# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 1 Introduction  
Under changing climatic conditions, irrigation plays a significant role in agricultural production in order to meet the global food requirement. In fact, better planning of irrigation is required to fulfil the high demand of food with the increase in the global population [1; 2]. Moreover, irrigated agriculture accounts now for more than 80% of water withdrawn from rivers, lakes, and groundwater aquifers being the principal consumer of fresh water resources [3; 4; 5]. Therefore, more focus is being set on theassessment of irrigation performance for improving water management in order to achieve higher water productivity and increase the agricultural water sustainability. Accurate information on the irrigated area extent helps in managing water resources that affect global food security and in analyzing the impact of climate change on the irrigation water requirement. However, the extent and distribution of irrigated areas remain indefinite in spite of the significant impact of irrigation on food security and water resources. At a global scale, several irrigation products are available at different spatial resolutions. Existing irrigation extent maps such as the FAO (Food and Agriculture Organization of the United Nations) database (Food, 2016), the Global Map of Irrigated Areas version 5 (GMIA 5.0) (Food, 2016) and the MIRCA2000 product (Food, 2016) have been primary derived from national statistical data. The Global Rain-Fed Irrigated and Paddy Croplands (GRIPC) with spatial resolution of 500 m was produced using MODIS (Moderate resolution imaging spectroradiometer) satellite data (Food, 2016). The low spatial resolution of these products is an obstacle for using them in irrigation management especially in regards to small to medium agricultural areas. At a regional scale, several efforts have been made to map irrigated areas using remote sensing data including both optical and radar data ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). Most studies that use optical data demonstrate that irrigated/non-irrigated areas can be classified based on the difference in temporal signals of vegetation indices such as NDVI (Normalized Differential Vegetation Index), NDVI (Normalized Differential Water Index) and GI (Greenness index) between irrigated and non-irrigated areas. Among optical sensors, MODIS and Landsat have been extensively used to map irrigated areas ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2019; [PERSON] et al., 2019). For example, [PERSON] et al. ([PERSON] et al., 2017) merged the NDVI derived from MODIS at 250 m spatial resolution and used statistical data to map irrigated areas of United States of America in 2002. In their study, they assumed that irrigated crops have a higher annual NDVI peak than non-irrigated crops especially under non-optimal precipitation conditions such as drought. [PERSON] et al. ([PERSON] et al., 2019) proposed a new approach to map irrigation extent, time and frequency in an arid region located in Hexi Corridor of northwest China by merging the 30 m spatial resolution Landsat images with 250 m MODIS data and ancillary data. In their study, they used the GI to detect irrigation events during the first half of the growing season. The overall accuracy for detecting irrigation water supplements reached 87%. Recently, [PERSON] et al. ([PERSON] et al., 2019) mapped irrigated areas of northeast China by comparing the MODIS derived LSWI (Land Surface Water Index) of the agricultural areas to the surrounding natural vegetation such as forests. They assumed that the canopy moisture indicated by the LSWI is higher for irrigated crops than the adjacent forest. Thus, they mapped irrigated areas by fixing a threshold for the difference between LSWI of agricultural plots and that of the forest. However, the overall accuracy of the produced map did not exceed 77.2% with a 0.49 kappa coefficient.