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Remote Sens. 2015, 7(5), 6489-6509; doi:10.3390/rs70506489

Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin

1,2,* , 1,2,* , 1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Joint Center for Global Change Studies (JCGCS), Beijing 100875, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Authors to whom correspondence should be addressed.
Academic Editors: Xin Li, Richard Gloaguen and Prasad S. Thenkabail
Received: 12 December 2014 / Accepted: 19 May 2015 / Published: 22 May 2015
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This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which were acquired simultaneously. This dataset provided the range of spatial heterogeneities of land surface necessary for the study, which was quantified by the dispersion variance. The LST scaling problem was studied by comparing the remotely sensed LST products in two ways. First, the LST products calculated in the distributed method and the lumped method were compared. Second, the airborne and satellite-based LST products derived from the TES algorithm were compared. Four upscaling methods of LST were used in the process. A scaling correction methodology was developed based on the comparisons. The results showed that the scaling effect could be as large as 0.8 when the spatial resolution of the TASI LST data was coarse. The scaling effect increases quickly with the spatial resolution until it reaches the characteristic scale of the landscape and is positively correlated with the spatial heterogeneity. The first two upscaling methods denoted as Methods 1–2 can upscale the LST more effectively when compared with the other two scaling methods (Methods 3–4). The scaling effect for the ASTER data is not notable. The comparison between the TASI and ASTER data showed that they were highly consistent, with a root mean square error (RMSE) of approximately 0.88 K, when the pixels were relatively homogeneous. When the spatial heterogeneity was significant, the RMSE was as large as 2.68 K The scaling correction methodology provided resolution-invariant results with scaling effects of less than 0.5 K. View Full-Text
Keywords: land surface temperature; TES; scaling problem; spatial heterogeneity; dispersion variance land surface temperature; TES; scaling problem; spatial heterogeneity; dispersion variance

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hu, T.; Liu, Q.; Du, Y.; Li, H.; Wang, H.; Cao, B. Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin. Remote Sens. 2015, 7, 6489-6509.

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