Abstract

Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain economic damage. Plenty of threshold-based index methods have been developed to detect surface algae pixels in various ocean color data with different manners; however, these methods cannot be used for every satellite sensor because of the spectral band configuration. Also, these traditional methods generally require other reliable indicators, and even visual inspection, in order to achieve an acceptable mapping of MAB that appears under diverse environmental conditions (cloud, aerosol, and sun glint). To overcome these drawbacks, a machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery. The method consists of two MLP models, which consider both spectral and spatial features of Rayleigh-corrected reflectance (Rrc) maps. Accuracy assessment and performance comparison showed that the proposed method has the capability to provide prediction maps of MAB with high accuracy (F1-score approaching 90% or more), and with more robustness than the traditional methods. Most importantly, the model is practically adaptable for other ocean color instruments. This allows customized models to be built and used for monitoring MAB in any regional areas. With the development of machine learning models, long-term mapping of MAB in global ocean is conducive to promoting the associated studies.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (5)

M. Wang and C. Hu, “On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS,” Int. J. Remote Sens. 39(12), 3852–3869 (2018).
[Crossref]

K. Jia, W. Jiang, J. Li, and Z. Tang, “Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images,” Remote Sens. Environ. 209, 1–18 (2018).
[Crossref]

C. Zhang, X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. M. Atkinson, “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens. 140, 133–144 (2018).
[Crossref]

H. Wu, J. Gu, and P. Zhu, “Winter counter-wind transport in the inner southwestern Yellow Sea,” J. Geophys. Res. Oceans 123(1), 411–436 (2018).
[Crossref]

Q. Xing, L. Wu, L. Tian, T. Cui, L. Li, F. Kong, X. Gao, and M. Wu, “Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign,” Mar. Pollut. Bull. 133, 150–156 (2018).
[Crossref] [PubMed]

2017 (2)

L. Qi, C. Hu, M. Wang, S. Shang, and C. Wilson, “Floating algae blooms in the East China Sea,” Geophys. Res. Lett. 44(22), 11,501–11,509 (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]

2016 (5)

M. Wang and C. Hu, “Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations,” Remote Sens. Environ. 183, 350–367 (2016).
[Crossref]

L. Qi, C. Hu, Q. Xing, and S. Shang, “Long-term trend of Ulva prolifera blooms in the western Yellow Sea,” Harmful Algae 58, 35–44 (2016).
[Crossref] [PubMed]

M. Belgiu and L. Drăgut, “Random forest in remote sensing: A review of applications and future directions,” ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016).
[Crossref]

H. J. Lie and C. H. Cho, “Seasonal circulation patterns of the Yellow and East China Seas derived from satellite-tracked drifter trajectories and hydrographic observations,” Prog. Oceanogr. 146, 121–141 (2016).
[Crossref]

Y. Yuan, Z. Qiu, D. Sun, S. Wang, and X. Yue, “Daytime sea fog retrieval based on GOCI data: a case study over the Yellow Sea,” Opt. Express 24(2), 787–801 (2016).
[Crossref] [PubMed]

2015 (5)

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]

C. Hu, L. Feng, R. F. Hardy, and E. J. Hochberg, “Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae,” Remote Sens. Environ. 167, 229–246 (2015).
[Crossref]

X. Huang, C. Xie, X. Fang, and L. Zhang, “Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2097–2110 (2015).
[Crossref]

C. Hu, B. B. Barnes, L. Qi, and A. A. Corcoran, “A harmful algal bloom of Karenia brevis in the northeastern Gulf of Mexico as revealed by MODIS and VIIRS: a comparison,” Sensors (Basel) 15(2), 2873–2887 (2015).
[Crossref] [PubMed]

Q. Xing, L. Tosi, F. Braga, X. Gao, and M. Gao, “Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data,” Nat. Hazards 78(1), 7–21 (2015).
[Crossref]

2014 (4)

X. Lou and C. Hu, “Diurnal changes of a harmful algal bloom in the East China Sea: observations from GOCI,” Remote Sens. Environ. 140, 562–572 (2014).
[Crossref]

S. Hu, H. Yang, J. Zhang, C. Chen, and P. He, “Small-scale early aggregation of green tide macroalgae observed on the Subei Bank, Yellow Sea,” Mar. Pollut. Bull. 81(1), 166–173 (2014).
[Crossref] [PubMed]

C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
[Crossref]

Q. Vanhellemont and K. Ruddick, “Turbid wakes associated with offshore wind turbines observed with Landsat 8,” Remote Sens. Environ. 145, 105–115 (2014).
[Crossref]

2013 (3)

M. Wang, J.-H. Ahn, L. Jiang, W. Shi, S. Son, Y.-J. Park, and J.-H. Ryu, “Ocean color products from the Korean Geostationary Ocean Color Imager (GOCI),” Opt. Express 21(3), 3835–3849 (2013).
[Crossref] [PubMed]

J. Gower, E. Young, and S. King, “Satellite images suggest a new Sargassum source region in 2011,” Remote Sens. Lett. 4(8), 764–773 (2013).
[Crossref]

