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.

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References

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  • Publication
  1. S. Manabe and R. T. Wetherald, “Thermal equilibrium of atmosphere with a given distribution of relative humidity,” J. Atmos. Sci. 24(3), 241–259 (1967).
    [Crossref]
  2. Y. J. Kaufman and B. C. Gao, “Remote sensing of water vapor in the near-IR from EOS/MODIS,” IEEE Trans. Geosci. Rem. Sens. 30(5), 871–884 (1992).
    [Crossref]
  3. V. Carrere and J. E. Conel, “Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—sensitivity analysis and application to airborne visible/ infrared imaging spectrometer (AVIRIS) data,” Remote Sens. Environ. 44(2-3), 179–204 (1993).
    [Crossref]
  4. B. C. Gao and Y. J. Kaufman, “Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels,” J. Geophys. Res. 108(D13), 4389 (2003), doi:.
    [Crossref]
  5. J. A. Sobrino, J. E. Kharraz, and Z. L. Li, “Surface temperature and water vapor retrieval from MODIS data,” Int. J. Remote Sens. 24(24), 5161–5182 (2003).
    [Crossref]
  6. M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
    [Crossref]
  7. B. Gao and Y. J. Kaufman, The MODIS Near-IR Water Vapor Algorithm: Product ID: MOD05-Total Precipitable Water, Algorithm Technical Background Document, Remote Sensing Division, Code 7212, Naval Research Laboratory, 4555 Overlook Avenue, SW, Washington, DC 20375 (1998).
  8. K. Mao, J. Shi, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
    [Crossref]
  9. K. Mao, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
    [Crossref]
  10. 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).
  11. 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).
  12. 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]
  13. K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).
  14. K. Mao, J. Shi, H. Tang, Z. L. 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]
  15. 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]

2008 (2)

K. Mao, J. Shi, H. Tang, Z. L. 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. 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]

2007 (1)

K. Mao, J. Shi, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
[Crossref]

2005 (1)

K. Mao, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
[Crossref]

2003 (2)

B. C. Gao and Y. J. Kaufman, “Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels,” J. Geophys. Res. 108(D13), 4389 (2003), doi:.
[Crossref]

J. A. Sobrino, J. E. Kharraz, and Z. L. Li, “Surface temperature and water vapor retrieval from MODIS data,” Int. J. Remote Sens. 24(24), 5161–5182 (2003).
[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]

1993 (1)

V. Carrere and J. E. Conel, “Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—sensitivity analysis and application to airborne visible/ infrared imaging spectrometer (AVIRIS) data,” Remote Sens. Environ. 44(2-3), 179–204 (1993).
[Crossref]

1992 (2)

M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
[Crossref]

Y. J. Kaufman and B. C. Gao, “Remote sensing of water vapor in the near-IR from EOS/MODIS,” IEEE Trans. Geosci. Rem. Sens. 30(5), 871–884 (1992).
[Crossref]

1967 (1)

S. Manabe and R. T. Wetherald, “Thermal equilibrium of atmosphere with a given distribution of relative humidity,” J. Atmos. Sci. 24(3), 241–259 (1967).
[Crossref]

Carrere, V.

V. Carrere and J. E. Conel, “Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—sensitivity analysis and application to airborne visible/ infrared imaging spectrometer (AVIRIS) data,” Remote Sens. Environ. 44(2-3), 179–204 (1993).
[Crossref]

Chen, K.

K. Mao, J. Shi, H. Tang, Z. L. 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]

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]

K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

Conel, J. E.

V. Carrere and J. E. Conel, “Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—sensitivity analysis and application to airborne visible/ infrared imaging spectrometer (AVIRIS) data,” Remote Sens. Environ. 44(2-3), 179–204 (1993).
[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]

Gao, B. C.

B. C. Gao and Y. J. Kaufman, “Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels,” J. Geophys. Res. 108(D13), 4389 (2003), doi:.
[Crossref]

Y. J. Kaufman and B. C. Gao, “Remote sensing of water vapor in the near-IR from EOS/MODIS,” IEEE Trans. Geosci. Rem. Sens. 30(5), 871–884 (1992).
[Crossref]

Gong, P.

K. Mao, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
[Crossref]

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]

Kaufman, Y. J.

B. C. Gao and Y. J. Kaufman, “Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels,” J. Geophys. Res. 108(D13), 4389 (2003), doi:.
[Crossref]

Y. J. Kaufman and B. C. Gao, “Remote sensing of water vapor in the near-IR from EOS/MODIS,” IEEE Trans. Geosci. Rem. Sens. 30(5), 871–884 (1992).
[Crossref]

M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
[Crossref]

Kharraz, J. E.

J. A. Sobrino, J. E. Kharraz, and Z. L. Li, “Surface temperature and water vapor retrieval from MODIS data,” Int. J. Remote Sens. 24(24), 5161–5182 (2003).
[Crossref]

King, M. D.

M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
[Crossref]

Li, Z.

K. Mao, J. Shi, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
[Crossref]

Li, Z. L.

K. Mao, J. Shi, H. Tang, Z. L. 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]

J. A. Sobrino, J. E. Kharraz, and Z. L. Li, “Surface temperature and water vapor retrieval from MODIS data,” Int. J. Remote Sens. 24(24), 5161–5182 (2003).
[Crossref]

K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

Manabe, S.

S. Manabe and R. T. Wetherald, “Thermal equilibrium of atmosphere with a given distribution of relative humidity,” J. Atmos. Sci. 24(3), 241–259 (1967).
[Crossref]

Mao, K.

K. Mao, J. Shi, H. Tang, Z. L. 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. 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, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
[Crossref]

K. Mao, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
[Crossref]

K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

Menzel, W. P.

