Abstract

In the atmospheric correction process of the satellite ocean color data, the removal of the aerosol scattering contribution over the coastal and inland water bodies has been a major challenge with the standard algorithms. In this work, a practical method is proposed based on a combination of NIR and ultraviolet (UV) bands (named as UVNIR-ex) for the succeeding generation of space borne multispectral and hyperspectral sensors. This scheme replaces the black-ocean assumption and accounts for non-zero water-leaving radiance contributions in the NIR and UV bands. The aerosol contributions are thus deduced for these two bands and used to select the appropriate aerosol models to retrieve aerosol optical properties and hence, water-leaving radiances in the UV, Visible and NIR bands. The performance of the UVNIR-ex algorithm was tested and evaluated based on match-ups between HICO and in-situ observations in optically complex coastal and inland waters and by comparison with three alternative aerosol correction methods based on UV-NIR, Spectral Shape Parameter (SSP) and iterative NIR (INIR) approaches. A preliminary comparison with in-situ aerosol optical thickness (AOT) measurements from AERONET-OC sites revealed that the UVNIR-ex algorithm significantly improved the AOT retrievals with a mean relative error (MRE) around 25%, while the UVNIR, SSP and INIR algorithms showed performance degradation with a MRE of 27%, 34%, and 42%, respectively. The comparison with AERONET-OC and regional in-situ measurements from turbid and productive waters further showed that the INIR algorithm underestimated the $n{L_{\textrm{w}}}$ retrievals in blue bands in turbid waters (MRE > 100%) and negligible $n{L_{\textrm{w}}}$ in red-NIR bands and high anomalous radiances in UV-Blue bands in productive waters (MRE 53%). The SSP and UVNIR algorithms performed better in retrieving the $n{L_{\textrm{w}}}$ in green-NIR bands but showed significant errors in UV-blue bands in both turbid and productive waters. Based on these match-up analyses, the UVNIR-ex algorithm yielded best $n{L_{\textrm{w}}}$ retrievals across all the UV-NIR bands in terms of accuracy and performance. The highest accuracy and consistency of the UVNIR-ex algorithm indicates that it is more suited for estimating the aerosol optical properties and water-leaving radiance and has a significant advantage over the requirement of shortwave infrared bands for turbid and productive waters.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Corrections

18 July 2019: Typographical corrections were made to Table 2.


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2018 (7)

A. Kulshreshtha and P. Shanmugam, “Assessment of trophic state and water quality of coastal-inland lakes based on Fuzzy Inference System,” J. Great Lakes Res. 44(5), 1010–1025 (2018).
[Crossref]

L. Qi, C. Hu, P. M. Visser, and R. Ma, “Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations,” Limnol. Oceanogr. 63(4), 1711–1726 (2018).
[Crossref]

M. Gao, P.-W. Zhai, B. A. Franz, Y. Hu, K. D. Knobelspiesse, P. J. Werdell, A. Ibrahim, F. Xu, and B. Cairns, “Retrieval of aerosol properties and water-leaving reflectance from multi-angular polarimetric measurements over coastal waters,” Opt. Express 26(7), 8968 (2018).
[Crossref]

P. Shanmugam, X. He, R. K. Singh, and T. Varunan, “A modern robust approach to remotely estimate chlorophyll in coastal and inland zones,” Adv. Space Res. 61(10), 2491–2509 (2018).
[Crossref]

T. Varunan and P. Shanmugam, “Use of Landsat 8 data for characterizing dynamic changes in physical and acoustical properties of coastal lagoon and estuarine waters,” Adv. Space Res. 62(9), 2393–2417 (2018).
[Crossref]

M. Wang and L. Jiang, “Atmospheric Correction Using the Information From the Short Blue Band,” IEEE Trans. Geosci. Remote Sens. 56(10), 6224–6237 (2018).
[Crossref]

A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
[Crossref]

2017 (4)

M. R. Al Shehhi, I. Gherboudj, J. Zhao, and H. Ghedira, “Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment,” ISPRS J. Photogramm. Remote Sens. 133, 46–60 (2017).
[Crossref]

Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
[Crossref]

T. Varunan and P. Shanmugam, “An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments,” J. Great Lakes Res. 43(1), 32–49 (2017).
[Crossref]

S. Vadakke-Chanat, P. Shanmugam, and Y.-H. Ahn, “A Model for Deriving the Spectral Backscattering Properties of Particles in Inland and Marine Waters From In Situ and Remote Sensing Data,” IEEE Trans. Geosci. Remote Sens. 55(3), 1461–1476 (2017).
[Crossref]

2016 (3)

K. Chakraborty, A. Gupta, A. A. Lotliker, and G. Tilstone, “Evaluation of model simulated and MODIS-Aqua retrieved sea surface chlorophyll in the eastern Arabian Sea,” Estuarine, Coastal Shelf Sci. 181, 61–69 (2016).
[Crossref]

M. Wang, “Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function,” Opt. Express 24(11), 12414 (2016).
[Crossref]

R. K. Singh and P. Shanmugam, “A Multidisciplinary Remote Sensing Ocean Color Sensor: Analysis of User Needs and Recommendations for Future Developments,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(11), 5223–5238 (2016).
[Crossref]

2015 (4)

Q. Vanhellemont and K. G. Ruddick, “Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8,” Remote Sens. Environ. 161, 89–106 (2015).
[Crossref]

T. Varunan and P. Shanmugam, “A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data,” Remote Sens. Environ. 158, 235–254 (2015).
[Crossref]

S. C. J. Palmer, T. Kutser, and P. D. Hunter, “Remote sensing of inland waters: Challenges, progress and future directions,” Remote Sens. Environ. 157, 1–8 (2015).
[Crossref]

S. Sterckx, S. Knaeps, S. Kratzer, and K. G. Ruddick, “SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters,” Remote Sens. Environ. 157, 96–110 (2015).
[Crossref]

2014 (5)

L. Qi, C. Hu, H. Duan, J. Cannizzaro, and R. Ma, “A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations,” Remote Sens. Environ. 154, 298–317 (2014).
[Crossref]

R. K. Singh and P. Shanmugam, “A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters,” Remote Sens. Environ. 142, 188–206 (2014).
[Crossref]

M. Tholkapiyan, P. Shanmugam, and T. Suresh, “Monitoring of ocean surface algal blooms in coastal and oceanic waters around India,” Environ. Monit. Assess. 186(7), 4129–4137 (2014).
[Crossref]

R. K. Singh and P. Shanmugam, “Corrigendum to “A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters” [Remote Sensing of Environment 142 (2014) 188–206],” Remote Sens. Environ. 148, 222–223 (2014).
[Crossref]

J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
[Crossref]

2013 (5)

Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
[Crossref]

G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti, “Assessment of MERIS ocean color data products for European seas,” Ocean Sci. 9(3), 521–533 (2013).
[Crossref]

C. Goyens, C. Jamet, and T. Schroeder, “Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters,” Remote Sens. Environ. 131, 63–75 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

2012 (3)

