# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results
### Performance of Crop Classification Models in Artificially Generated Cloudy Conditions  
In Section 4.1, we highlighted how Sentinel-1 PolSAR features accompanied by the use of a GA, as a feature selection module, can enhance the predictions of a S2 SVM model for crop classification. Although the results indicated a satisfactory improvement, we would not expect any dramatic increase. In our study site, cloud coverage was not a major problem and allowed us to collect temporally dense Sentinel-2 time-series and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crop Type** & **UA** & **PA** & **f1-Score** & **Support** \\ \hline soft wheat & 94.55 & 95.92 & 95.23 & 3823 \\ maize & 95.04 & 96.22 & 95.60 & 156 \\ barley & 93.84 & 94.35 & 94.10 & 2526 \\ oats & 93.70 & 89.13 & 91.35 & 820 \\ sunflower & 97.55 & 92.35 & 94.86 & 200 \\ rapeseed & 96.17 & 94.80 & 95.47 & 496 \\ broad beans & 95.83 & 90.48 & 93.06 & 147 \\ shrub grass & 85.67 & 81.84 & 83.69 & 228 \\ vineyards & 85.00 & 91.92 & 88.27 & 146 \\ cherry trees & 90.17 & 82.25 & 85.90 & 89 \\ \hline macro avg & 92.75 & 90.93 & 91.75 & 8631 \\ weighted avg & 94.01 & 94.00 & 93.98 & 8631 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification report of SVM with 111 features derived when used 15 as threshold. Figure 7: The performance of the SVM algorithm trained with features obtained when we apply the threshold t, in the different 100 subsets results from the GA experiments. The blue and the green lines present the OA and macro averaged f1-score, respectively, and are associated with the left y-axis. The red line indicated the number of features for each different run and is associated with the right y-axis. generate highly accurate classification models. However, we could expect the PolSAR data to make a real difference in regions where clouds are frequent, such as in Northern European countries. In order to demonstrate this, we generated artificial cloud masks, by applying over our study site the cloud coverage history per month for Copenhagen, Denmark. The coverage ranged from 43% for July to 70% for December. Specifically, we ingested null values into the Sentinel-2 time-series using the corresponding ratio for each month to simulate the cloud distribution. Then, two SVM models were trained, one using only the new clouded Sentinel-2 data (S2\({}^{\text{c}}\) model) and another one that combined them with the Sentinel-1 PolSAR (S1/S2\({}^{\text{c}}\) model). Table 5 presents f1-score, UA, and PA values for the two different models. Unlike the relevant case without artificial clouds, the S1/S2\({}^{\text{c}}\) model presents a significant increase in almost every single metric, with a notable improvement of more than 3.5% in f1-score. In the same fashion as in Section 4.1, we run 100 experiments of the GA to acquire the most dominant features. Regarding the threshold selection, a similar behavior is observed here too, namely thresholds from 13 to 16 achieved optimum results, with the number of features ranging from 159 to 94. Table 5 illustrates also the metrics of the model trained with 120 features derived when we set a threshold of 15 (GA\({}_{\text{c,15}}\) model). The feature selection procedure improves even more the predictions, compared to the model trained with all features. Specifically, macro average UA, PA, and f1-score are increased by 1.5%, 2.9%, and 2.2%, respectively. Finally, in Figure 8 we show a qualitative comparison of the three models presented in Table 5, where we can observe the same pattern. The S2\({}_{\text{c}}\) only model fails to identify several cases, whereas the other two predict almost every parcel correctly.