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Abstract:The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values.Large-and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C-or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.

Soil moisture retrieval from SAR images is always affected by speckle noise and uncertainities associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. In this paper a Bayesian model is proposed to address these issues. A soil moisture Bayesian estimator from polarimetric SAR images is presented. This estimator is based on a set of stochastic equations for the polarimetric soil backscattering coefficients, which naturally includes models for the soil scattering, the speckle and the soil spatial heterogeneity. Since it is a Bayesian estimator, it may extensively use a priori information about soil condition, enhancing the performance of the retrieval. The Oh model is used as scattering model, although it presents a limiting range of validity for retrieving. After fully stating the mathematical modeling, numerical simulations are presented. First, traditional minimization-based retrieval using Oh model is investigated. The Bayesian retrieval scheme is then compared with Oh's retrieval. The results indicate that Bayesian model enlarge the validity region of Oh's retrieval. Moreover, as speckle effects are reduced by multilooking, Bayesian retrieval approachs to Oh's retrieval. On the other hand, an improvement in the accuracy of the retrieval is achieved by using a precise prior when speckle effects are large. The proposed algorithm can be applied to investigate which are the optimum parameters regarding multi-loking process and prior information required to perform a precise retrieval in a given soil type/condition.

Accuracy of Kirchhoff approximation (KA) for rough-surface electromagnetic wave scattering is studied by comparison with accurate numerical solutions in the context of three-dimensional dielectric surfaces. The Kirchhoff tangent plane approximation is examined without resorting to the principle of stationary phase. In particular, it is shown that this additional assumption leads to zero cross-polarized backscattered power, but not the tangent plane approximation itself. Extensive numerical results in the case of a bisinusoidal surface are presented for a wide range of problem parameters: height-to-period, wavelength, incidence angles, and dielectric constants. In particular, this paper shows that the range of validity inherent in the KA includes surfaces whose curvature is not only much smaller, but also comparable to the incident wavelength, with errors smaller than 5% in total reflectivity, thus presenting a detailed and reliable source for the validity of the KA in a three-dimensional fully polarimetric formulation.

Validation of soil moisture products derived from synthetic aperture radar (SAR) remotely sensed observations involves a comparison against ground-truth data. This validation step helps one to state the performance of competing retrieval algorithms. Nevertheless, the design of a field experiment in the context of SAR retrieval is not straightforward. Ground-based measurements are affected by instrument errors due to both the physical limitations of the measurement technique and the uncertainties related to the spatial variability of the soil moisture. To properly assess the performance of the retrieved estimates, both of the mentioned sources of uncertainties should be considered in the ground-based sampling and in the subsequent error assessment analysis. This letter addresses the rationale behind an optimal field experiment designed to assess the suitability of soil moisture retrieval algorithms.