Brief description
This collection aims to provide land surface temperature (LST) data for benchmarking algorithmic refinements of spatiotemporal fusion approaches. It contains LST data generated through MODIS-Landsat fusion for 12 OzFlux regions across Australia during 2013-2021 (and Himawari-Landsat fusion for 3 OzFlux regions within southeast Australia during 2016-2021). The area of each region is 100 × 100 km. The spatial resolution is 100 m and the temporal frequency is daily (around 10:30 am local solar time). This collection also provides the MODIS (and Himawari) and Landsat data that were used as inputs into the fusion, as well as an independent LST collection from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) that was employed for cross-platform comparison. Several important updates within this version: - Updated the site description file with climate, land cover, and monthly emissivity values for each site. - Updated the OzFlux LST data using a new strategy. This strategy does not consider daylight saving time and does explicitly claim the 'seconds' timestep in the TOI (Time of Interests). Compared to the strategy used in our RSE paper, this strategy is expected to better coincide with the satellite overpass time. - Added MOD11A1 LST and viewtime data for the Australian continent during 2013-2021, with their native resolution of 1 km. Users can just simply clip the data to their targetted study areas. - Added the Himawari-Landsat fusion LST data for 3 OzFlux regions within southeast Australia during 2016-2021. The data format is identical to the MODIS-Landsat fusion LST data.Lineage
** Please refer to the GitHub repository (https://github.com/yuyi13/ubESTARFM) for algorithm details and citation information. ** The LST data was generated using a variant of a well-known spatiotemporal fusion algorithm, referred to as the unbiased Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ubESTARFM). Independent validation shows that ubESTARFM LST has an unbiased root mean squared error (ubRMSE) of 2.57 K and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11,290 observations at the 12 sites, both of which are better than those calculated for its baseline version (ESTARFM), being an ubRMSE of 3.80 K and R of 0.92. When spatially compared to ECOSTRESS LST, ubESTARFM LST has an ubRMSE of 2.00 K and R of 0.70 over 43 near clear-sky scenes, while ESTARFM LST has an ubRMSE of 2.68 K and R of 0.59. A further assessment underscored the potential of ubESTARFM for application using LST data acquired from geostationary platforms (e.g., Himawari-8), with a mean ubRMSE (R) of 2.22 K (0.97) against in-situ LST over 1327 observations at 3 sites from southeast Australia at 00:30 GMT.Data time period: 2013-01-01 to 2021-12-31
Subjects
Bias correction |
Earth Sciences |
ECOSTRESS |
Engineering |
ESTARFM |
Earth System Sciences |
Geomatic Engineering |
Himawari |
Land surface temperature |
Landsat |
MODIS |
Other Earth Sciences |
Photogrammetry and Remote Sensing |
Spatiotemporal fusion |
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Identifiers
- DOI : 10.25919/RRPG-M948
- Local : 102.100.100/486764
