# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 1 Introduction  
Efficient management of water resources is required to achieve environmentally sustainable development especially under changing climatic conditions and limited water resources. Fresh water is mainly consumed in the agricultural sector, which is considered the world's largest water user. In fact, with the increase of the global population, irrigating agricultural crops is essential in order toachieve satisfactory agricultural production and income. However, with the decreasing supplies of fresh water due to climate change, better management of irrigation policies is required to deal with the high demand of food and limited water resources. To support the management of irrigated agricultural policies, a spatially detailed quantification of the irrigation extent and timing is required. This quantification is crucial to monitoring fresh water consumption in the agricultural sector especially for regions suffering scarce water resources. Unfortunately, the extent and distribution of irrigated areas as well as the irrigation timing remain indefinite especially at large scale. Moreover, existing irrigation maps such as the Global Rain-fed, Irrigated and Paddy Croplands (GRIPC) [1] and the Global Map of Irrigated Areas (GMIA) [2] products remain inadequate for irrigation management at plot scale due to their low spatial resolutions (500 m and 5 arc minutes, respectively). With modern remote sensing, mapping irrigated areas has been the main concern for several studies [3; 4; 5]. Both optical and radar data have been exploited to perform an irrigated/non-irrigated classification maps. The use of multi-band optical data is mainly related to the assumption that irrigated/non-irrigated crops could be classified using the temporal series of vegetation indices such as the normalized differential vegetation index (NDVI) [3; 5], normalized differential water index (NDWI) [4] or greenness index (GI) [6]. However, optical data is not only limited to weather conditions but also to the specific studied crop type. For this reason, several studies tend to map irrigated/non-irrigated areas focusing on one specific crop type such as rice [7] wheat [8; 9] or maize [10]. Recent works have shown that a synthetic aperture radar (SAR) signal seems to be more adequate to map irrigated areas over different agricultural crops [8; 11; 12]. The use of a SAR signal for mapping irrigated/non-irrigated areas over any vegetation cover is related to the fact that the radar signal is sensitive to soil and vegetation water content [13; 14]. Since irrigation eventually increases the soil and the vegetation water content, the sensitivity of the radar signal to soil and vegetation water could help detect these irrigation events. Through literature, it has been widely demonstrated that the SAR backscattering coefficient (\(\sigma^{0}\)) is directly related to the soil and vegetation water content [14; 15; 16; 17; 18; 19; 20]. Mainly for the irrigation task, [PERSON] et al. [21] have reported that a three-day-old irrigation point could still be detected using X-band SAR data. In their study, they showed that the X-band radar signal increases by more than 1.4 dB due to irrigation events occurring one day before the acquisition with 90 cm vegetation height. They also showed that for low vegetation cover (vegetation height = 25 cm) the X-band SAR signal increases by 2.6 dB due to irrigation event one day before the SAR acquisition. Similarly, [PERSON] et al. [22] have shown that C-band SAR data could be used to detect irrigation activities over irrigated wheat plots with an interval of three days between the irrigation date and the SAR acquisition date. Since irrigation is a time dynamic activity, an extensive multi-temporal dataset is required to detect consecutive irrigation activities on the studied fields. In addition, high spatial resolution SAR data is required to obtain irrigation information at plot scale. In fact, irrigation information at plot scale is favorable especially in small agricultural areas.