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Journal of Applied Remote Sensing • Open Access

Estimation of daily average temperature using multisource spatial data in data sparse regions of Central Asia
Author(s): Mingyong Cai; Shengtian Yang; Changsen Zhao; Hongjuan Zeng; Qiuwen Zhou

Paper Abstract

The distribution of the daily average air temperature with high spatial resolution is vital for hydro-ecological applications. The air temperature usually recorded at fixed-point stations provides little distribution information and easily suffers from the scarce amount and uneven distribution of the stations in the data sparse regions. In this study, a method based on multisource spatial data was developed to estimate the spatial distribution of daily average temperature, especially for data sparse regions. In this method, the instantaneous temperature was retrieved first using the moderate resolution imaging spectroradiometer data, which was then transformed to a daily value using transformation equations. Second, the global land data assimilation system air temperature data were spatially downscaled and used to improve the data accuracy from step 1 at low temperatures. This method was applied in the Ili River basin in Central Asia, and the results were evaluated against data from two stations’ observations and in situ data from a field test site. The results showed the correlation coefficient varies from 0.90 to 0.94 and the root mean square deviation is ∼3°C , indicating the generated temperature matched the observations well. This suggests the method is an alternative for data sparse regions.

Paper Details

Date Published: 22 November 2013
PDF: 17 pages
J. Appl. Rem. Sens. 7(1) 073478 doi: 10.1117/1.JRS.7.073478
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Mingyong Cai, Beijing Normal Univ. (China)
Shengtian Yang, Beijing Normal Univ. (China)
Changsen Zhao, Beijing Normal Univ. (China)
Hongjuan Zeng, Changjiang Water Resources Commission (China)
Qiuwen Zhou, Beijing Normal Univ. (China)

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