Abstract

Several atmospheric correction algorithms for turbid waters have been developed based on an assumption of zero reflection in short–wave infrared (SWIR) bands. However, for the Landsat8–Operational Land Imager (OLI), some water reflections are so strong in the 1609 nm band that they cannot be ignored. In this study, we developed a novel atmospheric correction algorithm based on a zero assumption for the short–wave infrared band (ACZI). The ACZI algorithm uses the black pixel index (BPI) and the floating algae index (FAI) to distinguish black pixels, which are used to estimate the aerosol scattering of non–black pixels based on the assumption of spatial homogeneity of aerosol types. In Lake Taihu, compared with the SeaDAS (SeaWiFS Data Analysis System) –SWIR algorithm, the ACZI algorithm achieved better precision for visible bands MAPE (the mean absolute percentage error), < 30%, RMSE (the root mean square error) < 0.0117 sr–1) and provided more available water pixels. The accuracy of ACZI was close to that of the DSF (dark spectrum fitting) algorithm and was better than that of the EXP (exponential extrapolation) algorithm and L8SR (Landsat 8 OLI Surface Reflectance) product. The ACZI algorithm showed good applicability in turbid waters.

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

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References

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2019 (3)

D. Wang, R. Ma, K. Xue, and S. Loiselle, “The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters,” Remote Sens. 11(2), 169 (2019).
[Crossref]

K. Xue, R. Ma, H. Duan, M. Shen, E. Boss, and Z. Cao, “Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes,” Remote Sens. Environ. 225, 328–346 (2019).
[Crossref]

K. Xue, R. Ma, D. Wang, and M. Shen, “Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes,” Remote Sens. 11(2), 184 (2019).
[Crossref]

2018 (2)

I. Caballero, G. Navarro, and J. Ruiz, “Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system,” Int. J. Appl. Earth Obs. Geoinf. 68, 31–41 (2018).
[Crossref]

Q. Vanhellemont and K. Ruddick, “Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications,” Remote Sens. Environ. 216, 586–597 (2018).
[Crossref]

2017 (7)

L. Hu, C. Hu, and H. Ming-Xia, “Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea,” Remote Sens. Environ. 192, 217–227 (2017).
[Crossref]

Q. Liang, Y. Zhang, R. Ma, S. Loiselle, J. Li, and M. Hu, “A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu,” Remote Sens. 9(2), 133 (2017).
[Crossref]

K. Xue, Y. Zhang, H. Duan, and R. Ma, “Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China,” J. Great Lakes Res. 43(1), 17–31 (2017).
[Crossref]

Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sens. Environ. 192, 98–113 (2017).
[Crossref]

H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
[Crossref] [PubMed]

M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

F. Mushtaq and M. G. Nee Lala, “Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery,” Geocarto Int. 32(3), 274–285 (2017).
[Crossref]

2016 (5)

Z. Zheng, J. Ren, Y. Li, C. Huang, G. Liu, C. Du, and H. Lyu, “Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake,” Sci. Total Environ. 573, 39–54 (2016).
[Crossref] [PubMed]

J. A. Concha and J. R. Schott, “Retrieval of color producing agents in Case 2 waters using Landsat 8,” Remote Sens. Environ. 185, 95–107 (2016).
[Crossref]

Z. Lee, S. Shang, L. Qi, J. Yan, and G. Lin, “A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements,” Remote Sens. Environ. 177, 101–106 (2016).
[Crossref]

H. Qi, J. Lu, X. Chen, S. Sauvage, and J.-M. Sanchez-Pérez, “Water age prediction and its potential impacts on water quality using a hydrodynamic model for Poyang Lake, China,” Environ. Sci. Pollut. Res. Int. 23(13), 13327–13341 (2016).
[Crossref] [PubMed]

E. Vermote, C. Justice, M. Claverie, and B. Franch, “Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product,” Remote Sens. Environ. 185, 46–56 (2016).
[Crossref]

2015 (3)

Z. Wu, Y. Zhang, Y. Zhou, M. Liu, K. Shi, and Z. Yu, “Seasonal-Spatial Distribution and Long-Term Variation of Transparency in Xin’anjiang Reservoir: Implications for Reservoir Management,” Int. J. Environ. Res. Public Health 12(8), 9492–9507 (2015).
[Crossref] [PubMed]