C. Hu, Z. Lee, and B. A. Franz, “Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference,” J. Geophys. Res.: Oceans 117(C1), C01011 (2012).
[Crossref]

D. Odermatt, A. A. Gitelson, V. E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens. Environ. 118, 116–126 (2012).
[Crossref]

X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754 (2012).
[Crossref]

2011 (4)

X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353 (2011).
[Crossref]

F. Steinmetz, P.-Y. Deschamps, and D. Ramon, “Atmospheric correction in presence of sun glint: application to MERIS,” Opt. Express 19(10), 9783 (2011).
[Crossref]

C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
[Crossref]

P. Shanmugam, “A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters,” J. Geophys. Res.: Oceans 116(C4), C04016 (2011).
[Crossref]

2010 (4)

2009 (4)

M. Wang, S. Son, and W. Shi, “Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data,” Remote Sens. Environ. 113(3), 635–644 (2009).
[Crossref]

M. Wang, S. Son, and L. W. Harding, “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res.: Oceans 114(C10), C10011 (2009).
[Crossref]

C. Hu, “A novel ocean color index to detect floating algae in the global oceans,” Remote Sens. Environ. 113(10), 2118–2129 (2009).
[Crossref]

C. R. McClain, “A Decade of Satellite Ocean Color Observations,” Ann. Rev. Mar. Sci. 1(1), 19–42 (2009).
[Crossref]

2008 (3)

A. Mannino, M. E. Russ, and S. B. Hooker, “Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight,” J. Geophys. Res.: Oceans 113(C7), C07051 (2008).
[Crossref]

M. Wang and W. Shi, “Satellite-Observed Algae Blooms in China’s Lake Taihu,” Eos, Trans. Am. Geophys. Union 89(22), 201 (2008).
[Crossref]

M. Oo, M. Vargas, A. A. Gilerson, B. Gross, F. Moshary, and S. A. Ahmed, “Improving atmospheric correction for highly productive coastal waters using the short wave infrared retrieval algorithm with water-leaving reflectance constraints at 412 nm,” Appl. Opt. 47(21), 3846 (2008).
[Crossref]

2007 (3)

2006 (2)

P. V. Zimba and A. A. Gitelson, “Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization,” Aquaculture 256(1-4), 272–286 (2006).
[Crossref]

S. W. Bailey and P. J. Werdell, “A multi-sensor approach for the on-orbit validation of ocean color satellite data products,” Remote Sens. Environ. 102(1-2), 12–23 (2006).
[Crossref]

2005 (3)

S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
[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]

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
[Crossref]

2004 (3)

J. F. R. Gower, L. Brown, and G. A. Borstad, “Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor,” Can. J. Remote Sens. 30(1), 17–25 (2004).
[Crossref]

W. W. Gregg and N. W. Casey, “Global and regional evaluation of the SeaWiFS chlorophyll data set,” Remote Sens. Environ. 93(4), 463–479 (2004).
[Crossref]

X. He, D. Pan, and Z. Mao, “Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters,” Acta Oceanol. Sin. 23(4), 609–615 (2004).

2002 (1)

C. Brönmark and L.-A. Hansson, “Environmental issues in lakes and ponds: current state and perspectives,” Environ. Conserv. 29(3), 290–307 (2002).
[Crossref]

2001 (1)

2000 (3)

1999 (2)

1997 (1)

H. R. Gordon, “Atmospheric correction of ocean color imagery in the Earth Observing System era,” J. Geophys. Res.: Atmos. 102(D14), 17081–17106 (1997).
[Crossref]

1994 (2)

1992 (1)

Y.-H. Ahn, A. Bricaud, and A. Y. Morel, “Light backscattering efficiency and related properties of some phytoplankters,” Deep-Sea Res., Part A 39(11-12), 1835–1855 (1992).
[Crossref]

1983 (1)

Ahmad, Z.

A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
[Crossref]

Z. Ahmad, B. A. Franz, C. R. McClain, E. J. Kwiatkowska, P. J. Werdell, E. P. Shettle, and B. N. Holben, “New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans,” Appl. Opt. 49(29), 5545 (2010).
[Crossref]

Ahmed, S. A.

Ahn, Y.-H.

S. Vadakke-Chanat, P. Shanmugam, and Y.-H. Ahn, “A Model for Deriving the Spectral Backscattering Properties of Particles in Inland and Marine Waters From In Situ and Remote Sensing Data,” IEEE Trans. Geosci. Remote Sens. 55(3), 1461–1476 (2017).
[Crossref]

Y.-H. Ahn, A. Bricaud, and A. Y. Morel, “Light backscattering efficiency and related properties of some phytoplankters,” Deep-Sea Res., Part A 39(11-12), 1835–1855 (1992).
[Crossref]

Aiken, J.

S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
[Crossref]

J. Aiken, G. Moore, S. J. Lavender, C. Mazeran, and J.-P. Huot, MERIS ATBD 2.6 - Case II.S Bright Pixel Atmospheric Correction (EUM/OPS-SEN3/DOC/17/961973) (2017), (5.3).

Al Shehhi, M. R.

M. R. Al Shehhi, I. Gherboudj, J. Zhao, and H. Ghedira, “Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment,” ISPRS J. Photogramm. Remote Sens. 133, 46–60 (2017).
[Crossref]

Arnone, R. A.

R. P. Stumpf, R. A. Arnone, R. W. Gould, P. M. Martinolich, and V. Ransibrahmanakul, “A Partially Coupled Ocean–Atmosphere Model for Retrieval of Water-Leaving Radiance from SeaWiFS in Coastal Waters,” in SeaWiFS Postlaunch Technical Report Series, Volume 22, Algorithm Updates for the Fourth SeaWiFS Data Reprocessing, S. B. Hooker and E. R. Firestone, eds. (NASA Technical Memorandum, 2003), pp. 51–59.

Bai, Y.

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754 (2012).
[Crossref]

X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353 (2011).
[Crossref]

Bailey, S. W.

Berthon, J.-F.

G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti, “Assessment of MERIS ocean color data products for European seas,” Ocean Sci. 9(3), 521–533 (2013).
[Crossref]

Blondeau-Patissier, D.

S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
[Crossref]

Borstad, G. A.

J. F. R. Gower, L. Brown, and G. A. Borstad, “Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor,” Can. J. Remote Sens. 30(1), 17–25 (2004).
[Crossref]

Brando, V. E.

D. Odermatt, A. A. Gitelson, V. E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens. Environ. 118, 116–126 (2012).
[Crossref]

Bricaud, A.

Y.-H. Ahn, A. Bricaud, and A. Y. Morel, “Light backscattering efficiency and related properties of some phytoplankters,” Deep-Sea Res., Part A 39(11-12), 1835–1855 (1992).
[Crossref]

Brönmark, C.

C. Brönmark and L.-A. Hansson, “Environmental issues in lakes and ponds: current state and perspectives,” Environ. Conserv. 29(3), 290–307 (2002).
[Crossref]

Brown, L.

J. F. R. Gower, L. Brown, and G. A. Borstad, “Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor,” Can. J. Remote Sens. 30(1), 17–25 (2004).
[Crossref]

Cairns, B.

