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Abstract
The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11–14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11–14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11–14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.
Acknowledgement
The authors thank the following persons for their help with this study: Yu Chang Tzeng, The Centre for Space and Remote Sensing Research, National Central University, 32054 Chung-Li, Taiwan, ROC and the ASTER Science Team for providing the ASTER Spectral Library data. They would also like to thank the anonymous reviewers for their valuable comments, which greatly improved the presentation of this article. This work was supported by the NSFC (No. 40930101); the 973 Programme (Nos. 2010CB951503 and 2007CB714403) the open fund of the State Key Laboratory of Remote Sensing Science, jointly sponsored by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences and Beijing Normal University; and the open fund of the Key Laboratory of Agrometeorological Safeguard and Applied Technique, CMA.