Abstract

Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm­­­­­­­ blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.

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

Full Article  |  PDF Article
OSA Recommended Articles
UV-NIR approach with non-zero water-leaving radiance approximation for atmospheric correction of satellite imagery in inland and coastal zones

Rakesh Kumar Singh, Palanisamy Shanmugam, Xianqiang He, and Thomas Schroeder
Opt. Express 27(16) A1118-A1145 (2019)

Fuzzy c-means clustering based segmentation and the filtering method for discontinuous ESPI fringe patterns

Wenjun Xu, Chen Tang, Min Xu, and Zhenkun Lei
Appl. Opt. 58(6) 1442-1450 (2019)

References

  • View by:
  • |
  • |
  • |

  1. A. Morel and L. Prieur, “Analysis of variations in ocean color 1,” Limnol. Oceanogr. 22(4), 709–722 (1977).
    [Crossref]
  2. C. D. Mobley, “Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?” Oceanogr 17(2), 60–67 (2004).
    [Crossref]
  3. B. N. Seegers, R. P. Stumpf, B. A. Schaeffer, K. A. Loftin, and P. J. Werdell, “Performance metrics for the assessment of satellite data products: an ocean color case study,” Opt. Express 26(6), 7404–7422 (2018).
    [Crossref]
  4. J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
    [Crossref]
  5. 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]
  6. D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
    [Crossref]
  7. 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]
  8. Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
    [Crossref]
  9. C. Hu, Z. Lee, and B. Franz, “Chlorophyll aalgorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference,” J. Geophys. Res.: Oceans 117(C1), 117 (2012).
    [Crossref]
  10. G. Zheng and P. M. DiGiacomo, “Uncertainties and applications of satellite-derived coastal water quality products,” Prog. Oceanogr. 159, 45–72 (2017).
    [Crossref]
  11. T. S. Moore, J. W. Campbell, and M. D. Dowell, “A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product,” Remote Sens. Environ. 113(11), 2424–2430 (2009).
    [Crossref]
  12. T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
    [Crossref]
  13. J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
    [Crossref]
  14. T. Jackson, S. Sathyendranath, and F. Mélin, “An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications,” Remote Sens. Environ. 203, 152–161 (2017).
    [Crossref]
  15. N. G. Jerlov, “Classification of sea water in terms of quanta irradiance,” ICES J. Mar. Sci. 37(3), 281–287 (1977).
    [Crossref]
  16. N. G. Jerlov and F. F. Koczy, Photographic measurements of daylight in deep water (Elanders boktr., 1951).
  17. M. G. Solonenko and C. D. Mobley, “Inherent optical properties of Jerlov water types,” Appl. Opt. 54(17), 5392–5401 (2015).
    [Crossref]
  18. C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
    [Crossref]
  19. F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
    [Crossref]
  20. M. E. Smith, L. R. Lain, and S. Bernard, “An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters,” Remote Sens. Environ. 215, 217–227 (2018).
    [Crossref]
  21. M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
    [Crossref]
  22. M. W. Matthews and D. Odermatt, “Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters,” Remote Sens. Environ. 156, 374–382 (2015).
    [Crossref]
  23. K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
    [Crossref]
  24. F. Mélin and V. Vantrepotte, “How optically diverse is the coastal ocean?” Remote Sens. Environ. 160, 235–251 (2015).
    [Crossref]
  25. N. R. Pal, K. Pal, and J. C. Bezdek, “A mixed c-means clustering model,” in Proceedings of 6th International Fuzzy Systems Conference, (IEEE, 1997), 11–21.
  26. X. Wu, B. Wu, J. Sun, and H. Fu, “Unsupervised possibilistic fuzzy clustering,” J. Comput. Sci. 7, 1075–1080 (2010).
  27. W. R. Tobler, “A computer movie simulating urban growth in the Detroit region,” Economic Geography 46, 234–240 (1970).
    [Crossref]
  28. L. A. Zadeh, “Fuzzy sets,” Information and Control 8(3), 338–353 (1965).
    [Crossref]
  29. V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
    [Crossref]
  30. E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
    [Crossref]
  31. D. Dembele, “Multi-objective optimization for clustering 3-way gene expression data,” Adv. Data Anal. Classif. 2(3), 211–225 (2008).
    [Crossref]
  32. D. Dembele and P. Kastner, “Fuzzy C-means method for clustering microarray data,” Bioinformatics 19(8), 973–980 (2003).
    [Crossref]
  33. M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
    [Crossref]
  34. J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).
  35. S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
    [Crossref]
  36. K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
    [Crossref]
  37. 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]
  38. Y. Zhang, E. Zhang, and M. Liu, “Spectral absorption properties of chromophoric dissolved organic matter and particulate matter in Yunnan Palteau lakes,” J. Lake Sci. 21(2), 255–263 (2009).
    [Crossref]
  39. B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).
  40. 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]
  41. Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
    [Crossref]
  42. X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
    [Crossref]
  43. K. Xue, Y. Zhang, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
    [Crossref]
  44. K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
    [Crossref]
  45. B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
    [Crossref]
  46. Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
    [Crossref]
  47. J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms (Springer Science & Business Media, 2013).
  48. R Development Core Team, R: Probabilistic and Possibilistic Cluster Analysis, 2019.
  49. R Development Core Team, R: Fuzzy Clustering, 2019.
  50. W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems 158(19), 2095–2117 (2007).
    [Crossref]
  51. J. Yu, Q. Cheng, and H. Huang, “Analysis of the weighting exponent in the FCM,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 634–639 (2004).
    [Crossref]
  52. M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, “A new cluster validity index for the fuzzy c-mean,” Pattern Recognit. Lett. 19(3-4), 237–246 (1998).
    [Crossref]
  53. T. S. Moore, J. W. Campbell, and H. Feng, “A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms,” IEEE T. Geosci. Remote Sensing 39(8), 1764–1776 (2001).
    [Crossref]
  54. R. J. Campello and E. R. Hruschka, “A fuzzy extension of the silhouette width criterion for cluster analysis,” Fuzzy Sets and Systems 157(21), 2858–2875 (2006).
    [Crossref]
  55. S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
    [Crossref]
  56. Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
    [Crossref]
  57. X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
    [Crossref]
  58. B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
    [Crossref]
  59. A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
    [Crossref]
  60. A. A. Gilerson, A. A. Gitelson, J. Zhou, D. Gurlin, W. Moses, I. Ioannou, and S. A. Ahmed, “Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands,” Opt. Express 18(23), 24109–24125 (2010).
    [Crossref]
  61. M. Hieronymi, D. Müller, and R. Doerffer, “The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters,” Front. Mar. Sci. 4, 140 (2017).
    [Crossref]
  62. J. Wei, Z. Lee, and S. Shang, “A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments,” J. Geophys. Res.: Oceans 121, 8189–8207 (2016).
  63. H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
    [Crossref]