R. A. Garcia, P. Fearns, J. K. Keesing, and D. Liu, “Quantification of floating macroalgae blooms using the scaled algae index,” J. Geophys. Res. Oceans 118(1), 26–42 (2013).
[Crossref]

2012 (7)

Y. B. Son, J. E. Min, and J. H. Ryu, “Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data,” Ocean Sci. J. 47(3), 359–375 (2012).
[Crossref]

T. W. Cui, J. Zhang, L. E. Sun, Y. J. Jia, W. Zhao, Z. L. Wang, and J. M. Meng, “Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation,” Int. J. Remote Sens. 33(17), 5513–5527 (2012).
[Crossref]

Z. Zhu and C. E. Woodcock, “Object-based cloud and cloud shadow detection in Landsat imagery,” Remote Sens. Environ. 118, 83–94 (2012).
[Crossref]

J. H. Ryu, H. J. Han, S. Cho, Y. J. Park, and Y. H. Ahn, “Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS),” Ocean Sci. J. 47(3), 223–233 (2012).
[Crossref]

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

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

2011 (5)

G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: A review,” ISPRS J. Photogramm. Remote Sens. 66(3), 247–259 (2011).
[Crossref]

D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation,” J. Mach. Learn. Technol. 2, 37–63 (2011).

J. K. Keesing, D. Liu, P. Fearns, and R. Garcia, “Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007-2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China,” Mar. Pollut. Bull. 62(6), 1169–1182 (2011).
[Crossref] [PubMed]

J. F. R. Gower and S. A. King, “Distribution of floating Sargassum in the Gulf of Mexico and the Atlantic Ocean mapped using MERIS,” Int. J. Remote Sens. 32(7), 1917–1929 (2011).
[Crossref]

L. X. Dong, W. B. Guan, Q. Chen, X. H. Li, X. H. Liu, and X. M. Zeng, “Sediment transport in the Yellow Sea and East China Sea,” Estuar. Coast. Shelf Sci. 93(3), 248–258 (2011).
[Crossref]

2010 (4)

C. Hu, D. Li, C. Chen, J. Ge, F. E. Muller-Karger, J. Liu, F. Yu, and M. X. He, “On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea,” J. Geophys. Res. Oceans 115, 1–8 (2010).

D. Liu, J. K. Keesing, Z. Dong, Y. Zhen, B. Di, Y. Shi, P. Fearns, and P. Shi, “Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms,” Mar. Pollut. Bull. 60(9), 1423–1432 (2010).
[Crossref] [PubMed]

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. Oceans 115, 1–20 (2010).

C. Hu, J. Cannizzaro, K. L. Carder, F. E. Muller-Karger, and R. Hardy, “Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data,” Remote Sens. Environ. 114(9), 2048–2058 (2010).
[Crossref]

2009 (4)

D. Liu, J. K. Keesing, Q. Xing, and P. Shi, “World’s largest macroalgal bloom caused by expansion of seaweed aquaculture in China,” Mar. Pollut. Bull. 58(6), 888–895 (2009).
[Crossref] [PubMed]

X. H. Wang, L. Li, X. Bao, and L. D. Zhao, “Economic cost of an algae bloom cleanup in China’s 2008 olympic sailing venue,” Eos (Wash. D.C.) 90(28), 238–239 (2009).
[Crossref]

W. Shi and M. Wang, “Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008,” J. Geophys. Res. Oceans 114, 1–10 (2009).

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 (3)

C. Hu and M. X. He, “Origin and offshore entent of floating algae in Olympic sailing area,” Eos (Wash. D.C.) 89(33), 302–303 (2008).
[Crossref]

C. Hu, R. Luerssen, F. E. Muller-Karger, K. L. Carder, and C. A. Heil, “On the remote monitoring of Karenia brevis blooms of the west Florida shelf,” Cont. Shelf Res. 28(1), 159–176 (2008).
[Crossref]

J. F. Mas and J. J. Flores, “The application of artificial neural networks to the analysis of remotely sensed data,” Int. J. Remote Sens. 29(3), 617–663 (2008).
[Crossref]

2006 (4)

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

M. Wang and W. Shi, “Cloud masking for ocean color data processing in the coastal regions,” IEEE Trans. Geosci. Remote Sens. 44(11), 3196 (2006).
[Crossref]

S. E. Craig, S. E. Lohrenz, Z. Lee, K. L. Mahoney, G. J. Kirkpatrick, O. M. Schofield, and R. G. Steward, “Use of hyperspectral remote sensing reflectance for detection and assessment of the harmful alga, Karenia brevis,” Appl. Opt. 45(21), 5414–5425 (2006).
[Crossref] [PubMed]

J. Gower, C. Hu, G. Borstad, and S. King, “Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico,” IEEE Trans. Geosci. Remote Sens. 44(12), 3619–3625 (2006).
[Crossref]

2005 (1)

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[Crossref]

2004 (2)

M. Hiraoka, M. Ohno, S. Kawaguchi, and G. Yoshida, “Crossing test among floating Ulva thalli forming “green tide” in Japan,” Hydrobiologia 512(1-3), 239–245 (2004).
[Crossref]

M. Kahru, B. G. Michell, A. Diaz, and M. Miura, “MODIS detects a devastating algal bloom in Paracas Bay, Peru,” Eos (Wash. D.C.) 85(45), 465–472 (2004).
[Crossref]

2000 (1)

D. M. Anderson, P. Hoagland, Y. Kaoru, and A. W. White, “Estimated annual economic impacts from harmful algal blooms (HABs) in the United States,” Woods Hole Oceanogr. Inst. 25, 819–837 (2000).