Cannizzaro, J.

L. Qi, C. Hu, H. Duan, J. Cannizzaro, and R. Ma, “A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations,” Remote Sens. Environ. 154, 298–317 (2014).
[Crossref]

Canuti, E.

G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti, “Assessment of MERIS ocean color data products for European seas,” Ocean Sci. 9(3), 521–533 (2013).
[Crossref]

Carder, K. L.

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
[Crossref]

Z. Lee, K. L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch, “Hyperspectral remote sensing for shallow waters: 2 Deriving bottom depths and water properties by optimization,” Appl. Opt. 38(18), 3831 (1999).
[Crossref]

Casey, N. W.

W. W. Gregg and N. W. Casey, “Global and regional evaluation of the SeaWiFS chlorophyll data set,” Remote Sens. Environ. 93(4), 463–479 (2004).
[Crossref]

Chakraborty, K.

K. Chakraborty, A. Gupta, A. A. Lotliker, and G. Tilstone, “Evaluation of model simulated and MODIS-Aqua retrieved sea surface chlorophyll in the eastern Arabian Sea,” Estuarine, Coastal Shelf Sci. 181, 61–69 (2016).
[Crossref]

Chen, C.-T. A.

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

Chen, J.

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
[Crossref]

Z. Mao, J. Chen, and X. He, “Evaluation of CMODIS-measured radiance by a hyperspectral model,” Int. J. Remote Sens. 31(19), 5179–5198 (2010).
[Crossref]

Cheng, Q.

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

Cui, Q.

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

Darecki, M.

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
[Crossref]

Davis, C. O.

J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
[Crossref]

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
[Crossref]

DePinto, J. V.

J. V. DePinto, E. Verhamme, R. Lambert, and D. Rucinski, An Approach For Determination of Phosphorus Objectives and Target Loads for Lake Erie (2013).

Deschamps, P.-Y.

Dierssen, H. M.

H. M. Dierssen, “Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate,” Proc. Natl. Acad. Sci. 107(40), 17073–17078 (2010).
[Crossref]

H. M. Dierssen, The Backscattering Enigma in Natural Waters (2006).

Dong, X.

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

Duan, H.

L. Qi, C. Hu, H. Duan, J. Cannizzaro, and R. Ma, “A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations,” Remote Sens. Environ. 154, 298–317 (2014).
[Crossref]

Etzion, D.

A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Fan, Y.

Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
[Crossref]

Feng, H.

C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
[Crossref]

Franz, B. A.

Frouin, R. J.

R. J. Frouin and L. S. Gross-Colzy, “Contribution of ultraviolet and shortwave infrared observations to atmospheric correction of PACE ocean-color imagery,” in Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challanges - SPIE Asia-Pacific Remote Sensing, R. J. Frouin, S. C. Shenoi, and K. H. Rao, eds. (SPIE, 2016), 9878, p. 98780C.

Gao, B.-C.

A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
[Crossref]

J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
[Crossref]

Gao, M.

Gatebe, C. K.

Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
[Crossref]

Ghedira, H.

M. R. Al Shehhi, I. Gherboudj, J. Zhao, and H. Ghedira, “Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment,” ISPRS J. Photogramm. Remote Sens. 133, 46–60 (2017).
[Crossref]

Gherboudj, I.

M. R. Al Shehhi, I. Gherboudj, J. Zhao, and H. Ghedira, “Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment,” ISPRS J. Photogramm. Remote Sens. 133, 46–60 (2017).
[Crossref]

Gilerson, A. A.

Gitelson, A. A.

D. Odermatt, A. A. Gitelson, V. E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens. Environ. 118, 116–126 (2012).
[Crossref]

P. V. Zimba and A. A. Gitelson, “Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization,” Aquaculture 256(1-4), 272–286 (2006).
[Crossref]

A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Gong, F.

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353 (2011).
[Crossref]

Gordon, H. R.

Gould, R. W.

R. P. Stumpf, R. A. Arnone, R. W. Gould, P. M. Martinolich, and V. Ransibrahmanakul, “A Partially Coupled Ocean–Atmosphere Model for Retrieval of Water-Leaving Radiance from SeaWiFS in Coastal Waters,” in SeaWiFS Postlaunch Technical Report Series, Volume 22, Algorithm Updates for the Fourth SeaWiFS Data Reprocessing, S. B. Hooker and E. R. Firestone, eds. (NASA Technical Memorandum, 2003), pp. 51–59.

Gower, J. F. R.

J. F. R. Gower, L. Brown, and G. A. Borstad, “Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor,” Can. J. Remote Sens. 30(1), 17–25 (2004).
[Crossref]

Goyens, C.

C. Goyens, C. Jamet, and T. Schroeder, “Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters,” Remote Sens. Environ. 131, 63–75 (2013).
[Crossref]

Gregg, W. W.

W. W. Gregg and N. W. Casey, “Global and regional evaluation of the SeaWiFS chlorophyll data set,” Remote Sens. Environ. 93(4), 463–479 (2004).
[Crossref]

Gross, B.

Gross-Colzy, L. S.

R. J. Frouin and L. S. Gross-Colzy, “Contribution of ultraviolet and shortwave infrared observations to atmospheric correction of PACE ocean-color imagery,” in Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challanges - SPIE Asia-Pacific Remote Sensing, R. J. Frouin, S. C. Shenoi, and K. H. Rao, eds. (SPIE, 2016), 9878, p. 98780C.

Gupta, A.

K. Chakraborty, A. Gupta, A. A. Lotliker, and G. Tilstone, “Evaluation of model simulated and MODIS-Aqua retrieved sea surface chlorophyll in the eastern Arabian Sea,” Estuarine, Coastal Shelf Sci. 181, 61–69 (2016).
[Crossref]

Han, L.

A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Hansson, L.-A.

C. Brönmark and L.-A. Hansson, “Environmental issues in lakes and ponds: current state and perspectives,” Environ. Conserv. 29(3), 290–307 (2002).
[Crossref]

Hao, Z.

Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
[Crossref]

Harding, L. W.

M. Wang, S. Son, and L. W. Harding, “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res.: Oceans 114(C10), C10011 (2009).
[Crossref]

He, X.

P. Shanmugam, X. He, R. K. Singh, and T. Varunan, “A modern robust approach to remotely estimate chlorophyll in coastal and inland zones,” Adv. Space Res. 61(10), 2491–2509 (2018).
[Crossref]

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
[Crossref]

X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754 (2012).
[Crossref]

X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353 (2011).
[Crossref]

Z. Mao, J. Chen, and X. He, “Evaluation of CMODIS-measured radiance by a hyperspectral model,” Int. J. Remote Sens. 31(19), 5179–5198 (2010).
[Crossref]

X. He, D. Pan, and Z. Mao, “Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters,” Acta Oceanol. Sin. 23(4), 609–615 (2004).

Healy, R.

