Anterior|Siguiente

Advances in remote sensing of phycocyanin for inland waters

  • role： First author第一作者
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：lvlili@iga.ac.cn
  • Introduction：吕丽丽，1985年生，女，博士研究生， 研究方向为内陆水体藻蓝蛋白浓度遥感反演。E-mail： lvlili@iga.ac.cn
LYU Lili12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

    School of Environment and Planning, Liaocheng University, Liaocheng 252000, China

  • Email：songkaishan@iga.ac.cn
  • Introduction：宋开山，1974年生，男，研究员，研究方向为内陆水体水质参数遥感反演。E-mail： songkaishan@iga.ac.cn
SONG Kaishan13*,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

LIU Ge1,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

WEN Zhidan1,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

SHANG Yingxin1,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

LI Sijia1

resumen

Phycocyanin (PC), as the signature pigment of cyanobacteria, is usually used for remote sensing monitoring of cyanobacteria bloom. In recent years, the water quality of inland water has deteriorated, the eutrophication has intensified, and the algal blooms frequently occur. The research of remote sensing inversion of PC has attracted more and more attention. Therefore, it is urgent to sort out and write a comprehensive overview paper. In this paper, 178 relevant literatures were reviewed, and the development history and trend of PC remote sensing inversion research of PC in the past 30 years (1990—2020) were comprehensively summarized from the perspectives of PC optical characteristics, development of inversion algorithms, application of satellite sensors, difficulties and interference factors of quantitative remote sensing inversion of PC. This paper helps us to understand the new ideas and new methods emerging at home and abroad, master the new development trends of PC remote sensing inversion in the future, and provide data basis for the monitoring and management of water environment, water quality and water resources. Over the past 30 years, PC remote sensing inversion study number rising, and a breakthrough in the algorithm, has a lot of classic algorithms, such as band ratio method, the baseline method, nested band ratio method, biological optical model, derivative algorithm and machine learning algorithms, algorithm successfully isolated PC spectral characteristic of absorption coefficients at wavelength of 620 nm. Decreased the influence of other optically active substances (Chla、TSM) and obtained the high precision of inversion and validation . In addition, the development of PC inversion algorithms mostly based on in situ hyperspectral data or aerial images (CASI-2, AISA Eagle). In order to meet the needs of PC concentration distribution in a certain space and frequency, satellite image data sources are mostly used. PC. There are many types of multi-spectral satellite data sources to choose, such as Landsat series, MODIS, MERIS, Sentinel-2 MSI, Sentinel-3 OLCI, etc. However, due to the more appropriate band setting, MERIS and Sentinel-3 OLCI are still the most used data sources for PC remote sensing inversion research. Because PC spectrum signal is weak, and vulnerable to the interference of chlorophyll a, TSM, there is still a major difficult to get an accurate estimation result. Based on the above analysis, the future development direction of PC remote sensing inversion can be summarized as the following aspects: first, an international standard measurement method is urgently needed in PC extraction testing; secondly, the development of algorithm, adhere to the mechanism research related to the inherent optical properties, and the integrating machine learning algorithm to bring higher inversion accuracy; third, the scale of study area water body in space and time will toward a larger geographical space scale, longer time series history tracing and future prediction; fourth, at the aspect of the expansion of application, PC remote sensing inversion is not limited to the quantitative estimation of cyanobacteria biomass, but will predict distribution of algal toxins and related diseases based on the relationships between these parameters and PC concentration, furtherly establish a water risk factor rating system based on remote sensing in the future.

palabra clave

lake remote sensing;inland waters;Phycocyanin;Optical properties;Inversion algorithm;Satellite sensor

