# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 1 Introduction  
Irrigated agriculture accounts for more than 70 percent of the water withdrawn worldwide from lakes, rivers, and aquifers [1], in many countries even for more than 90 percent, thus being the greatest human disturbance in the terrestrial water cycle [2]. Even though only 17 percent of global crop land is irrigated, these lands already produce 40 percent of the world's food [3]. Since the global demand for food will further increase in the next decades due to population growth and dietary shifts, keeping pace with this growing demand will require even further expansion and intensification of irrigated agriculture [4; 5]. At the same time, urbanization pressure, market volatility, and climate variability associated with an increased recurrence and intensity of drought periods [6; 7; 8] have led to a call for improved water management involving reducing water withdrawals and increasing irrigation efficiency. Meeting these ends will require: (1) modeling irrigation water requirements at the global scale [9], (2) assessing irrigated food production [10], (3) quantifying the impact of irrigation on climate [11], river discharge [12], and groundwater depletion [13; 14], and (4) building plans for an optimal water resource allocation so that managers can accurately account for water use [15; 16; 17]. Central to all these steps is accurate knowledge about the spatial extent of irrigated lands, the amount of water applied as irrigation, and the timing when irrigation is applied. Obtaining this information with a high quality and on a global scale, however, is not trivial. Many wells are located on private lands, are used for small-scale agriculture, and are often illegal due to the reluctance of farmers to drill wells with official permission to avoid regulations [18]. Consequently, the overwhelming majority of agricultural water use worldwide--both from groundwater and surface water--remains unmeted [19]. Earth observing satellites provide the unique ability to monitor various processes strongly related to irrigation, e.g., soil moisture, land cover, or vegetation activity, quasi-globally and frequently. In the last decade, there has been a significant interest in using satellite Earth observations to retrieve information on irrigation extent, frequency, and amount. This has led to a proliferation of studies using new satellite platforms able to measure surface variables with relatively high spatial resolutions (i.e., less than 1 km) [20]. Many studies have also attempted to include irrigation modules in land surface models or ingest satellite observations to correct for the lack of irrigation representation. The main objectives of this study are:  
* To provide a comprehensive review of studies that have attempted to map irrigation, more specifically (i) where irrigation occurs, i.e., mapping methods, (ii) when irrigation occurs (frequency of irrigation), i.e., timing methods, and (iii) how much irrigation is applied. Reviewed studies include: (i) methods based on ground measurements and local statistics, (ii) remote sensing-based methods including multispectral, microwave, and gravimetric measurements, and (iii) methods based on modeling and data assimilation. * To report the results of a survey about user requirements on irrigation management in small-scale farming that targeted ten companies and organizations representative of the Mediterranean area (Spain, Italy, and France). * To confront the review of irrigation mapping studies with the user requirements survey in order to assess whether current remote sensing and modeling-based irrigation products can meet the requirements of actors working in the field of water resource management and agriculture. * To provide recommendations and guidelines for the future development of improved irrigation mapping techniques to help us meet the demands of farmers and stakeholders. The manuscript is organized as follows: Section 2 lists previous efforts to map irrigation using national statistics and ground observations.