Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN

K. B. Mao, ^* H. T. Li, D. Y. Hu, J. Wang, J. X. Huang,  Z. L. Li, Q. B. Zhou, and H. J. Tang^*

 

*hjtang@mail.caas.net.cn

*maokebiao@126.com

Abstract: 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 and 0.08 . 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 and 0.18 .

Ó2010 Optical Society of America

OCIS codes: (010.5630) Radiometry; (010.0280) Remote sensing and sensor; (280.4991) Passive remote sensing; (010.7340) Water vapor content; (010.3920) Metrology; (100.3190) Inverse problem

citation：K. B. Mao, H. T. Li, D. Y. Hu, J. Wang, J. X. Huang, Z. L. Li, Q. B. Zhou, and H. J. Tang, Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN , Optics Express, 2010, 18(9), pp. 9542–9554. PDF download（下载）

 

清晰版可以从以下网址： 

http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-18-9-9542

 View Full Text: Acrobat PDF (1485 KB)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-9-9542

转载本文请联系原作者获取授权，同时请注明本文来自毛克彪科学网博客。
链接地址：https://wap.sciencenet.cn/blog-67260-315162.html

上一篇：年轻人学习的榜样-周志华
下一篇：地球保卫战