Abstract

An estimation of the aerosol multiple-scattering reflectance is an important part of the atmospheric correction procedure in satellite ocean color data processing. Most commonly, the utilization of two near-infrared (NIR) bands to estimate the aerosol optical properties has been adopted for the estimation of the effects of aerosols. Previously, the operational Geostationary Color Ocean Imager (GOCI) atmospheric correction scheme relies on a single-scattering reflectance ratio (SSE), which was developed for the processing of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data to determine the appropriate aerosol models and their aerosol optical thicknesses. The scheme computes reflectance contributions (weighting factor) of candidate aerosol models in a single scattering domain then spectrally extrapolates the single-scattering aerosol reflectance from NIR to visible (VIS) bands using the SSE. However, it directly applies the weight value to all wavelengths in a multiple-scattering domain although the multiple-scattering aerosol reflectance has a non-linear relationship with the single-scattering reflectance and inter-band relationship of multiple scattering aerosol reflectances is non-linear. To avoid these issues, we propose an alternative scheme for estimating the aerosol reflectance that uses the spectral relationships in the aerosol multiple-scattering reflectance between different wavelengths (called SRAMS). The process directly calculates the multiple-scattering reflectance contributions in NIR with no residual errors for selected aerosol models. Then it spectrally extrapolates the reflectance contribution from NIR to visible bands for each selected model using the SRAMS. To assess the performance of the algorithm regarding the errors in the water reflectance at the surface or remote-sensing reflectance retrieval, we compared the SRAMS atmospheric correction results with the SSE atmospheric correction using both simulations and in situ match-ups with the GOCI data. From simulations, the mean errors for bands from 412 to 555 nm were 5.2% for the SRAMS scheme and 11.5% for SSE scheme in case-I waters. From in situ match-ups, 16.5% for the SRAMS scheme and 17.6% scheme for the SSE scheme in both case-I and case-II waters. Although we applied the SRAMS algorithm to the GOCI, it can be applied to other ocean color sensors which have two NIR wavelengths.

