## 2 Review of Existing Studies
### Regional Studies  
The most common method of mapping irrigated areas at regional scales is to use medium to coarse spatial resolution data from many time periods in the form of time series analysis. The image-based monitoring in the time domain relies heavily on the monitoring of crop evolution during the growing season by means of original spectral bands or vegetation indices. This requires knowledge of the growth schedules of crop types common to the area. One of the most cost effective ways of monitoring irrigation in large areas is to use freely available vegetation index data from coarse resolution sensors like AVHRR and MODIS [19, 21, 47-52]. There are various ways to translate multi-temporal information to a map of irrigation. For example, [PERSON] _et al._[48] developed a paddy rice mapping algorithm that uses time series of three vegetation indices, namely the Land Surface Water Index (LSWI), the Enhanced Vegetation Index (EVI) and NDVI, derived from MODIS images. This method relies on identification of the initial period of flooding and transplanting in paddy rice fields by exploiting the sensitivity of LSWI to the increased surface moisture during this period  
It is also possible to use the results of multi-temporal vegetation indices directly in modern classification algorithms such as decision trees [20, 49, 51]. Classification accuracies in such applications range from 60 percent to better than 90 percent [20, 21, 48, 51]. Notable regional differences in irrigated areas estimated this way stem from climate factors, localized precipitation patterns, and specific cropping practices [51]. Moreover, this form of classification benefits greatly from a multi-stage approach where the land cover is progressively classified [51] and from using ancillary information on climate, surface moisture status, elevation, natural land cover cartography, and crop area and water masks [20, 21, 53]. There are indications that multi-temporal image classification using the original spectral bands in addition to the band transformations leads to better classification results when satellite data with high temporal and spectral dimensionality such as MODIS are used [21,52]. For example, [PERSON] _et al._[21] developed innovative approaches to obtain precise information on the onset, peak, senescence and duration of each irrigated and rainfed land use class. In particular, their results demonstrated that the value of the MODIS band 5 centered at 1,240 nm provided the best separability, followed by bands 2 (859 nm), 7 (2,130 nm), 6 (1,640 nm) and 3 (648 nm). Algorithms that can automatically identify the location and the number of vegetation peaks in a time series plot improve classification accuracies. For example, [PERSON] _et al._[53] developed a Peak Detector Algorithm to identify the number of vegetative peaks based on time lag between peak rainfall and peak NDVI. Their results indicate that in rainfed rice there is high correlation at a 40-50 day lag between peak rainfall and a single peak NDVI, while in irrigated rice there are multiple peaks and multiple correlations with low values for at least 90 days after peak rainfall. One of the limitations of time series analysis of coarse spatial resolution data is the imprecise irrigated area estimates, especially in locations with small cultivated plots and fragmented landscapes. In these types of environments, remotely sensed estimates are generally higher than reported estimates because of: (a) inadequate accounting of irrigated areas in reported statistics, especially minor forms of irrigation; (b) definition issues involved in mapping using both remote sensing and statistics; (c) difficulties in arriving at precise estimates of irrigated area fractions; and (d) imagery resolution [19-21,52]. One approach to overcome these issues is to use high spatial resolution data such as Landsat to calibrate irrigated area from coarse resolution observations using regression [46,49].