# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 5 Discussion
### Random Forest with PC and WT Transformation  
A transformation of the S1 data by the means of principal component analysis and wavelet transformation was applied to the four temporal series of each plot (\(\sigma^{0}_{\text{p,VV}},\sigma^{0}_{\text{p,VH}},\Delta\sigma^{0}_{\text{PG,VV }},\Delta\sigma^{0}_{\text{PG,VH}}\)). This data transformation of the S1 data showed that using either the PC dimensions or the WT coefficients, the irrigated/non-irrigated plots were classified with high accuracy using a classical random forest classifier. As a classical machine learning method, the RF classifier remains a powerful  
Figure 10: Irrigation mapping using the (**a**) WT-RF model, (**b**) PC-RF model and (**c**) the CNN model. Irrigated areas are presented in blue while non-irrigated areas are shown in red. A zoom version of the yellow box in each map is provided to better visualize different classification results.  
tool. It is good to mention that adding \(\Delta\sigma^{0}_{\text{PG,VV}}\) and \(\Delta\sigma^{0}_{\text{PG,VH}}\) remarkably improved our classification accuracy where the overall accuracy increased by more than 15%. This enhancement confirms the relevance of our assumption of using conjointly \(\sigma^{0}_{\text{P}}\) to \(\sigma^{0}_{\text{PG}}\) to remove the rainfall-irrigation ambiguity for better detection of irrigation. However, in the performed RF classification, the majority of the irrigated plots misclassified as non-irrigated plots belong to winter cereals agricultural including wheat, barley and oat. To fully understand whether those plots were actually irrigated or not, the NDVI temporal behavior of these plots was analyzed and compared to similar non-irrigated agricultures. The cycle and maximum values of NDVI did not significantly differ from similar non-irrigated agricultures. Thus, some plots being in an irrigated district could be registered as irrigated but actually the irrigation did not take place. Moreover, if some winter cereal plots (which are usually not irrigated) were exceptionally irrigated and thus misclassified, then this may be due to the fact that these plots are irrigated only very few times during the growing cycle, and these few irrigation activities have occurred at a date far from the date of S1 acquisition. In this case, the soil moisture of a given plot irrigated five or six days before the S1 acquisition date will certainly decrease to reach a level equivalent to that attained before the irrigation. This was showed by [PERSON] et al. (2019) who have demonstrated that the radar signal could be used to identify three-day-old irrigated plots, but it could be difficult to detect irrigation event if the irrigation occurs far away from the SAR acquisition time (more than four days).  
Moreover, the assessment of the variable importance of the PC dimensions and the WT coefficients allowed us to significantly reduce the number of variables in the RF approach. From the results obtained, only 15 out of 328 variables were conserved in the PC-RF and 18 out of 256 variables were kept in the WT-RF. Using the conserved important variables in both methods, the accuracy slightly decreased compared to that obtained using all variables (in PC-RF the OA decreased by 0.5% while in WT the OA decreased by 2.3%). This reduction of variables' numbers allows reducing considerably the training time of the model.