# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 1 Introduction  
Irrigation plays a key role in increasing crop production [1], especially in arid and semi-arid regions. It has been shown that the amount of water used for agricultural irrigation accounts for 84% of total human water consumption [2]. Approximately 40% of the world's harvest is produced on irrigated arable land, which accounts for about 20% of the total arable land area [3], 70% of which is located in Asia [4]. Crop yields are usually higher when crops are fully irrigated as compared tonon-irrigated crops that usually suffer water deficiencies [5]. In China, irrigated land occupies more than 40% of total farmland, but produces 80% of the nation's food [3]. More than two-thirds of all water use in China is from the agriculture sector. It is a widespread concern that China will face serious water shortages as its economy booms and urbanization increases. Realistic estimations of irrigation area are important for evaluating regional water and carbon cycles and ensuring food security [6; 7; 8; 9; 10]. Currently, three major methods are used to classify irrigated and rainfed areas. (1) The water resources inventory, developed by the Food and Agriculture Organization of the United Nations (FAO), has been used to map the worldwide distribution of irrigated areas using statistical data [11; 12]. Generally, inventory data cannot accurately and timely reflect the spatial distribution of irrigated and rainfed areas. Statistical data are usually based on county/regional information (or even coarser scales) and span different time intervals. Recently, the FAO released a 2010 land cover map of Afghanistan, derived from the Satellite Pour l'Observation de la Terre (SPOT), Landsat imagery and aerial photographs [13]; (2) Unsupervised classification has been widely used for the classification of irrigated and rainfed areas [14; 15]. The U.S. Geological Survey (USGS) and International Water Management Institute (IWMI) classified and mapped global irrigated and rainfed areas in the U.S. Geological Survey Global Land Cover Characterization (GLCC) and Global Irrigated Area Map (GIMA), respectively [16; 17; 18]. This method can be easily used when local information is sparse; however, it is hard to control the number of classes. In addition, the calibration and combination of each cover type after unsupervised classification is strongly affected by subjective factors; (3) Supervised classification was used by [PERSON] and [PERSON] (2008) [19] to map irrigated and rainfed areas in the U.S. with a decision tree method based on eight-day synthesis Moderate Resolution Imaging Spectrometer (MODIS) Greenness Index data (500 \(\times\) 500 m resolution). [PERSON] _et al._ (2005) [20] classified irrigated and rainfed rice in Thailand using 10-day synthesis SPOT NDVI data at a 1 \(\times\) 1 km resolution, 10-day synthesis precipitation data and ancillary data. The advantage of the supervised classification method is that training samples can be controlled; therefore, unnecessary classes can be avoided, though classification accuracy is usually significantly influenced by training samples. Since the 1980s, satellite remote sensing has been widely used for the classification of irrigated areas because it can provide a near real-time capability to dynamically monitor land surfaces on a regional scale. However, improvements in classification methods for the identification of irrigated areas are still necessary [21]. Currently, the most commonly-used satellite data are MODIS products, with a mean spatial resolution of 250 \(\times\) 250 m. Such data are generally sufficient to characterize large-scale spatial patterns, but may bring large uncertainties in arid and semi-arid regions, where irrigation is typically practiced in small and scattered areas. [PERSON] _et al._ (2014) [22] used the same growth period to calculate the time-integrated NDVI (TIN) for irrigated and rainfed wheat.