Recently, a general polarimetric model-based decomposition framework was proposed by Chen et al., which addresses several well-known limitations in previous decomposition methods and implements a simultaneous full-parameter inversion by using complete polarimetric information. However, it only employs four typical models to characterize the volume scattering component, which limits the parameter inversion performance. To overcome this issue, this paper presents two general polarimetric model-based decomposition methods by incorporating the generalized volume scattering model (GVSM) or simplified adaptive volume scattering model, (SAVSM) proposed by Antropov et al. and Huang et al., respectively, into the general decomposition framework proposed by Chen et al. By doing so, the final volume coherency matrix structure is selected from a wide range of volume scattering models within a continuous interval according to the data itself without adding unknowns. Moreover, the new approaches rely on one nonlinear optimization stage instead of four as in the previous method proposed by Chen et al. In addition, the parameter inversion procedure adopts the modified algorithm proposed by Xie et al. which leads to higher accuracy and more physically reliable output parameters. A number of Monte Carlo simulations of polarimetric synthetic aperture radar (PolSAR) data are carried out and show that the proposed method with GVSM yields an overall improvement in the final accuracy of estimated parameters and outperforms both the version using SAVSM and the original approach. In addition, C-band Radarsat-2 and L-band AIRSAR fully polarimetric images over the San Francisco region are also used for testing purposes. A detailed comparison and analysis of decomposition results over different land-cover types are conducted. According to this study, the use of general decomposition models leads to a more accurate quantitative retrieval of target parameters. However, there exists a trade-off between parameter accuracy and model complexity which constrains the physical validity of solutions and must be further investigated.

This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popular machine learning regression methods, i.e., Random Forest Regression (RFR) and Support Vector Regression (SVR) are adopted and evaluated. A set of 27 representative polarimetric parameters are extracted from the PolSAR data and used as input features in the regression models for height estimation. Furthermore, based on the unique capability of the RFR method to determine variable importance contributing to the regression, a smaller number of polarimetric features (6 out of 27 in our study) are selected in the final regression models. Results of our study demonstrate that PolSAR observables can produce corn height estimates with root mean square error (RMSE) around 40–50 cm throughout the growth cycle. The RFR approach shows better overall accuracy in corn height estimation than the SVR method in all tests. The six selected polarimetric features by variable importance ranking can generate better results. This study provides a new perspective on the use of PolSAR data in retrieving agricultural crop height from space.

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.

In this paper, we analyze the robustness of the parameter inversion provided by general polarimetric model-based decomposition methods from the perspective of a quantitative application. The general model and algorithm we have studied is the method proposed recently by Chen et al., which makes use of the complete polarimetric information and outperforms traditional decomposition methods in terms of feature extraction from land covers. Nevertheless, a quantitative analysis on the retrieved parameters from that approach suggests that further investigations are required in order to fully confirm the links between a physically-based model (i.e., approaches derived from the Freeman–Durden concept) and its outputs as intermediate products before any biophysical parameter retrieval is addressed. To this aim, we propose some modifications on the optimization algorithm employed for model inversion, including redefined boundary conditions, transformation of variables, and a different strategy for values initialization. A number of Monte Carlo simulation tests for typical scenarios are carried out and show that the parameter estimation accuracy of the proposed method is significantly increased with respect to the original implementation. Fully polarimetric airborne datasets at L-band acquired by German Aerospace Center’s (DLR’s) experimental synthetic aperture radar (E-SAR) system were also used for testing purposes. The results show different qualitative descriptions of the same cover from six different model-based methods. According to the Bragg coefficient ratio (i.e., $\beta$ ), they are prone to provide wrong numerical inversion results, which could prevent any subsequent quantitative characterization of specific areas in the scene. Besides the particular improvements proposed over an existing polarimetric inversion method, this paper is aimed at pointing out the necessity of checking quantitatively the accuracy of model-based PolSAR techniques for a reliable physical description of land covers beyond their proven utility for qualitative features extraction.

The formulation of the polarimetric interferometric coherences derived by simple homogeneous-volume-over-ground models is revisited for the case of ground returns dominated by the double-bounce terms. The differences between single-transmit (single-tx) and alternate-transmit modes of the interferometer are analyzed by inspecting the positions of the coherences on the complex plane. An additional volume-decorrelation term appears when the interferometer is operated in a single-tx mode, which adds complexity in the potential inversion of the model parameters. This effect is even more noticeable for the cross-polar channels in the oriented case, since the phase of the coherence with an infinite ground-to-volume ratio does not correspond to the topographic phase, as observed for the copolar channels