1997 (1)

P. M. Atkinson and A. R. L. Tatnall, “Introduction neural networks in remote sensing,” Int. J. Remote Sens. 18(4), 699–709 (1997).
[Crossref]

1994 (1)

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

1991 (1)

R. G. Congalton, “A review of assessing the accuracy of classification of remotely sensed data,” Remote Sens. Environ. 37(1), 35–46 (1991).
[Crossref]

1986 (1)

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating erros,” Nature 323(6088), 533–536 (1986).
[Crossref]

1960 (1)

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[Crossref]

Ahn, J. H.

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

Ahn, J.-H.

Ahn, Y. H.

J. H. Ryu, H. J. Han, S. Cho, Y. J. Park, and Y. H. Ahn, “Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS),” Ocean Sci. J. 47(3), 223–233 (2012).
[Crossref]

Anderson, D. M.

D. M. Anderson, P. Hoagland, Y. Kaoru, and A. W. White, “Estimated annual economic impacts from harmful algal blooms (HABs) in the United States,” Woods Hole Oceanogr. Inst. 25, 819–837 (2000).

Atkinson, P. M.

C. Zhang, X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. M. Atkinson, “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens. 140, 133–144 (2018).
[Crossref]

P. M. Atkinson and A. R. L. Tatnall, “Introduction neural networks in remote sensing,” Int. J. Remote Sens. 18(4), 699–709 (1997).
[Crossref]

Bao, X.

X. H. Wang, L. Li, X. Bao, and L. D. Zhao, “Economic cost of an algae bloom cleanup in China’s 2008 olympic sailing venue,” Eos (Wash. D.C.) 90(28), 238–239 (2009).
[Crossref]

Barnes, B. B.

C. Hu, B. B. Barnes, L. Qi, and A. A. Corcoran, “A harmful algal bloom of Karenia brevis in the northeastern Gulf of Mexico as revealed by MODIS and VIIRS: a comparison,” Sensors (Basel) 15(2), 2873–2887 (2015).
[Crossref] [PubMed]

Belgiu, M.

M. Belgiu and L. Drăgut, “Random forest in remote sensing: A review of applications and future directions,” ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016).
[Crossref]

Berruti, B.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Blondel, M.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Borstad, G.

J. Gower, C. Hu, G. Borstad, and S. King, “Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico,” IEEE Trans. Geosci. Remote Sens. 44(12), 3619–3625 (2006).
[Crossref]

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[Crossref]

Braga, F.

Q. Xing, L. Tosi, F. Braga, X. Gao, and M. Gao, “Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data,” Nat. Hazards 78(1), 7–21 (2015).
[Crossref]

Brown, L.

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[Crossref]

Brucher, M.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Buongiorno, A.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Cannizzaro, J.

C. Hu, J. Cannizzaro, K. L. Carder, F. E. Muller-Karger, and R. Hardy, “Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data,” Remote Sens. Environ. 114(9), 2048–2058 (2010).
[Crossref]

Carder, K. L.

C. Hu, J. Cannizzaro, K. L. Carder, F. E. Muller-Karger, and R. Hardy, “Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data,” Remote Sens. Environ. 114(9), 2048–2058 (2010).
[Crossref]

C. Hu, R. Luerssen, F. E. Muller-Karger, K. L. Carder, and C. A. Heil, “On the remote monitoring of Karenia brevis blooms of the west Florida shelf,” Cont. Shelf Res. 28(1), 159–176 (2008).
[Crossref]

Chen, C.

S. Hu, H. Yang, J. Zhang, C. Chen, and P. He, “Small-scale early aggregation of green tide macroalgae observed on the Subei Bank, Yellow Sea,” Mar. Pollut. Bull. 81(1), 166–173 (2014).
[Crossref] [PubMed]

C. Hu, D. Li, C. Chen, J. Ge, F. E. Muller-Karger, J. Liu, F. Yu, and M. X. He, “On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea,” J. Geophys. Res. Oceans 115, 1–8 (2010).

Chen, Q.

L. X. Dong, W. B. Guan, Q. Chen, X. H. Li, X. H. Liu, and X. M. Zeng, “Sediment transport in the Yellow Sea and East China Sea,” Estuar. Coast. Shelf Sci. 93(3), 248–258 (2011).
[Crossref]

Chen, X.

C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
[Crossref]

Cheng, W.

W. Cheng, L. O. Hall, D. B. Goldgof, I. M. Soto, and C. Hu, “Automatic red tide detection from MODIS satellite images,” Conf. Proc. IEEE Int. Conf. Syst. Man Cybern, 1864–1868 (2009).

Cho, C. H.

