydrological processes, the soil water plays a decisive role in the transformation of water and energy falling on the Earth's surface to regulate the level of surface runoff and groundwater recharge (Ma et al, 2021). The dynamics of the shallow soil moisture reflect the changes in the rainfall intensity, infiltration and evapotranspiration potential, and antecedent soil moisture status (Nyamgerel et al, 2022).…”