In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric synthetic aperture radar (SAR) data. This is achieved either by fitting appropriate scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. As such, the received wave information depends on the received antenna configuration. Additionally, a polarimetric descriptor that is independent of the received antenna configuration might provide additional information which is missed by the individual elements of the coherency matrix. This implies that existing target characterization techniques might neglect this information. In this regard, we suitably utilize the 2-D and 3-D Barakat degree of polarization which is independent of the received antenna configuration to obtain distinct polarimetric information for target characterization. In this study, we introduce new roll-invariant scattering-type parameters for both full-polarimetric (FP) and compact-polarimetric (CP) SAR data. These new parameters jointly use the information of the 2-D and 3-D Barakat degree of polarization and the elements of the coherency (or covariance) matrix. We use these new scattering-type parameters, which provide equivalent information as the Cloude α for FP SAR data and the ellipticity parameter χ for CP SAR data, to characterize various targets adequately. Additionally, we appropriately utilize these new scattering-type parameters to obtain unique non-model-based three-component scattering power decomposition techniques. We obtain the even-bounce, and the odd-bounce scattering powers by modulating the total polarized power by a proper geometrical factor derived using the new scattering-type parameters for FP and CP SAR data. The diffused scattering power is obtained as the depolarized fraction of the total power. Moreover, due to the nature of its formulation, the decomposition scattering powers are non-negative and roll-invariant while the total power is conserved. The proposed method is both qualitatively and quantitatively assessed utilizing the L-band ALOS-2 and C-band Radarsat-2 FP and the associated simulated CP SAR data.

Target decomposition methods from polarimetric Synthetic Aperture Radar (PolSAR) data provides target scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the occurrence of negative power components. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the nonconventional 3-D Barakat degree of polarization to obtain the polarization state of scattered electromagnetic wave. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative, and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters' variations from one target to another according to their physical and geometrical properties. In this study, we utilized L-, C-, and X-band full-polarimetric SAR data. We used these three datasets to show the effectiveness of decomposition powers and the natural interpretability of clustering results. The code is available at: https://github.com/Subho07/MF4CF

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.

We require spatio-temporal information about rice for executing and planning diverse management practices. In this regard, data obtained from Synthetic Aperture Radar (SAR) sensors are well suited for tracking morphological developments of rice across its phenology stages. This study proposes different target characterization parameters from polarimetric SAR data for rice phenology mapping. Six C-band Radarsat-2 images acquired over Vijayawada, India, are used for complete analysis. It is known that polarimetric information provides excellent sensitivity for identifying crop phenology stages. Hence, in this study, we assessed phenology classification results using a scattering-type parameter and scattering powers for full-polarimetric (FP) and extracted dual-polarimetric (DP) SAR data. Here, we utilized the real 4 $\times$4 Kennaugh matrix elements to derive these parameters equivalently for the two polarimetric modes (i.e. FP and DP). We obtained better overall classification accuracy for each phenology stages using the proposed parameters than the existing ones from FP and DP SAR data. We noted that the overall classification accuracy using the DP SAR data was only marginally lower than the FP SAR data. This marginal difference in the accuracies could be due to the absence of the cross-polarized component in the DP SAR data. We also demonstrate the usefulness of the scattering powers from DP SAR data for rice phenology monitoring.