2019 (6)

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]

J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
[Crossref]

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

2018 (8)

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[Crossref]

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

M. E. Smith, L. R. Lain, and S. Bernard, “An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters,” Remote Sens. Environ. 215, 217–227 (2018).
[Crossref]

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

B. N. Seegers, R. P. Stumpf, B. A. Schaeffer, K. A. Loftin, and P. J. Werdell, “Performance metrics for the assessment of satellite data products: an ocean color case study,” Opt. Express 26(6), 7404–7422 (2018).
[Crossref]

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

2017 (8)

T. Jackson, S. Sathyendranath, and F. Mélin, “An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications,” Remote Sens. Environ. 203, 152–161 (2017).
[Crossref]

G. Zheng and P. M. DiGiacomo, “Uncertainties and applications of satellite-derived coastal water quality products,” Prog. Oceanogr. 159, 45–72 (2017).
[Crossref]

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (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]

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
[Crossref]

K. Xue, Y. Zhang, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
[Crossref]

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

M. Hieronymi, D. Müller, and R. Doerffer, “The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters,” Front. Mar. Sci. 4, 140 (2017).
[Crossref]

2016 (3)

J. Wei, Z. Lee, and S. Shang, “A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments,” J. Geophys. Res.: Oceans 121, 8189–8207 (2016).

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

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]

2015 (7)

M. W. Matthews and D. Odermatt, “Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters,” Remote Sens. Environ. 156, 374–382 (2015).
[Crossref]

F. Mélin and V. Vantrepotte, “How optically diverse is the coastal ocean?” Remote Sens. Environ. 160, 235–251 (2015).
[Crossref]

M. G. Solonenko and C. D. Mobley, “Inherent optical properties of Jerlov water types,” Appl. Opt. 54(17), 5392–5401 (2015).
[Crossref]

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

2014 (3)

X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
[Crossref]

T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
[Crossref]

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

2013 (1)

K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
[Crossref]

2012 (2)

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

V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
[Crossref]

2011 (2)

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

2010 (2)

2009 (2)

Y. Zhang, E. Zhang, and M. Liu, “Spectral absorption properties of chromophoric dissolved organic matter and particulate matter in Yunnan Palteau lakes,” J. Lake Sci. 21(2), 255–263 (2009).
[Crossref]

T. S. Moore, J. W. Campbell, and M. D. Dowell, “A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product,” Remote Sens. Environ. 113(11), 2424–2430 (2009).
[Crossref]

2008 (1)

D. Dembele, “Multi-objective optimization for clustering 3-way gene expression data,” Adv. Data Anal. Classif. 2(3), 211–225 (2008).
[Crossref]

2007 (1)

W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems 158(19), 2095–2117 (2007).
[Crossref]

2006 (1)

R. J. Campello and E. R. Hruschka, “A fuzzy extension of the silhouette width criterion for cluster analysis,” Fuzzy Sets and Systems 157(21), 2858–2875 (2006).
[Crossref]

2004 (2)

J. Yu, Q. Cheng, and H. Huang, “Analysis of the weighting exponent in the FCM,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 634–639 (2004).
[Crossref]

C. D. Mobley, “Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?” Oceanogr 17(2), 60–67 (2004).
[Crossref]

2003 (1)

D. Dembele and P. Kastner, “Fuzzy C-means method for clustering microarray data,” Bioinformatics 19(8), 973–980 (2003).
[Crossref]

2002 (1)

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

2001 (1)

T. S. Moore, J. W. Campbell, and H. Feng, “A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms,” IEEE T. Geosci. Remote Sensing 39(8), 1764–1776 (2001).
[Crossref]

2000 (1)

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

1998 (1)

M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, “A new cluster validity index for the fuzzy c-mean,” Pattern Recognit. Lett. 19(3-4), 237–246 (1998).
[Crossref]

1977 (2)

A. Morel and L. Prieur, “Analysis of variations in ocean color 1,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

N. G. Jerlov, “Classification of sea water in terms of quanta irradiance,” ICES J. Mar. Sci. 37(3), 281–287 (1977).
[Crossref]

1970 (1)

W. R. Tobler, “A computer movie simulating urban growth in the Detroit region,” Economic Geography 46, 234–240 (1970).
[Crossref]

1965 (1)

L. A. Zadeh, “Fuzzy sets,” Information and Control 8(3), 338–353 (1965).
[Crossref]

Ahmed, S. A.