H. J. Lie and C. H. Cho, “Seasonal circulation patterns of the Yellow and East China Seas derived from satellite-tracked drifter trajectories and hydrographic observations,” Prog. Oceanogr. 146, 121–141 (2016).
[Crossref]

Cho, S.

J. H. Ryu, H. J. Han, S. Cho, Y. J. Park, and Y. H. Ahn, “Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS),” Ocean Sci. J. 47(3), 223–233 (2012).
[Crossref]

Cohen, J.

J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas. 20(1), 37–46 (1960).
[Crossref]

Congalton, R. G.

R. G. Congalton, “A review of assessing the accuracy of classification of remotely sensed data,” Remote Sens. Environ. 37(1), 35–46 (1991).
[Crossref]

Corcoran, A. A.

C. Hu, B. B. Barnes, L. Qi, and A. A. Corcoran, “A harmful algal bloom of Karenia brevis in the northeastern Gulf of Mexico as revealed by MODIS and VIIRS: a comparison,” Sensors (Basel) 15(2), 2873–2887 (2015).
[Crossref] [PubMed]

Cournapeau, D.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Craig, S. E.

Cui, T.

Q. Xing, L. Wu, L. Tian, T. Cui, L. Li, F. Kong, X. Gao, and M. Wu, “Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign,” Mar. Pollut. Bull. 133, 150–156 (2018).
[Crossref] [PubMed]

Cui, T. W.

T. W. Cui, J. Zhang, L. E. Sun, Y. J. Jia, W. Zhao, Z. L. Wang, and J. M. Meng, “Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation,” Int. J. Remote Sens. 33(17), 5513–5527 (2012).
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Deering, D. W.

J. W. Rouse, R. H. Hass, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the great plains with ERTS,” Third Earth Resour. Technol. Satell. Symp.1, 309–317 (1973).

Di, B.

D. Liu, J. K. Keesing, Z. Dong, Y. Zhen, B. Di, Y. Shi, P. Fearns, and P. Shi, “Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms,” Mar. Pollut. Bull. 60(9), 1423–1432 (2010).
[Crossref] [PubMed]

Diaz, A.

M. Kahru, B. G. Michell, A. Diaz, and M. Miura, “MODIS detects a devastating algal bloom in Paracas Bay, Peru,” Eos (Wash. D.C.) 85(45), 465–472 (2004).
[Crossref]

Dong, L. X.

L. X. Dong, W. B. Guan, Q. Chen, X. H. Li, X. H. Liu, and X. M. Zeng, “Sediment transport in the Yellow Sea and East China Sea,” Estuar. Coast. Shelf Sci. 93(3), 248–258 (2011).
[Crossref]

Dong, Z.

D. Liu, J. K. Keesing, Z. Dong, Y. Zhen, B. Di, Y. Shi, P. Fearns, and P. Shi, “Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms,” Mar. Pollut. Bull. 60(9), 1423–1432 (2010).
[Crossref] [PubMed]

Donlon, C.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Dragut, L.

M. Belgiu and L. Drăgut, “Random forest in remote sensing: A review of applications and future directions,” ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016).
[Crossref]

Dubourg, V.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Duchesnay, É.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Fang, X.

X. Huang, C. Xie, X. Fang, and L. Zhang, “Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2097–2110 (2015).
[Crossref]

Fawcett, T.

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Fearns, P.

R. A. Garcia, P. Fearns, J. K. Keesing, and D. Liu, “Quantification of floating macroalgae blooms using the scaled algae index,” J. Geophys. Res. Oceans 118(1), 26–42 (2013).
[Crossref]

J. K. Keesing, D. Liu, P. Fearns, and R. Garcia, “Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007-2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China,” Mar. Pollut. Bull. 62(6), 1169–1182 (2011).
[Crossref] [PubMed]

D. Liu, J. K. Keesing, Z. Dong, Y. Zhen, B. Di, Y. Shi, P. Fearns, and P. Shi, “Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms,” Mar. Pollut. Bull. 60(9), 1423–1432 (2010).
[Crossref] [PubMed]

Féménias, P.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Feng, L.

C. Hu, L. Feng, R. F. Hardy, and E. J. Hochberg, “Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae,” Remote Sens. Environ. 167, 229–246 (2015).
[Crossref]

Ferreira, M. H.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Flores, J. J.

J. F. Mas and J. J. Flores, “The application of artificial neural networks to the analysis of remotely sensed data,” Int. J. Remote Sens. 29(3), 617–663 (2008).
[Crossref]

Frerick, J.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Gao, M.

Q. Xing, L. Tosi, F. Braga, X. Gao, and M. Gao, “Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data,” Nat. Hazards 78(1), 7–21 (2015).
[Crossref]

Gao, X.

Q. Xing, L. Wu, L. Tian, T. Cui, L. Li, F. Kong, X. Gao, and M. Wu, “Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign,” Mar. Pollut. Bull. 133, 150–156 (2018).
[Crossref] [PubMed]

Q. Xing, L. Tosi, F. Braga, X. Gao, and M. Gao, “Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data,” Nat. Hazards 78(1), 7–21 (2015).
[Crossref]

Garcia, R.

