Retrieval of Land Surface Component Temperature by Particle Swarm Optimization Algorithm

Sensors & Transducers

WANG Lu¹  Manqin Hu²  Zhenhua LIU²  Yueming Hu²  Qiaoyi Chen³ 

[1] Guangzhou Institute of Geochemistry, Chinese Academic of Science, Guangzhou, 510642, China;College of Information, South China Agricultural University, Guangzhou 510642, China;Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510642, China;

关键词: Component temperature;    ASTER data;    Particle swarm optimization algorithm.;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown