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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 9 — Apr. 26, 2010
  • pp: 9542–9554

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  »View Author Affiliations


Optics Express, Vol. 18, Issue 9, pp. 9542-9554 (2010)
http://dx.doi.org/10.1364/OE.18.009542


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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 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.

© 2010 OSA

OCIS Codes
(010.3920) Atmospheric and oceanic optics : Meteorology
(010.7340) Atmospheric and oceanic optics : Water
(100.3190) Image processing : Inverse problems
(280.4991) Remote sensing and sensors : Passive remote sensing
(010.5630) Atmospheric and oceanic optics : Radiometry
(010.0280) Atmospheric and oceanic optics : Remote sensing and sensors

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: February 11, 2010
Revised Manuscript: April 1, 2010
Manuscript Accepted: April 7, 2010
Published: April 22, 2010

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," Opt. Express 18, 9542-9554 (2010)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-9-9542


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