B. A. Franz, S. W. Bailey, N. Kuring, and P. J. Werdell, “Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS,” J. Appl. Remote Sens. 9(1), 096070 (2015).
[Crossref]

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

2014 (4)

E. Knight and G. Kvaran, “Landsat-8 Operational Land Imager Design, Characterization and Performance,” Remote Sens. 6(11), 10286–10305 (2014).
[Crossref]

N. Pahlevan, Z. Lee, J. Wei, C. B. Schaaf, J. R. Schott, and A. Berk, “On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing,” Remote Sens. Environ. 154, 272–284 (2014).
[Crossref]

J. Barsi, K. Lee, G. Kvaran, B. Markham, and J. Pedelty, “The Spectral Response of the Landsat-8 Operational Land Imager,” Remote Sens. 6(10), 10232–10251 (2014).
[Crossref]

Q. He and C. Chen, “A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary,” Remote Sens. Lett. 5(3), 249–257 (2014).
[Crossref]

2013 (1)

2010 (1)

C. Hu, Z. Lee, R. Ma, K. Yu, D. Li, and S. Shang, “Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,” J. Geophys. Res. 115(C4), C04002 (2010).
[Crossref]

2009 (2)

W. Shi and M. Wang, “An assessment of the black ocean pixel assumption for MODIS SWIR bands,” Remote Sens. Environ. 113(8), 1587–1597 (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]

2008 (1)

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[Crossref]

2007 (2)

2006 (1)

R. Ma, J. Tang, and J. Dai, “Bio-optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing,” Int. J. Remote Sens. 27(19), 4305–4328 (2006).
[Crossref]

2005 (2)

R. Ma and J. Dai, “Investigation of chlorophyll‐a and total suspended matter concentrations using Landsat ETM and field spectral measurement in Taihu Lake, China,” Int. J. Remote Sens. 26(13), 2779–2795 (2005).
[Crossref]

M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005).
[Crossref]

2000 (2)

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters,” Remote Sens. Environ. 74(2), 195–206 (2000).
[Crossref]

D. A. Siegel, M. Wang, S. Maritorena, and W. Robinson, “Atmospheric correction of satellite ocean color imagery: the black pixel assumption,” Appl. Opt. 39(21), 3582–3591 (2000).
[Crossref] [PubMed]

1999 (1)

1997 (1)

1994 (1)

M. Wang and H. R. Gordon, “A Simple, Moderately Accurate, Atmospheric correction algorithn for SeaWiFS,” Remote Sens. Environ. 50(3), 231–239 (1994).
[Crossref]

Antoine, D.

Bailey, S. W.

Barrow, T.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[Crossref]

Barsi, J.

J. Barsi, K. Lee, G. Kvaran, B. Markham, and J. Pedelty, “The Spectral Response of the Landsat-8 Operational Land Imager,” Remote Sens. 6(10), 10232–10251 (2014).
[Crossref]

Berk, A.

N. Pahlevan, Z. Lee, J. Wei, C. B. Schaaf, J. R. Schott, and A. Berk, “On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing,” Remote Sens. Environ. 154, 272–284 (2014).
[Crossref]

Boss, E.

Brando, V. E.

Caballero, I.

I. Caballero, G. Navarro, and J. Ruiz, “Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system,” Int. J. Appl. Earth Obs. Geoinf. 68, 31–41 (2018).
[Crossref]

Cao, Z.

K. Xue, R. Ma, H. Duan, M. Shen, E. Boss, and Z. Cao, “Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes,” Remote Sens. Environ. 225, 328–346 (2019).
[Crossref]

Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sens. Environ. 192, 98–113 (2017).
[Crossref]

H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
[Crossref] [PubMed]

M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

Carder, K. L.

C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters,” Remote Sens. Environ. 74(2), 195–206 (2000).
[Crossref]

Chen, C.