© 2016 Optical Society of America

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References

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  1. H. R. Gordon, J. W. Brown, and R. H. Evans, “Exact Rayleigh scattering calculations for use with the Nimbus-7 coastal zone color scanner,” Appl. Opt. 27(5), 862–871 (1988).
    [Crossref] [PubMed]
  2. H. R. Gordon and M. Wang, “Surface-roughness considerations for atmospheric correction of ocean color sensors. I: The Rayleigh-scattering component,” Appl. Opt. 31(21), 4247–4260 (1992).
    [Crossref] [PubMed]
  3. M. Wang, “The Rayleigh lookup tables for the SeaWiFS data processing: accounting for the effects of ocean surface roughness,” Int. J. Remote Sens. 23(13), 2693–2702 (2002).
    [Crossref]
  4. M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005).
    [Crossref]
  5. A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
    [Crossref]
  6. M. Wang and H. R. Gordon, “A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS,” Remote Sens. Environ. 50(3), 231–239 (1994).
    [Crossref]
  7. 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]
  8. H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
    [Crossref]
  9. D. Antoine and A. Morel, “A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones,” Int. J. Remote Sens. 20(9), 1875–1916 (1999).
    [Crossref]
  10. M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
    [Crossref]
  11. J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
    [Crossref]
  12. J. H. Ahn, Y. J. Park, W. Kim, B. Lee, and I. S. Oh, “Vicarious calibration of the Geostationary Ocean Color Imager,” Opt. Express 23(18), 23236–23258 (2015).
    [Crossref] [PubMed]
  13. B. Franz, “rhoa_to_rhoas() - MS aerosol reflectance to SS aerosol reflectance,” aerosol.c in SeaDAS code, http://seadas.gsfc.nasa.gov (2004).
  14. E. P. Shettle, and R. W. Fenn, “Models for the aerosols of the lower atmosphere and the effects of humidity variations on their optical properties,” Air Force Geophysics Lab Hanscom AFB MA, AFGL-TR-79–0214 (1979).
  15. M. Wang, “Atmospheric correction of ocean color sensors: computing atmospheric diffuse transmittance,” Appl. Opt. 38(3), 451–455 (1999).
    [Crossref] [PubMed]
  16. D. Antoine, “Atmospheric corrections over Case 1 waters (CWAC),” OLCI Level 2 ATBD, v.2.2. S3–L2-SD-03–C07-LOV-ATBD (2010).
  17. E. Vermote, D. Tanré, J. L. Deuzé, M. Herman, J. J. Morcrette, and S. Y. Kotchenova, “Second simulation of a satellite signal in the solar spectrum-vector (6SV),” 6S User Guide Version 3 (2006).
  18. A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters- A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001).
    [Crossref]
  19. A. Morel, D. Antoine, and B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt. 41(30), 6289–6306 (2002).
    [Crossref] [PubMed]
  20. A. Morel, B. Gentili, and D. Antoine, “Assessing the Atmospheric and Marine Signal from a Geostationary Orbit (part II),” COMS Ocean Data Processing System Development Project(3) (2005).
  21. M. Wang, “Effects of ocean surface reflectance variation with solar elevation on normalized water-leaving radiance,” Appl. Opt. 45(17), 4122–4128 (2006).
    [Crossref] [PubMed]
  22. M. Wang, “Extrapolation of the aerosol reflectance from the near-infrared to the visible: the single-scattering epsilon vs multiple-scattering epsilon method,” Int. J. Remote Sens. 25(18), 3637–3650 (2004).
    [Crossref]
  23. Z. Ahmad, “model_select-ahmad() – select two aerosol models whose epsilon values bracket the observed ms epsilon, eps_obs,” aerosol.c in SeaDAS code, http://seadas.gsfc.nasa.gov (2014).
  24. Z. Ahmad, and B. Franz, “Atmospheric correction using multiple-scattering epsilon values,” in proceeding of Ocean Optics XXII (2014).
  25. Z. Ahmad and B. Franz, “Recent enhancements in atmospheric correction algorithm for ocean color retrievals from remotely sensed data,” Proceeding of Ocean Optics XXIII (2016).
  26. B. Franz, “model_select_wang() - M. Wang aerosol model selection,” aeosol.c in SeaDAS code, http://seadas.gsfc.nasa.gov (2004).

2015 (1)

2012 (1)

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

2007 (1)

M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
[Crossref]

2006 (1)

2005 (1)

M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005).
[Crossref]

2004 (1)

M. Wang, “Extrapolation of the aerosol reflectance from the near-infrared to the visible: the single-scattering epsilon vs multiple-scattering epsilon method,” Int. J. Remote Sens. 25(18), 3637–3650 (2004).
[Crossref]

2002 (2)

M. Wang, “The Rayleigh lookup tables for the SeaWiFS data processing: accounting for the effects of ocean surface roughness,” Int. J. Remote Sens. 23(13), 2693–2702 (2002).
[Crossref]

A. Morel, D. Antoine, and B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt. 41(30), 6289–6306 (2002).
[Crossref] [PubMed]

2001 (1)

A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters- A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001).
[Crossref]

1999 (2)

D. Antoine and A. Morel, “A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones,” Int. J. Remote Sens. 20(9), 1875–1916 (1999).
[Crossref]

M. Wang, “Atmospheric correction of ocean color sensors: computing atmospheric diffuse transmittance,” Appl. Opt. 38(3), 451–455 (1999).
[Crossref] [PubMed]

1998 (1)

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

1994 (2)

M. Wang and H. R. Gordon, “A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS,” Remote Sens. Environ. 50(3), 231–239 (1994).
[Crossref]

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]

1992 (1)

1988 (1)

1977 (1)

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Ahn, J. H.

J. H. Ahn, Y. J. Park, W. Kim, B. Lee, and I. S. Oh, “Vicarious calibration of the Geostationary Ocean Color Imager,” Opt. Express 23(18), 23236–23258 (2015).
[Crossref] [PubMed]

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

Antoine, D.

