Quantification of the Scale Effect in Downscaling Remotely Sensed Land Surface Temperature

Most current statistical models for downscaling the remotely sensed land surface temperature (LST) are based on the assumption of the scale-invariant LST-descriptors relationship, which is being debated and requires an in-depth examination. Additionally, research on downscaling LST to high or very high resolutions (~10 m) is still rare. Here, a simple analytical model was developed to quantify the scale effect in downscaling the LST from a medium resolution (~100 m) to high resolutions. The model was verified in the Zhangye oasis and Beijing city. Examinations of the simulation datasets that were generated based on airborne and space station LSTs demonstrate that the developed model can predict the scale effect in LST downscaling; the scale effect exists in both of these two study areas. The model was further applied to 12 ASTER images in the Zhangye oasis during a complete crop growing season and one Landsat-8 TIRS image in Beijing city in the summer. The results demonstrate that the scale effect is intrinsically caused by the varying probability distribution of the LST and its descriptors at the native and target resolutions. The scale effect depends on the values of the descriptors, the phenology, and the ratio of the native resolution to the target resolution. Removing the scale effect would not necessarily improve the accuracy of the downscaled LST.

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