## 3 Emerging Patterns from Existing Studies
### Spatial resolution requirements
The sensor observation in any grid cell is only partially derived from the location of the cell, with the average overlap between observations and their grid cells being less than 30 percent. This mismatch between grid cells and observations has important implications for the use of reference data for the validation of MODIS products or the training of MODIS algorithms. When generating multi-date composites, gridding artifacts also introduce bias when spectral compositing criteria are used. The band-to-band registration for the different spatial resolutions of gridded MODIS data can be poor if the different resolutions of data are gridded before aggregation. In all cases it is imprecise to characterize the subpixel properties of the coarser resolution bands using the finer resolution bands due to poor correspondence in the areas from which the observations are derived. All of the band-to-band registration problems are minimized when the MODIS data are aggregated to coarser resolutions. When validating algorithm accuracy, data on the observation dimensions and the offsets between the grid cell and the observation should be included to ensure the quality of validation results. If this information is not available, MODIS data should be aggregated to coarser resolutions to improve the correspondence between the location of observations and grid cells. Although the choice of remote sensing measurements today is from a wider variety of sensors than at any time in the past, the selection of a sensor for irrigation mapping is still primarily dictated by the geographic range of the study. For large areas such as continents, the primary sources of data have typically been coarse resolution sensors such as AVHRR, MODIS, MERIS, and SPOT VEGETATION with wide area coverage [24, 65, 66, 67]. Similarly, data from high spatial resolution sensors like Landsat and SPOT have been used in support of local and regional scale applications that require increased spatial detail. What is less known, however, is the magnitude of errors incurred in irrigated area estimates as a function of the spatial resolutions of different sensors. Previous studies show that while coarsening the spatial resolution clearly leads to a loss of spatial detail [68], the magnitude of errors in area estimation as pixels increase in size is dependent on the spatial structure of the landscape [69, 70]. _Spectral data requirements_  
Remote sensing of irrigated lands over different geographic regions involves significant challenges, first in selecting spectral bands or indices that contain the maximum amount of irrigation-related information, then in relating this information to complex forms of irrigation presence. For instance,while satellite-derived indices are extremely useful in detecting vegetated areas in agricultural lands, the cause of spatial and temporal variability in biomass, whether as a result of rainfall or irrigation, is much less straightforward, hence more difficult to determine. A further complication may arise when only supplemental irrigation is practiced [71]. While, the characteristic shape of the fields where center pivot irrigation is practiced is obvious, this visual discrimination is neither evident nor can it easily be incorporated in automated image classification. To overcome these challenges the characteristic attributes of irrigated lands, especially those characteristics that are observable with remotely sensed measurements must first be identified. One such attribute is related to vegetation \"greenness.\" There is an overwhelming consensus that the NDVI is an important tool to monitor vegetation, including irrigated lands [65,72-75]. Studies of agricultural lands have shown that in many semi-arid regions, NDVI alone is a good indicator of irrigation [30,33,36,37], as well as irrigation status [35], crop condition [76] and large-scale vegetation dynamics [77].