# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 6 Conclusions  
In this paper, a large number of multi-temporal Sentinel-1 and Sentinel-2 dual-PolSAR features (818) were incorporated into various SVM classification scenarios in order to obtain high-quality crop-type maps of an agricultural region located in Navarre, Spain. One of the main contributions of this work is the assessment of the full set of 23 different polarimetric parameters that can be extracted, by applying Cloude-Pottier decomposition technique available at the PolSARpro software, for the task of crop classification. Moreover, a custom GA has been implemented and utilized as a feature selection method to derive an optimal feature subset out of the high dimensional feature space, as well as to perform a feature importance analysis for acquiring the most informative features. Aiming to examine the added value of PolSAR data, as well as the effectiveness of GAs, we trained and evaluated numerous classification models by making use of the available mixed-type features and GA's outputs. The main conclusions of the study come be summed up to the following:  
1. The use of all the available optical and polarimetric features improved slightly the crop classification accuracy. However, when artificial cloud masks were injected into the original Sentinel-2 imagery, simulating a real world scenario, the added value of PolSAR data was revealed. The corresponding polarimetric/optical synergistic SVM model presented an accuracy improvement of more than 3.5%, in comparison with the optical-based model under artificially cloudy conditions. This experimental result showcases the potential value of this approach in relevant tasks above agricultural regions that suffer from frequent cloud cover. 2. By employing our custom GA, we re-identified the most important features in the scenario of artificial clouds and used them an input data in the SVM classifier. This particular model exhibited an increased OA by 1.5%, approaching 90.66%. 3. Through a computationally demanding feature importance estimation analysis of carrying out more than 100 GA experiments, we derived a sorted list of the most important individual predictors in both scenarios of the original cloud-free Sentinel-2 dataset and the one with the artificial cloud masks that could be effectively utilized in future studies. This feature importance analysis verified the great contribution of Sentinel-2 attributes in the original case, as expected, and highlighted the great relative importance of several polarimetric SAR parameters, such as Shannon entropy, especially in the case of injecting artificial cloud coverage. The following are available online at [[https://www.mdpi.com/article/10.3390/rs14225739/s1](https://www.mdpi.com/article/10.3390/rs14225739/s1)]([https://www.mdpi.com/article/10.3390/rs14225739/s1](https://www.mdpi.com/article/10.3390/rs14225739/s1)), Figure S1: Flowchart of genetic algorithm's implementation; Table S1: Information about Sentinel-1 and Sentinel-2 images; Table S2: GA's parametrization; Table S3: SVM parametrization for each classification scenario; Table S4: SVM classification report using as input only the Sentinel-2 features (S2 model); Table S5: SVM classification report using as input all the available features (S1/S2 model); Table S6: SVM classification report using as input only the Sentinel-1 features; Table S7: SVM classification report using as input the optimal 80-feature subset derived from a single genetic algorithm implementation (GA model); Table S8: Most important features with the relevant numbers of occurrences (e) among the 100 different feature sets acquired from the 100 different GA runs, for the scenario with no artificial clouds; Table S9: Most important features with the relevant numbers of occurrences (e) among the 100 different feature sets acquired from the 100 different GA runs, for the scenario with artificial clouds. Conceptualization, V.S., A.K. and I.P. ; methodology, M.I., A.K. and V.S. ; software, M.I. ; validation, A.K. and V.S. ; formal analysis, M.I., A.K. and V.S. ; investigation, M.I., A.K.