Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3105873
Keywords
Spatial resolution; Land surface temperature; Data models; MODIS; Predictive models; Spatiotemporal phenomena; Geostationary satellites; Disaggregation; diurnal temperature cycle (DTC); land surface temperature (LST); spatiotemporal fusion
Categories
Funding
- Science and Engineering Research Board, Department of Science and Technology, Government of India [SRG/2019/000372]
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This study tested the application of simplified spatial disaggregation models across the temporal domain to obtain higher spatial resolution land surface temperature observations and capture diurnal temperature changes. The results indicated that the DisTrad model performed well in capturing spatiotemporal patterns, while the NL-DisTrad model showed inconsistency.
The thermal infrared (TIR) sensors aboard geostationary satellites provide multi-temporal land surface temperature (LST) observations for characterizing the diurnal temperature cycle (DTC) at a coarse spatial resolution. This study aims to test if simpler spatial disaggregation models developed to improve the spatial resolution of LST data can be applied across the temporal domain to obtain LST at relatively higher spatial resolution and to capture the DTC. LST from geostationary satellites Kalpana-1 and INSAT-3D were downscaled from 8- and 5-km spatial resolutions respectively to 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired during its daytime overpass. Two disaggregation models, DisTrad and NL-DisTrad and a data fusion model spatiotemporal integrated fusion model (STITFM) were selected for the purpose. The downscaled LSTs were compared against the MODIS LST observations from Terra nighttime and Aqua daytime overpasses in addition to in situ data at four sites in India. The results indicated that the DisTrad model (RMSE: 3.61 K, R-2: 0.95) was able to capture the spatiotemporal patterns with an accuracy comparable to that of the STITFM model (RMSE: 3.22 K, R-2: 0.96). However, the NL-DisTrad performed inconsistently, leading to higher errors.
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