Comparison of split window algorithms for land surface temperature retrieval from NOAA-AVHRR data

文献类型： 外文期刊

第一作者： Qin, ZH

作者： Qin, ZH;Xu, B;Zhang, WC;Li, WJ;Chen, ZX;Zhang, HO

作者机构：

关键词： split window algorithm;land surface temperature;NOAA-AVHRR

期刊名称：IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET

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年卷期： 2004 年

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收录情况： SCI

摘要： Land surface temperature (LST) retrieval froni NOAAA-AVHRR data is mainly through so-called split window algorithms. During the last 20 years 17 split window algorithms has been published. These algorithms call he grouped into four categories: emissivity-dependent models, two-factors model,. complicated models and radiance model. hi this paper we intend to compare these split window algorithms in terms of their computation and accuracy. Two methods are used for the comparison: ground datasets aild simulation datasets. Results from comparison shows that different algorithms have different performances under different situations. For simulation datasets. the algorithms of Qin et al. and Sobrino et al. are the best. The average root mean square (RMS) error of the two algorithms is less than 0.3degreesC. The algorithms of Fran a aild Cracknell. Prata and Uliverir et al. also have very low RMS errors (0.5-0.7degreesC). Results from comparison with ground datasets indicates that the algorithms of Qin et al. and Sobrino et al. are among the best for the dataset Without precise in situ atmospheric water vapor contents. These algorithms are able to provide LST retrieval with average RMS error less than 1.9degreesC for the 3361 measurements of the two Australian sites. An obvious contrast to the generally higher RMS error for the dataset is the much lower RMS error of the algorithms for the intensive experiments with precise in situ atmospheric water vapor contents. Based oil the above two methods for comparison, it can be concluded that. comprehensively, the algorithm of Qin et al. is the best alternative for LST retrieval from AVHRR. followed by Sobrino el al.. Franca and Cracknell. and Prata when data are available to estimate both emissivity and transmittance.

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