References

  1. 1.
    Ahn C Y, Joung S H, Yoon S K and Oh H M. 2007. Alternative alert system for cyanobacterial bloom, using phycocyanin as a level determinant. Journal of Microbiology, 45(2): 98-104]
  2. 2.
    Beck R, Xu M, Zhan S G, Johansen R, Liu H X, Tong S S N, Yang B, Shu S, Wu Q S, Wang S J, Berling K, Murray A, Emery E, Reif M, Harwood J, Young J, Nietch C, Macke D, Martin M, Stillings G, Stumpf R, Su H B, Ye Z X and Huang Y. 2019. Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations. Journal of Great Lakes Research, 45(3): 413-433
  3. 3.
    Beck R, Xu M, Zhan S G, Liu H X, Johansen R A, Tong S S N, Yang B, Shu S, Wu Q S, Wang S J, Berling K, Murray A, Emery E, Reif M, Harwood J, Young J, Martin M, Stillings G, Stumpf R, Su H B, Ye Z X and Huang Y. 2017. Comparison of satellite reflectance algorithms for estimating phycocyanin values and cyanobacterial total biovolume in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing, 9(6): 538
  4. 4.
    Becker R H, Sultan M I, Boyer G L, Twiss M R and Konopko E. 2009. Mapping cyanobacterial blooms in the Great Lakes using MODIS. Journal of Great Lakes Research, 35(3): 447-453
  5. 5.
    Bennett A and Bogorad L. 1973. Complementary chromatic adaptation in a filamentous blue-green-alga. Journal of Cell Biology, 58(2): 419-435
  6. 6.
    Castagna A, Simis S, Dierssen H, Vanhellemont Q, Sabbe K and Vyverman W. 2020. Extending Landsat 8: retrieval of an orange contra-band for inland water quality applications. Remote Sensing, 12(4): 637
  7. 7.
    Chawira M, Dube T and Gumindoga W. 2013. Remote sensing based water quality monitoring in Chivero and Manyame lakes of Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 66: 38-44
  8. 8.
    Chi G Y, Ma J, Shi Y and Chen X. 2016. Hyperspectral remote sensing of cyanobacterial pigments as indicators of the iron nutritional status of cyanobacteria-dominant algal blooms in eutrophic lakes. Ecological Indicators, 71: 609-617
  9. 9.
    Dash P, Walker N D, Mishra D R, Hu C M, Pinckney J L and D'Sa E J. 2011. Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data. Remote Sensing of Environment. 115(12): 3409-3423 [10.1016/j.rse.2011.08. (Dekker A G, Malthus T J and Seyhan E. 1991. Quantitative modeling of inland water quality for high-resolution MSS systems. IEEE Transactions on Geoscience and Remote Sensing, 29(1): 89-95)
  10. 10.
    Dekker A G. 1993. Detection of Optical Water Quality Parameters for Eutrophic Waters by High Resolution Remote Sensing. Amsterdam: Vrije Universiteit Amsterdam
  11. 11.
    Duan H T, Ma R H and Hu C M. 2012. Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China. Remote Sensing of Environment, 126: 126-135
  12. 12.
    Duan H T, Tao M, Loiselle S A, Zhao W, Cao Z G, Ma R H and Tang X X. 2017. MODIS observations of cyanobacterial risks in a eutrophic lake: implications for long-term safety evaluation in drinking-water source. Water Research, 122: 455-470
  13. 13.
    Effler S W, Strait C M, Perkins M G and O’Donnell D M. 2012. Optical characterization and tests of closure for Oneida Lake, New York, USA. Inland Waters. 2(4): 211-228 [10.5268/iw-2.4.503]
  14. 14.
    Gitelson A A, Laorawat S, Keydan G P and Vonshak A. 1995. Optical properties of dense algal cultures outdoors and their application to remote estimation of biomass and pigment concentration in Spirulina platensis (cyanobacteria). Journal of Phycology, 31(5): 828-834
  15. 15.
    Gordon H R, Clark D K, Mueller J L and Hovis W A. 1980. Phytoplankton pigments from the nimbus-7 coastal zone color scanner: comparisons with surface measurements. Science, 210(4465): 63-66
  16. 16.
    Guanter L, Ruiz-Verdú A, Odermatt D, Giardino C, Simis S, Estellés V, Heege T, Antonio Domínguez-Gómez J and Moreno J. 2010. Atmospheric correction of ENVISAT/MERIS data over inland waters: validation for European lakes. Remote Sensing of Environment, 114(3): 467-480
  17. 17.
