# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 5 Discussion
### Inter-Comparison and Quality Assessment  
An inter-comparison was performed between the maps obtained from the PC and that from the CNN in the three different scenarios: SAR only, optical only and combined SAR and optical. Figure 11 summarizes the accuracy indices (OA, Kappa, and F-score) obtained for the RF and CNN classifiers in the three different scenarios. The comparison between different classifications was first evaluated through the obtained accuracy indices. Moreover, to properly assess the performance of the tested machine learning algorithms (CNN and RF), the significance of the overall accuracy between classifications was calculated using the McNemar statistical test ([PERSON], 1965; [PERSON], 1966). Several studies have reported the use of the McNemar test to compare between two classification approaches ([PERSON], 1966; [PERSON], 1966). In this test, a null hypothesis that there is no significant difference between OA values of the two compared classifications is proposed. The McNemar test will reject this null hypothesis if the calculated probability (\(p\)-value) is less than 0.05 (i.e., considering 95% confidence level). In this case, the two classification schemes show a statistically significant difference. Using SAR data only, the cross-comparison showed a 92% of agreement between both maps. This means that 92.2% of the plots were commonly correctly classified using both models. Among this percentage, 95.5% of the plots being non-irrigated were classified as non-irrigated plots while 88.0% of the plots registered as irrigated are correctly classified as irrigated in both models. The CNN model was able to correctly classify 4317 plots (forming 6.4% of the total number of irrigated plots) as irrigated which were not classified using the RF approach. Among these plots, 43% are irrigated cereals plots including wheat, barley and oat. The CNN thus was able to improve the accuracy for irrigated cereals compared to the RF classifier. This improvement was clearly visible in terms of the precision, recall and F-score of the irrigated class in the CNN compared to that in the RF-classifier. Moreover, the McNemar test reveals that the two classification approaches using SAR data have a significant statistical difference with \(p\)-value less than 0.05. The use of the NDVI temporal series only shows approximately similar performance between the RF classifier and the CNN in terms of overall accuracy (89.5% and 91.6% for RF and CNN respectively). However, the inter-comparison between the classifications' results using NDVI time series shows that 90.2% of the plots were commonly correctly classified using both models. Among this percentage, 96.0% of the non-irrigated plots were correctly classified in both maps while 80.0% of the irrigated plots were commonly classified as irrigated plots. In terms of \(p\)-value obtained from the McNemar test, the results also show that the two approaches have significant statistical difference with a \(p\)-value \(<\) 0.05. Finally, it is good to mention that, compared to the use of S1 data, the use of the NDVI data performed well in classifying irrigated areas in both approaches. As irrigation in the region usually takes place between May and September, the probability of a plot being irrigated increases when a growing cycle exists in summer season. The combined use of optical and radar data in the RF classifier has slightly increased the classification results compared to that obtained using SAR data only while the results did not significantly change in the CNN approach. The inter-comparison between the results here shows that 94.0% of the plots were commonly correctly classified including 97.1% of the non-irrigated plots and 87.8% of the irrigated plots. The \(p\)-value obtained from the McNemar test also indicates that the classification approaches have significant statistical difference. Moreover, to properly assess the quality of the obtained results, the proposed method was compared with the method recently adopted by [PERSON] et al. (2018).