Q. He and C. Chen, “A new approach for atmospheric correction of MODIS imagery in turbid coastal waters: a case study for the Pearl River Estuary,” Remote Sens. Lett. 5(3), 249–257 (2014).
[Crossref]

Chen, X.

H. Qi, J. Lu, X. Chen, S. Sauvage, and J.-M. Sanchez-Pérez, “Water age prediction and its potential impacts on water quality using a hydrodynamic model for Poyang Lake, China,” Environ. Sci. Pollut. Res. Int. 23(13), 13327–13341 (2016).
[Crossref] [PubMed]

Claverie, M.

E. Vermote, C. Justice, M. Claverie, and B. Franch, “Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product,” Remote Sens. Environ. 185, 46–56 (2016).
[Crossref]

Concha, J. A.

J. A. Concha and J. R. Schott, “Retrieval of color producing agents in Case 2 waters using Landsat 8,” Remote Sens. Environ. 185, 95–107 (2016).
[Crossref]

d’Andon, O. H.

Dai, J.

R. Ma, J. Tang, and J. Dai, “Bio-optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing,” Int. J. Remote Sens. 27(19), 4305–4328 (2006).
[Crossref]

R. Ma and J. Dai, “Investigation of chlorophyll‐a and total suspended matter concentrations using Landsat ETM and field spectral measurement in Taihu Lake, China,” Int. J. Remote Sens. 26(13), 2779–2795 (2005).
[Crossref]

Dall’Olmo, G.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[Crossref]

Devred, E.

Dowell, M.

Du, C.

Z. Zheng, J. Ren, Y. Li, C. Huang, G. Liu, C. Du, and H. Lyu, “Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake,” Sci. Total Environ. 573, 39–54 (2016).
[Crossref] [PubMed]

Duan, H.

K. Xue, R. Ma, H. Duan, M. Shen, E. Boss, and Z. Cao, “Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes,” Remote Sens. Environ. 225, 328–346 (2019).
[Crossref]

M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sens. Environ. 192, 98–113 (2017).
[Crossref]

K. Xue, Y. Zhang, H. Duan, and R. Ma, “Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China,” J. Great Lakes Res. 43(1), 17–31 (2017).
[Crossref]

H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
[Crossref] [PubMed]

Feldman, G. C.

Feng, L.

Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sens. Environ. 192, 98–113 (2017).
[Crossref]

Fisher, T. R.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
[Crossref]

Franch, B.

E. Vermote, C. Justice, M. Claverie, and B. Franch, “Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product,” Remote Sens. Environ. 185, 46–56 (2016).
[Crossref]

Franz, B. A.

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Lee, K.

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Z. Lee, S. Shang, L. Qi, J. Yan, and G. Lin, “A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements,” Remote Sens. Environ. 177, 101–106 (2016).
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N. Pahlevan, Z. Lee, J. Wei, C. B. Schaaf, J. R. Schott, and A. Berk, “On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing,” Remote Sens. Environ. 154, 272–284 (2014).
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Li, D.

C. Hu, Z. Lee, R. Ma, K. Yu, D. Li, and S. Shang, “Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,” J. Geophys. Res. 115(C4), C04002 (2010).
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Li, J.

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Z. Zheng, J. Ren, Y. Li, C. Huang, G. Liu, C. Du, and H. Lyu, “Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake,” Sci. Total Environ. 573, 39–54 (2016).
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Z. Zheng, J. Ren, Y. Li, C. Huang, G. Liu, C. Du, and H. Lyu, “Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake,” Sci. Total Environ. 573, 39–54 (2016).
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Z. Wu, Y. Zhang, Y. Zhou, M. Liu, K. Shi, and Z. Yu, “Seasonal-Spatial Distribution and Long-Term Variation of Transparency in Xin’anjiang Reservoir: Implications for Reservoir Management,” Int. J. Environ. Res. Public Health 12(8), 9492–9507 (2015).
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Loiselle, S.