A. Morel, D. Antoine, and B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt. 41(30), 6289–6306 (2002).
[Crossref] [PubMed]

D. Antoine and A. Morel, “A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones,” Int. J. Remote Sens. 20(9), 1875–1916 (1999).
[Crossref]

Brown, J. W.

Evans, R. H.

Fukushima, H.

M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
[Crossref]

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Gentili, B.

Gordon, H. R.

Higurashi, A.

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Kim, W.

Lee, B.

J. H. Ahn, Y. J. Park, W. Kim, B. Lee, and I. S. Oh, “Vicarious calibration of the Geostationary Ocean Color Imager,” Opt. Express 23(18), 23236–23258 (2015).
[Crossref] [PubMed]

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

Maritorena, S.

A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters- A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001).
[Crossref]

Mitomi, Y.

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Morel, A.

A. Morel, D. Antoine, and B. Gentili, “Bidirectional reflectance of oceanic waters: accounting for Raman emission and varying particle scattering phase function,” Appl. Opt. 41(30), 6289–6306 (2002).
[Crossref] [PubMed]

A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters- A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001).
[Crossref]

D. Antoine and A. Morel, “A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones,” Int. J. Remote Sens. 20(9), 1875–1916 (1999).
[Crossref]

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Murakami, H.

M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
[Crossref]

Nakajima, T.

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Noguchi, T.

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Oh, I. S.

J. H. Ahn, Y. J. Park, W. Kim, B. Lee, and I. S. Oh, “Vicarious calibration of the Geostationary Ocean Color Imager,” Opt. Express 23(18), 23236–23258 (2015).
[Crossref] [PubMed]

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

Park, Y. J.

J. H. Ahn, Y. J. Park, W. Kim, B. Lee, and I. S. Oh, “Vicarious calibration of the Geostationary Ocean Color Imager,” Opt. Express 23(18), 23236–23258 (2015).
[Crossref] [PubMed]

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

Prieur, L.

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Ryu, J. H.

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

Tanaka, A.

M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
[Crossref]

Tanaka, T.

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Toratani, M.

M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
[Crossref]

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Wang, M.

M. Wang, “Effects of ocean surface reflectance variation with solar elevation on normalized water-leaving radiance,” Appl. Opt. 45(17), 4122–4128 (2006).
[Crossref] [PubMed]

M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005).
[Crossref]

M. Wang, “Extrapolation of the aerosol reflectance from the near-infrared to the visible: the single-scattering epsilon vs multiple-scattering epsilon method,” Int. J. Remote Sens. 25(18), 3637–3650 (2004).
[Crossref]

M. Wang, “The Rayleigh lookup tables for the SeaWiFS data processing: accounting for the effects of ocean surface roughness,” Int. J. Remote Sens. 23(13), 2693–2702 (2002).
[Crossref]

M. Wang, “Atmospheric correction of ocean color sensors: computing atmospheric diffuse transmittance,” Appl. Opt. 38(3), 451–455 (1999).
[Crossref] [PubMed]

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]

M. Wang and H. R. Gordon, “A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS,” Remote Sens. Environ. 50(3), 231–239 (1994).
[Crossref]

H. R. Gordon and M. Wang, “Surface-roughness considerations for atmospheric correction of ocean color sensors. I: The Rayleigh-scattering component,” Appl. Opt. 31(21), 4247–4260 (1992).
[Crossref] [PubMed]

Appl. Opt. (6)

Int. J. Remote Sens. (4)

M. Wang, “Extrapolation of the aerosol reflectance from the near-infrared to the visible: the single-scattering epsilon vs multiple-scattering epsilon method,” Int. J. Remote Sens. 25(18), 3637–3650 (2004).
[Crossref]

M. Wang, “The Rayleigh lookup tables for the SeaWiFS data processing: accounting for the effects of ocean surface roughness,” Int. J. Remote Sens. 23(13), 2693–2702 (2002).
[Crossref]

