Towards large area application of remotely sensed surface temperature

Studies were conducted in the summers of 1989 and 1990 with a general objective of developing procedures to help utilize remote sensing for measurement of surface temperature over large areas for agricultural and/or meteorological applications. Mid-day temperature measurements were made over several plots of prairie vegetation with varying percentages of vegetative cover and background conditions. The deviation of the composite temperature from the canopy temperature as a function of view zenith angle was investigated and found to be linearly related (r = 0.98) to the gap distribution in the canopy and exponentially related (r = 0.99) to the Leaf Area Index divided by the cosine of the view zenith angle (LAI/cos$\Theta$). Models which predict the canopy temperature as a function of the composite temperature, view angle and gap distribution or LAI were developed. Good agreement was observed between the theoretical and measured values (RMSE of 0.54 and 0.56). The problem of obtaining the representative composite temperature of surfaces with incomplete vegetative cover for use in radiation/energy balance studies was also studied. It was found that the mean of all the composite temperature readings taken from different azimuths and view zenith angles best represented the composite temperature of surfaces with incomplete cover. Mean composite temperature can be obtained by viewing the surface near the 40$\sp\circ$ view zenith angle. Temperatures obtained from view zenith angles other than 40$\sp\circ$ overestimated/underestimated the mean composite temperature. Models that predict the mean composite temperature as a function of view angle and gap distribution were developed. Good agreement was found between the theoretical and the observed values (RMSE = 0.39). Models to estimate gap distribution in the canopy from directional reflectance measurements were developed. The models permit the prediction of canopy temperature and mean composite temperature using remotely-sensed information as a primary input. Results of the study suggest that the ratio of the reflectance in channel 4 (760-900nm) to the reflectance in channel 3 (630-690nm) of the Barnes-MMR may be used to estimate the gap distribution in the plant canopies (r = 0.99)