Antoine, D.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Arnone, R.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Balch, W. M.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Banks, A. C.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Barbosa, C. C.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Barker, K.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Bernard, S.

M. E. Smith, L. R. Lain, and S. Bernard, “An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters,” Remote Sens. Environ. 215, 217–227 (2018).
[Crossref]

Bezdek, J. C.

N. R. Pal, K. Pal, and J. C. Bezdek, “A mixed c-means clustering model,” in Proceedings of 6th International Fuzzy Systems Conference, (IEEE, 1997), 11–21.

J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms (Springer Science & Business Media, 2013).

Bi, S.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

Binding, C. E.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Blondeau-Patissier, D.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Boss, E.

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]

Bradt, S.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
[Crossref]

Brando, V.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Brewin, R. J.

J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
[Crossref]

Brockmann, C.

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Brotas, V.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Campbell, J. W.

T. S. Moore, J. W. Campbell, and M. D. Dowell, “A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product,” Remote Sens. Environ. 113(11), 2424–2430 (2009).
[Crossref]

T. S. Moore, J. W. Campbell, and H. Feng, “A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms,” IEEE T. Geosci. Remote Sensing 39(8), 1764–1776 (2001).
[Crossref]

Campello, R. J.

R. J. Campello and E. R. Hruschka, “A fuzzy extension of the silhouette width criterion for cluster analysis,” Fuzzy Sets and Systems 157(21), 2858–2875 (2006).
[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, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[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]

Carder, K.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Chen, X.

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
[Crossref]

X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
[Crossref]

Cheng, Q.

J. Yu, Q. Cheng, and H. Huang, “Analysis of the weighting exponent in the FCM,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 634–639 (2004).
[Crossref]

Cherukuru, N.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Clementson, L.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Davis, C.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Dekker, A.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Dembele, D.

D. Dembele, “Multi-objective optimization for clustering 3-way gene expression data,” Adv. Data Anal. Classif. 2(3), 211–225 (2008).
[Crossref]

D. Dembele and P. Kastner, “Fuzzy C-means method for clustering microarray data,” Bioinformatics 19(8), 973–980 (2003).
[Crossref]

Dessailly, D.

V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
[Crossref]

DiGiacomo, P. M.

G. Zheng and P. M. DiGiacomo, “Uncertainties and applications of satellite-derived coastal water quality products,” Prog. Oceanogr. 159, 45–72 (2017).
[Crossref]

Ding, X.

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

Doerffer, R.

M. Hieronymi, D. Müller, and R. Doerffer, “The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters,” Front. Mar. Sci. 4, 140 (2017).
[Crossref]

Dowell, M. D.

T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
[Crossref]

T. S. Moore, J. W. Campbell, and M. D. Dowell, “A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product,” Remote Sens. Environ. 113(11), 2424–2430 (2009).
[Crossref]

Du, C.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

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]

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

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]

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[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]

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
[Crossref]

K. Xue, Y. Zhang, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
[Crossref]

Eleveld, M.

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Fang, S.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

Feng, H.

T. S. Moore, J. W. Campbell, and H. Feng, “A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms,” IEEE T. Geosci. Remote Sensing 39(8), 1764–1776 (2001).
[Crossref]

Feng, L.

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (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]

X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
[Crossref]

Franz, B.

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

Frouin, R.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Fu, H.

X. Wu, B. Wu, J. Sun, and H. Fu, “Unsupervised possibilistic fuzzy clustering,” J. Comput. Sci. 7, 1075–1080 (2010).

Fukushima, T.

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Gao, G.

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

Gilerson, A. A.

Gitelson, A. A.

Grant, M.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Groom, S.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Guo, Y.

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

Gurlin, D.

Han, X.

X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
[Crossref]

Hieronymi, M.

M. Hieronymi, D. Müller, and R. Doerffer, “The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters,” Front. Mar. Sci. 4, 140 (2017).
[Crossref]

Hommersom, A.

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Hooker, S.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Hou, X.

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
[Crossref]

Hruschka, E. R.

R. J. Campello and E. R. Hruschka, “A fuzzy extension of the silhouette width criterion for cluster analysis,” Fuzzy Sets and Systems 157(21), 2858–2875 (2006).
[Crossref]

Hu, C.

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

Hu, S.

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

Huang, 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]

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

Huang, H.

J. Yu, Q. Cheng, and H. Huang, “Analysis of the weighting exponent in the FCM,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 634–639 (2004).
[Crossref]

Hunter, P. D.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Ioannou, I.

Jackson, T.

T. Jackson, S. Sathyendranath, and F. Mélin, “An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications,” Remote Sens. Environ. 203, 152–161 (2017).
[Crossref]

Jerlov, N. G.

N. G. Jerlov, “Classification of sea water in terms of quanta irradiance,” ICES J. Mar. Sci. 37(3), 281–287 (1977).
[Crossref]

N. G. Jerlov and F. F. Koczy, Photographic measurements of daylight in deep water (Elanders boktr., 1951).

Kahru, M.

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

Kastner, P.

D. Dembele and P. Kastner, “Fuzzy C-means method for clustering microarray data,” Bioinformatics 19(8), 973–980 (2003).
[Crossref]

Koczy, F. F.

N. G. Jerlov and F. F. Koczy, Photographic measurements of daylight in deep water (Elanders boktr., 1951).

Lain, L. R.

