Abstract

Atmospheric correction over turbid waters can be problematic if atmospheric haze is spatially variable. In this case the retrieval of water quality is hampered by the fact that haze variations could be partly mistaken for variations in suspended sediment concentration (SSC). In this study we propose the suppression of local haze variations while leaving sediment variations intact. This is accomplished by a multispectral data projection (MDP) method based on a linear spectral mixing model, and applied prior to the actual standard atmospheric correction. In this linear model, the hazesediment spectral mixing was simulated by a coupled water-atmosphere radiative transfer (RT) model. As a result, local haze variations were largely suppressed and transformed into an approximately homogenous atmosphere over the MERIS top-of-atmosphere (TOA) radiance scene. The suppression of local haze variations increases the number of satellite images that are still suitable for standard atmospheric correction processing and subsequent water quality analysis.

© 2010 Optical Society of America

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References

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  1. Y. J. Kaufman, “The atmospheric effect on remote sensing and its correction,” In Theory and applications of optical remote sensing, G. Asrar, ed., (Wiley, New York, 1998), pp.734.
  2. J. Lavreau, “De-hazing Landsat Thematic Mapper images,” Photogramm. Eng. Remote Sensing 57, 1297–1302 (1991).
  3. Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sens. Environ. 82(2–3), 173–187 (2002).
    [CrossRef]
  4. Y. Zhang, and B. Guindon, “Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery: Effect on Land Cover Classification Performance,” IEEE Trans. Geosci. Rem. Sens. 41(5), 1082–1089 (2003).
    [CrossRef]
  5. Z. Zhang, “Studies on basic characteristics of fine sediment in Changjiang Estuary,” J. Sediment. Res. 1, 67–73 (1996) (in Chinese with English abstract).
  6. P. M. Teillet, N. T. O'Neill, A. Kalinauskas, D. Stugeon, and G. Fedosejevs, “A dynamic regression algorithm for incorporating atmospheric models into image correction procedures,” in Proceedings of the IGARSS'87 Symposium, Ann Arbor, MI, pp. 913−918 (1987).
  7. R. J. Kauth, and G. S. Thomas, “The Tasseled Cap—a graphic description of the spectral-temporal development of the agricultural crops as seen by Landsat,” in Proceedings of the Symposium on Machine processing of Remotely Sensed Data, Purdue University, pp. 41−51 (1976).
  8. R. Richter, “Atmospheric correction of satellite data with haze removal including a haze/clear transition region,” Comput. Geosci. 22(6), 675–681 (1996).
    [CrossRef]
  9. H. R. Gordon, and M. Wang, “Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm,” Appl. Opt. 33(3), 443–452 (1994).
    [CrossRef] [PubMed]
  10. C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
    [CrossRef]
  11. Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
    [CrossRef]
  12. C. Y. Ji, “Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment,” Remote Sens. Environ. 112(4), 1773–1783 (2008).
    [CrossRef]
  13. European Space Agency, “MERIS user guide,” http://envisat.esa.int/handbooks/meris.
  14. F. Shen, W. Verhoef, Y-X. Zhou, Mhd. S. Salama, and X–L. Liu, “Satellite estimates of wide-range suspended sediment concentrations in estuarine and coastal waters using MERIS data,” (submitted).
  15. A. Berk, G. P. Anderson, P. K. Acharya, J. H. Chetwynd, L. S. Bernstein, E. P. Shettle,  et al.MODTRAN4 USERS MANUAL, Air Force Research Laboratory, Space Vehicles Directorate, Air Force Materiel Command, Hanscom AFB, MA 01731–3010, pp.97 (2000).

2008 (1)

C. Y. Ji, “Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment,” Remote Sens. Environ. 112(4), 1773–1783 (2008).
[CrossRef]

2007 (1)

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

2003 (1)

Y. Zhang, and B. Guindon, “Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery: Effect on Land Cover Classification Performance,” IEEE Trans. Geosci. Rem. Sens. 41(5), 1082–1089 (2003).
[CrossRef]

2002 (1)

Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sens. Environ. 82(2–3), 173–187 (2002).
[CrossRef]

2000 (1)

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

1996 (2)

Z. Zhang, “Studies on basic characteristics of fine sediment in Changjiang Estuary,” J. Sediment. Res. 1, 67–73 (1996) (in Chinese with English abstract).

R. Richter, “Atmospheric correction of satellite data with haze removal including a haze/clear transition region,” Comput. Geosci. 22(6), 675–681 (1996).
[CrossRef]

1994 (1)

1991 (1)

J. Lavreau, “De-hazing Landsat Thematic Mapper images,” Photogramm. Eng. Remote Sensing 57, 1297–1302 (1991).

Behnert, I.

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

Carder, K. L.

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

Cihlar, J.

Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sens. Environ. 82(2–3), 173–187 (2002).
[CrossRef]

Doerffer, R.

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

Fischer, J.

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

Gordon, H. R.

Guindon, B.

Y. Zhang, and B. Guindon, “Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery: Effect on Land Cover Classification Performance,” IEEE Trans. Geosci. Rem. Sens. 41(5), 1082–1089 (2003).
[CrossRef]

Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sens. Environ. 82(2–3), 173–187 (2002).
[CrossRef]

Hu, C.

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

Ji, C. Y.

C. Y. Ji, “Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment,” Remote Sens. Environ. 112(4), 1773–1783 (2008).
[CrossRef]

Lavreau, J.

J. Lavreau, “De-hazing Landsat Thematic Mapper images,” Photogramm. Eng. Remote Sensing 57, 1297–1302 (1991).

Muller-Karger, F. E.

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

Richter, R.

