A Comparative Study of Algorithms for Estimating Land Surface Temperature from AVHRR Data


A knowledge of land su$ace temperature (LST) is strongly required for many applications, notably agrometeorology, climate, and environmental studies. Satellite remote sensing in the infrared provides an interesting alternative for the global and continuous measurements of this parameter. Two main problems arise in the use of remote sensing data for evaluation of LST. These are the atmospheric effect and the land emissivity eflect. Diflerent algorithms have been developed to correct these two efiects. In this work we present the results of a comparative test of LST algorithms. We have used AVHRR images corresponding to a flat homogeneous region characterized by the presence of natural grassland with patches of bare soil. The images cover difierent seasons, thus allowing for changes in su$ace emissivity due to changes in vegetation cover. The results show that the Ulivieri et al. algorithm and the Price algorithm are statistically indi,stinguishable, according to the Kolmogorov-Smirnov test. These two algorithms provide LST estimates close to su7face temperature used as ground data. We have tested also the algorithm proposed by Kerr et al., which use,s a ‘surrogate” for the emissivity, the NDVI based on the visibleand‘ near-infrared radiances measured by the AVHRR j&s two channels. The use of this approach provides results &se to the Ulivieri et al. algorithm. Thus .suggesting the convenieme of this method that does not require previous emissivity estimates. Alternative formulations for the vegetation index has been also tested with the Kerr et al. algorithm, but the be.st results correspond to the use (If NDV7. A .study of the error propagation in the different algorithms due to errotx in land sur-

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@inproceedings{Vhquex2003ACS, title={A Comparative Study of Algorithms for Estimating Land Surface Temperature from AVHRR Data}, author={D. Poxo Vhquex and Francisco Jos{\'e} Olmo and L. Alados Arboledas}, year={2003} }