M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005).
[Crossref]

D. Antoine and A. Morel, “A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument): principle and implementation for atmospheres carrying various aerosols including absorbing ones,” Int. J. Remote Sens. 20(9), 1875–1916 (1999).
[Crossref]

J. Geophys. Res. (1)

A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters- A reappraisal,” J. Geophys. Res. 106(C4), 7163–7180 (2001).
[Crossref]

J. Oceanogr. (2)

M. Toratani, H. Fukushima, H. Murakami, and A. Tanaka, “Atmospheric correction scheme for GLI with absorptive aerosol correction,” J. Oceanogr. 63(3), 525–532 (2007).
[Crossref]

H. Fukushima, A. Higurashi, Y. Mitomi, T. Nakajima, T. Noguchi, T. Tanaka, and M. Toratani, “Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance,” J. Oceanogr. 54(5), 417–430 (1998).
[Crossref]

Limnol. Oceanogr. (1)

A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Ocean Sci. J. (1)

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
[Crossref]

Opt. Express (1)

Remote Sens. Environ. (1)

M. Wang and H. R. Gordon, “A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS,” Remote Sens. Environ. 50(3), 231–239 (1994).
[Crossref]

Other (9)

B. Franz, “rhoa_to_rhoas() - MS aerosol reflectance to SS aerosol reflectance,” aerosol.c in SeaDAS code, http://seadas.gsfc.nasa.gov (2004).

E. P. Shettle, and R. W. Fenn, “Models for the aerosols of the lower atmosphere and the effects of humidity variations on their optical properties,” Air Force Geophysics Lab Hanscom AFB MA, AFGL-TR-79–0214 (1979).

D. Antoine, “Atmospheric corrections over Case 1 waters (CWAC),” OLCI Level 2 ATBD, v.2.2. S3–L2-SD-03–C07-LOV-ATBD (2010).

E. Vermote, D. Tanré, J. L. Deuzé, M. Herman, J. J. Morcrette, and S. Y. Kotchenova, “Second simulation of a satellite signal in the solar spectrum-vector (6SV),” 6S User Guide Version 3 (2006).

Z. Ahmad, “model_select-ahmad() – select two aerosol models whose epsilon values bracket the observed ms epsilon, eps_obs,” aerosol.c in SeaDAS code, http://seadas.gsfc.nasa.gov (2014).

Z. Ahmad, and B. Franz, “Atmospheric correction using multiple-scattering epsilon values,” in proceeding of Ocean Optics XXII (2014).

Z. Ahmad and B. Franz, “Recent enhancements in atmospheric correction algorithm for ocean color retrievals from remotely sensed data,” Proceeding of Ocean Optics XXIII (2016).

B. Franz, “model_select_wang() - M. Wang aerosol model selection,” aeosol.c in SeaDAS code, http://seadas.gsfc.nasa.gov (2004).

A. Morel, B. Gentili, and D. Antoine, “Assessing the Atmospheric and Marine Signal from a Geostationary Orbit (part II),” COMS Ocean Data Processing System Development Project(3) (2005).

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Figures (4)

Fig. 1
Fig. 1 (a) Spectral dependence of ρam for a range of aerosol optical depths τa (865) from 0.01 to 1.2 for the maritime aerosol model with a relative humidity of 50% and solar-sensor geometries θs = 60°, θv = 40°, ϕsv = 40°. (b) Spectral relationships between aerosol multiple-scattering reflectances at several wavelengths for the same aerosol model and solar-sensor geometries.
Fig. 2
Fig. 2 Flowchart describing the process of the Spectral Relationship of Aerosol Multiple-Scattering reflectance (SRAMS) approach for estimating ρam(VIS) from ρam(NIR).
Fig. 3
Fig. 3 Validation results from simulation data; the aerosol reflectance error ∆ρam for the SRAMS (a) and the SSE scheme (b), and errors in the ρwn,, the chlorophyll-a (chl-a), aerosol optical thickness at 555 nm (AOT555), and the Ångström exponent for 443 nm relative to 865 nm (Å) in the absolute percentage deviation (APD) for the SRAMS (c) and the SSE scheme (d).
Fig. 4
Fig. 4 Validation results of in situ Rrs match-ups at different visible wavelengths of GOCI, 412, 443, 490, 555, and 660 nm for SRAMS and SSE scheme. In situ Rrs data are from Korea Ocean Satellite Center (KOSC) cruises since 2010 (65 match-ups) and the ocean color component of the Aerosol Robotics Network (AERONET-OC) sites (67 match-ups).

