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This paper investigates and compares the potential of five model-based polarimetric decompositions, namely those developed by Eigenvector-based decomposition (Van Zyl), Model-based decomposition (Freeman-Durden three-component decomposition and Yamaguchi four-component decomposition), An & Yang3 and An & Yang4 for crop biomass detection over agricultural fields covered by various crops. The time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the ground truth of soil and vegetation characteristics collected during the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012 (SMAPVEX12) were used to compare the five decomposition methods with related to the scattering mechanisms and the biomass retrieval performances. The results show that the performance of each decomposition method for biomass retrieval depends on the crop types and the crop phenological stages. Finally, an overall biomass underestimation was observed from the five decompositions, and the highest regression value of 99% was obtained from Freeman decomposition as a result of the enhanced volume scattering. Indeed, Freeman-Durden model provided the best results.

In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image. In this process, the difference image is classified into three classes; negative change class, positive change class and no change class. However the SAR images suffer from speckle noise, the proposed method is able to map the changes without speckle filtering. To evaluate the performance of this method, two dates of SAR data acquired by Uninhabited Aerial Vehicle Synthetic from an agriculture area are used. Change detection results show better efficiency when compared to the state-of-the-art methods.