    Guo Y Y, Zhu L, Wu C Q, Li J S and Zhang F F. 2016. The retrieval of phycocyanin concentrations in Taihu Lake based on water reflectance spectra classification. Acta Scientiae Circumstantiae, 36(8): 2905-2910
  18. 18.
    Hmimina G, Hulot F D, Humbert J F, Quiblier C, Tambosco K, Lemaire B J, Vinçon-Leite B, Audebert L and Soudani K. 2019. Linking phytoplankton pigment composition and optical properties: a framework for developing remote-sensing metrics for monitoring cyanobacteria. Water Research, 148: 504-514
  19. 19.
    Hunter P D, Tyler A N, Carvalho L, Codd G A and Maberly S C. 2010. Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sensing of Environment, 114(11): 2705-2718
  20. 20.
    Hunter P D, Tyler A N, Willby N J and Gilvear D J. 2008. The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: a case study using high spatial resolution time-series airborne remote sensing. Limnology and Oceanography, 53(6): 2391-2406
  21. 21.
    Jin Q, Lyu H, Shi L, Miao S, Wu Z M, Li Y M and Wang Q. 2017. Developing a two-step method for retrieving cyanobacteria abundance from inland eutrophic lakes using MERIS data. Ecological Indicators 81：543-554 [10.1016/j.ecolind.2017.06.027]
  22. 22.
    Jupp D L B, Kirk J T O and Harris G P. 1994. Detection, identification and mapping of cyanobacteria-using remote sensing to measure the optical quality of turbid inland waters. Australian Journal of Marine and Freshwater Research, 45(5): 801-828
  23. 23.
    Kudela R M, Palacios S L, Austerberry D C, Accorsi E K, Guild L S and Torres-Perez J. 2015. Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment, 167: 196-205
  24. 24.
    Kwon Y S, Pyo J, Kwon Y H, Duan H T, Cho K H and Park Y. 2020. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sensing of Environment, 236: 111517
  25. 25.
    Le C F, Li Y M, Zha Y, Wang Q, Zhang H and Yin B. 2011. Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu, China. International Journal of Remote Sensing, 32(23): 8253-8269
  26. 26.
    Lee Z P, Carder K L and Arnone R A. 2002. Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41(27): 5755-5772
  27. 27.
    Li L, Sengpiel R E, Pascual D L, Tedesco L P, Wilson J S and Soyeux E. 2010. Using hyperspectral remote sensing to estimate chlorophyll-a and phycocyanin in a mesotrophic reservoir. International Journal of Remote Sensing, 31(15): 4147-4162
  28. 28.
    Li L H, Li L, Shi K, Li Z C and Song K S. 2012. A semi-analytical algorithm for remote estimation of phycocyanin in inland waters. Science of the Total Environment, 435-436: 141-150
  29. 29.
    Li L H, Li L and Song K S. 2015. Remote sensing of freshwater cyanobacteria: an extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin. Remote Sensing of Environment, 157: 9-23
  30. 30.
    Liu G, Simis S G H, Li L, Wang Q, Li Y M, Song K S, Lyu H, Zheng Z B and Shi K. 2018. A four-band semi-analytical model for estimating phycocyanin in inland waters from simulated MERIS and OLCI data. IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1374-1385
  31. 31.
    Lyu H, Wang Q, Wu C Q, Zhu L, Yin B, Li Y M and Huang J Z. 2013. Retrieval of phycocyanin concentration from remote-sensing reflectance using a semi-analytic model in eutrophic lakes. Ecological Informatics, 18: 178-187
  32. 32.
    Matthews M W, Bernard S, Evers-King H and Lain L R. 2020. Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. Remote Sensing of Environment, 248: 111981
  33. 33.
    Matthews M W, Bernard S and Winter K. 2010. Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS. Remote Sensing of Environment, 114(9): 2070-2087
  34. 34.
    Medina-Cobo M, Domínguez J A, Quesada A and de Hoyos C. 2014. Estimation of cyanobacteria biovolume in water reservoirs by MERIS sensor. Water Research, 63: 10-20
  35. 35.
    Miao S, Li Y Y, Wu Z M, Lyu H, Li Y M, Bi S, Xu J, Lei S H, Mu M and Wang Q. 2020. A semianalytical algorithm for mapping proportion of cyanobacterial biomass in Eutrophic Inland Lakes based on OLCI data. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 5148-5161
