期刊
OPTICS EXPRESS
卷 18, 期 9, 页码 9542-9554出版社
OPTICAL SOC AMER
DOI: 10.1364/OE.18.009542
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资金
- State Key Laboratory of Remote Sensing Science
- Chinese Academy of Sciences and Beijing Normal University
- National Natural Science Foundation of China (NSFC) [40930101, 40971218]
- 973 Program [2007CB714403]
- Special Fund for Basic Research Work of Central Scientific Research Institutions for Public Welfare [901-40]
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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