Estimation of water vapor content in near-infrared bands around 1 mu m from MODIS data by using RM-NN
文献类型： 外文期刊
第一作者： Mao, K. B.
作者： Mao, K. B.;Zhou, Q. B.;Tang, H. J.;Mao, K. B.;Zhou, Q. B.;Tang, H. J.;Mao, K. B.;Zhou, Q. B.;Tang, H. J.;Mao, K. B.;Mao, K. B.;Li, H. T.;Hu, D. Y.;Wang, J.;Huang, J. X.;Li, Z. L.
作者机构：
期刊名称：OPTICS EXPRESS （ 影响因子：3.894； 五年影响因子：3.701 ）
ISSN： 1094-4087
年卷期： 2010 年 18 卷 9 期
页码：
收录情况： SCI
摘要： An algorithm based on the radiance transfer model (RM) and a dynamic learning neural network (NN) for estimating water vapor content from moderate resolution imaging spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 is used to simulate the sun-surface-sensor process with different conditions. The dynamic learning neural network is used to estimate water vapor content. Analysis of the simulation data indicates that the mean and standard deviation of estimation error are under 0.06 gcm(-2) and 0.08 gcm(-2). The comparison analysis indicates that the estimation result by RM-NN is comparable to that of a MODIS water vapor content product (MYD05_L2). Finally, validation with ground measurement data shows that RM-NN can be used to accurately estimate the water vapor content from MODIS 1B data, and the mean and standard deviation of the estimation error are about 0.12 gcm(-2) and 0.18 gcm(-2). (C) 2010 Optical Society of America
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