  36. 36.
    Millie D F, Baker M C, Tucker C S, Vinyard B T and Dionigi C P. 1992. High-resolution airborne remote sensing of bloom-forming phytoplankton. Journal of Phycology, 28(3): 281-290
  37. 37.
    Mishra S, Mishra D R, Lee Z and Tucker C S. 2013. Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach. Remote Sensing of Environment, 133: 141-151
  38. 38.
    Mishra S, Mishra D R and Schluchter W M. 2009. A novel algorithm for predicting phycocyanin concentrations in cyanobacteria: a proximal hyperspectral remote sensing approach. Remote Sensing, 1(4): 758-775
  39. 39.
    Ogashawara I. 2020. Determination of phycocyanin from space-a bibliometric analysis. Remote Sensing, 12(3): 567
  40. 40.
    Ogashawara I, Mishra D R, Mishra S, Curtarelli M P and Stech J L. 2013. A performance review of reflectance based algorithms for predicting phycocyanin concentrations in inland waters. Remote Sensing, 5(10): 4774-4798
  41. 41.
    Ortiz J D, Witter D L, Ali K A, Fela N, Duff M and Mills L. 2013. Evaluating multiple colour-producing agents in Case II waters from Lake Erie. International Journal of Remote Sensing. 34(24): 8854-8880 [10.1080/01431161.2013.853892]
  42. 42.
    Park Y, Pyo J, Kwon Y S, Cha Y, Lee H, Kang T and Cho K H. 2017. Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea. Water Research, 126: 319-328
  43. 43.
    Peterson K T, Sagan V, Sidike P, Hasenmueller E A, Sloan J J and Knouft J H. 2019. Machine learning-based ensemble prediction of water-quality variables using feature-level and decision-level fusion with proximal remote sensing. Photogrammetric Engineering and Remote Sensing, 85(4): 269-280
  44. 44.
    Pyo J C, Duan H T, Baek S, Kim M S, Jeon T, Kwon Y S, Lee H and Cho K H. 2019. A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sensing of Environment. 233: 111350 [10.1016/j.rse.2019.111350]
  45. 45.
    Pyo J C, Duan H T, Ligaray M, Kim M, Baek S, Kwon Y S, Lee H, Kang T, Kim K, Cha Y K and Cho K H. 2020. An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery. Remote Sensing, 12(7): 1073
  46. 46.
    Pyo J C, Ligaray M, Kwon Y S, Ahn M H, Kim K, Lee H, Kang T, Cho S B, Park Y and Cho K H. 2018. High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery. Remote Sensing. 10(8): 1180 [10.3390/rs10081180]
  47. 47.
    Pyo J C, Pachepsky Y, Baek S S, Kwon Y S, Kim M J, Lee H, Park S, Cha Y, Ha R, Nam G, Park Y and Cho K H. 2017. Optimizing semi-analytical algorithms for estimating Chlorophyll-a and Phycocyanin concentrations in inland waters in Korea. Remote Sensing, 9(6): 542
  48. 48.
    Qi L, Hu C M, Duan H T, Cannizzaro J and Ma R H. 2014. A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: theoretical basis and practical considerations. Remote Sensing of Environment, 154: 298-317
  49. 49.
    Randolph K, Wilson J, Tedesco L, Li L, Pascual D L and Soyeux E. 2008. Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sensing of Environment, 112(11): 4009-4019
  50. 50.
    Riddick C A L, Hunter P D, Dominguez Gomez J A, Martinez-Vicente V, Presing M, Horvath H, Kovacs A W, Voros L, Zsigmond E and Tyler A N. 2019. Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake. 11(13): 1613 [10.3390/rs11131613]
  51. 51.
    Ruiz-Verdú A, Simis S G H, de Hoyos C, Gons H J and Peña-Martínez R. 2008. An evaluation of algorithms for the remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 112(11): 3996-4008
  52. 52.
    Sarada R, Pillai M G and Ravishankar G A. 1999. Phycocyanin from Spirulina sp: influence of processing of biomass on phycocyanin yield, analysis of efficacy of extraction methods and stability studies on phycocyanin. Process Biochemistry, 34(8): 795-801
  53. 53.