M. E. Smith, L. R. Lain, and S. Bernard, “An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters,” Remote Sens. Environ. 215, 217–227 (2018).
[Crossref]

Le, C.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

Lee, Z.

J. Wei, Z. Lee, and S. Shang, “A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments,” J. Geophys. Res.: Oceans 121, 8189–8207 (2016).

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

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Lei, S.

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

Lelieveldt, B. P.

M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, “A new cluster validity index for the fuzzy c-mean,” Pattern Recognit. Lett. 19(3-4), 237–246 (1998).
[Crossref]

Li, J.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Li, L.

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
[Crossref]

Li, Q.

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

Li, Y.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

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]

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
[Crossref]

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

Lin, J.

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

Liu, D.

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[Crossref]

Liu, G.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

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]

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

Liu, H.

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

Liu, M.

Y. Zhang, E. Zhang, and M. Liu, “Spectral absorption properties of chromophoric dissolved organic matter and particulate matter in Yunnan Palteau lakes,” J. Lake Sci. 21(2), 255–263 (2009).
[Crossref]

Liu, X.

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

Loftin, K. A.

Loisel, H.

V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
[Crossref]

Lu, H.

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
[Crossref]

Lu, Z.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Lv, H.

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

Lyu, H.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

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]

Ma, R.

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]

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[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, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
[Crossref]

Matsushita, B.

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Matthews, M. W.

M. W. Matthews and D. Odermatt, “Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters,” Remote Sens. Environ. 156, 374–382 (2015).
[Crossref]

McLean, S.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Mélin, F.

T. Jackson, S. Sathyendranath, and F. Mélin, “An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications,” Remote Sens. Environ. 203, 152–161 (2017).
[Crossref]

F. Mélin and V. Vantrepotte, “How optically diverse is the coastal ocean?” Remote Sens. Environ. 160, 235–251 (2015).
[Crossref]

Mériaux, X.

V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
[Crossref]

Miao, S.

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

Miller, C.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Mitchell, B. G.

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

Mobley, C. D.

M. G. Solonenko and C. D. Mobley, “Inherent optical properties of Jerlov water types,” Appl. Opt. 54(17), 5392–5401 (2015).
[Crossref]

C. D. Mobley, “Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?” Oceanogr 17(2), 60–67 (2004).
[Crossref]

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Moore, T.

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Moore, T. S.

T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
[Crossref]

T. S. Moore, J. W. Campbell, and M. D. Dowell, “A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product,” Remote Sens. Environ. 113(11), 2424–2430 (2009).
[Crossref]

T. S. Moore, J. W. Campbell, and H. Feng, “A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms,” IEEE T. Geosci. Remote Sensing 39(8), 1764–1776 (2001).
[Crossref]

Morel, A.

A. Morel and L. Prieur, “Analysis of variations in ocean color 1,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Moreno-Madriñán, M. J.

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Moses, W.

Mu, M.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

Mueller, J.

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

Mueller, J. L.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Müller, D.

M. Hieronymi, D. Müller, and R. Doerffer, “The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters,” Front. Mar. Sci. 4, 140 (2017).
[Crossref]

Nechad, B.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Neil, C.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

O’Donnell, R.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Odermatt, D.

M. W. Matthews and D. Odermatt, “Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters,” Remote Sens. Environ. 156, 374–382 (2015).
[Crossref]

Oubelkheir, K.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Oyama, Y.

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Pal, K.

N. R. Pal, K. Pal, and J. C. Bezdek, “A mixed c-means clustering model,” in Proceedings of 6th International Fuzzy Systems Conference, (IEEE, 1997), 11–21.

Pal, N. R.

N. R. Pal, K. Pal, and J. C. Bezdek, “A mixed c-means clustering model,” in Proceedings of 6th International Fuzzy Systems Conference, (IEEE, 1997), 11–21.

Pan, Y.

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

Peters, S.

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Pitarch, J.

J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
[Crossref]

Prieur, L.

A. Morel and L. Prieur, “Analysis of variations in ocean color 1,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Qin, B.

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

Reiber, J. H.

M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, “A new cluster validity index for the fuzzy c-mean,” Pattern Recognit. Lett. 19(3-4), 237–246 (1998).
[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).
[Crossref]

Rezaee, M. R.

M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, “A new cluster validity index for the fuzzy c-mean,” Pattern Recognit. Lett. 19(3-4), 237–246 (1998).
[Crossref]

Ruddick, K.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Ruescas, A.

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Sathyendranath, S.

T. Jackson, S. Sathyendranath, and F. Mélin, “An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications,” Remote Sens. Environ. 203, 152–161 (2017).
[Crossref]

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Schaeffer, B. A.

Schroeder, T.

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

Scott, M.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Seegers, B. N.

Shang, S.

J. Wei, Z. Lee, and S. Shang, “A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments,” J. Geophys. Res.: Oceans 121, 8189–8207 (2016).

Shen, M.

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]

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[Crossref]

Shen, Q.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Shi, K.

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
[Crossref]

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
[Crossref]

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

Simis, S. G.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Smith, M. E.

M. E. Smith, L. R. Lain, and S. Bernard, “An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters,” Remote Sens. Environ. 215, 217–227 (2018).
[Crossref]

Solonenko, M. G.

Song, K.

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

Spyrakos, E.

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

Steward, R.

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Stramska, M.

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

Stumpf, R. P.

Sun, D.

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (2017).
[Crossref]

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

Sun, J.

X. Wu, B. Wu, J. Sun, and H. Fu, “Unsupervised possibilistic fuzzy clustering,” J. Comput. Sci. 7, 1075–1080 (2010).

Taberner, M.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Tobler, W. R.

