Ocean Color
Ocean-color remote sensing
• Radiative transfer models and atmospheric correction algorithms
• Water optics and ocean-color remote sensing
1. Radiative transfer models and atmospheric correction algorithms
Radiative transfer models and atmospheric correction algorithms are core technologies for satellite detection of ocean carbon parameters. For offshore or inland water bodies significantly affected by terrestrial sources, high-precision radiative transfer models and atmospheric correction algorithms remain challenging. At the same time, with the development of geostationary orbit satellite technology (and the ability to continuously observe from morning to evening), improvement in the performance of radiative transfer models and atmospheric correction algorithms under large zenith angles (occurring in the morning or evening) is urgent.
He et al. (2010) established an ocean-atmosphere coupled vector radiative transfer model, which considered air-sea coupling, rough sea surface, and polarization (PCOART). The model uses the matrix algorithm (or accumulative addition method) to realize the numerical solution of the polarization radiation transfer equation. The Cox-Munk model is used to simulate wind-induced rough sea surface and the “infinite thin sea-air interface layer superposition technology” is used for sea-air coupling. PCOART can not only simulate total radiance but also estimate polarization information. Based on the PCOART model with a parallel layer assumption, He et al. (2018) developed a sea-air coupled vector radiative transfer model (PCOART-SA), which considers the curvature of the Earth. For the first time, a model generated atmospheric Rayleigh scattering, aerosol scattering, and atmospheric diffuse transmittance look-up tables under the effect of the Earth’s curvature. An atmospheric correction model considering the Earth’s curvature has also been developed, which lays a foundation for processing observation data from geostationary orbit satellites.
Regarding atmospheric correction of offshore turbid waters, He et al. (2012) found that water leaving radiance at the blue band is small due to the strong absorption of short waves by terrestrial organic matter (yellow matter, organic debris), and the blue band can replace the near-infrared band for aerosol scattering estimates based on the measured spectral characteristics of high-turbidity estuary waters. An atmospheric correction algorithm (UV-AC) based on the ultraviolet band suitable for high-turbidity water bodies was proposed by He et al. (2012). Compared with the standard algorithm using the near-infrared band as a reference, the UV-AC model can invert the near-infrared radiance of water, which is beneficial to the inversion of suspended matter concentration. Verification results based on both simulation and field observations show that the UV-AC model can effectively improve the performance of the operational algorithms in high-turbidity water.
Representative articles：
1. He, X., Bai, Y., Wei, J., Ding, J., Shanmugam, P., Wang, D., et al. (2017). Ocean color retrieval from MWI onboard the Tiangong-2 Space Lab: preliminary results. Optics Express, 25(20), 23955-23973.
2. He, X., Bai, Y., Zhu, Q., & Gong, F. (2010). A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface. Journal Of Quantitative Spectroscopy & Radiative Transfer, 111(10), 1426-1448.
3. He, X., Pan, D., Bai, Y., Mao, Z., Wang, T., & Hao, Z. (2016). A Practical Method for On-Orbit Estimation of Polarization Response of Satellite Ocean Color Sensor. IEEE Transactions on Geoscience & Remote Sensing, 54(4), 1967-1976.
4. He, X., Pan, D., Yan, B., Wang, D., & Hao, Z. (2014). A new simple concept for ocean colour remote sensing using parallel polarisation radiance. Sci Rep, 4(6168), 3748.
5. Liu, J., He, X., Liu, J., Bai, Y., Wang, D., Chen, T., et al. (2017). Polarization-based enhancement of ocean color signal for estimating suspended particulate matter: radiative transfer simulations and laboratory measurements. Optics Express, 25(8), A323.
6. Mao, Z., Pan, D., Hao, Z., Chen, J., Tao, B., & Zhu, Q. (2014). A potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data. Remote Sensing of Environment, 142(3), 131-140.
7. Mao, Z., Pan, D., He, X., Chen, J., Tao, B., Chen, P., et al. (2016). A Unified Algorithm for the Atmospheric Correction of Satellite Remote Sensing Data over Land and Ocean. Remote Sensing, 8(7), 536.
