## 2 Review of Existing Studies
### Local Scale Studies
#### Digital image classification  
Although visual interpretation of satellite images is useful, more recent work has emphasized digital image classification, partly because the analysis times are shorter and costs associated with mapping are lower. Common methods include multi-stage classification [31, 41], unsupervised clustering [31, 33, 42], density slicing with thresholds [7, 43, 44], and decision tree classifications [45]. The multi-stage approach involves identification of land cover at increasingly refined categorical levels following the concept that irrigated areas are a subclass of cultivated lands, which themselves belong to vegetated landscapes. As noted, the accuracy of land-cover maps is often inversely related to their categorical detail. Thus, categorizing the landscape at each stage into more refined classes enables better identification of irrigated areas as a subclass. As in visual interpretation, digital image classification benefits from spectral transformations [7, 33, 35, 42, 44]. In particular, the Normalized Difference Vegetation Index (NDVI) proves to be indispensible for identifying irrigated areas in local scale studies, in part because of differential spectral response between irrigated and non-irrigated fields. Common use of NDVI would include direct insertion into a classification algorithm as an input feature. Thresholds are set to distinguish productive fields from non-productive and non-irrigated areas, and also multi-temporal NDVI features such as the range and maximum NDVI. When multiple NDVI observations are available from the same growing season for several years, it is also possible to identify crop types as well as the changes in irrigated areas. Multi-temporal data availability has also proved to be very useful in identifying irrigated areas. When using data from multiple time periods, the discrimination procedure is based on the different spectral responses of crops according to their phenological evolution. A number of studies have demonstrated that using spectral information from two consecutive seasons in a crop-year is sufficient to identify the irrigated crops, especially when only a few are dominant. However, for each season, the estimates require multiple images [36]. This is because single-date analysis in visible cropping intensity often does not take into account planting dates that vary from year to year. Therefore, multi-temporal analysis has greater potential to define irrigated areas [46]. Ultimately, classification results are conditional upon the temporal and spatial variability of the spectral signature of the land cover type in question, so suitable images must be available for the temporal approach to provide a complete inventory of all irrigated fields in a study area. It is also possible to map irrigation with a single image during the peak irrigation season as long as its peak can be identified using a time-series of observations. One technique is to use coarse spatial resolution time series data to select the ideal time period where irrigation information is maximized and then acquire high resolution data to do the actual mapping at local and regional scales [7]. The review of local scale studies suggests that both methods baed on both visual intepretation and statistical and non-parametric classifications of image data work equally well in identifying irrigated areas. In general accuracies better tht 85 percent have been achived with both approaches. In terms of spectral bands of importance, near-infrared combined with at least one visible light containing band have consistently produced higher classification accuracies than mapping applications that do not include these bands. It is also possible to map irrigation using single image NDVI data as long as the study area contains only a few classes to be distinguished. For example, these forms of classifications were more successful in mono cropped areas like the US mid-west when compared with fragmented irrigated landscapes of Asia and were quite futile in highly mixed cropping in Africa.