W. R. Tobler, “A computer movie simulating urban growth in the Detroit region,” Economic Geography 46, 234–240 (1970).
[Crossref]

Valente, A.

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

van der Woerd, H. J.

J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
[Crossref]

Vantrepotte, V.

F. Mélin and V. Vantrepotte, “How optically diverse is the coastal ocean?” Remote Sens. Environ. 160, 235–251 (2015).
[Crossref]

V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
[Crossref]

Verdu, A. R.

T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
[Crossref]

Wang, D.

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, G.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Wang, Q.

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

Wang, S.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Wang, W.

W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems 158(19), 2095–2117 (2007).
[Crossref]

Wei, J.

J. Wei, Z. Lee, and S. Shang, “A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments,” J. Geophys. Res.: Oceans 121, 8189–8207 (2016).

Wen, S.

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

Werdell, P. J.

Wieland, J.

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

Wu, B.

X. Wu, B. Wu, J. Sun, and H. Fu, “Unsupervised possibilistic fuzzy clustering,” J. Comput. Sci. 7, 1075–1080 (2010).

Wu, C.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Wu, G.

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

Wu, X.

X. Wu, B. Wu, J. Sun, and H. Fu, “Unsupervised possibilistic fuzzy clustering,” J. Comput. Sci. 7, 1075–1080 (2010).

Wu, Y.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Wu, Z.

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

Xiao, Q.

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[Crossref]

Xu, J.

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

Xu, Y.

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

Xue, K.

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]

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[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, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
[Crossref]

Yan, X.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

Yang, W.

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Yao, X.

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

Yesou, H.

X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
[Crossref]

Yoshimura, K.

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Yu, G.

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

Yu, J.

J. Yu, Q. Cheng, and H. Huang, “Analysis of the weighting exponent in the FCM,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 634–639 (2004).
[Crossref]

Zadeh, L. A.

L. A. Zadeh, “Fuzzy sets,” Information and Control 8(3), 338–353 (1965).
[Crossref]

Zha, Y.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

Zhang, B.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Zhang, E.

Y. Zhang, E. Zhang, and M. Liu, “Spectral absorption properties of chromophoric dissolved organic matter and particulate matter in Yunnan Palteau lakes,” J. Lake Sci. 21(2), 255–263 (2009).
[Crossref]

Zhang, F.

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

Zhang, H.

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

Zhang, Y.

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

K. Xue, Y. Zhang, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
[Crossref]

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

Y. Zhang, E. Zhang, and M. Liu, “Spectral absorption properties of chromophoric dissolved organic matter and particulate matter in Yunnan Palteau lakes,” J. Lake Sci. 21(2), 255–263 (2009).
[Crossref]

W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems 158(19), 2095–2117 (2007).
[Crossref]

Zheng, G.

G. Zheng and P. M. DiGiacomo, “Uncertainties and applications of satellite-derived coastal water quality products,” Prog. Oceanogr. 159, 45–72 (2017).
[Crossref]

Zheng, Z.

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

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]

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

Zhou, J.

Zhou, Q.

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

Zhou, Y.

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

Zhu, G.

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

Zhu, L.

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

Zielinski, O.

J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
[Crossref]

Adv. Data Anal. Classif. (1)

D. Dembele, “Multi-objective optimization for clustering 3-way gene expression data,” Adv. Data Anal. Classif. 2(3), 211–225 (2008).
[Crossref]

Appl. Opt. (1)

Bioinformatics (1)

D. Dembele and P. Kastner, “Fuzzy C-means method for clustering microarray data,” Bioinformatics 19(8), 973–980 (2003).
[Crossref]

Earth Syst. Sci. Data (2)

B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, and A. C. Banks, “CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters,” Earth Syst. Sci. Data 7(2), 319–348 (2015).
[Crossref]

A. Valente, S. Sathyendranath, V. Brotas, S. Groom, M. Grant, M. Taberner, D. Antoine, R. Arnone, W. M. Balch, and K. Barker, “A compilation of global bio-optical in situ data for ocean-colour satellite applications,” Earth Syst. Sci. Data 8(1), 235–252 (2016).
[Crossref]

Ecological Indicators (1)

J. Lin, H. Lyu, S. Miao, Y. Pan, Z. Wu, Y. Li, and Q. Wang, “A two-step approach to mapping particulate organic carbon (POC) in inland water using OLCI images,” Ecological Indicators 90, 502–512 (2018).
[Crossref]

Economic Geography (1)

W. R. Tobler, “A computer movie simulating urban growth in the Detroit region,” Economic Geography 46, 234–240 (1970).
[Crossref]

Environ. Sci. Pollut. Res. (2)

M. Mu, C. Wu, Y. Li, H. Lyu, S. Fang, X. Yan, G. Liu, Z. Zheng, C. Du, and S. Bi, “Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake,” Environ. Sci. Pollut. Res. 26(11), 11012–11028 (2019).
[Crossref]

Y. Zhang, K. Shi, Y. Zhang, M. J. Moreno-Madriñán, G. Zhu, Y. Zhou, and X. Yao, “Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management,” Environ. Sci. Pollut. Res. 26(3), 3041–3054 (2019).
[Crossref]

Front. Mar. Sci. (1)

M. Hieronymi, D. Müller, and R. Doerffer, “The OLCI Neural Network Swarm (ONNS): A bio-geo-optical algorithm for open ocean and coastal waters,” Front. Mar. Sci. 4, 140 (2017).
[Crossref]

Fuzzy Sets and Systems (2)

