# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 1 Introduction
Among several SAR satellite constellations, the time series acquired via Sentinel-1 (S1) SAR constellation (S1A and S1B) provides an effective tool for large-scale irrigated area mapping and monitoring due to the unique combination of high revisit time (6 days revisit period) and high spatial resolution (10 m \(\times\) 10 m pixel spacing). To preform irrigated area mapping, as well as other large-scale area mapping, both deep and machine learning approaches have been extensively exploited since they provide acceptable results and allow large-scale analysis [23; 24]. Using the S1 temporal series and the machine-learning approaches, several studies have achieved high-quality classification mapping with a good accuracy [24; 25; 26; 27]. However, one of the most important questions about using machine learning approaches relates to the dependency of these models on the terrain calibration data and the studied geographical context [28]. Such supervised classifications always depend on a training-validation procedure that requires a rich set of labelled samples in order to build the predictive model. Moreover, for better performance, almost all classifications performed via machine learning techniques require complete temporal series data over the growing season, thus making the near real-time mapping more complicated. Most of the remote-sensing applications for irrigation monitoring mainly focus on the mapping of irrigation extent without taking frequency and timing into account. On the other hand, obtaining information about the period and frequency of irrigation over each agricultural cropland is more significant in the context of irrigation management [29; 30]. In fact, to understand the sustainability of the water resources, irrigation timing and frequency are important especially in arid and semiarid regions. Furthermore, in the context of irrigation water management, the early detection of existing irrigation episodes over each cropland is of great importance for crop modelling in order to estimate the water status and hence better schedule the irrigation episodes over croplands. Better scheduling of irrigation activities can save water that may be used to irrigate more land particularly where water is a limiting factor of agricultural production. In addition, the improvement of the water-use efficiency (WUE) in irrigation requires a real-time control and optimization of the irrigation activities. In irrigated agriculture, improvement of WUE is achieved by optimizing the timing and quantity of irrigation applications [31]. This optimization of the irrigation schedule requires an early detection of irrigation episodes over each irrigated agricultural plot. Moreover, the near real-time detection of irrigation episodes can help monitor and assess the water consumption over agricultural areas. The arrival of the Sentinel satellites (Sentinel-1/2), with high spatial and temporal resolutions, opens the way toward building operational models capable of detecting irrigation events at plot scale. Therefore, the challenge is to build an effective tool capable of detecting irrigation events at plot scale using simple models that may not require extensive labelled samples (training/validation) and independent of the studied geographical context. In the context of irrigation water management, the objective of this study is to build a near real-time irrigation detection approach at plot scale using Sentinel-1 time series. First, we analyzed the sensitivity of the radar signal following irrigation events over irrigated plots for a study site located in Montpellier, South-East France. Then, we build a tree-like approach for detecting irrigation events based on the change detection of the SAR signal at plot scale co-jointly with the change detection of the SAR signal obtained at grid scale (10 km \(\times\) 10 km) which was used to eliminate the ambiguity between rainfall and irrigation.