## 3 VIs as Proxies for Other Canopy Attributes
### VIs and \(f_{c}\)  
Fractional vegetation cover is also required in many SVAT and SEB models that divide the landscape into areas of vegetation and bare soil [6, 38-40]. These models use different methods to estimate carbon and moisture fluxes from vegetation and bare soil and require an estimate of the fraction of the landscape that is vegetated. For example, SEB models of ET use the difference between air temperature and soil temperature (measured by satellite sensors) to estimate bare soil evaporation, but they use the Priestly-Taylor formula for potential ET to estimate transpiration from vegetation [38-40]. Typically, a VI is used to partition the landscape into bare soil and vegetation. NDVI values for a  
Figure 1: Leaf shapes and angles in the canopies, the LAI at which 50% light absorption occurs, and k values for four riparian plant species growing in mixed stands in the Colorado River corridor (from data in [33]). scene (satellite image) are usually scaled between 0 and 1, representing bare soil (0) and 100% cover (1) to get \(\mathrm{f_{c}}\) for a given pixel or area of interest in the scene. In some models, linear scaling is used, and in others the scaling function is non-linear and is adjusted to represent the vegetation type of interest. Some models require both LAI and \(\mathrm{f_{c}}\), and these are often both estimated by VIs [39]. Ground-based information on vegetation type and canopy characteristics can be included to improve the estimates. Sometimes average leaf angles for a particular type of landscape are used to predict both \(\mathrm{f_{c}}\) and LAI from VIs [39]. Figure 2: Examples of [PERSON], NDVI and evapotranspiration (ET) of four riparian species from the Colorado River, with alfalfa for comparison. NDVI, [PERSON] and ET are from natural stands of plants at 100% cover. From data in [31-36]. Alfalfa is from a field along the Colorado River in Blythe, CA, (HayDay Farms, Inc., [PERSON] _et al._, unpublished data). [PERSON] and [PERSON] [41] showed that for partially vegetated scenes with [PERSON] in the range of 1-3, VIs were much more closely related to \(\mathrm{f_{c}}\) than to LAI of clumped vegetation, and that the relationship between NDVI and [PERSON]mathrm{f_{c}}\) was non-linear. They showed that for partially vegetated scenes of uniform vegetation type, VIs were a good measure of \(\mathrm{f_{c}}\). Other studies have also reported strong linear [42] or non-linear [43] relationships between VIs and [PERSON]mathrm{f_{c}}\) in a variety of landscape types. However, Figure 2 points out a potential practical problem in using VIs to estimate \(\mathrm{f_{c}}\) over mixed scenes. At 100% cover, different plant species may have different VIs due to differences in chlorophyll content and canopy architecture. Figure 3 shows an ETM\(+\) scene containing the same riparian vegetation and agricultural vegetation as in Figure 2 along the Colorado River. Pixels were converted to NDVI values. Fractional cover, determined on high-resolution aerial photographs, was nearly 1.0 for each plant stand highlighted in the figure. However, NDVI values varied among plant types, as expected from Figure 2, and estimates of \(\mathrm{f_{c}}\) based on NDVI differed by as much as 40% among species. Alfalfa NDVI was much higher than the riparian species, because fertilized crops tend to have higher chlorophyll content than nitrogen-limited plants in natural ecosystems, due to the large nitrogen cost of chlorophyll and chlorophyll-binding proteins. Figure 4 is a scatter plot showing the relationship between NDVI and \(\mathrm{f_{c}}\) for riparian vegetation along the Colorado River (agricultural fields excluded). High-resolution (0.3 m) aerial images were  
obtained with a DyCam digital camera having Red, Blue and NIR bands. Individual shrubs and bare soil were visible on the images, and a point-intercept method was used to visually determine \(\mathrm{f_{c}}\) on 25 images dominated by cottonwood, saltcedar or arrowweed. Then NDVI was determined for each image.