## 3 Results and Discussion
While the temporal pattern of the Kcp_4 (high bare cover) area closely follows the Kc pattern, the Kcp_4 (high tree cover) resembles that of the corresponding NDVI, except for the magnitude. Note that Kcp_4 is generated using Equation 7 to calculate the threshold NDVIIo of 0.17, while Kcp_3 used the same NDVIIo of 0.3 as in the case of Kcp_1 and Kcp_2 in Figure 2. The main reason for this difference is that the maximum NDVI for NDVI_4 is less than 0.4, triggering the use of Equation 7 to estimate NDVIIo. The cutoff value of 0.4 is established arbitrarily from visual inspection of several plots to handle sparsely vegetated areas. The peak NDVI is used as a measure of vegetation density. In this study, the peak NDVI magnitude was strongly correlated (r \(>\) 0.89) with the bare area percentage. From the examples of Figures 2 and 3, NDVI_4 has the lowest peak NDVI (\(<\) 0.3) with the highest bare percentage (42%), while NDVI_3 has the highest peak NDVI (\(>\) 0.65) with a bare percentage of 0.0%. Figure 4 show the spatial distribution of seasonal (1 May through 30 September) ETa for the generic cereal crops generated using the VegET model for 2005 and 2006, respectively, at 5-km resolution. Generally, it can be observed that the eastern United States has a higher ETa than the western United States in both years, which can be attributed to higher rainfall and vegetation cover. More important for decision makers is that the VegET output shows a gradual subcounty spatial variability and a dramatic year-to-year variability in some places. For example, the 2005 ETa (Figure 4) shows lower ETa (drought) in Illinois, Pennsylvania, and North Carolina with seasonal ETa values as low as 300 mm. By contrast, in 2006, the drought was located in the Dakotas, Oklahoma/Kansas, and Alabama/Georgia. These drought incidences have been reported by various organizations. For example, Illinois reported the second driest March-August (2005) on record ([[http://www.ncdc.noaa.gov/oa/climate/research/2005/aug/st011](http://www.ncdc.noaa.gov/oa/climate/research/2005/aug/st011) dv00 pcp200508.html]([http://www.ncdc.noaa.gov/oa/climate/research/2005/aug/st011](http://www.ncdc.noaa.gov/oa/climate/research/2005/aug/st011) dv00 pcp200508.html)). Furthermore, preliminary analysis with crop yield data has shown a strong correlation between seasonal ETa and wheat yield in South Dakota, where ETa has explained 60% of the spatial yield  
Figure 4: Seasonal VegETa ETa (May–September) maps for 2005 **(a)** and 2006 **(b)**. Kp_sites refer to locations where the Kcp (B1 – B42) and AmerixFlux (SD, AZ) latent flux data were extracted. variation in 2005 and 2006. The yield decrease from 2005 to 2006 was correctly identified in most counties in South Dakota [23]. It should be pointed out that the relatively low ET in the wet part of the northwest of the US can be explained by two important reasons: 1) the accumulation period for the ET map is between May and September, a generally dry time for the region and 2) the VegET model only monitors rainfed systems and does not take into account snowmelt sources that supply moisture to the natural vegetation and irrigation systems during the dry period. Although the VegET model was run assuming a generic cereal crop over the landscape, the relative ETa spatial distribution will also apply to other natural vegetation types that obtain their water from rainfall since the maximum crop coefficient value for cereal crop and natural vegetation such as grassland is comparable for similar ground cover fraction. The low ETa for the western part of the United States is mainly a result of the shortage of precipitation and thus the lack of adequate soil water, which affects the Ks factor in Equation 2. From the cereal crop assumption covering the whole modeling unit, these seasonal ETa estimates, despite their low value in the arid and semiarid west, are probably exaggerated on the high side because of the assumption of Kcmax for the entire modeling cell.