D. Wang, R. Ma, K. Xue, and S. Loiselle, “The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters,” Remote Sens. 11(2), 169 (2019).
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M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

Q. Liang, Y. Zhang, R. Ma, S. Loiselle, J. Li, and M. Hu, “A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu,” Remote Sens. 9(2), 133 (2017).
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H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
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H. Qi, J. Lu, X. Chen, S. Sauvage, and J.-M. Sanchez-Pérez, “Water age prediction and its potential impacts on water quality using a hydrodynamic model for Poyang Lake, China,” Environ. Sci. Pollut. Res. Int. 23(13), 13327–13341 (2016).
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Z. Zheng, J. Ren, Y. Li, C. Huang, G. Liu, C. Du, and H. Lyu, “Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake,” Sci. Total Environ. 573, 39–54 (2016).
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Ma, R.

D. Wang, R. Ma, K. Xue, and S. Loiselle, “The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters,” Remote Sens. 11(2), 169 (2019).
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K. Xue, R. Ma, D. Wang, and M. Shen, “Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes,” Remote Sens. 11(2), 184 (2019).
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K. Xue, R. Ma, H. Duan, M. Shen, E. Boss, and Z. Cao, “Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes,” Remote Sens. Environ. 225, 328–346 (2019).
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K. Xue, Y. Zhang, H. Duan, and R. Ma, “Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China,” J. Great Lakes Res. 43(1), 17–31 (2017).
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Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sens. Environ. 192, 98–113 (2017).
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H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
[Crossref] [PubMed]

Q. Liang, Y. Zhang, R. Ma, S. Loiselle, J. Li, and M. Hu, “A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu,” Remote Sens. 9(2), 133 (2017).
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C. Hu, Z. Lee, R. Ma, K. Yu, D. Li, and S. Shang, “Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,” J. Geophys. Res. 115(C4), C04002 (2010).
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R. Ma, J. Tang, and J. Dai, “Bio-optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing,” Int. J. Remote Sens. 27(19), 4305–4328 (2006).
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Maritorena, S.

Markham, B.

J. Barsi, K. Lee, G. Kvaran, B. Markham, and J. Pedelty, “The Spectral Response of the Landsat-8 Operational Land Imager,” Remote Sens. 6(10), 10232–10251 (2014).
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Mélin, F.

Ming-Xia, H.

L. Hu, C. Hu, and H. Ming-Xia, “Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea,” Remote Sens. Environ. 192, 217–227 (2017).
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Mobley, C. D.

Moore, T. S.

Moses, W.

A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
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C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters,” Remote Sens. Environ. 74(2), 195–206 (2000).
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J. Barsi, K. Lee, G. Kvaran, B. Markham, and J. Pedelty, “The Spectral Response of the Landsat-8 Operational Land Imager,” Remote Sens. 6(10), 10232–10251 (2014).
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Qi, H.

H. Qi, J. Lu, X. Chen, S. Sauvage, and J.-M. Sanchez-Pérez, “Water age prediction and its potential impacts on water quality using a hydrodynamic model for Poyang Lake, China,” Environ. Sci. Pollut. Res. Int. 23(13), 13327–13341 (2016).
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Qi, L.

Z. Lee, S. Shang, L. Qi, J. Yan, and G. Lin, “A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements,” Remote Sens. Environ. 177, 101–106 (2016).
[Crossref]

Ren, J.

Z. Zheng, J. Ren, Y. Li, C. Huang, G. Liu, C. Du, and H. Lyu, “Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: A case study of Dongting Lake,” Sci. Total Environ. 573, 39–54 (2016).
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Ruddick, K.

Q. Vanhellemont and K. Ruddick, “Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications,” Remote Sens. Environ. 216, 586–597 (2018).
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Q. Vanhellemont and K. 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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I. Caballero, G. Navarro, and J. Ruiz, “Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system,” Int. J. Appl. Earth Obs. Geoinf. 68, 31–41 (2018).
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A. A. Gitelson, G. Dall’Olmo, W. Moses, D. C. Rundquist, T. Barrow, T. R. Fisher, D. Gurlin, and J. Holz, “A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation,” Remote Sens. Environ. 112(9), 3582–3593 (2008).
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Sanchez-Pérez, J.-M.

