Downscaling land surface temperatures with multi-spectral and multi-resolution images

• Published in: International journal of applied earth observation and geoinformation Vol. 18; pp. 23 - 36
• Main Authors: Zhan, Wenfeng, Chen, Yunhao, Wang, Jinfei, Zhou, Ji, Quan, Jinling, Liu, Wenyu, Li, Jing
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.08.2012; Elsevier
• Subjects: Applied geophysics; Downscaling; Earth sciences; Earth, ocean, space; Exact sciences and technology; Internal geophysics; Land surface temperature; Multi-resolution; Multi-spectral; Sharpening; Thermal remote sensing; Sharpening; Multi-resolution; Thermal remote sensing; Downscaling; Land surface temperature; Multi-spectral; Thematic Mapper; accuracy; remote sensing; variance analysis; windows; thermal emission; currents; surface temperature; spatial resolution; frame structure; errors
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2012.01.003

▸ An enhanced generalized theoretical framework (EGTF) was proposed to integrate the proxy-sharpening of multi-spectral (MS) bands and the downscaling of land surface temperatures (LSTs). ▸ The EGTF was found to be effective in downscaling LSTs through MS and multi-resolution bands. ▸ The moving-window size (MWS) in LST downscaling can be determined by the range in a semi-variance analysis of scaling factor images. Land surface temperature (LST) plays an important role in many fields. However, the limited spatial resolution of current thermal sensors impedes the utilization of LSTs. Based on a theoretical framework of thermal sharpening, this report presents an Enhanced Generalized Theoretical Framework (EGTF) to downscale LSTs using multi-spectral (MS) and multi-resolution images. MS proxy-sharpening and LST downscaling are combined under EGTF. Simulated images upscaled from Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are produced for indirect validations. Validation of MS proxy-sharpening shows that EGTF is better than the Gram-Schmidt (GS) and the Principle Component (PC) methods, yielding a lower root mean square error (RMSE) and ERGAS (erreur relative globale adimensionnelle de synthèse) and, thus, maintaining higher spectral similarity. For LST downscaling, validations show that EGTF has a higher accuracy than the Unmixing-Based Image Fusion (UBIF) method and indicate that the proxy-sharpening process improves the accuracy of downscaled LSTs. Further discussions regarding the selection of the moving-window size (MWS) demonstrate that the MWS could be determined by the range in a semi-variance analysis of scaling factor images.