# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 6 Conclusions  
To deal with the aspect of managing water resources in the agricultural sector, this paper proposes a new approach for detecting irrigation events at plot scale in a near real-time scenario. The proposed method is a decision tree-based approach for detecting irrigation events using the change detection in the S1 SAR backscattering coefficients at plot scale. Several filters were applied in order to remove the ambiguity between irrigation events and rainfall, vegetation development and soil surface roughness. A thresholds-based method was proposed to detect irrigation events at each available S1 SAR date and for each agricultural plot mainly using the backscattering S1 SAR signal at plot and at grid scale (10 km \(\times\) 10 km). Finally, a post-processing filter based on the NDVI value was integrated to ameliorate the detection of irrigation events.  
To ensure the transferability and the possible operational application of the proposed approach, three study sites were examined in this study (Montpellier, Catalonia and Tarbes) with two different climatic properties. In terms of climate, both Montpellier and Catalonia are similar (Mediterranean) with dry summer season whereas Tarbes is a humid region with frequent rainfalls in the summer season. Results showed, first, that the proposed method was capable of detecting 84.8% of the irrigation events occurring at three plots encountering 33 irrigation events in Montpellier. Then, an analysis performed over the semi-arid region in Catalonia revealed that the proposed method was capable of classifying irrigated and non-irrigated plots with an overall accuracy of 85.9%. Finally, the analysis performed over irrigated plots in Tarbes revealed that our proposed method is able to detect irrigation events even in the presence of frequent rainfall events in the summer season where 90% of irrigated plots were detected with two and more irrigation events.  
Our irrigation events detection method opens the way toward building new semi-supervised approaches for irrigated area mapping at plot scale. Since several machine-learning models require an extensive dataset with costly terrain measurements, this method could be used to create a dataset of irrigated/non-irrigated labelled samples in order to be used in a machine-learning model that could be more efficient. Our future work will concentrate on combining this new method with a supervised classification model to obtain a semi-supervised model for irrigation mapping. This combination can permit the transfer of machine-learning classification models over several regions to perform irrigation mapping at plot scale.  
Conceptualization, H.B. and N.B.; Data curation, H.B.; Formal analysis, H.B., N.B. and I.F.; Methodology, H.B., N.B. and I.F.; Supervision, N.B.; Validation, M.Z., H.B. and V.D.; Writing--original draft, H.B.; Writing--review and editing, N.B., I.F., M.Z., H.B. and V.D. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the French Space Study Center (CNES, TOSCA 2020 project), the National Research Institute for Agriculture, Food and the Environment (INRAE) and the Mediterranean Agronomic Institute of Montpellier (CIIEAM-IAMM).  
**Acknowledgments:** Authors wish to thank the French Space Study Center (TOSCA 2020), the National Research Institute for Agriculture, Food and the Environment (INRAE) and the Mediterranean Agronomic Institute of Montpellier (CIIEAM IAMM). The authors wish to thank the Occitanie region, France for supporting this work. The authors wish also to thank the European Space Agency (ESA) for the Sentinel 1 and Sentinel 2 data and Theia pole for the calibration of the Sentinel 2 images. The authors would like to thank [PERSON] from the isardSAT, Catalunya for providing the SIGPAC data. Finally, authors would like to thank [PERSON] of the Chamber of Agriculture of Hautes Pyrenees, France, for providing terrain data over Tarbes, France.  
**Conflicts of Interest:** The authors declare no conflict of interest.