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; Z. L. Li

doi:10.1364/OE.20.020096

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

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

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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)

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Related Topics
Table of Contents Category
- Atmospheric and Oceanic Optics
Optics & Photonics Topics
?

The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Meteorology (010.3920)
- Water (010.7340)
- Radiometry (010.5630)
- Remote sensing and sensors (010.0280)
- Inverse problems (100.3190)
- Passive remote sensing (280.4991)

About this Article
History
- Original Manuscript: June 20, 2012
- Revised Manuscript: August 12, 2012
- Manuscript Accepted: August 13, 2012
- Published: August 17, 2012

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Open Access

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.

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References

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|
Year
|
Author
|
Publication

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).
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]
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. Change 19, 115–135 (1998).
[Crossref]
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.
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]
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]
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]
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]
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]
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).
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]
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]
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]
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).

2011 (1)

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]

2008 (2)

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]

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]

2003 (1)

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]

1998 (2)

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]

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. Change 19, 115–135 (1998).
[Crossref]

1997 (1)

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]

1994 (1)

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]

1992 (1)

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]

1976 (1)

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]

Boone, A.

Chang, S.

Chen, F.

Chen, K.

Chen, K. S.

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]

Cothern, J. S.

D’Aria, D. M.

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]

Dai, Y.

Dalu, G.

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]

Dickinson, R. E.

Duan, Q.

Ek, M.

Feng, Y.

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]

Fung, A. K.

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]

Gillespie, A.

Gusev, Y. M.

Habets, F.

Hook, S.

Irannejad, P.

Jacob, F.

Kahle, A. B.

Kao, W. L.

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]

Koster, R.

Lettenmaier, D. P.

Li, S.

Li, Z.

Lohmann, D.

Mao, K.

Matsunaga, T.

Mitchel, K. E.

Nasonova, O. N.

Noilhan, J.

Ogawa, K.

Prabhakara, C.

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]

Rokugawa, S.

Salisbury, J. W.

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]

Schaake, J.

Schlosser, A.

Schmugge, T.

Shao, Y.

Shi, J.

Shmakin, A. B.

Snyder, W. C.

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]

Tang, H.

Tzeng, Y. C.

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]

Verseghy, D.

Wan, Z.

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]

Wang, D.

Wang, X.

Warrach, K.

Wetzel, P.

Wood, E. F.

Xu, L.

Xue, Y.

Yang, Z. L.

Zeng, Q.

Zhang, L.

Zhang, Y.

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]

Zhou, Q.

Geophys. Res. Lett. (1)

Global Planet. Change (1)

IEEE Trans. Geosci. Rem. Sens. (3)

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]

Int. J. Remote Sens. (2)

J. Geophys. Res. (1)

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]

Remote Sens. Environ. (2)

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]

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]

Other (4)

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).

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).

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.

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).

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Fig. 1 The comparison between truth emissivity and emissivity estimated by regression method.

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Fig. 2 . The distribution of average error.

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Fig. 3 (a) Classification map (band 1, 2, 3), (b) broadband emissivity.

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Tables (3)

Table 1 The Estimated Error from Brightness Temperatures in Bands 10-14 at Satellite

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Table 2 The Estimated Error from Brightness Temperature in Bands 11-14 at Satellite

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Table 3 The Sensitivity Analysis for Algorithm

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Equations (2)

Equations on this page are rendered with MathJax. Learn more.

ε_{8 - 12} = \frac{\int_{λ = 8}^{λ = 12} ε (λ) B (λ, T) d λ}{\int_{λ = 8}^{λ = 12} B (λ, T) d λ},

ε_{8 - 12} = 0.014 ε_{10} + 0.145 ε_{11} + 0.241 ε_{12} + 0.467 ε_{13} + 0.004 ε_{14} + 0.128.

Hidden nodes	Emissivity
Hidden nodes	Average Error	Average Error (Percent)	R	SD
100-100	0.0264	0.0245	0.428	0.041
200-200	0.023	0.0238	0.465	0.0334
300-300	0.0221	0.0228	0.482	0.029
400-400	0.0157	0.0203	0.576	0.027
500-500	0.0113	0.0116	0.794	0.009
600-600	0.0126	0.0129	0.726	0.0111
700-700	0.0138	0.0141	0.69	0.0134
800-800	0.0148	0.0154	0.64	0.0211

Hidden nodes	Emissivity
Hidden nodes	Average Error	Average Error (Percent)	R	SD
100-100	0.0127	0.0131	0.733	0.0143
200-200	0.0125	0.0129	0.733	0.0131
300-300	0.0117	0.0121	0.752	0.012
400-400	0.0111	0.0114	0.766	0.01
500-500	0.0101	0.0114	0.786	0.011
600-600	0.0097	0.011	0.791	0.0099
700-700	0.0091	0.0095	0.837	0.0081
800-800	0.0094	0.0098	0.814	0.0092

Spectral Coverage	Rang of Bright Temperature at Satellite	Absolute Temperature Accuracy	Estimation Error of Broadband Emissivity
8.3- 11.65 μm	200 - 240 K	≤3 K	0.016
	240 - 270 K	≤2 K	0.0013
	270 - 340 K	≤1 K	0.0095
	340 - 370 K	≤2 K	0.001

Hidden nodes	Emissivity
Hidden nodes	Average Error	Average Error (Percent)	R	SD
100-100	0.0264	0.0245	0.428	0.041
200-200	0.023	0.0238	0.465	0.0334
300-300	0.0221	0.0228	0.482	0.029
400-400	0.0157	0.0203	0.576	0.027
500-500	0.0113	0.0116	0.794	0.009
600-600	0.0126	0.0129	0.726	0.0111
700-700	0.0138	0.0141	0.69	0.0134
800-800	0.0148	0.0154	0.64	0.0211

Hidden nodes	Emissivity
Hidden nodes	Average Error	Average Error (Percent)	R	SD
100-100	0.0127	0.0131	0.733	0.0143
200-200	0.0125	0.0129	0.733	0.0131
300-300	0.0117	0.0121	0.752	0.012
400-400	0.0111	0.0114	0.766	0.01
500-500	0.0101	0.0114	0.786	0.011
600-600	0.0097	0.011	0.791	0.0099
700-700	0.0091	0.0095	0.837	0.0081
800-800	0.0094	0.0098	0.814	0.0092