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Remotely sensed land surface temperature (LST) downscaling is an important issue in remote sensing. Geostatistical methods have\udshown their applicability in downscaling multi/hyperspectral images. In this paper, four geostatistical solutions, including regression\udkriging (RK), downscaling cokriging (DSCK), kriging with external drift (KED) and area-to-point regression kriging (ATPRK), are\udapplied for downscaling remotely sensed LST. Their differences are analyzed theoretically and the performances are compared\udexperimentally using a Landsat 7 ETM+ dataset. They are also compared to the classical TsHARP method

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

GEOSTATISTICAL SOLUTIONS FOR DOWNSCALING REMOTELY SENSED LAND SURFACE TEMPERATURE