# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
###### Abstract
This method was initially developed for the analysis of quad-pol data and it was later adapted for the dual-pol data case [37]. Covariance matrix's eigenvectors is considered to represent the scattering mechanisms, while the associated eigenvalues express the contribution of each mechanism to the total scattered power [38]. By applying this technique, three main polarimetric parameters can be extracted: entropy (H), anisotropy (A), and mean alpha angle (\(\overline{\alpha}\)). Entropy indicates the degree of statistical disorder of the scattering process, anisotropy is a measure of the relative importance of the secondary scattering mechanisms, providing valuable information in cases of targets with high values of entropy (H > 0.7), and mean alpha angle value represents the dominant mechanism taking place in the scattering phenomenon, being single-bounce scattering, volume scattering, or double-bounce scattering for values close to 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\), respectively [39]. Apart from the three above decomposition parameters, several others polarimetric parameters can be obtained by the specific method, using the open source \"Polarimetric SAR data Processing and Education Toolbox\" (PolSARPro) [40], provided by the European Space Agency (ESA). Some of them contain information about the eigenvectors and eigenvalues of the covariance matrix, while others are calculated based on different combination of H and A or they are related to Shannon entropy, a parameter that also quantifies the degree of randomness in the scattering phenomenon [41]. The potential of Sentinel-1 PolSAR data has been moderately examined in several application contexts, such as land-cover mapping [42; 43; 44; 45; 46; 47], crop monitoring [48; 49; 50; 51; 52; 53], crop yield estimation [54], crop damage detection [55], as well as in other specialized research areas, such as surface soil moisture estimation [56] and flood extent mapping [57]. Even though numerous studies have examined and demonstrated the contribution of PolSAR data collected from various SAR missions, such as Radarsat-2, in effective crop mapping [58; 59; 60], there is only a handful of relevant applications that utilize Sentinel-1 PolSAR data [61; 62; 32; 63; 64; 65; 66; 67]. This limited interest might be justified, considering that the pre-processing workflow of Sentinel-1 PolSAR data is a non-trivial and computationally expensive task. These studies undertaken to explore the usefulness of Sentinel-1 PolSAR data exhibit high heterogeneity concerning the study area characteristics, as well as, the input datasets and classification algorithms that were employed. Some of these studies use only polarimetric input features to build their crop classification models, reporting satisfactory overall accuracies from up to 96% [32], while some of them incorporate polarimetric features as supplementary information in combination with Sentinel-1 backscattering data [64; 65; 67], reporting contradictory results about the added value of PolSAR data. In most cases, H, A, \(\overline{\alpha}\) parameters derived from Cloude-Pottier polarimetric decomposition were used, as well as the elements of the covariance matrix. Additionally, two of these studies performed feature importance estimation analysis [32; 63], demonstrating that Sentinel-1 polarimetric attributes are more valuable compared with its corresponding backscattering data and highlighting that parameters related with Shannon entropy contain valuable information content for effectively discriminating crop types. The synergistic use of optical and SAR satellite data is an increasingly adopted strategy in crop classification studies [3]. By using multi-sensor information, supplementary and temporally dense information about vegetation's properties can be collected [2; 68], allowing to capture significant crop-specific phenological changes within crops' growth cycles [34; 69].