A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
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L. Qi, C. Hu, P. M. Visser, and R. Ma, “Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations,” Limnol. Oceanogr. 63(4), 1711–1726 (2018).
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L. Qi, C. Hu, H. Duan, J. Cannizzaro, and R. Ma, “A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations,” Remote Sens. Environ. 154, 298–317 (2014).
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C. Hu, Z. Lee, and B. A. Franz, “Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference,” J. Geophys. Res.: Oceans 117(C1), C01011 (2012).
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C. Hu, “A novel ocean color index to detect floating algae in the global oceans,” Remote Sens. Environ. 113(10), 2118–2129 (2009).
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Huang, N.

X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
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J. Aiken, G. Moore, S. J. Lavender, C. Mazeran, and J.-P. Huot, MERIS ATBD 2.6 - Case II.S Bright Pixel Atmospheric Correction (EUM/OPS-SEN3/DOC/17/961973) (2017), (5.3).

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M. Gao, P.-W. Zhai, B. A. Franz, Y. Hu, K. D. Knobelspiesse, P. J. Werdell, A. Ibrahim, F. Xu, and B. Cairns, “Retrieval of aerosol properties and water-leaving reflectance from multi-angular polarimetric measurements over coastal waters,” Opt. Express 26(7), 8968 (2018).
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A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
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Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
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C. Goyens, C. Jamet, and T. Schroeder, “Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters,” Remote Sens. Environ. 131, 63–75 (2013).
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C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
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H. Loisel, V. Vantrepotte, C. Jamet, and D. Ngoc Dat, “Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters,” in Topics in Oceanography, E. Zambianchi, ed. (InTechOpen, 2013), pp. 1–38.

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S. Sterckx, S. Knaeps, S. Kratzer, and K. G. Ruddick, “SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters,” Remote Sens. Environ. 157, 96–110 (2015).
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A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
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M. Gao, P.-W. Zhai, B. A. Franz, Y. Hu, K. D. Knobelspiesse, P. J. Werdell, A. Ibrahim, F. Xu, and B. Cairns, “Retrieval of aerosol properties and water-leaving reflectance from multi-angular polarimetric measurements over coastal waters,” Opt. Express 26(7), 8968 (2018).
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Kratzer, S.

S. Sterckx, S. Knaeps, S. Kratzer, and K. G. Ruddick, “SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters,” Remote Sens. Environ. 157, 96–110 (2015).
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C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
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J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
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Kulshreshtha, A.

A. Kulshreshtha and P. Shanmugam, “Assessment of trophic state and water quality of coastal-inland lakes based on Fuzzy Inference System,” J. Great Lakes Res. 44(5), 1010–1025 (2018).
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S. C. J. Palmer, T. Kutser, and P. D. Hunter, “Remote sensing of inland waters: Challenges, progress and future directions,” Remote Sens. Environ. 157, 1–8 (2015).
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S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
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Lee, Z.

C. Hu, Z. Lee, and B. A. Franz, “Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference,” J. Geophys. Res.: Oceans 117(C1), C01011 (2012).
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Li, W.

Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
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C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
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H. Loisel, V. Vantrepotte, C. Jamet, and D. Ngoc Dat, “Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters,” in Topics in Oceanography, E. Zambianchi, ed. (InTechOpen, 2013), pp. 1–38.

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K. Chakraborty, A. Gupta, A. A. Lotliker, and G. Tilstone, “Evaluation of model simulated and MODIS-Aqua retrieved sea surface chlorophyll in the eastern Arabian Sea,” Estuarine, Coastal Shelf Sci. 181, 61–69 (2016).
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A. Mannino, M. E. Russ, and S. B. Hooker, “Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight,” J. Geophys. Res.: Oceans 113(C7), C07051 (2008).
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J. Aiken, G. Moore, S. J. Lavender, C. Mazeran, and J.-P. Huot, MERIS ATBD 2.6 - Case II.S Bright Pixel Atmospheric Correction (EUM/OPS-SEN3/DOC/17/961973) (2017), (5.3).

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Meister, G.

C. R. McClain, G. Meister, and B. Monosmith, “Satellite Ocean Color Sensor Design Concepts and Performance Requirements,” in Experimental Methods in the Physical Sciences: Optical Radiometry for Ocean Climate Measurements, G. Zibordi, C. J. Donlon, and A. C. Parr, eds. (Elsevier Academic, 2014), 47, pp. 73–119.

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J. Aiken, G. Moore, S. J. Lavender, C. Mazeran, and J.-P. Huot, MERIS ATBD 2.6 - Case II.S Bright Pixel Atmospheric Correction (EUM/OPS-SEN3/DOC/17/961973) (2017), (5.3).

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S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
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Odermatt, D.

D. Odermatt, A. A. Gitelson, V. E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens. Environ. 118, 116–126 (2012).
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Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
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Pinkerton, M. H.

S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
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A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
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Qi, L.

L. Qi, C. Hu, P. M. Visser, and R. Ma, “Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations,” Limnol. Oceanogr. 63(4), 1711–1726 (2018).
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L. Qi, C. Hu, H. Duan, J. Cannizzaro, and R. Ma, “A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations,” Remote Sens. Environ. 154, 298–317 (2014).
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Ransibrahmanakul, V.

R. P. Stumpf, R. A. Arnone, R. W. Gould, P. M. Martinolich, and V. Ransibrahmanakul, “A Partially Coupled Ocean–Atmosphere Model for Retrieval of Water-Leaving Radiance from SeaWiFS in Coastal Waters,” in SeaWiFS Postlaunch Technical Report Series, Volume 22, Algorithm Updates for the Fourth SeaWiFS Data Reprocessing, S. B. Hooker and E. R. Firestone, eds. (NASA Technical Memorandum, 2003), pp. 51–59.

Rhea, W. J.

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
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Robinson, W. D.

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J. V. DePinto, E. Verhamme, R. Lambert, and D. Rucinski, An Approach For Determination of Phosphorus Objectives and Target Loads for Lake Erie (2013).

Ruddick, K. G.

S. Sterckx, S. Knaeps, S. Kratzer, and K. G. Ruddick, “SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters,” Remote Sens. Environ. 157, 96–110 (2015).
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Russ, M. E.

A. Mannino, M. E. Russ, and S. B. Hooker, “Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight,” J. Geophys. Res.: Oceans 113(C7), C07051 (2008).
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J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
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Schaepman, M.

D. Odermatt, A. A. Gitelson, V. E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens. Environ. 118, 116–126 (2012).
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Schalles, J. F.

A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Schroeder, T.

Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
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P. Shanmugam, X. He, R. K. Singh, and T. Varunan, “A modern robust approach to remotely estimate chlorophyll in coastal and inland zones,” Adv. Space Res. 61(10), 2491–2509 (2018).
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T. Varunan and P. Shanmugam, “Use of Landsat 8 data for characterizing dynamic changes in physical and acoustical properties of coastal lagoon and estuarine waters,” Adv. Space Res. 62(9), 2393–2417 (2018).
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A. Kulshreshtha and P. Shanmugam, “Assessment of trophic state and water quality of coastal-inland lakes based on Fuzzy Inference System,” J. Great Lakes Res. 44(5), 1010–1025 (2018).
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T. Varunan and P. Shanmugam, “An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments,” J. Great Lakes Res. 43(1), 32–49 (2017).
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S. Vadakke-Chanat, P. Shanmugam, and Y.-H. Ahn, “A Model for Deriving the Spectral Backscattering Properties of Particles in Inland and Marine Waters From In Situ and Remote Sensing Data,” IEEE Trans. Geosci. Remote Sens. 55(3), 1461–1476 (2017).
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R. K. Singh and P. Shanmugam, “A Multidisciplinary Remote Sensing Ocean Color Sensor: Analysis of User Needs and Recommendations for Future Developments,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(11), 5223–5238 (2016).
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T. Varunan and P. Shanmugam, “A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data,” Remote Sens. Environ. 158, 235–254 (2015).
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M. Tholkapiyan, P. Shanmugam, and T. Suresh, “Monitoring of ocean surface algal blooms in coastal and oceanic waters around India,” Environ. Monit. Assess. 186(7), 4129–4137 (2014).
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R. K. Singh and P. Shanmugam, “A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters,” Remote Sens. Environ. 142, 188–206 (2014).
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P. Shanmugam, “A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters,” J. Geophys. Res.: Oceans 116(C4), C04016 (2011).
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Shi, W.

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P. Shanmugam, X. He, R. K. Singh, and T. Varunan, “A modern robust approach to remotely estimate chlorophyll in coastal and inland zones,” Adv. Space Res. 61(10), 2491–2509 (2018).
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R. K. Singh and P. Shanmugam, “A Multidisciplinary Remote Sensing Ocean Color Sensor: Analysis of User Needs and Recommendations for Future Developments,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(11), 5223–5238 (2016).
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R. K. Singh and P. Shanmugam, “A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters,” Remote Sens. Environ. 142, 188–206 (2014).
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A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Steinmetz, F.

Sterckx, S.

S. Sterckx, S. Knaeps, S. Kratzer, and K. G. Ruddick, “SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters,” Remote Sens. Environ. 157, 96–110 (2015).
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Steward, R. G.

Stramski, D.

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
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Stumpf, R. P.

R. P. Stumpf, R. A. Arnone, R. W. Gould, P. M. Martinolich, and V. Ransibrahmanakul, “A Partially Coupled Ocean–Atmosphere Model for Retrieval of Water-Leaving Radiance from SeaWiFS in Coastal Waters,” in SeaWiFS Postlaunch Technical Report Series, Volume 22, Algorithm Updates for the Fourth SeaWiFS Data Reprocessing, S. B. Hooker and E. R. Firestone, eds. (NASA Technical Memorandum, 2003), pp. 51–59.

Suresh, T.

M. Tholkapiyan, P. Shanmugam, and T. Suresh, “Monitoring of ocean surface algal blooms in coastal and oceanic waters around India,” Environ. Monit. Assess. 186(7), 4129–4137 (2014).
[Crossref]

Tang, J.

X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754 (2012).
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M. Wang, J. Tang, and W. Shi, “MODIS-derived ocean color products along the China east coastal region,” Geophys. Res. Lett. 34(6), L06611 (2007).
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Tanré, D.

Tao, B.

Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
[Crossref]

Tholkapiyan, M.

M. Tholkapiyan, P. Shanmugam, and T. Suresh, “Monitoring of ocean surface algal blooms in coastal and oceanic waters around India,” Environ. Monit. Assess. 186(7), 4129–4137 (2014).
[Crossref]

Tilstone, G.

K. Chakraborty, A. Gupta, A. A. Lotliker, and G. Tilstone, “Evaluation of model simulated and MODIS-Aqua retrieved sea surface chlorophyll in the eastern Arabian Sea,” Estuarine, Coastal Shelf Sci. 181, 61–69 (2016).
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Tufillaro, N. B.

J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
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Vadakke-Chanat, S.

S. Vadakke-Chanat, P. Shanmugam, and Y.-H. Ahn, “A Model for Deriving the Spectral Backscattering Properties of Particles in Inland and Marine Waters From In Situ and Remote Sensing Data,” IEEE Trans. Geosci. Remote Sens. 55(3), 1461–1476 (2017).
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Vanhellemont, Q.

Q. Vanhellemont and K. G. Ruddick, “Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8,” Remote Sens. Environ. 161, 89–106 (2015).
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Vantrepotte, V.

H. Loisel, V. Vantrepotte, C. Jamet, and D. Ngoc Dat, “Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters,” in Topics in Oceanography, E. Zambianchi, ed. (InTechOpen, 2013), pp. 1–38.

Vargas, M.

Varunan, T.

P. Shanmugam, X. He, R. K. Singh, and T. Varunan, “A modern robust approach to remotely estimate chlorophyll in coastal and inland zones,” Adv. Space Res. 61(10), 2491–2509 (2018).
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T. Varunan and P. Shanmugam, “Use of Landsat 8 data for characterizing dynamic changes in physical and acoustical properties of coastal lagoon and estuarine waters,” Adv. Space Res. 62(9), 2393–2417 (2018).
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T. Varunan and P. Shanmugam, “An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments,” J. Great Lakes Res. 43(1), 32–49 (2017).
[Crossref]

T. Varunan and P. Shanmugam, “A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data,” Remote Sens. Environ. 158, 235–254 (2015).
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Verhamme, E.

J. V. DePinto, E. Verhamme, R. Lambert, and D. Rucinski, An Approach For Determination of Phosphorus Objectives and Target Loads for Lake Erie (2013).

Visser, P. M.

L. Qi, C. Hu, P. M. Visser, and R. Ma, “Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations,” Limnol. Oceanogr. 63(4), 1711–1726 (2018).
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Wang, D.

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
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X. He, Y. Bai, D. Pan, J. Tang, and D. Wang, “Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters,” Opt. Express 20(18), 20754 (2012).
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Wang, M.

M. Wang and L. Jiang, “Atmospheric Correction Using the Information From the Short Blue Band,” IEEE Trans. Geosci. Remote Sens. 56(10), 6224–6237 (2018).
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M. Wang, “Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function,” Opt. Express 24(11), 12414 (2016).
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M. Wang, S. Son, and L. W. Harding, “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res.: Oceans 114(C10), C10011 (2009).
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M. Wang, S. Son, and W. Shi, “Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data,” Remote Sens. Environ. 113(3), 635–644 (2009).
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M. Wang and W. Shi, “Satellite-Observed Algae Blooms in China’s Lake Taihu,” Eos, Trans. Am. Geophys. Union 89(22), 201 (2008).
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M. Wang, J. Tang, and W. Shi, “MODIS-derived ocean color products along the China east coastal region,” Geophys. Res. Lett. 34(6), L06611 (2007).
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M. Wang, “Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations,” Appl. Opt. 46(9), 1535 (2007).
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M. Wang and W. Shi, “The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing,” Opt. Express 15(24), 15722 (2007).
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M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005).
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M. Wang and S. W. Bailey, “Correction of sun glint contamination on the SeaWiFS ocean and atmosphere products,” Appl. Opt. 40(27), 4790 (2001).
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D. A. Siegel, M. Wang, S. Maritorena, and W. D. Robinson, “Atmospheric correction of satellite ocean color imagery: the black pixel assumption,” Appl. Opt. 39(21), 3582 (2000).
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H. R. Gordon and M. Wang, “Influence of oceanic whitecaps on atmospheric correction of ocean-color sensors,” Appl. Opt. 33(33), 7754 (1994).
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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 (1994).
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Werdell, P. J.