J. K. Keesing, D. Liu, P. Fearns, and R. Garcia, “Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007-2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China,” Mar. Pollut. Bull. 62(6), 1169–1182 (2011).
[Crossref] [PubMed]

Garcia, R. A.

R. A. Garcia, P. Fearns, J. K. Keesing, and D. Liu, “Quantification of floating macroalgae blooms using the scaled algae index,” J. Geophys. Res. Oceans 118(1), 26–42 (2013).
[Crossref]

Gardiner, A.

C. Zhang, X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. M. Atkinson, “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens. 140, 133–144 (2018).
[Crossref]

Ge, J.

C. Hu, D. Li, C. Chen, J. Ge, F. E. Muller-Karger, J. Liu, F. Yu, and M. X. He, “On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea,” J. Geophys. Res. Oceans 115, 1–8 (2010).

Goldgof, D. B.

W. Cheng, L. O. Hall, D. B. Goldgof, I. M. Soto, and C. Hu, “Automatic red tide detection from MODIS satellite images,” Conf. Proc. IEEE Int. Conf. Syst. Man Cybern, 1864–1868 (2009).

Gordon, H. R.

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

Goryl, P.

C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
[Crossref]

Gower, J.

J. Gower, E. Young, and S. King, “Satellite images suggest a new Sargassum source region in 2011,” Remote Sens. Lett. 4(8), 764–773 (2013).
[Crossref]

J. Gower, C. Hu, G. Borstad, and S. King, “Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico,” IEEE Trans. Geosci. Remote Sens. 44(12), 3619–3625 (2006).
[Crossref]

J. Gower, S. King, G. Borstad, and L. Brown, “Detection of intense plankton blooms using the 709nm band of the MERIS imaging spectrometer,” Int. J. Remote Sens. 26(9), 2005–2012 (2005).
[Crossref]

Gower, J. F. R.

J. F. R. Gower and S. A. King, “Distribution of floating Sargassum in the Gulf of Mexico and the Atlantic Ocean mapped using MERIS,” Int. J. Remote Sens. 32(7), 1917–1929 (2011).
[Crossref]

Gramfort, A.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Grisel, O.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

Gu, J.

H. Wu, J. Gu, and P. Zhu, “Winter counter-wind transport in the inner southwestern Yellow Sea,” J. Geophys. Res. Oceans 123(1), 411–436 (2018).
[Crossref]

Guan, W. B.

L. X. Dong, W. B. Guan, Q. Chen, X. H. Li, X. H. Liu, and X. M. Zeng, “Sediment transport in the Yellow Sea and East China Sea,” Estuar. Coast. Shelf Sci. 93(3), 248–258 (2011).
[Crossref]

Hall, L. O.

W. Cheng, L. O. Hall, D. B. Goldgof, I. M. Soto, and C. Hu, “Automatic red tide detection from MODIS satellite images,” Conf. Proc. IEEE Int. Conf. Syst. Man Cybern, 1864–1868 (2009).

Han, H. J.

J. H. Ryu, H. J. Han, S. Cho, Y. J. Park, and Y. H. Ahn, “Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS),” Ocean Sci. J. 47(3), 223–233 (2012).
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C. Hu, D. Li, C. Chen, J. Ge, F. E. Muller-Karger, J. Liu, F. Yu, and M. X. He, “On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea,” J. Geophys. Res. Oceans 115, 1–8 (2010).

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C. Zhang, X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. M. Atkinson, “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens. 140, 133–144 (2018).
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K. Jia, W. Jiang, J. Li, and Z. Tang, “Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images,” Remote Sens. Environ. 209, 1–18 (2018).
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Q. Xing, L. Wu, L. Tian, T. Cui, L. Li, F. Kong, X. Gao, and M. Wu, “Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign,” Mar. Pollut. Bull. 133, 150–156 (2018).
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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, D. Li, C. Chen, J. Ge, F. E. Muller-Karger, J. Liu, F. Yu, and M. X. He, “On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea,” J. Geophys. Res. Oceans 115, 1–8 (2010).

Liu, X. H.

L. X. Dong, W. B. Guan, Q. Chen, X. H. Li, X. H. Liu, and X. M. Zeng, “Sediment transport in the Yellow Sea and East China Sea,” Estuar. Coast. Shelf Sci. 93(3), 248–258 (2011).
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Loiselle, S.

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X. Lou and C. Hu, “Diurnal changes of a harmful algal bloom in the East China Sea: observations from GOCI,” Remote Sens. Environ. 140, 562–572 (2014).
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C. Hu, R. Luerssen, F. E. Muller-Karger, K. L. Carder, and C. A. Heil, “On the remote monitoring of Karenia brevis blooms of the west Florida shelf,” Cont. Shelf Res. 28(1), 159–176 (2008).
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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]

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. Oceans 115, 1–20 (2010).

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T. W. Cui, J. Zhang, L. E. Sun, Y. J. Jia, W. Zhao, Z. L. Wang, and J. M. Meng, “Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation,” Int. J. Remote Sens. 33(17), 5513–5527 (2012).
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Michell, B. G.

