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An Advanced Radiative Transfer and Neural Network Scheme and Evaluation for Estimating Water Vapor Content from MODIS Data

National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Science and Beijing Normal University, Beijing 100086, China
College of Resources and Environments, Hunan Agricultural University, Changsha 410128, China
Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, OK 73072, USA
International Agricultural Big Data and Nutrition Academy in China, Hong Kong, China
Author to whom correspondence should be addressed.
Atmosphere 2017, 8(8), 139;
Received: 13 June 2017 / Revised: 24 July 2017 / Accepted: 26 July 2017 / Published: 29 July 2017
(This article belongs to the Special Issue Water Vapor in the Atmosphere)
This work made an improvement upon and a further evaluation of previous work for estimating water vapor content from near-infrared around 1 μm from MODIS data. The accuracy of RM-NN is determined by the complicated relationship of the geophysical parameters. An advanced scheme is proposed for building different training databases for different seasons in different regions to reduce the complexity. The training database includes three parts. The first part is a simulation database by MODTRAN for different weather conditions, which is made as a basic database; the second part is reliable field measurement data in observation stations; and the third part is the MYD05_L2 product on clear days, which is produced by the standard product algorithm for water vapor content. The comparative analyses based on simulation data indicate that maximum accuracy of single condition could be improved by about 34% relative to the “all conditions” results. Two study regions in China and America are selected as test areas, and the evaluation shows that the mean and the standard deviation of estimation error are about 0.08 g cm−2 and 0.09 g cm−2, respectively. All the analysis indicates that the advanced scheme can improve the retrieval accuracy of water vapor content, which can make full use of the advantages of previous methods. View Full-Text
Keywords: MODIS; water vapor content; neural network MODIS; water vapor content; neural network
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Mao, K.; Shen, X.; Zuo, Z.; Ma, Y.; Liu, G.; Tang, H. An Advanced Radiative Transfer and Neural Network Scheme and Evaluation for Estimating Water Vapor Content from MODIS Data. Atmosphere 2017, 8, 139.

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