8. Pan, D. (2004). Study on Marine Application Potentiality of CMODIS/SZ-3. Engineering Sciences,2, 1-5.
9. Pan, D., Xianqiang, H. E., & Mao, T. (2003). Preliminary study on the orbit cross-calibration of CMODIS by SeaWiFS. Progress in Natural Science, 13(10), 745-749.
10. Teng, L. I., Pan, D., Yan, B., Gang, L. I., Xianqiang, H. E., Chen, C. T. A., et al. (2015). Satellite remote sensing of ultraviolet irradiance on the ocean surface. Acta Oceanologica Sinica, 34(6), 101-112.
11. Xianqiang, H. E., Pan, D., Bai, Y., & Gong, F. (2006). A general purpose exact Rayleigh scattering look-up table for ocean color remote sensing. Acta Oceanologica Sinica, 25(1), 48-56.
12. Xianqiang, H. E., Pan, D. L., Yan, B., Zhu, Q. K., & Fang, G. (2007). Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method. Science in China, 50(3), 442-452.
13. Xianqiang He*, Knut Stamnes, Yan Bai, Wei Li, Difeng Wang. Effects of Earth curvature on atmospheric correction for ocean color remote sensing. Remote Sensing of Environment, 209，118–133，2018.
14. Xuchen Jin, Delu Pan, Xianqiang He*, Yan Bai, Palanisamy Shanmugam, Fang Gong, Qiankun Zhu. A vector radiative transfer model for sea surface salinity retrieval from space: a non-raining case. International Journal of Remote Sensing, 2018, DOI: 10.1080/01431161.2018.1488283.
15. He, X., Bai, Y., Pan, D., Tang, J., & Wang, D. (2012). Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters. Optics Express, 20(18), 20754-20770.
16. Wei, J. A., Wang, D., Gong, F., He, X., & Bai, Y. (2017). The Influence of Increasing Water Turbidity on Sea Surface Emissivity. IEEE Transactions on Geoscience & Remote Sensing, PP(99), 1-15.
2. Water optics and ocean-color remote sensing
The radiation transfer theory of light in water (water optics) is the physical foundation for ocean-color remote sensing. Ocean-color remote sensing refers to the method used for studying marine characteristics via electromagnetic information in the near-ultraviolet, visible, or near-infrared bands. The basic mechanism states that when the concentration of each optical component (chlorophyll concentration, suspended matter concentration, or colored dissolved organic matter) in the water changes, it will cause changes in absorption and scattering characteristics of the water body, which will lead to variation in water leaving radiance. By analyzing changes in water leaving radiance received by satellite sensors, the content of one or more optical components in the water body, such as chlorophyll concentration, suspended matter concentration, or colored dissolved organic matter, can be inverted.
The current theoretical framework of ocean-color remote sensing is based on the total radiance (I) received by the satellite sensor while ignoring the deficiency of the polarized signal. He et al. (2014) innovatively proposed the concept of parallel polarization equivalent radiance (I+Q). This concept uses a new theoretical framework for ocean-color polarization remote sensing. Both PCOART simulation and POLDER (POLarization and Directionality of the Earth’s Reflectances) satellite polarization observations show that parallel polarization equivalent radiance (PPR) can effectively reduce the influence of solar flares and improve the signal-to-noise ratio, which is beneficial for ocean-color remote sensing compared with traditional radiance-based ocean-color remote sensing.
For traditional passive ocean-color remote sensing, signals can only be observed during the daytime and the observation results can only characterize components in the surface layer. To obtain the vertical profile of a component, laser remote sensing technology has been developed. Behrenfeld et al. (2013) used lidar data to estimate POC profiles and obtained remote sensing estimates of global ocean POC reserves in the upper 22.5-m layer. Based on the observed data, Chen et al. (2015) established an inversion method for phytoplankton species using laser fluorescence radar signals. In addition, lidar has been used for remote sensing studies of components in near-shore and inland waters (Chen et al., 2017).