M. Kahru, B. G. Michell, A. Diaz, and M. Miura, “MODIS detects a devastating algal bloom in Paracas Bay, Peru,” Eos (Wash. D.C.) 85(45), 465–472 (2004).
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M. Kahru, B. G. Michell, A. Diaz, and M. Miura, “MODIS detects a devastating algal bloom in Paracas Bay, Peru,” Eos (Wash. D.C.) 85(45), 465–472 (2004).
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G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: A review,” ISPRS J. Photogramm. Remote Sens. 66(3), 247–259 (2011).
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Muller-Karger, F. E.

C. Hu, J. Cannizzaro, K. L. Carder, F. E. Muller-Karger, and R. Hardy, “Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data,” Remote Sens. Environ. 114(9), 2048–2058 (2010).
[Crossref]

C. Hu, D. Li, C. Chen, J. Ge, F. E. Muller-Karger, J. Liu, F. Yu, and M. X. He, “On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea,” J. Geophys. Res. Oceans 115, 1–8 (2010).

C. Hu, R. Luerssen, F. E. Muller-Karger, K. L. Carder, and C. A. Heil, “On the remote monitoring of Karenia brevis blooms of the west Florida shelf,” Cont. Shelf Res. 28(1), 159–176 (2008).
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C. Donlon, B. Berruti, A. Buongiorno, M. H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, J. Nieke, H. Rebhan, B. Seitz, J. Stroede, and R. Sciarra, “The global monitoring for environment and security (GMES) Sentinel-3 mission,” Remote Sens. Environ. 120, 37–57 (2012).
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G. Mountrakis, J. Im, and C. Ogole, “Support vector machines in remote sensing: A review,” ISPRS J. Photogramm. Remote Sens. 66(3), 247–259 (2011).
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J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
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M. Hiraoka, M. Ohno, S. Kawaguchi, and G. Yoshida, “Crossing test among floating Ulva thalli forming “green tide” in Japan,” Hydrobiologia 512(1-3), 239–245 (2004).
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Pan, X.

C. Zhang, X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. M. Atkinson, “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens. 140, 133–144 (2018).
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Park, Y. J.

J. H. Ahn, Y. J. Park, J. H. Ryu, B. Lee, and I. S. Oh, “Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI),” Ocean Sci. J. 47(3), 247–259 (2012).
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F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

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F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2012).

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Shi, P.

D. Liu, J. K. Keesing, Z. Dong, Y. Zhen, B. Di, Y. Shi, P. Fearns, and P. Shi, “Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms,” Mar. Pollut. Bull. 60(9), 1423–1432 (2010).
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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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Q. Vanhellemont and K. Ruddick, “Acolite for Sentinel-2: Aquatic applications of MSI imagery,” Proc. ESA Living Planet Symp. Pragur, Czech Repub. SP-740, 9–13 (2016).

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M. Wang and C. Hu, “On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS,” Int. J. Remote Sens. 39(12), 3852–3869 (2018).
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W. Shi and M. Wang, “Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008,” J. Geophys. Res. Oceans 114, 1–10 (2009).

M. Wang and W. Shi, “Cloud masking for ocean color data processing in the coastal regions,” IEEE Trans. Geosci. Remote Sens. 44(11), 3196 (2006).
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T. W. Cui, J. Zhang, L. E. Sun, Y. J. Jia, W. Zhao, Z. L. Wang, and J. M. Meng, “Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation,” Int. J. Remote Sens. 33(17), 5513–5527 (2012).
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L. Qi, C. Hu, M. Wang, S. Shang, and C. Wilson, “Floating algae blooms in the East China Sea,” Geophys. Res. Lett. 44(22), 11,501–11,509 (2017).
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Z. Zhu and C. E. Woodcock, “Object-based cloud and cloud shadow detection in Landsat imagery,” Remote Sens. Environ. 118, 83–94 (2012).
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H. Wu, J. Gu, and P. Zhu, “Winter counter-wind transport in the inner southwestern Yellow Sea,” J. Geophys. Res. Oceans 123(1), 411–436 (2018).
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Q. Xing, L. Wu, L. Tian, T. Cui, L. Li, F. Kong, X. Gao, and M. Wu, “Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign,” Mar. Pollut. Bull. 133, 150–156 (2018).
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Q. Xing, L. Wu, L. Tian, T. Cui, L. Li, F. Kong, X. Gao, and M. Wu, “Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign,” Mar. Pollut. Bull. 133, 150–156 (2018).
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L. Qi, C. Hu, Q. Xing, and S. Shang, “Long-term trend of Ulva prolifera blooms in the western Yellow Sea,” Harmful Algae 58, 35–44 (2016).
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Q. Xing, L. Tosi, F. Braga, X. Gao, and M. Gao, “Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data,” Nat. Hazards 78(1), 7–21 (2015).
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D. Liu, J. K. Keesing, Q. Xing, and P. Shi, “World’s largest macroalgal bloom caused by expansion of seaweed aquaculture in China,” Mar. Pollut. Bull. 58(6), 888–895 (2009).
[Crossref] [PubMed]

Xu, L.

C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
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C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
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Yoshida, G.

