## 3 Emerging Patterns from Existing Studies
### Spatial resolution requirements
The complex nature of this landscape with multiple land cover types surrounding irrigated fields illustrates the need for high spatial resolution data which may come at a cost of frequency of observations needed to map paddy fields. Image is provided courtesy of [PERSON] from McGill University. (B) A large irrigated alfalfa field in Utah (USA) and a rotating drum irrigation system. In these types of environments, the spatial requirements for irrigation mapping can be relaxed but the spectral properties of the image signal must be enhanced to distinguish irrigation. Photo courtesy of USDA NRCS. (C) A large irrigated landscape interspersed with multiple land cover types in Idaho (USA). This photograph illustrates the fact that even though many landscapes may be heterogeneous, the size of individual landscape elements (e.g., cultivated fields) determines the spatial resolution requirements of an irrigation mapping exercise. Photo courtesy of USDA NRCS. Figure 1: Cont. The second approach attempts to derive the information on subpixel composition of irrigated pixels directly from the remotely sensed signal using a pixel decomposition method. For example, the authors in [20,62] reported on the use of land surface temperature data, especially the diurnal difference (day _vs._ night) in land surface temperature, and how this may relate to irrigated area intensity. As an example, Figure 2 shows that as the difference between minimum and maximum land surface temperatures decreases, irrigated crop intensity increases due to increased modulation of radiative temperature by vegetation/moisture presence. This information can be used to directly map the intensity of irrigated areas using land surface temperature sensors at high observation frequencies. The spatial resolution dependent changes in irrigated area were also explored by [PERSON] _et al._[63] who determined how irrigated areas change with resolution (or scale) of imagery in the Krishna River basin in India. The irrigated areas were mapped using satellite sensor data at four resolutions: (a) AVHRR (10,000 m); (b) MODIS (500 m); (c) MODIS (250 m), and (d) Landsat (30 m). The proportion of irrigated areas relative to Landsat derived irrigation figures (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 meter data, (b) 93 percent using MODIS  
Figure 2: The relationship between diurnal difference (day minus night) in land surface temperature and subpixel intensity of irrigated area at a location in western US as observed by MODIS. As the irrigation intensity increases, the modulating effect of evaporation and associated cooling of the land surface becomes more pronounced. This indicates that the land surface temperature difference data which is routinely available from remote sensing could be used to map fractional coverage of irrigation using coarse resolution sensors. [PERSON] unpublished data. 500 meter data, and (c) 86 percent using AVHRR at 10,000 meters. The study also found that the precise locations of the irrigated areas were better established using finer spatial resolution data. This study suggested that \"the finer the spatial resolution of the sensor, the greater was the irrigated area,\" since at finer spatial resolutions fragmented areas are better detected. Accuracies and errors were established consistently across the four resolutions mentioned above for three classes: surface water irrigation, ground water/conjunctive use irrigation, and no irrigation. The results showed that Landsat data provided significantly higher overall accuracy (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent). Even if the satellite sensor has sufficient spatial resolution to map irrigated areas in a particular location, the pre-processing of the sensor data may distort the original resolution. For example, [PERSON] _et al._[64] showed that gridding artifacts between satellite observations and predefined grid cells strongly influence the local spatial properties of MODIS images.