The relationships between spectral reflectance in the Landsat Thematic Mapper (TM) bands and grass canopy variables were evaluated using in situ remote sensing techniques. Reflectance data were collected from experimental plots of annual ryegrass (Lolium multiflorum) and tall fescue (Festuca arundinacea) using a Barnes Modular Multiband Radiometer (MMR). The canopy variables used were canopy height, canopy cover, total wet biomass, total dry biomass, aboveground plant water, and leaf area index. Statistically significant relationships were found between the spectral bands and the canopy variables. Inverse relationships in the visible (TM1, TM2, TM3) and middle infrared (TM5, TM7) regions were related to spectral absorption by plant pigments (visible) and moisture within plant tissue (middle infrared). Direct relationships in the near infrared (TM4, MMR5) were attributed to enhanced reflectance resulting from spectral scattering. Overall, no one spectral band was found to be superior in all situations, but TM5 consistently showed the lowest correlations with the canopy variables. Data sets were collected during three annual ryegrass phenological stages: early stem extension (June), anthesis (July), and senescence (August). The most significant correlations between reflectance and the canopy variables were found for the June data. High levels of biomass in July and plant senescence in August adversely affected the spectral reflectance/canopy relationships. Data from the tall fescue plots were obtained from a wide range of total wet biomass levels (16.5 - 1677.9 g/m²). The asymptotic limits, or the biomass range for which the reflectance could be used to predict changes in the canopy variables, were studied. The reflection asymptotes were nearly twice as high for the near infrared (TM4) as for the visible and middle infrared bands (TM1, TM2, TM3, TM7). The use of band ratios and normalized difference transformations did not consistantly increase correlations of spectral reflectance with the grass canopy variables. Logarithmic transformations of both the spectral bands and the canopy variables were successfully used to linearize the spectral reflectance/canopy regression functions. Redundancy was found among the absorption bands (TM1, TM2, TM3, TM7) and between the near infrared bands (TM4, MMR5). Principal component transformations were utilized to eliminate these spectral band redundancies. The seven spectral bands were reduced to two principal components, while maintaining nearly all of the variability found in the original bands