    Sayers M J, Bosse K R, Shuchman R A, Ruberg S A, Fahnenstiel G L, Leshkevich G A, Stuart D G, Johengen T H, Burtner A M and Palladino D. 2019. Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: implications for water quality remote sensing. Journal of Great Lakes Research, 45(3): 490-507
  54. 54.
    Schalles J F and Yacobi Y Z. 2000. Remote detection and seasonanl patterns of phycocyanin, carotenoid and chlorophyll pigments in eutrophic waters. Limnology and Lake Management : Arch.Hydrobiol.Spec.Issues Advanc. Limnol. 55 : 153-168
  55. 55.
    Shi K, Zhang Y L, Li Y M, Li L, Lv H and Liu X H. 2015a. Remote estimation of cyanobacteria-dominance in inland waters. Water Research, 68: 217-226
  56. 56.
    Shi K, Zhang Y L, Qin B Q and Zhou B T. 2019. Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges. Science Bulletin, 64(20): 1540-1556
  57. 57.
    Shi K, Zhang Y L, Xu H, Zhu G W, Qin B Q, Huang C C, Liu X H, Zhou Y Q and Lv H. 2015b. Long-term satellite observations of microcystin concentrations in Lake Taihu during cyanobacterial bloom periods. Environmental Science and Technology, 49(11): 6448-6456
  58. 58.
    Simis S G H, Peters S W M and Gons H J. 2005. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnology and Oceanography, 50(1): 237-245
  59. 59.
    Simis S G H, Ruiz-Verdú A, Antonio Domínguez-Gómez J, Peña-Martinez R, Peters S W M and Gons H J. 2007. Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass. Remote Sensing of Environment, 106(4): 414-427
  60. 60.
    Soja-Woźniak M, Craig S E, Kratzer S, Wojtasiewicz B, Darecki M and Jones C T. 2017. A novel statistical approach for ocean colour estimation of inherent optical properties and cyanobacteria abundance in optically complex waters. Remote Sensing, 9(4): 343
  61. 61.
    Song K S, Fang C, Jacinthe P A, Wen Z D, Liu G, Xu X F, Shang Y X and Lyu L. 2021. Climatic versus anthropogenic controls of decadal trends (1983-2017) in algal blooms in lakes and reservoirs across China. Environmental Science and Technology, 55(5): 2929-2938
  62. 62.
    Song K S, Li L, Li S, Tedesco L, Hall B and Li Z C. 2012a. Hyperspectral retrieval of phycocyanin in potable water sources using genetic algorithm-partial least squares (GA-PLS) modeling. International Journal of Applied Earth Observation and Geoinformation, 18: 368-385
  63. 63.
    Song K S, Li L, Tedesco L, Clercin N, Hall B, Li S, Shi K, Liu D W and Sun Y. 2013. Remote estimation of phycocyanin (PC) for inland waters coupled with YSI PC fluorescence probe. Environmental Science and Pollution Research, 20(8): 5330-5340
  64. 64.
    Song K S, Li L, Tedesco L P, Li S, Clercin N A, Hall B E, Li Z C and Shi K. 2012b. Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling. Science of the Total Environment, 426: 220-232
  65. 65.
    Song K S, Li L, Tedesco L P, Li S, Hall B E and Du J. 2014. Remote quantification of phycocyanin in potable water sources through an adaptive model. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 68-80
  66. 66.
    Song K S, Shang Y X, Wen Z D, Jacinthe P A, Liu G, Lyu L and Fang C. 2019. Characterization of CDOM in saline and freshwater lakes across China using spectroscopic analysis. Water Research, 150: 403-417
  67. 67.
    Sòria-Perpinyà X, Vicente E, Urrego P, Pereira-Sandoval M, Ruíz-Verdú A, Delegido J, Miguel Soria J and Moreno J. 2020. Remote sensing of cyanobacterial blooms in a hypertrophic lagoon (Albufera of València, eastern Iberian Peninsula) using multitemporal Sentinel-2 images. Science of the Total Environment, 698: 134305
  68. 68.
    Sridhar B B M and Vincent R K. 2007. Spectral reflectance measurements of a Microcystis bloom in Upper Klamath Lake, Oregon. Journal of Great Lakes Research, 33(1): 279-284 [DOI: [279:SRMOAM]2.0.CO;2]
  69. 69.
    Sun D Y, Hu C M, Qiu Z F and Shi K. 2015. Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: a case study in Lake Dianchi. Optics Express, 23(3): 3055-3074
  70. 70.