Representative articles：
1. Bai, Y., Pan, D., Cai, W. J., He, X., Wang, D., Tao, B., & Zhu, Q. (2013). Remote sensing of salinity from satellite‐derived CDOM in the Changjiang River dominated East China Sea. Journal of Geophysical Research Oceans, 118(1), 227-243.
2. Chen, J., He, X., Zhou, B., & Pan, D. (2017). Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm. Journal of Geophysical Research-Oceans, 122(1).
3. Chen, J., Ni, X., Liu, M., Chen, J., Mao, Z., Jin, H., & Pan, D. (2015). Monitoring the occurrence of seasonal low‐oxygen events off the Changjiang Estuary through integration of remote sensing, buoy observations, and modeling. Journal of Geophysical Research Oceans, 119(8), 5311-5322.
4. Chen, P., Pan, D., & Mao, Z. (2015). Application of a laser fluorometer for discriminating phytoplankton species. Optics & Laser Technology, 67(67), 50-56.
5. Chen, P., Pan, D., Wang, T., Mao, Z., & Zhang, Y. (2017). Coastal and inland water monitoring using a portable hyperspectral laser fluorometer. Marine Pollution Bulletin.
6. He, X., Bai, Y., Pan, D., Huang, N., Dong, X., Chen, J., et al. (2013). Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sensing of Environment, 133(12), 225-239.
7. Hu, Z., Pan, D., He, X., & Bai, Y. (2016). Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing. Remote Sensing, 8(2), 147.
8. Hu, Z., Pan, D., He, X., Song, D., Huang, N., Bai, Y., et al. (2017). Assessment of the MCC method to estimate sea surface currents in highly turbid coastal waters from GOCI. International Journal of Remote Sensing, 38(2), 572-597.
9. Hu, Z., Wang, D. P., He, X., Li, M., Wei, J., Pan, D., & Bai, Y. (2017). Episodic surface intrusions in the Yellow Sea during relaxation of northerly winds. Journal of Geophysical Research, 122.
10. Hu, Z., Wang, D. P., Pan, D., He, X., Miyazawa, Y., Bai, Y., et al. (2016). Mapping surface tidal currents and Changjiang plume in the East China Sea from Geostationary Ocean Color Imager. Journal of Geophysical Research Oceans, 121(3).
11. Li, H., He, X., Bai, Y., Gong, F., & Zhu, Q. (2017). Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions. Journal of Applied Remote Sensing, 11(1), 012004.
12. Mao, Z., Stuart, V., Pan, D., Chen, J., Gong, F., Huang, H., & Zhu, Q. (2010). Effects of phytoplankton species composition on absorption spectra and modeled hyperspectral reflectance. Ecological Informatics, 5(5), 359-366.
13. Peng, C., Pan, D., Mao, Z., & Tao, B. (2015). Detection of water quality parameters in Hangzhou Bay using a portable laser fluorometer. Marine Pollution Bulletin, 93(1-2), 163-171.
14. Tao, B., Mao, Z., Lei, H., Pan, D., Bai, Y., Zhu, Q., & Zhang, Z. (2017). A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea. Journal of Geophysical Research Oceans, 122(3).
15. Tao, B., Mao, Z., Lei, H., Pan, D., Shen, Y., Bai, Y., et al. (2015). A novel method for discriminating Prorocentrum donghaiense from diatom blooms in the East China Sea using MODIS measurements. Remote Sensing of Environment, 158, 267-280.
16. Tao, B., Pan, D., Mao, Z., Shen, Y., Zhu, Q., & Chen, J. (2013). Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance. Acta Oceanologica Sinica, 32(10), 48-56.
17. Wang, D., Gong, F., Pan, D., Hao, Z., & Zhu, Q. (2010). Introduction to the airborne marine surveillance platform and its application to water quality monitoring in China. Acta Oceanologica Sinica, 29(2), 33-39.
18. Tao, B., Mao, Z., Pan, D., Shen, Y., Zhu, Q., & Chen, J. (2013). Influence of bio-optical parameter variability on the reflectance peak position in the red band of algal bloom waters. Ecological Informatics, 16(16), 17-24.
