GEOSTATISTICAL SOLUTIONS FOR DOWNSCALING REMOTELY SENSED LAND SURFACE TEMPERATURE
- 1Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
- 2Department of Physical Geography, University of Seville, Seville 41004, Spain
- 3School of Geography, Archaeology and Palaeoecology, Queen's University Belfast, BT7 1NN, Northern Ireland, UK
- 4Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Keywords: Image fusion, Downscaling, Geostatistics, Land surface temperature (LST), Landsat thermal imagery
Abstract. 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.