W. Wang and Y. Zhang, “On fuzzy cluster validity indices,” Fuzzy Sets and Systems 158(19), 2095–2117 (2007).
[Crossref]

R. J. Campello and E. R. Hruschka, “A fuzzy extension of the silhouette width criterion for cluster analysis,” Fuzzy Sets and Systems 157(21), 2858–2875 (2006).
[Crossref]

Hydrobiologia (1)

D. Sun, Y. Li, Q. Wang, C. Le, C. Huang, and K. Shi, “Development of optical criteria to discriminate various types of highly turbid lake waters,” Hydrobiologia 669(1), 83–104 (2011).
[Crossref]

ICES J. Mar. Sci. (1)

N. G. Jerlov, “Classification of sea water in terms of quanta irradiance,” ICES J. Mar. Sci. 37(3), 281–287 (1977).
[Crossref]

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing (2)

F. Zhang, J. Li, Q. Shen, B. Zhang, C. Wu, Y. Wu, G. Wang, S. Wang, and Z. Lu, “Algorithms and Schemes for ChlorophyllaEstimation by Remote Sensing and Optical Classification for Turbid Lake Taihu, China,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 8(1), 350–364 (2015).
[Crossref]

K. Shi, Y. Li, Y. Zhang, L. Li, H. Lv, and K. Song, “Classification of inland waters based on bio-optical properties,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(2), 543–561 (2014).
[Crossref]

IEEE T. Geosci. Remote Sensing (1)

T. S. Moore, J. W. Campbell, and H. Feng, “A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms,” IEEE T. Geosci. Remote Sensing 39(8), 1764–1776 (2001).
[Crossref]

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) (1)

J. Yu, Q. Cheng, and H. Huang, “Analysis of the weighting exponent in the FCM,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1), 634–639 (2004).
[Crossref]

Information and Control (1)

L. A. Zadeh, “Fuzzy sets,” Information and Control 8(3), 338–353 (1965).
[Crossref]

Int. J. Appl. Earth Obs. Geoinf. (1)

Z. Cao, H. Duan, M. Shen, R. Ma, K. Xue, D. Liu, and Q. Xiao, “Using VIIRS/NPP and MODIS/Aqua data to provide a continuous record of suspended particulate matter in a highly turbid inland lake,” Int. J. Appl. Earth Obs. Geoinf. 64, 256–265 (2018).
[Crossref]

Int. J. Appl. Earth Obs. Geoinfor. (1)

H. Liu, S. Hu, Q. Zhou, Q. Li, and G. Wu, “Revisiting effectiveness of turbidity index for the switching scheme of NIR-SWIR combined ocean color atmospheric correction algorithm,” Int. J. Appl. Earth Obs. Geoinfor. 76, 1–9 (2019).
[Crossref]

Int. J. Remote Sensing (1)

X. Han, L. Feng, X. Chen, and H. Yesou, “MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009,” Int. J. Remote Sensing 35(24), 8309–8322 (2014).
[Crossref]

ISPRS J. Photogramm. and Remote Sensing (1)

B. Matsushita, W. Yang, G. Yu, Y. Oyama, K. Yoshimura, and T. Fukushima, “A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters,” ISPRS J. Photogramm. and Remote Sensing 102, 28–37 (2015).
[Crossref]

J. Comput. Sci. (1)

X. Wu, B. Wu, J. Sun, and H. Fu, “Unsupervised possibilistic fuzzy clustering,” J. Comput. Sci. 7, 1075–1080 (2010).

J. Geophys. Res. Biogeosci. (1)

K. Shi, Y. Li, L. Li, and H. Lu, “Absorption characteristics of optically complex inland waters: Implications for water optical classification,” J. Geophys. Res. Biogeosci. 118(2), 860–874 (2013).
[Crossref]

J. Geophys. Res.: Oceans (2)

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

J. Wei, Z. Lee, and S. Shang, “A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments,” J. Geophys. Res.: Oceans 121, 8189–8207 (2016).

J. Lake Sci. (2)

Y. Zhang, E. Zhang, and M. Liu, “Spectral absorption properties of chromophoric dissolved organic matter and particulate matter in Yunnan Palteau lakes,” J. Lake Sci. 21(2), 255–263 (2009).
[Crossref]

S. Bi, Y. Li, H. Lu, L. Zhu, M. Mu, S. Lei, S. Wen, and X. Ding, “Estimation of chlorophyll-a concentration in Lake Erhai based on OLCI data,” J. Lake Sci. 30(3), 701–712 (2018).
[Crossref]

Limnol. Oceanogr. (2)

E. Spyrakos, R. O’Donnell, P. D. Hunter, C. Miller, M. Scott, S. G. Simis, C. Neil, C. C. Barbosa, C. E. Binding, and S. Bradt, “Optical types of inland and coastal waters,” Limnol. Oceanogr. 63(2), 846–870 (2018).
[Crossref]

A. Morel and L. Prieur, “Analysis of variations in ocean color 1,” Limnol. Oceanogr. 22(4), 709–722 (1977).
[Crossref]

Limnol. Oceanogr.: Methods (1)

K. Xue, Y. Zhang, R. Ma, and H. Duan, “An approach to correct the effects of phytoplankton vertical nonuniform distribution on remote sensing reflectance of cyanobacterial bloom waters,” Limnol. Oceanogr.: Methods 15(3), 302–319 (2017).
[Crossref]

Ocean optics protocols for satellite ocean color sensor validation Revision (1)

B. G. Mitchell, M. Kahru, J. Wieland, M. Stramska, and J. Mueller, “Determination of spectral absorption coefficients of particles, dissolved material and phytoplankton for discrete water samples,” Ocean optics protocols for satellite ocean color sensor validation Revision 3, 231 (2002).

