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The model-based polarimetric decomposition under multi-angular condition is refined to estimate soil moisture over agricultural fields covered by different crops from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) time series. The approach allows to disentangle the vegetation and ground scattering components in order to invert the latter for the retrieval of soil moisture. For the vegetation volume separation, the crop orientations were estimated from SAR observations acquired at different incidence angles, and the associated volume scattering component was subtracted from each acquisition individually. Afterward, the soil moisture was retrieved from both ground scattering components (surface, dihedral), using the developed multi-angular cost functions comprised of dominant Bragg surface (β) or Fresnel dihedral (α) scattering parameters. Compared to former soil moisture retrievals from model-based decomposition of multi-angular polarimetric SAR data, the present refined approach that integrated both ground components, surface and dihedral, is independent of the power attenuation and loss during the microwave propagation through the vegetation. In this way, the ambiguity in the dihedral scattering component (most prominent around 45° incidence angle) was overcome, enabling a more robust retrieval methodology by clearly decoupling the soil and vegetation dielectric constants. The proposed multi-angular approach for soil moisture retrieval was validated with respect to the ground measurements conducted during the Soil Moisture Active Passive Validation Experiment in 2012. Due to the increased number of valid dominant surface/dihedral components which are used to retrieve the soil moisture in the multi-angular approach, an overall retrieval rate of 90%, significantly higher than that of the single-angular condition (50%), is obtained. The results indicate an overall retrieval rmse of 0.07–0.09 m3/m3 for the early crop growth stage, and a rmse of 0.09–0.12 m3/m3 for the later crop development until mature stage. However, the retrieval performance is highly dependent on the crop structure and phenological development stages, but the multi-angular rmse range is mostly lower than all single-angular rmses, indicating better quality of the multi-angular inversion than the single-angular one. [ABSTRACT FROM AUTHOR]

The objective of this study is to evaluate the potential of multiangular SAR data at C-band for soil parameters’ discrimination over bare agricultural fields. In order to exploit the incidence angle diversity to enhance the soil parameter retrievals, the conventional multiangular roughness descriptor \DeltaHH formulated originally as the backscattering difference between two specific incidence angles is adapted to take into account a general incidence angle effect. Moreover, a new angular coherence SAR descriptor \gammaHH is proposed by using a pair of low and high incidence angle SAR data. The adapted \DeltaHH and the proposed \gammaHH are applied to the RADARSAT-2 data with three different incidence angles. The results indicate that the proposed \gammaHH is more sensitive to surface roughness than the adapted \DeltaHH. Thus, the \gammaHH is selected to retrieve the surface roughness, and then the retrieved surface roughness is substituted into a low incidence angle data to retrieve the soil moisture. The RMSE of 6.1–8.5 \textm^3/\textm^3 is obtained for soil moisture retrieval. [ABSTRACT FROM PUBLISHER]

This letter investigates the polarimetric decomposition for monitoring the crop growth status over agricultural fields. Based on an existing polarimetric decomposition, the vegetation volume scattering component is removed from the full polarimetric synthetic aperture radar (SAR). Then, the estimated crop orientation is combined with the dominant scattering mechanism in the remaining ground coherency matrix to define the vegetation growth indicators for the crop growth monitoring from SAR. The proposed method is evaluated on the time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the extensive ground truth measurements collected in the framework of the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012. The results show that the scattering characteristics vary with the crop types and the phenological development stage. The random orientation is the most important case during the crop development period considered in this letter. For canola, corn, and wheat, the dihedral scattering is significant in the remaining ground coherency matrix at the early growth stage, and then, the surface scattering becomes important in the further growth. For soybean and pasture, the surface scattering dominates the remaining ground coherency matrix during the considered crop development stage. The vegetation growth indicators derived from UAVSAR data are well correlated with the ground measurements of crop height and biomass. This letter demonstrates, for the first time, the crop growth monitoring by using polarimetric decomposition via the vegetation orientation and scattering mechanisms. [ABSTRACT FROM PUBLISHER]