中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint

文献类型:SCI/SSCI论文

作者Wu H.
发表日期2011
关键词Emissivity hyperspectral thermal infrared (TIR) land surface temperature (LST) linear constraint retrieval land-surface emissivity images separation design
英文摘要Owing to the ill-posed problem of radiometric equations, the separation of land surface temperature (LST) and land surface emissivity (LSE) from observed data has always been a troublesome problem. On the basis of the assumption that the LSE spectrum can be described by a piecewise linear function, a new method has been proposed to retrieve LST and LSE from atmospherically corrected hyperspectral thermal infrared data using linear spectral emissivity constraint. Comparisons with the existing methods found in literature show that our proposed method is more noise immune than the existing methods. Even with a NE Delta T of 0.5 K, the rmse of LST is observed to be only 0.16 K, and that of LSE is 0.006. In addition, our proposed method is simple and efficient and does not encounter the problem of singular values unlike the existing methods. As for the impact of the atmosphere, the results show that our proposed method performs well with the uncertainty of the atmospheric downwelling radiance but suffers from the inaccuracy of the atmospheric upwelling radiance and atmospheric transmittance, which implies that an accurate atmospheric correction is still needed to convert the radiance measured at the satellite level to the at-ground radiance. To validate the proposed method, a field experiment was conducted, and the results show that 80% of the samples have an accuracy of LST within 1 K and that the mean values of LSE are accurate to 0.01.
出处Ieee Transactions on Geoscience and Remote Sensing
49
4
1291-1303
收录类别SCI
语种英语
ISSN号0196-2892
源URL[http://ir.igsnrr.ac.cn/handle/311030/22892]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Wu H.. Temperature and Emissivity Retrievals From Hyperspectral Thermal Infrared Data Using Linear Spectral Emissivity Constraint. 2011.

入库方式: OAI收割

来源:地理科学与资源研究所

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