M. Hiraoka, M. Ohno, S. Kawaguchi, and G. Yoshida, “Crossing test among floating Ulva thalli forming “green tide” in Japan,” Hydrobiologia 512(1-3), 239–245 (2004).
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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. Oceans 115, 1–20 (2010).

Yu, Z.

C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
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Zhang, C.

C. Zhang, X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. M. Atkinson, “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens. 140, 133–144 (2018).
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Zhang, J.

S. Hu, H. Yang, J. Zhang, C. Chen, and P. He, “Small-scale early aggregation of green tide macroalgae observed on the Subei Bank, Yellow Sea,” Mar. Pollut. Bull. 81(1), 166–173 (2014).
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Zhang, L.

X. Huang, C. Xie, X. Fang, and L. Zhang, “Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2097–2110 (2015).
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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).
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C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
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Zhao, L. D.

X. H. Wang, L. Li, X. Bao, and L. D. Zhao, “Economic cost of an algae bloom cleanup in China’s 2008 olympic sailing venue,” Eos (Wash. D.C.) 90(28), 238–239 (2009).
[Crossref]

Zhao, W.

T. W. Cui, J. Zhang, L. E. Sun, Y. J. Jia, W. Zhao, Z. L. Wang, and J. M. Meng, “Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation,” Int. J. Remote Sens. 33(17), 5513–5527 (2012).
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Zhen, Y.

D. Liu, J. K. Keesing, Z. Dong, Y. Zhen, B. Di, Y. Shi, P. Fearns, and P. Shi, “Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms,” Mar. Pollut. Bull. 60(9), 1423–1432 (2010).
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Zhu, P.

H. Wu, J. Gu, and P. Zhu, “Winter counter-wind transport in the inner southwestern Yellow Sea,” J. Geophys. Res. Oceans 123(1), 411–436 (2018).
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Z. Zhu and C. E. Woodcock, “Object-based cloud and cloud shadow detection in Landsat imagery,” Remote Sens. Environ. 118, 83–94 (2012).
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Appl. Opt. (1)

Cont. Shelf Res. (1)

C. Hu, R. Luerssen, F. E. Muller-Karger, K. L. Carder, and C. A. Heil, “On the remote monitoring of Karenia brevis blooms of the west Florida shelf,” Cont. Shelf Res. 28(1), 159–176 (2008).
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Environ. Earth Sci. (1)

C. Huang, Y. Li, H. Yang, D. Sun, Z. Yu, Z. Zhang, X. Chen, and L. Xu, “Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS,” Environ. Earth Sci. 71(8), 3705–3714 (2014).
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Eos (Wash. D.C.) (3)

M. Kahru, B. G. Michell, A. Diaz, and M. Miura, “MODIS detects a devastating algal bloom in Paracas Bay, Peru,” Eos (Wash. D.C.) 85(45), 465–472 (2004).
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X. H. Wang, L. Li, X. Bao, and L. D. Zhao, “Economic cost of an algae bloom cleanup in China’s 2008 olympic sailing venue,” Eos (Wash. D.C.) 90(28), 238–239 (2009).
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C. Hu and M. X. He, “Origin and offshore entent of floating algae in Olympic sailing area,” Eos (Wash. D.C.) 89(33), 302–303 (2008).
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Estuar. Coast. Shelf Sci. (1)

L. X. Dong, W. B. Guan, Q. Chen, X. H. Li, X. H. Liu, and X. M. Zeng, “Sediment transport in the Yellow Sea and East China Sea,” Estuar. Coast. Shelf Sci. 93(3), 248–258 (2011).
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Geophys. Res. Lett. (1)

L. Qi, C. Hu, M. Wang, S. Shang, and C. Wilson, “Floating algae blooms in the East China Sea,” Geophys. Res. Lett. 44(22), 11,501–11,509 (2017).
[Crossref]

Harmful Algae (1)

L. Qi, C. Hu, Q. Xing, and S. Shang, “Long-term trend of Ulva prolifera blooms in the western Yellow Sea,” Harmful Algae 58, 35–44 (2016).
[Crossref] [PubMed]

Hydrobiologia (1)

M. Hiraoka, M. Ohno, S. Kawaguchi, and G. Yoshida, “Crossing test among floating Ulva thalli forming “green tide” in Japan,” Hydrobiologia 512(1-3), 239–245 (2004).
[Crossref]

IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (1)

X. Huang, C. Xie, X. Fang, and L. Zhang, “Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2097–2110 (2015).
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IEEE Trans. Geosci. Remote Sens. (2)

J. Gower, C. Hu, G. Borstad, and S. King, “Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico,” IEEE Trans. Geosci. Remote Sens. 44(12), 3619–3625 (2006).
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M. Wang and W. Shi, “Cloud masking for ocean color data processing in the coastal regions,” IEEE Trans. Geosci. Remote Sens. 44(11), 3196 (2006).
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Int. J. Remote Sens. (6)

M. Wang and C. Hu, “On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS,” Int. J. Remote Sens. 39(12), 3852–3869 (2018).
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Figures (11)

