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.

Abstract:The aim of this paper is to evaluate the most used radar backscattering models (Integral Equation Model "IEM", Oh, Dubois, and Advanced Integral Equation Model "AIEM") using a wide dataset of SAR (Synthetic Aperture Radar) data and experimental soil measurements. These forward models reproduce the radar backscattering coefficients (σ 0 ) from soil surface characteristics (dielectric constant, roughness) and SAR sensor parameters (radar wavelength, incidence angle, polarization). The analysis dataset is composed of AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR, ERS, RADARSAT, ASAR and TerraSAR-X data and in situ measurements (soil moisture and surface roughness). Results show that Oh model version developed in 1992 gives the best fitting of the backscattering coefficients in HH and VV polarizations with RMSE values of 2.6 dB and 2.4 dB, respectively. Simulations performed with the Dubois model show a poor correlation between real data and model simulations in HH polarization (RMSE = 4.0 dB) and better correlation with real data in VV polarization (RMSE = 2.9 dB). The IEM and the AIEM simulate the backscattering coefficient with high RMSE when using a Gaussian correlation function. However, better simulations are performed with IEM and AIEM by using an exponential correlation function (slightly better fitting with AIEM than IEM). Good agreement was found between the radar data and the simulations using the calibrated version of the IEM modified by Baghdadi (IEM_B) with bias less than 1.0 dB and RMSE less than 2.0 dB. These results confirm that, up to date, the IEM modified by Baghdadi (IEM_B) is the most adequate to estimate soil moisture and roughness from SAR data.

Abstract:The purpose of this study is to analyze the potential of Sentinel-1 C-band SAR data in VV polarization for estimating the surface roughness (Hrms) over bare agricultural soils. An inversion technique based on Multi-Layer Perceptron neural networks is used. It involves two steps. First, a neural network (NN) is used for estimating the soil moisture without taking into account the soil roughness. Then, a second neural network is used for retrieving the soil roughness when using as an input to the network the soil moisture that was estimated by the first network. The neural networks are trained and validated using simulated datasets generated from the radar backscattering model IEM (Integral Equation Model) with the range of soil moisture and surface roughness encountered in agricultural environments. The inversion approach is then validated using Sentinel-1 images collected over two agricultural study sites, one in France and one in Tunisia. Results show that the use of C-band in VV polarization for estimating the soil roughness does not allow a reliable estimate of the soil roughness. From the synthetic dataset, the achievable accuracy of the Hrms estimates is about 0.94 cm when using the soil moisture estimated by the NN built with a priori information on the moisture volumetric content "mv" (accuracy of mv is about 6 vol. %). In addition, an overestimation of Hrms for low Hrms-values and an underestimation of Hrms for Hrms higher than 2 cm are observed. From a real dataset, results show that the accuracy of the estimates of Hrms in using the mv estimated over a wide area (few km 2 ) is similar to that in using the mv estimated at the plot scale (RMSE about 0.80 cm).

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