H. Qi, J. Lu, X. Chen, S. Sauvage, and J.-M. Sanchez-Pérez, “Water age prediction and its potential impacts on water quality using a hydrodynamic model for Poyang Lake, China,” Environ. Sci. Pollut. Res. Int. 23(13), 13327–13341 (2016).
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Sauvage, S.

H. Qi, J. Lu, X. Chen, S. Sauvage, and J.-M. Sanchez-Pérez, “Water age prediction and its potential impacts on water quality using a hydrodynamic model for Poyang Lake, China,” Environ. Sci. Pollut. Res. Int. 23(13), 13327–13341 (2016).
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N. Pahlevan, Z. Lee, J. Wei, C. B. Schaaf, J. R. Schott, and A. Berk, “On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing,” Remote Sens. Environ. 154, 272–284 (2014).
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J. A. Concha and J. R. Schott, “Retrieval of color producing agents in Case 2 waters using Landsat 8,” Remote Sens. Environ. 185, 95–107 (2016).
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Z. Lee, S. Shang, L. Qi, J. Yan, and G. Lin, “A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements,” Remote Sens. Environ. 177, 101–106 (2016).
[Crossref]

C. Hu, Z. Lee, R. Ma, K. Yu, D. Li, and S. Shang, “Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,” J. Geophys. Res. 115(C4), C04002 (2010).
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K. Xue, R. Ma, D. Wang, and M. Shen, “Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes,” Remote Sens. 11(2), 184 (2019).
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K. Xue, R. Ma, H. Duan, M. Shen, E. Boss, and Z. Cao, “Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes,” Remote Sens. Environ. 225, 328–346 (2019).
[Crossref]

M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

Shi, K.

Z. Wu, Y. Zhang, Y. Zhou, M. Liu, K. Shi, and Z. Yu, “Seasonal-Spatial Distribution and Long-Term Variation of Transparency in Xin’anjiang Reservoir: Implications for Reservoir Management,” Int. J. Environ. Res. Public Health 12(8), 9492–9507 (2015).
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W. Shi and M. Wang, “An assessment of the black ocean pixel assumption for MODIS SWIR bands,” Remote Sens. Environ. 113(8), 1587–1597 (2009).
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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), 15722–15733 (2007).
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Smyth, T. J.

Tang, J.

R. Ma, J. Tang, and J. Dai, “Bio-optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing,” Int. J. Remote Sens. 27(19), 4305–4328 (2006).
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Tang, X.

H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
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Tao, M.

H. Duan, M. Tao, S. A. Loiselle, W. Zhao, Z. Cao, R. Ma, and X. Tang, “MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source,” Water Res. 122, 455–470 (2017).
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Vanhellemont, Q.

Q. Vanhellemont and K. Ruddick, “Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications,” Remote Sens. Environ. 216, 586–597 (2018).
[Crossref]

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

Vermote, E.

E. Vermote, C. Justice, M. Claverie, and B. Franch, “Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product,” Remote Sens. Environ. 185, 46–56 (2016).
[Crossref]

Wang, D.

D. Wang, R. Ma, K. Xue, and S. Loiselle, “The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters,” Remote Sens. 11(2), 169 (2019).
[Crossref]

K. Xue, R. Ma, D. Wang, and M. Shen, “Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes,” Remote Sens. 11(2), 184 (2019).
[Crossref]

Wang, M.

W. Shi and M. Wang, “An assessment of the black ocean pixel assumption for MODIS SWIR bands,” Remote Sens. Environ. 113(8), 1587–1597 (2009).
[Crossref]

M. Wang and W. Shi, “The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing,” Opt. Express 15(24), 15722–15733 (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), 1535–1547 (2007).
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M. Wang and W. Shi, “Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies,” Geophys. Res. Lett. 32(13), L13606 (2005).
[Crossref]

D. A. Siegel, M. Wang, S. Maritorena, and W. Robinson, “Atmospheric correction of satellite ocean color imagery: the black pixel assumption,” Appl. Opt. 39(21), 3582–3591 (2000).
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M. Wang and H. R. Gordon, “A Simple, Moderately Accurate, Atmospheric correction algorithn for SeaWiFS,” Remote Sens. Environ. 50(3), 231–239 (1994).
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Wei, J.

