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GEOSTATISTICAL SOLUTIONS FOR DOWNSCALING REMOTELY SENSED LAND SURFACE TEMPERATURE
| Content Provider | Scilit |
|---|---|
| Author | Wang, Q. Rodriguez-Galiano, V. Atkinson, P. M. |
| Copyright Year | 2017 |
| Description | 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. |
| Ending Page | 917 |
| Page Count | 5 |
| Starting Page | 913 |
| e-ISSN | 21949034 |
| DOI | 10.5194/isprs-archives-xlii-2-w7-913-2017 |
| Journal | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume Number | XLII-2/W7 |
| Language | English |
| Publisher | Copernicus GmbH |
| Publisher Date | 2017-09-13 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Downscaling Remotely Sensed |
| Content Type | Text |
| Resource Type | Article |