Xu, F.

Yacobi, Y. Z.

A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Zhai, P.-W.

A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
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M. Gao, P.-W. Zhai, B. A. Franz, Y. Hu, K. D. Knobelspiesse, P. J. Werdell, A. Ibrahim, F. Xu, and B. Cairns, “Retrieval of aerosol properties and water-leaving reflectance from multi-angular polarimetric measurements over coastal waters,” Opt. Express 26(7), 8968 (2018).
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Zhao, J.

M. R. Al Shehhi, I. Gherboudj, J. Zhao, and H. Ghedira, “Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment,” ISPRS J. Photogramm. Remote Sens. 133, 46–60 (2017).
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Zhu, Q.

Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
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X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353 (2011).
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Zibordi, G.

Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
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G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti, “Assessment of MERIS ocean color data products for European seas,” Ocean Sci. 9(3), 521–533 (2013).
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C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
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P. V. Zimba and A. A. Gitelson, “Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization,” Aquaculture 256(1-4), 272–286 (2006).
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Acta Oceanol. Sin. (1)

X. He, D. Pan, and Z. Mao, “Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters,” Acta Oceanol. Sin. 23(4), 609–615 (2004).

Adv. Limnol. (1)

A. A. Gitelson, Y. Z. Yacobi, J. F. Schalles, D. C. Rundquist, L. Han, R. Stark, and D. Etzion, “Remote estimation of phytoplankton density in productive waters,” Adv. Limnol. 55, 121–136 (2000).

Adv. Space Res. (2)

P. Shanmugam, X. He, R. K. Singh, and T. Varunan, “A modern robust approach to remotely estimate chlorophyll in coastal and inland zones,” Adv. Space Res. 61(10), 2491–2509 (2018).
[Crossref]

T. Varunan and P. Shanmugam, “Use of Landsat 8 data for characterizing dynamic changes in physical and acoustical properties of coastal lagoon and estuarine waters,” Adv. Space Res. 62(9), 2393–2417 (2018).
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Ann. Rev. Mar. Sci. (1)

C. R. McClain, “A Decade of Satellite Ocean Color Observations,” Ann. Rev. Mar. Sci. 1(1), 19–42 (2009).
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Appl. Opt. (12)

Z. Lee, K. L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch, “Hyperspectral remote sensing for shallow waters: 2 Deriving bottom depths and water properties by optimization,” Appl. Opt. 38(18), 3831 (1999).
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M. Wang, “Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations,” Appl. Opt. 46(9), 1535 (2007).
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K. G. Ruddick, F. Ovidio, and M. Rijkeboer, “Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters,” Appl. Opt. 39(6), 897 (2000).
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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 (1994).
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D. A. Siegel, M. Wang, S. Maritorena, and W. D. Robinson, “Atmospheric correction of satellite ocean color imagery: the black pixel assumption,” Appl. Opt. 39(21), 3582 (2000).
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C. D. Mobley, “Estimation of the remote-sensing reflectance from above-surface measurements,” Appl. Opt. 38(36), 7442 (1999).
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Z. Ahmad, B. A. Franz, C. R. McClain, E. J. Kwiatkowska, P. J. Werdell, E. P. Shettle, and B. N. Holben, “New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans,” Appl. Opt. 49(29), 5545 (2010).
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X. He, D. Pan, Y. Bai, Q. Zhu, and F. Gong, “Evaluation of the aerosol models for SeaWiFS and MODIS by AERONET data over open oceans,” Appl. Opt. 50(22), 4353 (2011).
[Crossref]

M. Wang and S. W. Bailey, “Correction of sun glint contamination on the SeaWiFS ocean and atmosphere products,” Appl. Opt. 40(27), 4790 (2001).
[Crossref]

H. R. Gordon and M. Wang, “Influence of oceanic whitecaps on atmospheric correction of ocean-color sensors,” Appl. Opt. 33(33), 7754 (1994).
[Crossref]

Aquaculture (1)

P. V. Zimba and A. A. Gitelson, “Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems: Model tuning and accuracy optimization,” Aquaculture 256(1-4), 272–286 (2006).
[Crossref]

Biogeosciences (1)

X. He, Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong, “Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998-2011),” Biogeosciences 10(7), 4721–4739 (2013).
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Can. J. Remote Sens. (1)

J. F. R. Gower, L. Brown, and G. A. Borstad, “Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor,” Can. J. Remote Sens. 30(1), 17–25 (2004).
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Cont. Shelf Res. (1)

S. J. Lavender, M. H. Pinkerton, G. F. Moore, J. Aiken, and D. Blondeau-Patissier, “Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters,” Cont. Shelf Res. 25(4), 539–555 (2005).
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Deep-Sea Res., Part A (1)

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Environ. Conserv. (1)

C. Brönmark and L.-A. Hansson, “Environmental issues in lakes and ponds: current state and perspectives,” Environ. Conserv. 29(3), 290–307 (2002).
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Environ. Monit. Assess. (1)

M. Tholkapiyan, P. Shanmugam, and T. Suresh, “Monitoring of ocean surface algal blooms in coastal and oceanic waters around India,” Environ. Monit. Assess. 186(7), 4129–4137 (2014).
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Eos, Trans. Am. Geophys. Union (1)

M. Wang and W. Shi, “Satellite-Observed Algae Blooms in China’s Lake Taihu,” Eos, Trans. Am. Geophys. Union 89(22), 201 (2008).
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Estuarine, Coastal Shelf Sci. (1)

K. Chakraborty, A. Gupta, A. A. Lotliker, and G. Tilstone, “Evaluation of model simulated and MODIS-Aqua retrieved sea surface chlorophyll in the eastern Arabian Sea,” Estuarine, Coastal Shelf Sci. 181, 61–69 (2016).
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Geophys. Res. Lett. (1)

M. Wang, J. Tang, and W. Shi, “MODIS-derived ocean color products along the China east coastal region,” Geophys. Res. Lett. 34(6), L06611 (2007).
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IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (1)

R. K. Singh and P. Shanmugam, “A Multidisciplinary Remote Sensing Ocean Color Sensor: Analysis of User Needs and Recommendations for Future Developments,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(11), 5223–5238 (2016).
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IEEE Trans. Geosci. Remote Sens. (2)

M. Wang and L. Jiang, “Atmospheric Correction Using the Information From the Short Blue Band,” IEEE Trans. Geosci. Remote Sens. 56(10), 6224–6237 (2018).
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S. Vadakke-Chanat, P. Shanmugam, and Y.-H. Ahn, “A Model for Deriving the Spectral Backscattering Properties of Particles in Inland and Marine Waters From In Situ and Remote Sensing Data,” IEEE Trans. Geosci. Remote Sens. 55(3), 1461–1476 (2017).
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Int. J. Remote Sens. (2)