N. Pahlevan, Z. Lee, J. Wei, C. B. Schaaf, J. R. Schott, and A. Berk, “On-orbit radiometric characterization of OLI (Landsat-8) for applications in aquatic remote sensing,” Remote Sens. Environ. 154, 272–284 (2014).
[Crossref]

Werdell, P. J.

Wu, Z.

Z. Wu, Y. Zhang, Y. Zhou, M. Liu, K. Shi, and Z. Yu, “Seasonal-Spatial Distribution and Long-Term Variation of Transparency in Xin’anjiang Reservoir: Implications for Reservoir Management,” Int. J. Environ. Res. Public Health 12(8), 9492–9507 (2015).
[Crossref] [PubMed]

Xue, K.

K. Xue, R. Ma, D. Wang, and M. Shen, “Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes,” Remote Sens. 11(2), 184 (2019).
[Crossref]

K. Xue, R. Ma, H. Duan, M. Shen, E. Boss, and Z. Cao, “Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China’s three largest freshwater lakes,” Remote Sens. Environ. 225, 328–346 (2019).
[Crossref]

D. Wang, R. Ma, K. Xue, and S. Loiselle, “The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters,” Remote Sens. 11(2), 169 (2019).
[Crossref]

M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

Z. Cao, H. Duan, L. Feng, R. Ma, and K. Xue, “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales,” Remote Sens. Environ. 192, 98–113 (2017).
[Crossref]

K. Xue, Y. Zhang, H. Duan, and R. Ma, “Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China,” J. Great Lakes Res. 43(1), 17–31 (2017).
[Crossref]

Yan, J.

Z. Lee, S. Shang, L. Qi, J. Yan, and G. Lin, “A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements,” Remote Sens. Environ. 177, 101–106 (2016).
[Crossref]

Yesou, H.

M. Shen, H. Duan, Z. Cao, K. Xue, S. Loiselle, and H. Yesou, “Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI,” Remote Sens. 9(12), 1246 (2017).
[Crossref]

Yu, K.

C. Hu, Z. Lee, R. Ma, K. Yu, D. Li, and S. Shang, “Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,” J. Geophys. Res. 115(C4), C04002 (2010).
[Crossref]

Yu, Z.

Z. Wu, Y. Zhang, Y. Zhou, M. Liu, K. Shi, and Z. Yu, “Seasonal-Spatial Distribution and Long-Term Variation of Transparency in Xin’anjiang Reservoir: Implications for Reservoir Management,” Int. J. Environ. Res. Public Health 12(8), 9492–9507 (2015).
[Crossref] [PubMed]

Zhang, Y.

Q. Liang, Y. Zhang, R. Ma, S. Loiselle, J. Li, and M. Hu, “A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu,” Remote Sens. 9(2), 133 (2017).
[Crossref]

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Figures (13)

