This study addresses the fundamental science question of modeling land surface temperature (LST) by combining the field experiment, spatial interpolation, and satellite thermal infrared remote sensing. Landsat thermal data were applied to derive surface emissivity and brightness temperature. Field-observed temperatures were used to develop algorithms for predicting air temperature profiles in a forest canopy. The accuracy of LST estimates for the study site was significantly improved by calibration using satellite-derived surface emissivity. The results showed that split-window method can be used to estimate LST using brightness temperatures derived from Landsat thermal data. Twenty-four polynomial models were developed using field observations to simulate and predict the air temperature profiles in a forest canopy at any hour during a summer day. This study demonstrated that the combination of satellite thermal remote sensing, spatial interpolation, and field empirical models is capable of obtaining accurate LST from Landsat data and predicting air temperature profiles in forest canopy.

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