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Spectral Reflectance as a Covariate for Estimating Pasture Productivity and Composition

^a USDA-NASS, Des Moines, IA 50309
^b Dep. of Statistics, Iowa State Univ., Ames, IA 50011

View larger version (17K): [in a new window]   Fig. 1. Three sampling schemes at n = 30 density.

View larger version (12K): [in a new window]   Fig. 2. Map of plant sampling points and spectrometer covariate points.

View larger version (50K): [in a new window]   Fig. 3. Representative comparisons of kriged (OK) and cokriged (Co-K) maps for a) n = 30 grid sampling scheme, biomass, b) n = 30 triangular sampling scheme, percentage of grass and c) n = 30 random sampling scheme, percentage of legume.

View larger version (13K): [in a new window]   Fig. 4. Reduction in root mean square error (RMSE) of prediction due to cokriging as a function of the absolute correlation (r) between canopy reflectance at covariate wavebands and the three target plant variables: biomass (Bio.), percentage of grass (Grass), and percentage of legume (Leg.). Data is plotted for each sampling pattern.