## 3 Emerging Patterns from Existing Studies  
### Spatial resolution requirements  
Irrigation is practiced in many forms around the world and the spatial resolution requirements go hand in hand with its heterogeneous distribution. Figure 1(A-C) shows ground level photographs of several landscapes from very heterogeneous small plot sizes in SE Asia to vast expanses of irrigated fields in the U.S. The purpose of these pictures is to illustrate the great variation in irrigation presence from subsistence farming to major enterprises. This diversity directly affects our ability to map irrigation with remote sensing. In general, to map irrigated croplands accurately, the spatial resolution of the sensor should be at or below the size of the fields of interest. However, enhanced information brought about by high spatial resolution data does not tell the whole story--automatic identification of irrigated lands also relies heavily on temporal availability of information. If the low spatial resolution option is chosen, then frequent observations are available, but the low resolving power of pixels would lead to the mixed pixel problem. Consider a single AVHRR 10 km pixel, which encompasses an area of 10,000 hectares, while a MODIS 500 meter pixel covers 25 hectares. Obviously in many cases only a fraction of a coarse resolution pixel falls in a particular land cover category, such as irrigation. We also know that spatial resolution of a sensor is supply driven, that is, spatial resolution is fixed because of sensor design and flight characteristics. Research has shown that the finer the spatial resolution, the greater the accuracy of irrigated area class designations [7, 58]. For example, a recent study of the irrigated areas of the Ogallala Aquifer in the United States based on Landsat imagery and 364 ground truth points has shown that 18.5 percent of the areas actually irrigated were not included (errors of omission) in the GIAM dataset at 10 km resolution but 20.4 percent of the non-irrigated areas were included as irrigated areas (errors of commission) [59]. The reason for the errors of omission was that at coarser resolutions, smaller, more fragmented patches of irrigated croplands could not be delineated. In contrast, the errors of commission resulted because the large pixel sizes of coarse resolutions can at times map patchy non-irrigated areas that surround irrigated areas as if they too were irrigated. In either case, the need for finer spatial resolution is essential to resolve the confusion. To overcome the mixed pixel issue present in low spatial resolution data, several researchers have developed schemes to \"unmix\" land cover classes--including irrigation--from the remotely sensed signal to estimate the sub-pixel areas of irrigated lands. Two approaches are common in the literature to estimate these irrigated area fractions. The first is a two-stage approach and determines the subpixel composition of a class label following initial categorical labeling with a classification algorithm [7, 58]. For example, [PERSON] _et al._[58] developed innovative methods to compute sub-pixel areas from coarse resolution data. They used the global GIAM dataset at 10 km resolution based on AVHRR data and the irrigated area map for India at 500 m based on MODIS data to test and verify their methods. The sub-pixel irrigated areas from coarse-resolution satellite sensor data were estimated by multiplying the full pixel irrigated areas with irrigated area fractions. The results indicated that the sub pixel areas provided significantly better correlation with the national statistics than full pixel areas, which were also shown to grossly overestimate reported areas. Figure 1: Near ground photographs of irrigation practices in three different irrigated locations. (A) Irrigated rice paddies with a bore well in the background in north-central India.