# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results
### Crop Classification Results
#### 4.1.1 Crop Classification Results Based on Sentinel-2 Imagery (S2 Model)  
The OA and weighted average f1-score achieved by this model are both around 92.40%. As is also discussed in [19], all metrics are quite high for most crop types, demonstrating the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **UA** & **PA** & **f1-Score** & **OA** \\ \hline S2 & 91.06 & 88.95 & 89.93 & 92.42 \\ S1 & 76.10 & 67.68 & 71.12 & 82.83 \\ S1/S2 & 91.09 & 87.99 & 89.41 & 92.28 \\ GA & 91.85 & 90.04 & 90.85 & 93.58 \\ GA\({}_{15}\) & **92.75** & **90.93** & **91.75** & **94.00** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall accuracy and macro average values of UA, PA, and f1-score of 5 different SVM classification models constructed based on the original dataset. With bold is highlighted the best model for each metric.  
high sensitivity of Sentinel-2 measurements to the distinct biophysical properties of crops. In particular, this model exhibited exceptional distinguishing ability of maize, sunflower, and soft wheat, as it is quantitatively expressed by the high f1-scores values obtained (94.99%, 93.97%, and 93.84%, respectively). However, this is not the case for the three all-year crop classes, namely vineyards, shrub grass and cherry trees, being categorized with relatively low f1-scores below 86.00%. The poorer separability of these three crop types might be justified, considering that their spectral signatures, for the majority of optical features, do not contain any specific temporal characteristics that could distinguish them from the other ones. Especially, shrub grass presents by far the worst performance in terms of f1-score values (81.51%). This vegetation type is of a diverse nature, providing various spectral profiles and, hence, misclassifications are expected. Table S4 presents in detail the performance of the SVM model that was built using as input data only the Sentinel-2 multi-temporal features.  
1.2 Crop Classification Results Based on the Combination of Sentinel-1 and Sentinel-2 Multi-Temporal Imagery (S1/S2 Model)  
In this scenario, we use all the available variables, both optical and polarimetric, to train the SVM classifier. The full integration of features did not result in a performance improvement, compared to the S2 model, achieving an OA of around 92.25%. In terms of the class-specific accuracy metrics, the S1/S2 model achieved comparable or better results concerning the discrimination of the majority of crop classes. Specifically, the inclusion of polarimetric features contributed to a considerable improvement of 1.09% and 1.35% in the f1-scores of maize and rapeseed, respectively. However, it exhibited a poorer performance (up to 2.8%) compared to the S2 model for the all-year crop types, as well as for oats. One possible explanation of this under-performance might be the fact that some polarimetric features, which are included into the input feature dataset, might not be adequately descriptive of the complex scattering processes occurring on the vegetation cover of these particular crop types, causing a confusion to the SVM classifier. Another explanation might be the fact the several crop types, such as oats and soft wheat, may have diverse spectral properties, but quite similar dielectric and structural temporal characteristics during several months of the total observation period, with subtle differences that cannot be captured by Sentinel's 1 dual-polarized sensor, requiring the collection of PolSAR data from a system with full polarization capabilities. Table S5 presents the metrics of the SVM model using both types of data, in detail.