Near-Surface Air Temperature Estimation From ASTER Data Using Neural Network Kebiao Mao , Huajun Tang, Xiufeng Wang , Qingbo Zhou, Daolong Wang, An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near-surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM-NN can be used to estimate near surface air temperature from ASTER data. Kebiao Mao , Huajun Tang, Xiufeng Wang , Qingbo Zhou, Daolong Wang, Near-Surface Air Temperature Estimation From ASTER Data Using Neural Network , International Journal of Remote Sensing,2008, 29(20): 6021-6028. PDF download(点击下载) 相关连接 ： http://www.sciencenet.cn/blog/user_content.aspx?id=230867
A Neural Network Technique for Separating Land Surface Emissivity and Temperature from ASTER Imagery Kebiao Mao , Jiancheng Shi, Huajun Tang, Zhao-Liang Li, Xiufeng Wang, Kunshan Chen, Abstract Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11, 12, 13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11, 12, 13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11, 12, 13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated. Kebiao Mao , Jiancheng Shi, Huajun Tang, Zhao-Liang Li, Xiufeng Wang, Kunshan Chen, A Neural Network Technique for Separating Land Surface Emissivity and Temperature from ASTER Imagery, IEEE Trans. Geosci. Remote Sensing, 2008, 46(1), 200-208. PDF download(点击下载） 相关连接： http://www.sciencenet.cn/blog/user_content.aspx?id=230867
标签: ASTER
