## 2 Review of Existing Studies
### Local Scale Studies
These investigations recognized that satellite data provided a cost-effective method of obtaining multiple images during the growing season, an important consideration for identifying irrigation. Initially, traditional photo interpretation methods were used on Landsat color composite images from multiple dates to draw boundaries of irrigated fields by hand. For visual interpretation, particular attributes of satellite data that proved useful were spectral availability in several channels including near-infrared, and temporal availability of imagery at low cost. Archival image data from multiple years also proved useful because newly irrigated croplands showed a color signature distinct from those previously irrigated. Since manual interpretation of satellite images is performed by an analyst, it is possible to use shapes of irrigated fields as an additional feature for identification [32]. Irrigated acreage inventories based on identifying and mapping the characteristic circular pivot system as seen in photographic enlargements of Landsat imagery are prime examples of these shape-based interpretation methods in the U.S. While this form of detection is simplistic, it provides an excellent example of the practical utility of remote sensing. Visual interpretation of satellite data also includes automated approaches [29,30,38,39]. These studies benefit from the strong spectral separation of irrigated fields from harvested and fallow fields in the visible and near-infrared portions of the electromagnetic spectrum. One study [29] even demonstrated that in semiarid areas, croplands irrigated by surface water could be distinguished from those irrigated with ground water by visual interpretation of single-date Landsat imagery, although it was harder to identify different crop types. Studies involving visual interpretation of satellite data also benefit from radiometric enhancements and manipulations in the form of spectral indices. Studies that compare these data to automated techniques of image classification report superior performance of manual mapping of area estimates when compared to reported acreage [30]. For example, by visually interpreting satellite imagery, [PERSON] and [PERSON] [40] conducted an inventory of the cropland in a command area while evaluating various vegetation indices derived from Landsat-TM in India. The accuracy of the estimated irrigated crop area was within five percent of the estimate for the entire command area made by the Directorate of Economics and Statistics (DES) of the Indian government. Studies like this demonstrate that remote sensing is an effective time-saving technique to provide seasonal assessment of irrigated croplands and that the per hectare cost using remote sensing is often between one-half and one-fifth the cost of a conventional system based on air-photo interpretation supplemented by ground surveys. While considered simplistic by today's image processing standards, the early work with visual interpretation of satellite data established several facts that hold true today. First, visual interpretation by analysts is by far the most accurate method of mapping land cover, irrigation included. This is because the human brain and expertise are the best image processors. However, visual interpretation can be expensive, although it is less costly than traditional air photo interpretations of large areas. Second, the red and the NIR portions of the electromagnetic spectrum provide tremendous information for recognizing irrigation. Third, multiple images during a single growing season increase accuracy because of differential growth stage times. Finally, while irrigation identification is possible with visual interpretation, identification of crop types is less reliable unless multiple images are available from the same growing season.