• Water optics and ocean-color remote sensing
1. Radiative transfer models and atmospheric correction algorithms
Radiative transfer models and atmospheric correction algorithms are core technologies for satellite detection of ocean carbon parameters. For offshore or inland water bodies significantly affected by terrestrial sources, high-precision radiative transfer models and atmospheric correction algorithms remain challenging. At the same time, with the development of geostationary orbit satellite technology (and the ability to continuously observe from morning to evening), improvement in the performance of radiative transfer models and atmospheric correction algorithms under large zenith angles (occurring in the morning or evening) is urgent.
He et al. (2010) established an ocean-atmosphere coupled vector radiative transfer model, which considered air-sea coupling, rough sea surface, and polarization (PCOART). The model uses the matrix algorithm (or accumulative addition method) to realize the numerical solution of the polarization radiation transfer equation. The Cox-Munk model is used to simulate wind-induced rough sea surface and the “infinite thin sea-air interface layer superposition technology” is used for sea-air coupling. PCOART can not only simulate total radiance but also estimate polarization information. Based on the PCOART model with a parallel layer assumption, He et al. (2018) developed a sea-air coupled vector radiative transfer model (PCOART-SA), which considers the curvature of the Earth. For the first time, a model generated atmospheric Rayleigh scattering, aerosol scattering, and atmospheric diffuse transmittance look-up tables under the effect of the Earth’s curvature. An atmospheric correction model considering the Earth’s curvature has also been developed, which lays a foundation for processing observation data from geostationary orbit satellites.
Regarding atmospheric correction of offshore turbid waters, He et al. (2012) found that water leaving radiance at the blue band is small due to the strong absorption of short waves by terrestrial organic matter (yellow matter, organic debris), and the blue band can replace the near-infrared band for aerosol scattering estimates based on the measured spectral characteristics of high-turbidity estuary waters. An atmospheric correction algorithm (UV-AC) based on the ultraviolet band suitable for high-turbidity water bodies was proposed by He et al. (2012). Compared with the standard algorithm using the near-infrared band as a reference, the UV-AC model can invert the near-infrared radiance of water, which is beneficial to the inversion of suspended matter concentration. Verification results based on both simulation and field observations show that the UV-AC model can effectively improve the performance of the operational algorithms in high-turbidity water.
Representative articles：
1. He, X., Bai, Y., Wei, J., Ding, J., Shanmugam, P., Wang, D., et al. (2017). Ocean color retrieval from MWI onboard the Tiangong-2 Space Lab: preliminary results. Optics Express, 25(20), 23955-23973.
2. He, X., Bai, Y., Zhu, Q., & Gong, F. (2010). A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface. Journal Of Quantitative Spectroscopy & Radiative Transfer, 111(10), 1426-1448.
3. He, X., Pan, D., Bai, Y., Mao, Z., Wang, T., & Hao, Z. (2016). A Practical Method for On-Orbit Estimation of Polarization Response of Satellite Ocean Color Sensor. IEEE Transactions on Geoscience & Remote Sensing, 54(4), 1967-1976.
4. He, X., Pan, D., Yan, B., Wang, D., & Hao, Z. (2014). A new simple concept for ocean colour remote sensing using parallel polarisation radiance. Sci Rep, 4(6168), 3748.
5. Liu, J., He, X., Liu, J., Bai, Y., Wang, D., Chen, T., et al. (2017). Polarization-based enhancement of ocean color signal for estimating suspended particulate matter: radiative transfer simulations and laboratory measurements. Optics Express, 25(8), A323.
6. Mao, Z., Pan, D., Hao, Z., Chen, J., Tao, B., & Zhu, Q. (2014). A potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data. Remote Sensing of Environment, 142(3), 131-140.
7. Mao, Z., Pan, D., He, X., Chen, J., Tao, B., Chen, P., et al. (2016). A Unified Algorithm for the Atmospheric Correction of Satellite Remote Sensing Data over Land and Ocean. Remote Sensing, 8(7), 536.