Tables (2)

Tables Icon

Table 1 Summary of the spectral relationships for various GOCI bands.

Tables Icon

Table 2 Statistical values of the Rrs match-ups (SRAMS and SSE scheme)

Equations (15)

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

ρ TOA (λ)= ρ r (λ)+ ρ am (λ)+ t d v (λ) t d s (λ) ρ wn (λ),
ρ as ( M i ,λ, θ s , θ v , ϕ sv )= n=1 4 a n ( M i ,λ, θ s , θ v , ϕ sv ) ρ am (λ) n ,
ε( M i , λ 1 , λ 2 , θ s , θ v , ϕ sv )= ρ as ( M i , λ 1 , θ s , θ v , ϕ sv )/ ρ as ( M i , λ 2 , θ s , θ v , ϕ sv ).
ε pre ( M L , NIR S , NIR L ) ε ave ( NIR S , NIR L )< ε pre ( M H , NIR S , NIR L ),
ε ave ( NIR S , NIR L )= N 1 i=1 N ε( M i , NIR S , NIR L ) .
w M L = ε pre ( M H , NIR S , NIR L ) ε ave ( NIR S , NIR L ) ε pre ( M H , NIR S , NIR L ) ε pre ( M L , NIR S , NIR L ) ,
w M H =1 w M L .
ρ am (λ)= w M L n=1 4 b n ( M L ,λ, θ s , θ v , ϕ sv ) ρ as ( M L ,λ) n + w M H n=1 4 b n ( M H ,λ, θ s , θ v , ϕ sv ) ρ as ( M H ,λ) n ,
ρ am Mod ( M i , λ 2 )= n=1 D c n ( M i , λ 1 , λ 2 , θ s , θ v , ϕ sv ) ρ am ( λ 1 ) n ,
ρ am Mod ( M L , NIR S ) ρ am ( NIR S )< ρ am Mod ( M H , NIR S ).
ρ am ( NIR S )= n=1 2 c n ( M H ,λ, θ s , θ v , ϕ sv ) [ w M H ρ am ( NIR L ) ] n + n=1 2 c n ( M L ,λ, θ s , θ v , ϕ sv ) [ w M L ρ am ( NIR L ) ] n .
A ( w M H ) 2 +B( w M H )+C=0,
A= ρ am ( NIR L ) 2 [ c 2 ( M L ,λ, θ s , θ v , ϕ sv ) + c 2 ( M H ,λ, θ s , θ v , ϕ sv ) ], B= ρ am ( NIR L )[ c 1 ( M H ,λ, θ s , θ v , ϕ sv ) c 1 ( M L ,λ, θ s , θ v , ϕ sv ) 2 c 2 ( M L ,λ, θ s , θ v , ϕ sv ) ρ am ( NIR L ) ], C= ρ am ( NIR L )[ c 1 ( M L ,λ, θ s , θ v , ϕ sv ) + c 2 ( M L ,λ, θ s , θ v , ϕ sv ) ρ am ( NIR L ) ] ρ am ( NIR S ).
ρ am ( λ 2 )= n=1 D c n ( M H ,λ, θ s , θ v , ϕ sv ) [ w M H ρ am Mod ( M H , λ 1 ) ] n + n=1 D c n ( M L ,λ, θ s , θ v , ϕ sv ) [ (1 w M H ) ρ am Mod ( M L , λ 1 ) ] n .
APD (%)= 100 K n=1 K ( | v n t v n e | v n t ) ,

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