# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 5 Discussion
### Relevance of Sentinel-1 PolSAR Data for Crop Classification
The relative importance of each feature type was calculated, by taking into account only the features that were selected more than 10 times out of 100 GA implementations, since 10 is the minimum threshold for acquiring satisfactory classification accuracies (Figure 7). In general, the contribution of polarimetric parameters is estimated at around 25-30%, demonstrating the usefulness of PolSAR data as ancillary information in crop classification tasks. The contribution of Sentinel-1 features is more evident in the case of adding artificial cloud noise in the original cloud-free Sentinel-2 imagery. In this scenario, the synergistic use of both optical and polarimetric data contributed to a much better discrimination of crops (OA increased more than 3.5%), compared with the S2 model. This result clearly demonstrates the great value of incorporating the cloud-penetrating PolSAR data when trying to produce accurate crop maps of agricultural areas that suffer from dense cloud cover conditions. The great usefulness of this type of data in cloudy conditions is also clearly demonstrated in Figure 9 (clouds), where the relative importance of optical and polarimetric acquire almost equal values. Figure 10 illustrates how the number of features of each data type changes inside the list of the most valuable attributes as the threshold t increases. This ratio-threshold relationship was examined in both scenarios of clear and artificially cloudy Sentinel-2 imagery. As it is observed, in both cases, when all individual features are considered  
Figure 9: The aggregated importance of Sentinel-1 and Sentinel-2 features with and without clouds. (\(t=0\)), the relative ratio of Sentinel-1 and Sentinel-2 features is 67% and 33%, respectively, as expected (see Table S1). According to Figure 9(a), which refers to the original case, as the threshold increases from 0 to 7, the percentage of Sentinel-2 features gradually increases against the percentage of Sentinel-1 features, which decreases in a complementary fashion. After the threshold value of 7, where the ratios of the two types are almost equal, the Sentinel-2 data prevails, approaching a ratio of more than 95% for \(t>20\). As discussed in Section 4.1.4, for threshold values ranging between 13 and 17, SVM classifier has optimal performance. At this range, Sentinel-2 features dominate the list of the best predictors with a mean ratio of 83%. The corresponding graph in the scenario of artificial cloud masks exhibits some interesting differences (Figure 9(b)). As the threshold increases from 0 to 10, Sentinel-2 ratio increases with a slower rate, compared to the original case, and until \(t=25\) it present a stable behavior. At the optimal performance threshold range, the two types of features interestingly contribute in an almost equal degree. For t larger than 25, Sentinel-2 features slowly occupies a larger part of the list. However, contrary to the original case, this ratio does not surpass 80%, which indicates that the presence of polarimetric features is significant even when only a handful of features that present the highest selection frequencies are considered.