Abstract
The objective of this paper is to propose a new semi-empirical radar backscattering model for bare soil surfaces based on the Dubois model. A wide dataset of backscattering coefficients extracted from synthetic aperture radar (SAR) images and in situ soil surface parameter measurements (moisture content and roughness) is used. The retrieval of soil parameters from SAR images remains challenging because the available backscattering models have limited performances. Existing models, physical, semi-empirical, or empirical, do not allow for a reliable estimate of soil surface geophysical parameters for all surface conditions. The proposed model, developed in HH, HV, and VV polarizations, uses a formulation of radar signals based on physical principles that are validated in numerous studies. Never before has a backscattering model been built and validated on such an important dataset as the one proposed in this study. It contains a wide range of incidence angles (18°–57°) and radar wavelengths (L, C, X), well distributed, geographically, for regions with different climate conditions (humid, semi-arid, and arid sites), and involving many SAR sensors. The results show that the new model shows a very good performance for different radar wavelengths (L, C, X), incidence angles, and polarizations (RMSE of about 2 dB). This model is easy to invert and could provide a way to improve the retrieval of soil parameters.
Highlights
Soil moisture content and surface roughness play important roles in meteorology, hydrology, agronomy, agriculture, and risk assessment
The results indicate a slight underestimation of the radar signal by the Dubois model in the case of kHrms lower than 2.5 (Dubois validation domain) for both HH and VV polarizations (Figures 1b and 2b; Table 2)
The new model is based on the formulation made in the Dubois model where the mv < 35 vol%
Summary
Soil moisture content and surface roughness play important roles in meteorology, hydrology, agronomy, agriculture, and risk assessment These soil surface characteristics can be estimated using synthetic aperture radar (SAR). It is possible to obtain SAR and optical data for global areas at high spatial and temporal resolutions with free and open access Sentinel-1/2 satellites (six days with the two Sentinel-1 satellites and five days with the two Sentinel-2 satellites at 10 m spatial resolution). This availability of both Sentinel-1 satellites and Sentinel-2 sensors, in addition to Remote Sens. The retrieval of soil moisture content and surface roughness requires the use of radar backscatter models capable of correctly modelling the radar signal for a wide range of soil parameter values
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