Fig. 1
Fig. 1 Study lake and in situ sampling points in Lake Taihu. Triangles and crosses represent the in situ sampling locations on May 27, 2017 and December 21, 2017, respectively.
Fig. 2
Fig. 2 Processing procedure of the ACZI algorithm.
Fig. 3
Fig. 3 OLI–RGB images, ρrc(SWIR) images and frequency of ρrc(1609)/ρrc(2201) in the Xin’ anjiang Reservoir (a–e), Yangtze River estuary (f–j), Lake Hongze (k–o) and Lake Taihu (p–t).
Fig. 4
Fig. 4 Average ρrc spectra of five water types: (a) floating bloom, (b) submerged macrophytes, (c) extremely turbid water, (d) turbid water, and (e) clean water. The average ρrc of each water type was extracted from 1000 pixels of images in Fig. 3.
Fig. 5
Fig. 5 Histogram of the BPI index of water pixels. The colors indicate different water types: dark is floating bloom, yellow is turbid water, pink is extremely turbid water, green is submerged macrophyte, and blue is clean water.
Fig. 6
Fig. 6 Measured remote sensing reflectance (Rrs) of Lake Taihu onMay 27 and December 21 2017. The red lines represent data on May 27, 2017, and the black lines represent data on December 21, 2017.
Fig. 7
Fig. 7 Two examples of the identification of black pixels in OLI images of Lake Taihu on May 27 and December 21, 2017. The dark areas are masked pixels, and red indicates identified black pixels (b and f). The spatial distribution (c and g) and frequency histograms (d and h) of ρrc(1609)/ρrc(2201) are also illustrated.
Fig. 8
Fig. 8 Comparison of the in situ data and atmospheric correction algorithm–driven Rrs in 443, 482, 561, 655, 865 nm.
Fig. 9
Fig. 9 Comparison of SeaDAS–SWIR and ACZI for OLI Rrs retrievals over Lake Taihu on May 27 and December 21, 2017. The “proc_ocean” option of SeaDAS was set to “2–force all pixels to be processed as ocean”.
Fig. 10
Fig. 10 Comparison of the in situ data and atmospheric correction algorithm–driven Rrs in 443, 482, 561, 655, 865 nm.
Fig. 11
Fig. 11 (a) Calibration: relationship between in situ Rrs and measured SPM. (b) Scatter plots of validation data using measured SPM and estimated SPM.
Fig. 12
Fig. 12 OLI true color image (a), and distribution of estimated SPM patterns in Lake Taihu on May 27, 2017: ACZI (b) and DSF (c). Comparison of measured SPM and OLI–estimated SPM derived by ACZI and DSF algorithms (d). The frequency of OLI–estimated SPM on May 27, 2017 (e).
Fig. 13
Fig. 13 An example of the identification of black pixels in an OLI image of Xin’ anjiang Reservoir (March 11, 2018).

Tables (4)

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Table 1 Bands of the OLI on Landsat–8, with band range, band center, ground sampling distance (GSD), and signal–to–noise ratio (SNR) at the reference radiance [1].

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Table 2 Variations in the bio–optical properties of Lake Taihu. SD is the standard deviation. Note that not all the sample sites had bio–optical parameters in the two cruises.

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Table 3 Band errors between the in situ Rrs and OLI Rrs obtained with SeaDAS–SWIR and ACZI atmospheric correction algorithm. The minimal statistical value is shown in bold.

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Table 4 Band errors between the in situ Rrs and OLI Rrs obtained with four atmospheric correction algorithms (EXP, DSF, L8SR, and ACZI). The minimal statistical value is shown in bold.

Equations (17)

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R rs = L w E d = ( L t σ L sky ) ρ P π L P .
L t = M L DN+ A L ,
ρ t = π L t d 2 F0cos( θ 0 ) ,
ρ t = ρ r + ρ a +t ρ w ,
ρ rc = ρ t ρ r ,
ρ rc = ρ a +t ρ w ,
ε( λ i , λ j )= ρ a ( λ i ) ρ a ( λ j ) =exp[C( λ j λ i )],
ε b ( SWI R 1 ,SWI R 2 )= ρ a b ( SWI R 1 ) ρ a b ( SWI R 2 ) =exp[C(SWI R 2 SWI R 1 )] ρ t b ( SWI R 1 ) ρ r b ( SWI R 1 ) ρ t b ( SWI R 2 ) ρ r b ( SWI R 2 ) ,
C( λ SWIR1 , λ SWIR 2 )= 1 λ SWIR 2 λ SWIRI ln( ρ rc ( λ SWIR1 ) ρ rc ( λ SWIR 2 ) ),
ρ a ( λ i )= ε b ( λ i , λ j )* ρ rc ( λ j ),
ρ w (λ)=[ ρ rc (λ) ε b (λ)* ρ rc (SWIR2)]/t(λ),
BPI= | ρ rc (655) ρ rc (561) | ρ rc (655) ρ rc (865) ,
FAI= ρ rc (865){ ρ rc (655)+[ ρ rc (1609) ρ rc (655)]*[ ( 865655 ) ( 1609655 ) ]},
RMSE= i=1 n ( R rs m R rs i ) 2 n ,
MAPE= i=1 n | R rs m R rs i R rs i | n ×100%,
Bias= i=1 n [ ( R rs m R rs i ) R rs i ] n ×100%,
SP M insitu =6270.3* R rs (865)2.238,