# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 5 Discussion  
### Relevance of Sentinel-1 PolSAR Data for Crop Classification  
Despite the recent advance in Deep Learning methods for crop classification [135; 136], SVM still remains an excellent alternative. OA when using only Sentinel-2 time-series as input data is over 92%. Moreover, f1-score is more than 80% for each of the 10 different crop types, and more than 92% for half of them. This great overall performance of S2 model was expected, considering that we used high quality imagery with minimum cloud coverage and high temporal density. Although it is not fair to compare these results with studies of different areas, several recent studies achieve similar performance [18; 22; 23; 137; 24]. Our study verifies the value of Sentinel-2 optical images, render this type of data indispensable for achieving highly accurate crop maps. To our knowledge, this research is the first one examining the potential of the entire set of polarimetric parameters that can be extracted using the H/A/\(\overline{a}\) decomposition technique in crop mapping. In Section 2.3.2, we present in detail each of the aforementioned polarimetric features. The potential of PolSAR data in identifying different crop types was assessed both alone and in synergy with optical data. Integrating multi-temporal optical and polarimetric features resulted in a very large feature space of 818 attributes. The S1 model, as expected, achieved a lower, but very promising, performance (OA more than 82%), compared to the S2 model. In terms of individual crop type metrics, the polarimetric-based SVM model fails to correctly identify the all-year crop types, but successfully manages to classify other crop types, such as rapeseed, sunflower, and maize, with class-wise metrics ranging between 90 and 95%. This is a strong indicator that polarimetric features hold important information about crops' phenology [51]. Regarding the S1/S2 model's results, even though the joint use of Sentinel-1 PolSAR and Sentinel-2 optical imagery did not have a significant impact in the OA obtained, we observed some interesting improvements in the metrics of several classes. This classification ability enhancement of certain crop types is due to the fact that PolSAR data contains enriched information about vegetation's  
Figure 8: Crop type maps attained by the three different SVM models. **(i)** ground truth, **(ii)** predictions from the S2 model, **(iii)** predictions from the S1/S2 mode,l and **(iv)** predictions from the GA optimal model. structural and dielectric properties that is complementary to the spectral reflectances of plants. However, SVM exhibited a worse performance in categorizing four crop types; all-year crops (cherry trees, vineyards, shrub grass), as well as oats, compared to the S2 model. This deterioration might indicate that the large feature space (818) contains several polarimetric parameters that cannot adequately describe the complex scattering processes of plants due to the fact that Sentinel-1 mission has not full polarization capabilities [64]. By utilizing GA as a feature selection module, we acquired an 80-feature subset, which included 33 polarimetric attributes. When SVM classifier was trained using, as input data, this particular mixed-type feature subset, classification accuracies were further improved. This finding not only showcases the great value of polarimetric parameters, but also highlights the effectiveness of GAs and the benefits of incorporating them as feature selection methods in crop classification tasks. Exceptional performance was also exhibited by the GA\({}_{\text{c,15}}\) model (i.e., the SVM trained using as input data the first 111 most important features, which were selected more than 15 times out of 100 GA runs). Figure 9 (no clouds) illustrates the relative importance of both types of data.