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Early yield prediction of winter cereals is a very powerful tool assisting in fertilization strategies and optimization. Satellite based vegetation indexes show interesting correlation with yield in winter cereals and is commonly used by technician in fertilization management zone. The aim of this paper is to investigate and develop a tool for quantitative early yield prediction, relying on machine-learning models in order to implement it in a decision support system platform.

In this paper we derive a simple model to describe the distortions on focused airborne SAR data rising from the usage of inaccurate external information of the topography of the illuminated area. The model is based on the assumption that an accurate time-domain strategy is applied to focus the data. Moreover, perfect knowledge of the aircraft trajectory is also assumed, in order to better highlight the relation between true topography and airborne SAR focused data. The presented model takes into account the coupling between the trajectory deviations affecting the aircraft flight track and the topography errors. A closed form expression for both the amplitude and phase distortions corrupting the focused signal is provided. To validate the presented analysis some simulation results relevant to a realistic SAR acquisition scenario are finally shown.

We present a method able to retrieve the soil moisture content and the surface roughness of a moderately vegetated soil in a possibly hilly area, from a fully polarimetric SAR dataset. The estimation procedure is based on the prediction of the second order statistics of the scattered field provided by the Slope-Corrected Polarimetric Two-Scale Model combined with a two-component scattering model. The performance of the estimation method is assessed by comparing obtained retrieval results to “in situ” measurements. To this aim, data from AGRISAR 2006 campaign are employed. Keywords—SAR Polarimetry; soil moisture retrieval

This work deals with the analysis of a novel data processing approach to estimate the local depth in a shallow coastal area starting from X-band radar data. The approach is based on the maximization of the Normalized Scalar Product (NSP) between the measured and the theoretical wave dispersion relations, which embed the dependence on the searched for parameters (current vector and depth). In the present work, we presented an improvement version of NSP approach able to estimate the local depth, which allows to obtain a high resolution spatial map (subpixel) of the investigated area. The performances of the NSP approach is evaluated by comparing the estimated bathymetry with the ground truth provided the multi-beam echo sounder reported on the same grid resolution of the bathymetric maps of the X-band radar images. © 2013 IEEE.

In this paper we introduce an algorithm to retrieve the soil moisture content and the surface roughness of a moderately vegetated soil from a fully polarimetric SAR dataset. The estimation procedure is founded on the prediction of the second order statistics of the scattered field provided by the Polarimetric Two-Scale Model combined with a two-component scattering model. The performance of the estimation algorithm is assessed by comparing obtained retrieval results to “in situ” measurements. To this aim, data from Little Washita SMEX'03 campaign available in literature are employed.

An algorithm able to retrieve the soil moisture content and the surface roughness of a bare soil from a set of measured HH-, VV-SAR images is here presented. The estimation procedure is founded on the prediction of power densities and correlation between the copolar channels provided by the Polarimetric Two-Scale Model that we recently proposed. The performance of the estimation algorithm is assessed by comparing obtained retrieval results to “in situ” measurements. To this aim, data from Little Washita SMEX′03 campaign available in literature are employed.

A time domain simulator useful to obtain the pulse response for the generic bistatic SAR configuration is here presented. Moreover, we focus on two particular bistatic systems, i.e. the twin SAR and the space-surface SAR, for which we obtain, under some analytical approximations, the Transfer Functions. Finally, to investigate the possibility to perform an efficient processing in the frequency domain for these two systems, we perform a numerical comparison between the point spread functions, obtained using the time domain simulator and the inverse FFT of the Transfer Functions.

In this paper we introduce an original formulation for the electromagnetic field diffraction by a knife edge with random roughness: the formulation, based on the asymptotic physical optics (APO) approach, leads to closed form evaluations of the statistics of the diffracted field. A very interesting conclusion is that, for moderate edge roughness, the diffracted field propagation can be described in terms of the same ray congruence as in the straight (smooth) edge case, with the only difference that the field associated to each ray is a random variable whose statistics are, in this paper, computed in closed form. The presented approach can be then considered as a first step toward a general stochastic theory of edge diffraction (STED).