# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 4 Results
### Classifying Irrigated and Non-Irrigated Plots over Catalonia  
Based on the results obtained in Section 4.3, we propose to classify irrigated and non-irrigated plots over the Catalonia site using the obtained distributions of detected irrigated points present in Figure 8. Four different classifications were performed. The first classification was performed using the results obtained from the morning SAR acquisitions (Figure 8), and the second classification was executed using the results of evening SAR acquisitions (Figure 8). A third classification was undertaken using the intersection between the morning and evening results (Figure 8), and finally a fourth classification was executed using the combination of the morning and evening results (Figure 8). As stated in 4.4, the intersection means that at each SAR date, a point is considered an irrigation point if it exists in both SAR acquisition modes while the combination means that the point is considered as irrigation point if it exists in either one of the two modes. For the morning and the evening classification scenarios, we suppose that a plot is considered irrigated if our method identifies two and more irrigation points within the complete SAR temporal series (morning or evening). For the intersection classification scenario, we consider that a plot is an irrigated plot if the intersection between the morning and the evening results gives one and more irrigation points. Finally, for the combined classification we consider that a plot is irrigated if the combination of the morning and the evening results gives three and more irrigation points. For each of the four classifications, a confusion matrix is built and Table 3 reports the accuracy metrics by means of the overall accuracy, weighted F-measure, and the F-Measure at each class (irrigated, non-irrigated). The overall accuracy is a standard metric used for remote-sensing applications. The weighted F-measure corresponds to the harmonic mean affected by the number of samples. Since the number of non-irrigated plots is bigger than the irrigated plots, the weighted F-measure is well suited to evaluate the performance of the classification [47]. Figure 9: Distribution of the irrigation points detected over irrigated plots in Catalonia Spain as a function of months for (**a**) morning SAR acquisition, (**b**) evening SAR acquisition. The highest overall accuracy (85.9%) was recorded for the intersection scenario with a weighted F-measure of 86.0%. The F-score of the irrigated class in the intersection scenario reaches 0.70 and that for the non-irrigated class reaches 0.90. On the other hand, the lowest overall accuracy was recorded for the evening SAR acquisition (82.5%) accompanied with the lowest weighted F-measure (83.4%). The combined scenario shows the highest accuracy for the irrigated class (F-measure = 0.72) with an overall classification accuracy of 85.4%. Generally, the F-measure of each class was nearly the same between the four scenarios. In fact, the F-measure varies between 0.68 and 0.72 for the irrigated class, and varies between 0.88 and 0.90 for the non-irrigated class. The four proposed classification scenarios were re-established using only the detected irrigation events between April and September 2018 which corresponded to the irrigation period in Catalonia. Figure 10 shows a comparison between the accuracy metrics previously obtained when using all the detected irrigation events (between September 2017 and December 2018) and that obtained when using only irrigation events between April 2018 and September 2018. For the morning and evening classification scenarios, the results show that the overall accuracy increased by 2.8% and 4.8%, respectively, when considering only detected irrigation events between April and September (Figure 10a). The F-measure of the irrigated class increased by 2.2% for the morning scenario and significantly increased by 5.0% for the evening classification (Figure 10b).