A Modified Single-Channel Algorithm for Estimating Land Surface Temperature from UAV TIR Imagery

Thermal Infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs) provide low-cost, high spatial and temporal resolutions TIR data. This paper develops a novel single-channel algorithm adaptive to UAV TIR data. Atmospheric parameters were estimated using atmospheric reanalysis data and surface emissivity were acquired by the Portable Fourier transform thermal infrared spectrometer (102F). Then the effective atmospheric transmittance and emissivity were calculated owing to the broad spectral range of the UAV TIR channel. The results were validated using in-situ land surface temperature (LST) derived from SI-111 radiometers at an area of Baotou City, China. The root mean square error (RMSE) were 2.31K on 24 September and 1.82K on 26 September, which indicates that the proposed algorithm is a promising method to estimate LST from UAV TIR images.