GEOSTATISTICAL SOLUTIONS FOR DOWNSCALING REMOTELY SENSED LAND SURFACE TEMPERATURE

Wang, Q.; Rodriguez-Galiano, V.; Atkinson, P. M.

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

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Wang, Q. / Rodriguez-Galiano, V. / Atkinson, P. M.: GEOSTATISTICAL SOLUTIONS FOR DOWNSCALING REMOTELY SENSED LAND SURFACE TEMPERATURE. 2017. Copernicus Publications.

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