8. Pan, D. (2004). Study on Marine Application Potentiality of CMODIS/SZ-3. Engineering Sciences,2, 1-5.
9. Pan, D., Xianqiang, H. E., & Mao, T. (2003). Preliminary study on the orbit cross-calibration of CMODIS by SeaWiFS. Progress in Natural Science, 13(10), 745-749.
10. Teng, L. I., Pan, D., Yan, B., Gang, L. I., Xianqiang, H. E., Chen, C. T. A., et al. (2015). Satellite remote sensing of ultraviolet irradiance on the ocean surface. Acta Oceanologica Sinica, 34(6), 101-112.
11. Xianqiang, H. E., Pan, D., Bai, Y., & Gong, F. (2006). A general purpose exact Rayleigh scattering look-up table for ocean color remote sensing. Acta Oceanologica Sinica, 25(1), 48-56.
12. Xianqiang, H. E., Pan, D. L., Yan, B., Zhu, Q. K., & Fang, G. (2007). Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method. Science in China, 50(3), 442-452.
13. Xianqiang He*, Knut Stamnes, Yan Bai, Wei Li, Difeng Wang. Effects of Earth curvature on atmospheric correction for ocean color remote sensing. Remote Sensing of Environment, 209，118–133，2018.
14. Xuchen Jin, Delu Pan, Xianqiang He*, Yan Bai, Palanisamy Shanmugam, Fang Gong, Qiankun Zhu. A vector radiative transfer model for sea surface salinity retrieval from space: a non-raining case. International Journal of Remote Sensing, 2018, DOI: 10.1080/01431161.2018.1488283.
15. He, X., Bai, Y., Pan, D., Tang, J., & Wang, D. (2012). Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters. Optics Express, 20(18), 20754-20770.
16. Wei, J. A., Wang, D., Gong, F., He, X., & Bai, Y. (2017). The Influence of Increasing Water Turbidity on Sea Surface Emissivity. IEEE Transactions on Geoscience & Remote Sensing, PP(99), 1-15.
2. Water optics and ocean-color remote sensing
The radiation transfer theory of light in water (water optics) is the physical foundation for ocean-color remote sensing. Ocean-color remote sensing refers to the method used for studying marine characteristics via electromagnetic information in the near-ultraviolet, visible, or near-infrared bands. The basic mechanism states that when the concentration of each optical component (chlorophyll concentration, suspended matter concentration, or colored dissolved organic matter) in the water changes, it will cause changes in absorption and scattering characteristics of the water body, which will lead to variation in water leaving radiance. By analyzing changes in water leaving radiance received by satellite sensors, the content of one or more optical components in the water body, such as chlorophyll concentration, suspended matter concentration, or colored dissolved organic matter, can be inverted.
The current theoretical framework of ocean-color remote sensing is based on the total radiance (I) received by the satellite sensor while ignoring the deficiency of the polarized signal. He et al. (2014) innovatively proposed the concept of parallel polarization equivalent radiance (I+Q). This concept uses a new theoretical framework for ocean-color polarization remote sensing. Both PCOART simulation and POLDER (POLarization and Directionality of the Earth’s Reflectances) satellite polarization observations show that parallel polarization equivalent radiance (PPR) can effectively reduce the influence of solar flares and improve the signal-to-noise ratio, which is beneficial for ocean-color remote sensing compared with traditional radiance-based ocean-color remote sensing.
For traditional passive ocean-color remote sensing, signals can only be observed during the daytime and the observation results can only characterize components in the surface layer. To obtain the vertical profile of a component, laser remote sensing technology has been developed. Behrenfeld et al. (2013) used lidar data to estimate POC profiles and obtained remote sensing estimates of global ocean POC reserves in the upper 22.5-m layer. Based on the observed data, Chen et al. (2015) established an inversion method for phytoplankton species using laser fluorescence radar signals. In addition, lidar has been used for remote sensing studies of components in near-shore and inland waters (Chen et al., 2017).
