OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 18 — Aug. 27, 2012
  • pp: 20096–20101

Estimation of broadband emissivity (8-12um) from ASTER data by using RM-NN

K. B. Mao, Y. Ma, X. Y. Shen, B. P. Li, C. Y. Li, and Z. L. Li  »View Author Affiliations


Optics Express, Vol. 20, Issue 18, pp. 20096-20101 (2012)
http://dx.doi.org/10.1364/OE.20.020096


View Full Text Article

Enhanced HTML    Acrobat PDF (1575 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Land surface window emissivity is a key parameter for estimating the longwave radiative budget. The combined radiative transfer model (RM) with neural network (NN) algorithm is utilized to directly estimate the window (8–12 um) emissivity from the brightness temperature of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with 90 m spatial resolution. Although the estimation accuracy is very high when the broadband emissivity is estimated from AST05 (ASTER Standard Data Product) by using regression method, the accuracy of AST05 is about ± 0.015 for 86 spectra which is determined by the atmosphere correction for ASTER 1B data. The MODTRAN 4 is used to simulate the process of radiance transfer, and the broadband emissivity is directly estimated from the brightness temperature of ASTER 1B data at satellite. The comparison analysis indicates that the RM-NN is more competent to estimate broadband emissivity than other method when the brightness temperatures of band 11, 12, 13, 14 are made as input nodes of dynamic neural network. The estimation average accuracy is about 0.009, and the estimation results are not sensitive to instrument noise. The RM-NN is applied to extract broadband emissivity from an image of ASTER 1B data in China, and the comparison against a classification based multiple bands with 15 m spatial resolution shows that the estimation results from RM-NN are very good.

© 2012 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: June 20, 2012
Revised Manuscript: August 12, 2012
Manuscript Accepted: August 13, 2012
Published: August 17, 2012

Citation
K. B. Mao, Y. Ma, X. Y. Shen, B. P. Li, C. Y. Li, and Z. L. Li, "Estimation of broadband emissivity (8-12um) from ASTER data by using RM-NN," Opt. Express 20, 20096-20101 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-18-20096


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. A. C. Wilber, D. P. Kratz, and S. K. Gupta, “Surface emissivity maps for use in satellite retrievals of longwave radiation,” NASA/TP-1999–209362, 30 (1999).
  2. C. Prabhakara and G. Dalu, “Remote sensing of the surface emissivity at 9 um over the globe,” J. Geophys. Res.81(21), 3719–3724 (1976). [CrossRef]
  3. E. F. Wood, D. P. Lettenmaier, L. Xu, D. Lohmann, A. Boone, S. Chang, F. Chen, Y. Dai, R. E. Dickinson, Q. Duan, M. Ek, Y. M. Gusev, F. Habets, P. Irannejad, R. Koster, K. E. Mitchel, O. N. Nasonova, J. Noilhan, J. Schaake, A. Schlosser, Y. Shao, A. B. Shmakin, D. Verseghy, K. Warrach, P. Wetzel, Y. Xue, Z. L. Yang, and Q. Zeng, “The project for intercomparison of land-surface parameterization scheme (PILPS) Phase 2(c) Red-Arkansas River basin experiment:1. Experiment description and summary intercomparisons,” Global Planet. Change19, 115–135 (1998). [CrossRef]
  4. C. Blondin, Parameterization of Land-Surface Processes in Numerical Weather Prediction, in Land Surface Evaporation, T. J. Schmugge and J. Andre, eds. (Springer-Verlag, New York, 1991) pp. 31– 54.
  5. K. Mao, J. Shi, H. Tang, Z. Li, X. Wang, and K. Chen, “A Neural network technique for separating and surface emissivity and temperature from ASTER imagery,” IEEE Trans. Geosci. Rem. Sens.46(1), 200–208 (2008). [CrossRef]
  6. K. Ogawa, T. Schmugge, and F. Jacob, “Estimation of land surface window (8–12 um) emissivity from multi-spectral thermal infrared remote sensing — A case study in a part of Sahara Desert,” Geophys. Res. Lett.30(2), 1067 (2003), doi:. [CrossRef]
  7. A. Gillespie, S. Rokugawa, T. Matsunaga, J. S. Cothern, S. Hook, and A. B. Kahle, “ A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images,” IEEE Trans. Geosci. Rem. Sens.36(4), 1113–1126 (1998). [CrossRef]
  8. J. W. Salisbury and D. M. D’Aria, “Emissivity of terrestrial materials in the 8–14 mm atmospheric window,” Remote Sens. Environ.42(2), 83–106 (1992). [CrossRef]
  9. W. C. Snyder, Z. Wan, Y. Zhang, and Y. Feng, “Thermal infrared (3– 14mm) bi-directional reflectance measurement of sands and soils,” Remote Sens. Environ.60(1), 101–109 (1997). [CrossRef]
  10. D. E. Bowker, R. E. Davis, D. L. Myrick, K. Stacy, and W. T. Jones, “Spectral reflectances of natural targets for use in remote sensing studies, ” NASA Reference Pub.1139 (1985).
  11. Y. C. Tzeng, K. S. Chen, W. L. Kao, and A. K. Fung, “A dynamic learning neural network for remote sensing applications,” IEEE Trans. Geosci. Rem. Sens.32(5), 1096–1102 (1994). [CrossRef]
  12. K. Mao, H. Tang, X. Wang, Q. Zhou, and D. Wang, “Near-surface air temperature estimation from ASTER data based on neural network algorithm,” Int. J. Remote Sens.29(20), 6021–6028 (2008). [CrossRef]
  13. K. Mao, S. Li, D. Wang, L. Zhang, H. Tang, X. Wang, and Z. Li, “Retrieval of land surface temperature and emissivity from ASTER1B data using dynamic learning neural Network,” Int. J. Remote Sens.32(19), 5413–5423 (2011). [CrossRef]
  14. A. Berk, L. S. Bemstein, and D. C. Roberttson, “MODTRAN: a moderate resolution model for LOWTRAN,” Burlington, MA, Spectral Science, Inc. Rep. AFGL-TR-87–0220 (1987).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Figures

Fig. 1 Fig. 2 Fig. 3
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited