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Vol. 164, Issue 2, February 2014, pp. 256-264




Retrieval of Land Surface Component Temperature by Particle Swarm Optimization Algorithm

1 Zhenhua LIU, 2 Manqin Hu, 3 Qiaoyi Chen, 4 Yueming Hu * WANG Lu

1, 2, *, 4 College of Information, South China Agricultural University, Guangzhou 510642, China

3 Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510642, China

* Guangzhou Institute of Geochemistry, Chinese Academic of Science, Guangzhou, 510642, China

* Graduate University of Chinese Academic of Science, Beijing 100039, China

* Guangdong Province Land Use and Remediation of the Key Laboratory, Guangzhou, 510642, China

E-mail: 1 grassmoutain@163.com, 2 cqy3929198@21cn.com, 3 985354903@qq.com, 4 ymhu163@163.com, *selinapple@163.com


Received: 29 October 2013 /Accepted: 27 December 2013 /Published: 28 February 2014

Digital Sensors and Sensor Sysstems


Abstract: The temperature of the individual components can differ significantly, introducing errors in the quantity estimations by remote sensing technique. Because the measured radiation by these sensors can be an aggregation of radiation emitted by the different canopy components, the objective of this research was to create an inversion scheme to retrieve three component temperatures: vegetation, sunlit soil and shade soil temperature by Particle swarm optimization algorithm in the YingKe wheat study area. Given Aster spatial resolution varies with wavelength: 15 m in the visible and 90 m in the thermal infrared (TIR), area ratios of components in the pixel is acquired by the optical part of the spectrum to improve component temperature retrieval precision. Comparing with field measured data, the results showed that comparing simultaneous field data, the error range of simulated temperature under condition of considering thermal radiation and reflectance data was 1.5271 %-9.58 %. There for, the retrieval method for land Surface Component Temperature by Particle Swarm Optimization Algorithm is feasible.


Keywords: Component temperature, ASTER data, Particle swarm optimization algorithm.


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