J. L. Mueller, C. Davis, R. Arnone, R. Frouin, K. Carder, Z. Lee, R. Steward, S. Hooker, C. D. Mobley, and S. McLean, “Above-water radiance and remote sensing reflectance measurements and analysis protocols,” Ocean Optics protocols for satellite ocean color sensor validation Revision 2, 98–107 (2000).

Oceanogr (1)

C. D. Mobley, “Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?” Oceanogr 17(2), 60–67 (2004).
[Crossref]

Opt. Express (2)

Pattern Recognit. Lett. (1)

M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, “A new cluster validity index for the fuzzy c-mean,” Pattern Recognit. Lett. 19(3-4), 237–246 (1998).
[Crossref]

Prog. Oceanogr. (1)

G. Zheng and P. M. DiGiacomo, “Uncertainties and applications of satellite-derived coastal water quality products,” Prog. Oceanogr. 159, 45–72 (2017).
[Crossref]

Remote Sens. (4)

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]

Z. Zheng, Y. Li, Y. Guo, Y. Xu, G. Liu, and C. Du, “Landsat-based long-term monitoring of total suspended matter concentration pattern change in the wet season for Dongting Lake, China,” Remote Sens. 7(10), 13975–13999 (2015).
[Crossref]

S. Bi, Y. Li, Q. Wang, H. Lyu, G. Liu, Z. Zheng, C. Du, M. Mu, J. Xu, S. Lei, and S. Miao, “Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations,” Remote Sens. 10(7), 1002 (2018).
[Crossref]

M. Eleveld, A. Ruescas, A. Hommersom, T. Moore, S. Peters, and C. Brockmann, “An optical classification tool for global lake waters,” Remote Sens. 9(5), 420 (2017).
[Crossref]

Remote Sens. Environ. (12)

M. W. Matthews and D. Odermatt, “Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters,” Remote Sens. Environ. 156, 374–382 (2015).
[Crossref]

M. E. Smith, L. R. Lain, and S. Bernard, “An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters,” Remote Sens. Environ. 215, 217–227 (2018).
[Crossref]

C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Zhang, “Remote estimation of chlorophyll a in optically complex waters based on optical classification,” Remote Sens. Environ. 115(2), 725–737 (2011).
[Crossref]

F. Mélin and V. Vantrepotte, “How optically diverse is the coastal ocean?” Remote Sens. Environ. 160, 235–251 (2015).
[Crossref]

V. Vantrepotte, H. Loisel, D. Dessailly, and X. Mériaux, “Optical classification of contrasted coastal waters,” Remote Sens. Environ. 123, 306–323 (2012).
[Crossref]

X. Hou, L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, “Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China,” Remote Sens. Environ. 190, 107–121 (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, 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]

T. S. Moore, J. W. Campbell, and M. D. Dowell, “A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product,” Remote Sens. Environ. 113(11), 2424–2430 (2009).
[Crossref]

T. S. Moore, M. D. Dowell, S. Bradt, and A. R. Verdu, “An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters,” Remote Sens. Environ. 143, 97–111 (2014).
[Crossref]

J. Pitarch, H. J. van der Woerd, R. J. Brewin, and O. Zielinski, “Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations,” Remote Sens. Environ. 231, 111249 (2019).
[Crossref]

T. Jackson, S. Sathyendranath, and F. Mélin, “An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications,” Remote Sens. Environ. 203, 152–161 (2017).
[Crossref]

Sci. Rep. (1)

K. Shi, Y. Zhang, Y. Zhou, X. Liu, G. Zhu, B. Qin, and G. Gao, “Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors,” Sci. Rep. 7(1), 40326 (2017).
[Crossref]

Sci. Total Environ. (1)

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]

Water Res. (1)

Y. Zhang, Y. Zhou, K. Shi, B. Qin, X. Yao, and Y. Zhang, “Optical properties and composition changes in chromophoric dissolved organic matter along trophic gradients: Implications for monitoring and assessing lake eutrophication,” Water Res. 131, 255–263 (2018).
[Crossref]

Other (5)

N. G. Jerlov and F. F. Koczy, Photographic measurements of daylight in deep water (Elanders boktr., 1951).

N. R. Pal, K. Pal, and J. C. Bezdek, “A mixed c-means clustering model,” in Proceedings of 6th International Fuzzy Systems Conference, (IEEE, 1997), 11–21.

J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms (Springer Science & Business Media, 2013).

R Development Core Team, R: Probabilistic and Possibilistic Cluster Analysis, 2019.

R Development Core Team, R: Fuzzy Clustering, 2019.

Supplementary Material (1)

NameDescription
» Data File 1       This file includes the detailed spectra value of cluster centers at OLCI bands by using FCM-m. If interest in cluster centers of other sensors, please find them at https://github.com/bishun945

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (10)