R. Richter, “Atmospheric correction of satellite data with haze removal including a haze/clear transition region,” Comput. Geosci. 22(6), 675–681 (1996).
[CrossRef]

Schaale, M.

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

Schroeder, Th.

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

Wang, M.

Zhang, Y.

Y. Zhang, and B. Guindon, “Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery: Effect on Land Cover Classification Performance,” IEEE Trans. Geosci. Rem. Sens. 41(5), 1082–1089 (2003).
[CrossRef]

Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sens. Environ. 82(2–3), 173–187 (2002).
[CrossRef]

Zhang, Z.

Z. Zhang, “Studies on basic characteristics of fine sediment in Changjiang Estuary,” J. Sediment. Res. 1, 67–73 (1996) (in Chinese with English abstract).

Appl. Opt. (1)

Comput. Geosci. (1)

R. Richter, “Atmospheric correction of satellite data with haze removal including a haze/clear transition region,” Comput. Geosci. 22(6), 675–681 (1996).
[CrossRef]

IEEE Trans. Geosci. Rem. Sens. (1)

Y. Zhang, and B. Guindon, “Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery: Effect on Land Cover Classification Performance,” IEEE Trans. Geosci. Rem. Sens. 41(5), 1082–1089 (2003).
[CrossRef]

Int. J. Remote Sens. (1)

Th. Schroeder, I. Behnert, M. Schaale, J. Fischer, and R. Doerffer, “Atmospheric correction algorithm for MERIS above case-2 waters,” Int. J. Remote Sens. 28(7), 1469–1486 (2007).
[CrossRef]

J. Sediment. Res. (1)

Z. Zhang, “Studies on basic characteristics of fine sediment in Changjiang Estuary,” J. Sediment. Res. 1, 67–73 (1996) (in Chinese with English abstract).

Photogramm. Eng. Remote Sensing (1)

J. Lavreau, “De-hazing Landsat Thematic Mapper images,” Photogramm. Eng. Remote Sensing 57, 1297–1302 (1991).

Remote Sens. Environ. (3)

Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sens. Environ. 82(2–3), 173–187 (2002).
[CrossRef]

C. Y. Ji, “Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment,” Remote Sens. Environ. 112(4), 1773–1783 (2008).
[CrossRef]

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric correction of SeaWiFS imagery over turbid coastal waters: A practical method,” Remote Sens. Environ. 74(2), 195–206 (2000).
[CrossRef]

Other (6)

European Space Agency, “MERIS user guide,” http://envisat.esa.int/handbooks/meris.

F. Shen, W. Verhoef, Y-X. Zhou, Mhd. S. Salama, and X–L. Liu, “Satellite estimates of wide-range suspended sediment concentrations in estuarine and coastal waters using MERIS data,” (submitted).

A. Berk, G. P. Anderson, P. K. Acharya, J. H. Chetwynd, L. S. Bernstein, E. P. Shettle,  et al.MODTRAN4 USERS MANUAL, Air Force Research Laboratory, Space Vehicles Directorate, Air Force Materiel Command, Hanscom AFB, MA 01731–3010, pp.97 (2000).

Y. J. Kaufman, “The atmospheric effect on remote sensing and its correction,” In Theory and applications of optical remote sensing, G. Asrar, ed., (Wiley, New York, 1998), pp.734.

P. M. Teillet, N. T. O'Neill, A. Kalinauskas, D. Stugeon, and G. Fedosejevs, “A dynamic regression algorithm for incorporating atmospheric models into image correction procedures,” in Proceedings of the IGARSS'87 Symposium, Ann Arbor, MI, pp. 913−918 (1987).

R. J. Kauth, and G. S. Thomas, “The Tasseled Cap—a graphic description of the spectral-temporal development of the agricultural crops as seen by Landsat,” in Proceedings of the Symposium on Machine processing of Remotely Sensed Data, Purdue University, pp. 41−51 (1976).

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Figures (8)

Fig. 1.
Fig. 1.

Two-dimensional vector diagram on the modelling of haze and sediment variations by linear spectral mixing and the suppression of haze variations by subtracting the increased haze contribution.

Fig. 2.
Fig. 2.

Diagram of processing steps in the MDP method for suppression of local haze variations and subsequent sediment concentrations retrieval.

Fig. 3.
Fig. 3.

Model-simulated TOA radiance spectra at MERIS bands for 36 combinations of haze and sediment

Fig. 4.
Fig. 4.

Three basis spectra (end members) at MERIS bands for the MDP method

Fig. 5.
Fig. 5.

TOA radiance spectra at MERIS bands at standard level of haze with a 40-km visibility. All 36 modelled spectra (shown in Fig. 3) are transformed by the MDP method and shown in (a). A subset of 9 real spectra at 40 km visibility is shown in (b) for comparison.

Fig. 6.
Fig. 6.

MERIS scene of 23 June 2005 over the Changjiang Estuary and adjacent coast. Original TOA radiance scene at MERIS 620 nm is shown in (a). The scene where spatial variations of haze were suppressed by the MDP transform is shown in (b). The spatially varying haze on the scene is shown in (c).

Fig. 7.
Fig. 7.

Scatter plot of original TOA radiance at MERIS 442 nm vs. the one at 620 nm in the region of interest (red dashed-circled area in Fig. 6(a)) shown in the (a) and of transformed by the MDP method in the same region of interest (green solid-circled area in Fig. 6(b)) shown in the (b).

Fig. 8.
Fig. 8.

Mappings of SSC retrieved from MERIS TOA radiance scene of 23 June 2005. The MERIS haze-unsuppressed SSC is shown in (a) and the haze-suppressed SSC in (b).

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