Z. Mao, J. Chen, and X. He, “Evaluation of CMODIS-measured radiance by a hyperspectral model,” Int. J. Remote Sens. 31(19), 5179–5198 (2010).
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M. Wang, “A refinement for the Rayleigh radiance computation with variation of the atmospheric pressure,” Int. J. Remote Sens. 26(24), 5651–5663 (2005).
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ISPRS J. Photogramm. Remote Sens. (1)

M. R. Al Shehhi, I. Gherboudj, J. Zhao, and H. Ghedira, “Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment,” ISPRS J. Photogramm. Remote Sens. 133, 46–60 (2017).
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J. Geophys. Res.: Atmos. (1)

H. R. Gordon, “Atmospheric correction of ocean color imagery in the Earth Observing System era,” J. Geophys. Res.: Atmos. 102(D14), 17081–17106 (1997).
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J. Geophys. Res.: Oceans (5)

Z.-P. Lee, M. Darecki, K. L. Carder, C. O. Davis, D. Stramski, and W. J. Rhea, “Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods,” J. Geophys. Res.: Oceans 110(C2), C02017 (2005).
[Crossref]

M. Wang, S. Son, and L. W. Harding, “Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,” J. Geophys. Res.: Oceans 114(C10), C10011 (2009).
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A. Mannino, M. E. Russ, and S. B. Hooker, “Algorithm development and validation for satellite-derived distributions of DOC and CDOM in the U.S. Middle Atlantic Bight,” J. Geophys. Res.: Oceans 113(C7), C07051 (2008).
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P. Shanmugam, “A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters,” J. Geophys. Res.: Oceans 116(C4), C04016 (2011).
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C. Hu, Z. Lee, and B. A. Franz, “Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference,” J. Geophys. Res.: Oceans 117(C1), C01011 (2012).
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J. Great Lakes Res. (2)

T. Varunan and P. Shanmugam, “An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments,” J. Great Lakes Res. 43(1), 32–49 (2017).
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A. Kulshreshtha and P. Shanmugam, “Assessment of trophic state and water quality of coastal-inland lakes based on Fuzzy Inference System,” J. Great Lakes Res. 44(5), 1010–1025 (2018).
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Limnol. Oceanogr. (1)

L. Qi, C. Hu, P. M. Visser, and R. Ma, “Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations,” Limnol. Oceanogr. 63(4), 1711–1726 (2018).
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Ocean Sci. (1)

G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti, “Assessment of MERIS ocean color data products for European seas,” Ocean Sci. 9(3), 521–533 (2013).
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Opt. Express (6)

Proc. Natl. Acad. Sci. (1)

H. M. Dierssen, “Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate,” Proc. Natl. Acad. Sci. 107(40), 17073–17078 (2010).
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Remote Sens. (1)

J. P. Ryan, C. O. Davis, N. B. Tufillaro, R. M. Kudela, and B.-C. Gao, “Application of the Hyperspectral Imager for the Coastal Ocean to Phytoplankton Ecology Studies in Monterey Bay, CA, USA,” Remote Sens. 6(2), 1007–1025 (2014).
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Remote Sens. Environ. (18)

S. W. Bailey and P. J. Werdell, “A multi-sensor approach for the on-orbit validation of ocean color satellite data products,” Remote Sens. Environ. 102(1-2), 12–23 (2006).
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Y. Fan, W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, “Atmospheric correction over coastal waters using multilayer neural networks,” Remote Sens. Environ. 199, 218–240 (2017).
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R. K. Singh and P. Shanmugam, “Corrigendum to “A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters” [Remote Sensing of Environment 142 (2014) 188–206],” Remote Sens. Environ. 148, 222–223 (2014).
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A. Ibrahim, B. A. Franz, Z. Ahmad, R. Healy, K. D. Knobelspiesse, B.-C. Gao, C. Proctor, and P.-W. Zhai, “Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO),” Remote Sens. Environ. 204, 60–75 (2018).
[Crossref]

Z. Mao, J. Chen, Z. Hao, D. Pan, B. Tao, and Q. Zhu, “A new approach to estimate the aerosol scattering ratios for the atmospheric correction of satellite remote sensing data in coastal regions,” Remote Sens. Environ. 132, 186–194 (2013).
[Crossref]

Q. Vanhellemont and K. G. Ruddick, “Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8,” Remote Sens. Environ. 161, 89–106 (2015).
[Crossref]

D. Odermatt, A. A. Gitelson, V. E. Brando, and M. Schaepman, “Review of constituent retrieval in optically deep and complex waters from satellite imagery,” Remote Sens. Environ. 118, 116–126 (2012).
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S. C. J. Palmer, T. Kutser, and P. D. Hunter, “Remote sensing of inland waters: Challenges, progress and future directions,” Remote Sens. Environ. 157, 1–8 (2015).
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X. He, Y. Bai, D. Pan, N. Huang, X. Dong, J. Chen, C.-T. A. Chen, and Q. Cui, “Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters,” Remote Sens. Environ. 133, 225–239 (2013).
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L. Qi, C. Hu, H. Duan, J. Cannizzaro, and R. Ma, “A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations,” Remote Sens. Environ. 154, 298–317 (2014).
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W. W. Gregg and N. W. Casey, “Global and regional evaluation of the SeaWiFS chlorophyll data set,” Remote Sens. Environ. 93(4), 463–479 (2004).
[Crossref]

T. Varunan and P. Shanmugam, “A model for estimating size-fractioned phytoplankton absorption coefficients in coastal and oceanic waters from satellite data,” Remote Sens. Environ. 158, 235–254 (2015).
[Crossref]

S. Sterckx, S. Knaeps, S. Kratzer, and K. G. Ruddick, “SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters,” Remote Sens. Environ. 157, 96–110 (2015).
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C. Hu, “A novel ocean color index to detect floating algae in the global oceans,” Remote Sens. Environ. 113(10), 2118–2129 (2009).
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C. Jamet, H. Loisel, C. P. Kuchinke, K. G. Ruddick, G. Zibordi, and H. Feng, “Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements,” Remote Sens. Environ. 115(8), 1955–1965 (2011).
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C. Goyens, C. Jamet, and T. Schroeder, “Evaluation of four atmospheric correction algorithms for MODIS-Aqua images over contrasted coastal waters,” Remote Sens. Environ. 131, 63–75 (2013).
[Crossref]

M. Wang, S. Son, and W. Shi, “Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data,” Remote Sens. Environ. 113(3), 635–644 (2009).
[Crossref]

R. K. Singh and P. Shanmugam, “A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters,” Remote Sens. Environ. 142, 188–206 (2014).
[Crossref]

Other (9)

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Figures (14)