Fig. 1
Fig. 1 (a) GOCI AFAI image acquired on 26 May 2017 (03:16:43 GMT) showing floating macroalgae blooms and clouds outlined in the white dashed ellipses over the YS. Dark purple color indicates relatively turbid water, especially at the Subei Shoal. (b) GOCI measured mean Rayleigh-corrected reflectance (Rrc, unitless) spectra of macroalgae, cloud, turbid and clear water pixels collected using ENVI ROI tool with respective standard deviation.
Fig. 2
Fig. 2 Illustration of the generic form of an error matrix and the equations of statistical measures considered in this paper.
Fig. 3
Fig. 3 (a) Probability and cumulative probability distributions of AFAI for macroalgae pixels. (b)–(d) show the probability distributions of Br and Wh for cloud pixels, and NDVI and Rrc_ratio for turbid and clear water pixels, respectively.
Fig. 4
Fig. 4 Flow diagram showing the general framework of distinguishing floating macroalgae pixels. The final prediction map was yielded by combining results from the two models.
Fig. 5
Fig. 5 (a) The distribution of accuracy measures of macroalgae pixels predicted using a model A only for 14 independent testing Rrc images. (b) The distribution of final predicted accuracy of macroalgae pixels by using model A and B successively, where the testing images with significant improvements of Precision (> 25%) are highlighted by red dashed boxes and arrows. (c) and (d) briefly display the values of F1-score for three remaining classes (cloud, turbid water, and others) and the values of OA and K for each testing image, respectively.
Fig. 6
Fig. 6 (a) Pseudo color image of GOCI acquired on 7 June 2017 (04:16:42 GMT), showing the U. prolifera patches as red near Qingdao. (b) The final prediction map generated by the proposed method, where the pixels in individual class are displayed as the legend and black color indicates land. (c) and (d) are enlarged images of (a) and (b), respectively, for region 1 in (b). (e) is the corresponding intermediate prediction resulted by model A. (f)–(h) are similar as (c)–(e) but for region 2 in (b).
Fig. 7
Fig. 7 A total of 427,426 macroalgae pixels were predicted by the proposed method from the testing Rrc images. (a) exhibits the examples of Rrc spectra for these macroalgae pixels, with mean spectrum and standard deviation of all spectra. The color of each spectrum corresponds with its AFAI value as the color scale. (b) displays the probability and cumulative probability distributions of AFAI for predicted macroalgae pixels. (c) and (d) show the probability distributions of Br and Wh, and NDVI and Rrc_ratio for predicted macroalgae pixels, respectively.
Fig. 8
Fig. 8 Pseudo color images for three chosen scenes are shown in (a), (f) and (k), respectively, with individual location informed as an inserted map. Comparison of the prediction maps among MLP models (this study), NDVI, IGAG and AFAI are illustrated in (b)–(e), (g)–(j), (l)–(o) for the respective scene. The manmade cloud-masking product was overlaid for the cloudy scene (bottom panel). Green, white and blue color indicates macroalgae, cloud, and other pixels, respectively. The total number of predicted macroalgae pixels are displayed (denoted as N) for each result.
Fig. 9
Fig. 9 (a) exhibits the number of macroalgae pixels (denoted as N) predicted in the Subei Shoal between 13 and 18 in May 2017 (eight scenes per day), except for the scenes covered by clouds completely. The prediction maps of highlighted dates (red dashed circles) are displayed in (b)–(d), respectively.
Fig. 10
Fig. 10 (a) shows the sensitivity of F1-score in two typical scenes (cloudy and clear) to different window sizes used to calculate the spatial difference (SD). (b) shows the sensitivity of mean F1-score for the 14 testing images to different window sizes and SD computed by the mean and median value of Rrc difference within each window.
Fig. 11
Fig. 11 Examples of monitoring Sargassum bloom occurred in the ECS between March and April. (a)–(f) illustrate the distributions of Sargassum slicks at different dates.

Tables (2)

Tables Icon

Table 1 F1-score assessment of the final macroalgae prediction maps achieved by different methods for three chosen scenes.

Tables Icon

Table 2 Image information of downloaded L8/OLI data and comparison of macroalgae coverage between L8/OLI and quasi-simultaneous GOCI images. The Relative Error (RE) of estimated coverage for each image pair and Mean Relative Error (MRE) were calculated.

Equations (10)

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R rc,λ = π L TOA,λ Corr / ( F 0 cos θ s ) R r,λ
AFAI= R rc,NIR [ R rc,RED + ( R rc,LNIR R rc,RED ) NIR λ RED ) / LNIR λ RED ) ]
NDVI=( R rc,NIR R rc,RED ) / ( R rc,NIR + R rc,RED )
R rc _ratio = R rc,680 / R rc,745
B r = 1 N i = 1 N R r c , λ i
W h = i = 1 N | ( R r c , λ i B r ) / B r |
AFAI > 0
B r > 0 .2 or ( B r > 0 .05 and B r < 0 .2 and W h < 0 .8)
R rc _ratio > 1 .4 and NDVI < 0 .3
IGAG = ( R rc,555 + R rc,660 ) / ( R rc,745 R rc,660 ) + R rc,745 / R rc,660

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