M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
[Crossref]

Qin, Z.

K. Mao, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
[Crossref]

Shi, J.

K. Mao, J. Shi, H. Tang, Z. L. 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. Mao, J. Shi, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
[Crossref]

K. Mao, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
[Crossref]

K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

Sobrino, J. A.

J. A. Sobrino, J. E. Kharraz, and Z. L. Li, “Surface temperature and water vapor retrieval from MODIS data,” Int. J. Remote Sens. 24(24), 5161–5182 (2003).
[Crossref]

Tang, H.

K. Mao, J. Shi, H. Tang, Z. L. 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. 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, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
[Crossref]

K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

Tanre, D.

M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
[Crossref]

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]

Wang, D.

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]

Wang, X.

K. Mao, J. Shi, H. Tang, Z. L. 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. 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]

Wetherald, R. T.

S. Manabe and R. T. Wetherald, “Thermal equilibrium of atmosphere with a given distribution of relative humidity,” J. Atmos. Sci. 24(3), 241–259 (1967).
[Crossref]

Zhou, Q.

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, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

IEEE Trans. Geosci. Rem. Sens. (4)

Y. J. Kaufman and B. C. Gao, “Remote sensing of water vapor in the near-IR from EOS/MODIS,” IEEE Trans. Geosci. Rem. Sens. 30(5), 871–884 (1992).
[Crossref]

M. D. King, Y. J. Kaufman, W. P. Menzel, and D. Tanre, “Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS),” IEEE Trans. Geosci. Rem. Sens. 30(2), 1–27 (1992).
[Crossref]

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, J. Shi, H. Tang, Z. L. 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]

Int. J. Remote Sens. (3)

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, Z. Qin, J. Shi, and P. Gong, “A practical split-window algorithm for retrieving land surface temperature from MODIS data,” Int. J. Remote Sens. 26(15), 3181–3204 (2005).
[Crossref]

J. A. Sobrino, J. E. Kharraz, and Z. L. Li, “Surface temperature and water vapor retrieval from MODIS data,” Int. J. Remote Sens. 24(24), 5161–5182 (2003).
[Crossref]

J. Atmos. Sci. (1)

S. Manabe and R. T. Wetherald, “Thermal equilibrium of atmosphere with a given distribution of relative humidity,” J. Atmos. Sci. 24(3), 241–259 (1967).
[Crossref]

J. Geophys. Res (1)

K. Mao, J. Shi, H. Tang, Q. Zhou, Z. L. Li, and K. S. Chen, “A neural network technique for the retrieval of land surface temperature from advanced microwave scanning radiometer-EOS passive microwave data using a multiple-sensor/ multi-resolution remote sensing approach,” J. Geophys. Res., doi: 10.1029/ 2007JD009577 (to be published).

J. Geophys. Res. (2)

B. C. Gao and Y. J. Kaufman, “Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels,” J. Geophys. Res. 108(D13), 4389 (2003), doi:.
[Crossref]

K. Mao, J. Shi, Z. Li, and H. Tang, “An RM–NN algorithm for retrieving land surface temperature and emissivity from EOS/MODIS data,” J. Geophys. Res. 112(D21), D21102 (2007), doi:.
[Crossref]

Remote Sens. Environ. (1)

V. Carrere and J. E. Conel, “Recovery of atmospheric water vapor total column abundance from imaging spectrometer data around 940 nm—sensitivity analysis and application to airborne visible/ infrared imaging spectrometer (AVIRIS) data,” Remote Sens. Environ. 44(2-3), 179–204 (1993).
[Crossref]

Other (3)

B. Gao and Y. J. Kaufman, The MODIS Near-IR Water Vapor Algorithm: Product ID: MOD05-Total Precipitable Water, Algorithm Technical Background Document, Remote Sensing Division, Code 7212, Naval Research Laboratory, 4555 Overlook Avenue, SW, Washington, DC 20375 (1998).

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).

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).

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Figures (11)

Fig. 1
Fig. 1

Relationship between transmittance and wavelength for different water vapor content.

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Fig. 2
Fig. 2

(a)–(e) Radiance ratios versus the total atmospheric water vapor amounts in different regions and seasons.

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Fig. 3
Fig. 3

Radiance ratios versus the total atmospheric water vapor amounts.

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Fig. 4
Fig. 4

Distribution of retrieval error ( gcm − 2 ).

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Fig. 5
Fig. 5

Distribution of retrieval error (percentage).

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Fig. 6
Fig. 6

MODIS1B image (bands 3, 2, and 1).

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Fig. 7
Fig. 7

MYD05_L2 product of water vapor content provided by NASA.

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Fig. 8
Fig. 8

Water vapor content retrieved by RM–NN.

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Fig. 9
Fig. 9

Comparison of water vapor content between NASA product and that retrieved by NN.

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Fig. 10
Fig. 10

Water vapor content retrieved by NN after training again.

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Fig. 11
Fig. 11

Validation results.

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Tables (3)

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Table 1 Part of Surface Reflectance of MODIS Channels 2, 17, 18, 19 and 5 [11]

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Table 2 Summary of Retrieval Errora

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Table 3 Information of Precipitable Water Observation Station

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Equations (6)

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

L sensor ( λ ) = L sun ( λ ) τ ( λ ) ρ ( λ ) + L path ( λ ) ,
τ ( λ ) = L sensor ( λ ) L sun ( λ ) ρ ( λ ) − L path ( λ ) L sun ( λ ) ρ ( λ ) .
τ ( λ ) = L sensor ( λ ) L sun ( λ ) ρ ( λ ) .
R 17 = L 17 L 2 ,
R 18 = L 18 L 2 ,
R 19 = L 19 L 2 ,

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