Fig. 1.
Fig. 1. The flowchart for the estimation of κ from ${\rho _{\textrm{rc}}}(\lambda )$ ratios. Subscripts denote the specific bands: V – Violet (415 nm), B – Blue (490 nm), G – Green (536 nm), R – Red (667 nm), F – Fluorescence (684 nm) and N – Near-infrared (747 nm). κ values defined here are derived from the SS14 algorithm [39].
Fig. 2.
Fig. 2. The flowchart for the estimation of aerosol contributions using SSP and UVNIR algorithms used in this study; Subscripts: UV = 387 nm, R = 667 nm, and N = 747 nm. For coefficient and equation details see [39].
Fig. 3.
Fig. 3. Spectral plots of field and laboratory measured ${L_\textrm{w}}({\lambda } )$ and absorption coefficients of phytoplankton ( ${a_{\textrm{ph}}}({\lambda } )$ ), color dissolved organic matter ( ${a_{\textrm{CDOM}}}({\lambda } )$ ) and suspended sediments ( ${a_{\textrm{SS}}}({\lambda } )$ ) from highly turbid productive Muttukadu Lagoon waters during November 2013 (a) and July 2014 (b). Here, λUV=387 nm, λRED=667 nm, λNIRS=747 nm and λNIRL=787 nm.
Fig. 4.
Fig. 4. Relationships of the in-situ water-leaving radiances between UV and NIR bands (a) [ ${R_{\textrm{rs}}}({387} )$ vs ${R_{\textrm{rs}}}({747} )$ ] as well as UV and Blue bands (b) [ ${R_{\textrm{rs}}}({387} )$ vs ${R_{\textrm{rs}}}({443} )$ ] for moderately clear to extremely turbid productive waters. (c) Spectral plots for the in-situ data (N = 204) used to generate the scatterplots (a) and (b).
Fig. 5.
Fig. 5. Geographical locations of global and regional in-situ datasets: AERONET-OC and Muttukadu Lagoon/Coastal waters on the coast of Bay of Bengal. Data measured from Ganga River and turbid coastal waters around Chennai and Point Calimere are also included.
Fig. 6.
Fig. 6. Spectral plots of AOT (a – measured; b-e – retrieved) and $n{L_\textrm{w}}$ (f – measured; g-j – retrieved); Scatter plots of the retrieved AOT (k-n) and $n{L_\textrm{w}}$ values (o-r) by INIR, SSP, UVNIR and UVNIR-ex algorithms versus AERONET-OC measurements at the Gloria (4 points), Lucinda (3 points), LISCO (5 points), MVCO (4 points) and Venice (33 points). The black line represents the 1:1 line. Number of observations, N = 49.
Fig. 7.
Fig. 7. Variation of the MRE, RMSE and Bias as a function of the wavelength calculated between the retrieved AOT and $n{L_\textrm{w}}$ values from HICO data by INIR, SSP, UVNIR and UVNIR-ex algorithms and AERONET-OC match-up datasets. Number of observations, N = 49.
Fig. 8.
Fig. 8. The HICO RGB true color image composites of the total radiance (a) and normalized water-leaving radiances (b-e) generated using R-G-B = 645 nm – 553 nm – 467 nm for Muttukadu Lagoon water on 14 April 2014. (f) variation of the κ values, and variation of the aerosol radiance at 547 nm (g-j) on 14 April 2014.
Fig. 9.
Fig. 9. Spectral plots of $n{L_\textrm{w}}$ and ${L_\textrm{a}}$ retrieved from the HICO image of 14 April 2014 by INIR, SSP, UVNIR and UVNIR-ex algorithms in Muttukadu Lagoon water. The HICO retrieved $n{L_\textrm{w}}$ spectra are compared with the in-situ $n{L_\textrm{w}}$ measurements.
Fig. 10.
Fig. 10. Spectral plots of the in-situ $n{L_\textrm{w}}$ values (a) and retrieved $n{L_\textrm{w}}$ values (b-e) from the HICO images on 22 November 2013, 16 December 2013, 22 March 2014 and 14 April 2014. (f-i) Scatterplots of the HICO retrieved $n{L_\textrm{w}}$ values by INIR, SSP, UVNIR, and UVNIR-ex algorithms versus in-situ measurements from Muttukadu Lagoon water. Number of observations, N = 17.
Fig. 11.
Fig. 11. The chlorophyll concentration products derived from the HICO image for Muttukadu Lagoon water on 14 April 2014 using the GABI algorithm, which was applied with the inputs of the retrieved $n{L_\textrm{w}}$ values by the INIR, SSP, UVNIR and UVNIR-ex algorithms.
Fig. 12.
Fig. 12. The HICO RGB true color image composites of ${L_\textrm{t}}$ (a) and $n{L_\textrm{w}}$ (b-e) generated using R-G-B = 645–553–467 nm for the Yangtze River Estuary on 9 September 2013. (f) ${L_{\textrm{rc}}}$ spectra (g-j) Spectral plots of the retrieved $n{L_\textrm{w}}$ ; (k-n) Spectral plots of the retrieved ${L_\textrm{a}}$ , (o) variation of the κ values, and variation of the aerosol radiance at 547 nm (p-s).
Fig. 13.
Fig. 13. The HICO-derived $n{L_\textrm{w}}$ at three key wavelengths from the INIR, SSP, UVNIR and UVNIR-ex algorithms for the Yangtze River Estuary on 9 September 2013.
Fig. 14.
Fig. 14. The HICO RGB true color image composites (a) and $n{L_\textrm{w}}$ (denoted by letters b-e in first column) generated using R-G-B = 645 nm – 553 nm – 467 nm for the Lake Erie on 3 September 2011. Panels in second and third columns: Spectral plots of the retrieved $n{L_\textrm{w}}$ and ${L_\textrm{a}}$ . (f-m) from the INIR, SSP, UVNIR and UVNIR-ex algorithms. (n) Variation of the κ values. (o-r) Variation of the aerosol radiance at 547 nm from the respective algorithms. Second-fifth rows correspond to the results from INIR, SSP, UVNIR and UVNIR-ex algorithms.

Tables (3)

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Table 1. List of the HICO swaths used in this study

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Table 2. Statistical results for the retrieved values of AOT and $n{L_\textrm{w}}$ obtained with the INIR, SSP, UVNIR and UVNIR-ex algorithms for the HICO and AERONET-OC match-up datasets.

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Table 3. Statistical results for the retrieved values of $n{L_{\textrm{w}}}$ obtained with the INIR, SSP, UVNIR and UVNIR-ex algorithms for the HICO and Muttukadu Lagoon in-situ match-up datasets.

Equations (7)

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L t ( λ ) = L r ( λ ) + L a ( λ ) + L ra ( λ ) + T ( λ ) L g ( λ ) + t ( λ ) L wc ( λ ) + t ( λ ) L w ( λ ) .
ρ t ( λ ) = π L t ( λ ) F 0 ( λ ) c o s θ 0 .
R rs ( 387 ) = 0.267 R r s ( 443 ) 0.7597 .
MRE = 1 N N | X H I C O X o b s X o b s | .
RMSE = 1 N N ( X o b s X H I C O ) 2 .
Bias = 1 N N ( X o b s X H I C O ) .
α = cos 1 ( k = 1 B d k i k ( k = 1 B d k 2 ) 1 2 ( k = 1 B d k 2 ) 1 2 ) .

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