Fig. 1.
Fig. 1. Pictures from different water color types: (a) Lake Erhai, (b) Lake Dianchi, (c) Lake Hongze and (d) Lake Taihu; the inset plots indicate the magnitude and spectral shape of remote sensing reflectance (Rrs, unit: sr−1) with considerable variations in different kinds of lakes.
Fig. 2.
Fig. 2. The flowchart of FCM-m. The raw Rrs spectra are initially normalized by dividing their integrals to capture the spectral shape; then, the fuzzifier parameter (mused) were optimized by calculating the upper bound m value; finally, water optical clusters and membership degree are generated by Fuzzy C-Means method with Normalized-Rrs and mused as input.
Fig. 3.
Fig. 3. The distribution of membership degree (between 0 and 1) from fuzzy clustering algorithms (FCM, FPCM, and UPFC) influenced by the distinct fuzzifier parameter m; the data set includes 1280 spectra of inland waters with 15 OLCI bands; the jitter plots indicate the membership distribution of each cluster sorted by the sum of all samplings; the first row (a-c) and the first column (a, d, and g) are the membership degree distribution of FCM with increasing fuzzifier m (i.e., 1.1, 1.36, 2, 3.6, 5.6); (e-f) and (h-i) are the results of FPCM and UPFC with fuzzifier m = 1.36 and m = 2, respectively.
Fig. 4.
Fig. 4. (a) Combination of the mean raw remote sensing reflectance (Rrs, unit: sr−1) for seven clusters in inland waters with the highest membership which is defined by FCM (m = 1.36) with area-normalized Rrs spectra as input; (b-h) raw Rrs associated with seven optical water types; bold black lines denote the mean Rrs for each cluster; gray lines denote members of each cluster; among them, (g) has a wider y-axis range as its cluster is dominated by floating algae or very high concentrations of biomass; (i-l) boxplots for CDOM absorption coefficient at 440 nm (m−1), Chla concentration (mg/m3), TSM concentration (mg/L) and SSD (m) across the seven optical water types with log10 scale in y-axis.
Fig. 5.
Fig. 5. (a) True color map for an atmospherically corrected OLCI image using 2% linear stretching over Lake Taihu, a shallow and phytoplankton-dominated inland lake, on July 24, 2017; (b) the dominant cluster defined by the highest membership degree; (c-i) membership maps for seven clusters; purple areas denote zero or low membership; green areas denote medium membership; yellow areas denote high membership; red lines in (a) and (b) are transects for subsequent analysis.
Fig. 6.
Fig. 6. (a) True color map for an atmospherically corrected OLCI image using 2% linear stretching over Lake Hongze, a shallow and turbid inland lake, on May 18, 2017; (b) the dominant cluster defined by the highest membership degree; (c-i) membership maps for seven clusters; purple areas denote zero or low membership; green areas denote medium membership; yellow areas denote high membership.
Fig. 7.
Fig. 7. (a) True color map for an atmospherically corrected OLCI image using 2% linear stretching over Lake Erhai, a deep and low-reflected inland lake, on April 19, 2017; (b) the dominant cluster defined by the highest membership degree; (c-i) membership maps for seven clusters; purple areas denote zero or low membership; green areas denote medium membership; yellow areas denote high membership.
Fig. 8.
Fig. 8. The membership degree of seven clusters of one transect at Lake Taihu shown in Fig. 5 by FCM with (a) fuzzifier parameter m = 1.36 and (b) m = 2.
Fig. 9.
Fig. 9. The mean raw remote sensing reflectance (Rrs, unit: sr−1) of seven clusters in inland waters based on FCM-m and data set in this study (namely line C1-7), compared with the cluster results in the study of Moore, et al. [12] denoted as gray dashed lines (namely line TM1-7).
Fig. 10.
Fig. 10. The collected hyperspectral Rrs spectra of inland waters (n=1280) were re-sampled to 7 popular sensors, namely OLI (on Landsat-8 with 5 available bands), VIIRS (7), GOCI (8), MSI (9), MODIS (13), MERIS (13), and OLCI (15), and then used as the input of FCM with fuzzifier m in a predefined range (1.1, 1.3, 1.5, 1.7, 2.0, 2.5, 3.0, 4.0, and 5.0). The relationship between the optimal fuzzifier m (mused), determined by FCM-m, and sensor band numbers were shown in (a). The trend of SIL.F values, presenting the goodness of FCM results, within the predefined range of m was shown in (b). The circles of different colors represent the best m position of each sensor.

Tables (4)

Tables Icon

Table 1. Names, locations, elevation, and in situ numbers of the sampled inland waters including lakes (in green), reservoirs (in violet), and rivers (in yellow).

Tables Icon

Table 2. Dominant characteristics of clusters in inland waters.

Tables Icon

Table 3. Parameters used for the FCM algorithm. N, p, and K denote the number of samplings, dimension (or bands), and the optimized cluster number of each data set, respectively; each data set was clustered using both normalized (by dividing with the spectrum integral) and non-normalized spectra; FCM cluster results include upper bound m (mub), final used m (mused), and the mean of SIL.F with better performance in bold font; besides the data set of inland waters used in this study, the data set of this study excluded the spectrally vegetation-like type (this study 2) and other five additional data sets were included.

Tables Icon

Table 4. Median Relative Percent Error (MRPE), bias and Median Absolute Error (MAE) for Band-Ratio (BR), Three-Band algorithm (TBA), and blended CChla concentration based on results of FCM-m with fuzzifier parameter m equal to 1.36 and 2, assessed by the in situ measured value within the range from 1 to 200 mg/m3; the bold values denote optimal algorithm for that optical water cluster.

Equations (6)

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

min J ( U , V ) = j = 1 N i = 1 K u i j m | | x j v i | | A 2
with  i = 1 K u i j = 1  and  0 j = 1 N u i j N
c v { Y m } = s t d ( Y m ) m e a n ( Y m ) 0.03 p
with  Y m = { [ | | x j v i | | A 2 ] 1 m 1 ; k i = 1 , 2 , , N }
s j = b p j a p j max { a p j , b p j }
S I L . F = j = 1 N s j ( u p j u q j ) α j = 1 N ( u p j u q j ) α

Metrics