Representative articles：
1. Bai, Y., Pan, D., Cai, W. J., He, X., Wang, D., Tao, B., & Zhu, Q. (2013). Remote sensing of salinity from satellite‐derived CDOM in the Changjiang River dominated East China Sea. Journal of Geophysical Research Oceans, 118(1), 227-243.
2. Chen, J., He, X., Zhou, B., & Pan, D. (2017). Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm. Journal of Geophysical Research-Oceans, 122(1).
3. Chen, J., Ni, X., Liu, M., Chen, J., Mao, Z., Jin, H., & Pan, D. (2015). Monitoring the occurrence of seasonal low‐oxygen events off the Changjiang Estuary through integration of remote sensing, buoy observations, and modeling. Journal of Geophysical Research Oceans, 119(8), 5311-5322.
4. Chen, P., Pan, D., & Mao, Z. (2015). Application of a laser fluorometer for discriminating phytoplankton species. Optics & Laser Technology, 67(67), 50-56.
5. Chen, P., Pan, D., Wang, T., Mao, Z., & Zhang, Y. (2017). Coastal and inland water monitoring using a portable hyperspectral laser fluorometer. Marine Pollution Bulletin.
6. He, X., Bai, Y., Pan, D., Huang, N., Dong, X., Chen, J., et al. (2013). Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sensing of Environment, 133(12), 225-239.
7. Hu, Z., Pan, D., He, X., & Bai, Y. (2016). Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing. Remote Sensing, 8(2), 147.
8. Hu, Z., Pan, D., He, X., Song, D., Huang, N., Bai, Y., et al. (2017). Assessment of the MCC method to estimate sea surface currents in highly turbid coastal waters from GOCI. International Journal of Remote Sensing, 38(2), 572-597.
9. Hu, Z., Wang, D. P., He, X., Li, M., Wei, J., Pan, D., & Bai, Y. (2017). Episodic surface intrusions in the Yellow Sea during relaxation of northerly winds. Journal of Geophysical Research, 122.
10. Hu, Z., Wang, D. P., Pan, D., He, X., Miyazawa, Y., Bai, Y., et al. (2016). Mapping surface tidal currents and Changjiang plume in the East China Sea from Geostationary Ocean Color Imager. Journal of Geophysical Research Oceans, 121(3).
11. Li, H., He, X., Bai, Y., Gong, F., & Zhu, Q. (2017). Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions. Journal of Applied Remote Sensing, 11(1), 012004.
12. Mao, Z., Stuart, V., Pan, D., Chen, J., Gong, F., Huang, H., & Zhu, Q. (2010). Effects of phytoplankton species composition on absorption spectra and modeled hyperspectral reflectance. Ecological Informatics, 5(5), 359-366.
13. Peng, C., Pan, D., Mao, Z., & Tao, B. (2015). Detection of water quality parameters in Hangzhou Bay using a portable laser fluorometer. Marine Pollution Bulletin, 93(1-2), 163-171.
14. Tao, B., Mao, Z., Lei, H., Pan, D., Bai, Y., Zhu, Q., & Zhang, Z. (2017). A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea. Journal of Geophysical Research Oceans, 122(3).
15. Tao, B., Mao, Z., Lei, H., Pan, D., Shen, Y., Bai, Y., et al. (2015). A novel method for discriminating Prorocentrum donghaiense from diatom blooms in the East China Sea using MODIS measurements. Remote Sensing of Environment, 158, 267-280.
16. Tao, B., Pan, D., Mao, Z., Shen, Y., Zhu, Q., & Chen, J. (2013). Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance. Acta Oceanologica Sinica, 32(10), 48-56.
17. Wang, D., Gong, F., Pan, D., Hao, Z., & Zhu, Q. (2010). Introduction to the airborne marine surveillance platform and its application to water quality monitoring in China. Acta Oceanologica Sinica, 29(2), 33-39.
18. Tao, B., Mao, Z., Pan, D., Shen, Y., Zhu, Q., & Chen, J. (2013). Influence of bio-optical parameter variability on the reflectance peak position in the red band of algal bloom waters. Ecological Informatics, 16(16), 17-24.