    Sun D Y, Li Y M, Wang Q, Gao J, Le C F, Huang C C and Gong S Q. 2013. Hyperspectral remote sensing of the pigment C-phycocyanin in turbid inland waters, based on optical classification. IEEE Transactions on Geoscience and Remote Sensing, 51(7): 3871-3884
  71. 71.
    Tao M, Duan H T, Cao Z G, Loiselle S A and Ma R H. 2017. A hybrid EOF algorithm to improve MODIS cyanobacteria phycocyanin data quality in a Highly Turbid Lake: bloom and Nonbloom condition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10): 4430-4444
  72. 72.
    Torbick N and Corbiere M. 2015. A multiscale mapping assessment of Lake Champlain cyanobacterial harmful algal blooms. International Journal of Environmental Research and Public Health, 12(9): 11560-11578
  73. 73.
    Torbick N, Ziniti B, Stommel E, Linder E, Andrew A, Caller T, Haney J, Bradley W, Henegan P L and Shi X. 2018. Assessing cyanobacterial harmful algal blooms as risk factors for amyotrophic lateral sclerosis. Neurotoxicity Research, 33(1): 199-212
  74. 74.
    Varunan T and Shanmugam P. 2017. An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments. Journal of Great Lakes Research, 43(1): 32-49
  75. 75.
    Vincent R K, Qin X M, McKay R M L, Miner J, Czajkowski K, Savino J and Bridgeman T. 2004. Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment, 89(3): 381-392
  76. 76.
    Wang G Q, Lee Z, Mishra D R and Ma R H. 2016. Retrieving absorption coefficients of multiple phytoplankton pigments from hyperspectral remote sensing reflectance measured over cyanobacteria bloom waters. Limnology and Oceanography: Methods, 14(7): 432-447
  77. 77.
    Wang G Q, Lee Z and Mouw C. 2017. Multi-spectral remote sensing of phytoplankton pigment absorption properties in cyanobacteria bloom waters: a regional example in the western Basin of Lake Erie. Remote Sensing, 9(12): 1309
  78. 78.
    Wen Z D, Song K S, Liu G, Shang Y X, Fang C, Du J and Lyu L. 2019. Quantifying the trophic status of lakes using total light absorption of optically active components. Environmental Pollution, 245: 684-693
  79. 79.
    Wheeler S M, Morrissey L A, Levine S N, Livingston G P and Vincent W F. 2012. Mapping cyanobacterial blooms in Lake Champlain’s Missisquoi Bay using QuickBird and MERIS satellite data. Journal of Great Lakes Research, 38: 68-75
  80. 80.
    Wynne T T, Stumpf R P, Tomlinson M C and Dyble J. 2010. Characterizing a cyanobacterial bloom in western Lake Erie using satellite imagery and meteorological data. Limnology and Oceanography, 55(5): 2025-2036
  81. 81.
    Wynne T T, Stumpf R P, Tomlinson M C, Warner R A, Tester P A, Dyble J and Fahnenstiel G L. 2008. Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. International Journal of Remote Sensing, 29(12): 3665-3672
  82. 82.
    Yan Y E, Bao Z J and Shao J G. 2018. Phycocyanin concentration retrieval in inland waters: a comparative review of the remote sensing techniques and algorithms. Journal of Great Lakes Research, 44(4): 748-755
  83. 83.
    Yang D T, Pan D L, Zhang X Y, Zhang X F, He X Q and Li S Q. 2005. Retrieval of water quality parameters by hyperspectral remote sensing in lake TaiHu, China. Conference on Optical Technologies for Atmospheric, Ocean, and Environmental Studies. Beijing, PEOPLES R CHINA. Oct 18-22. 5832: 431-439.
  84. 84.
    Yim I, Shin J, Lee H, Park S, Nam G, Kang T, Cho K H and Cha Y. 2020. Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data. Ecological Indicators, 110: 105879
  85. 85.
    Yin B, Lü H, Li Y M, Wu C Q, Zhu L and Wang Y F. 2011. Retrieve phycocyanin concentrations based on semi-analytical model in the Dianchi Lake, China. Environmental Science, 32(2): 472-478
  86. 86.
    Zhang F, Lee J, Liang S and Shum C K. 2015. Cyanobacteria blooms and non-alcoholic liver disease: evidence from a county level ecological study in the United States. Environmental Health, 14: 41
  87. 87.
    Zhao Q H，Sun G D ，Wang J J，Yu G Z and Jiang B. 2018. Coupling effect of water temperature and light energy on the algal growth in Lake Taihu. Journal of Lake Sciences, 30(2) : 385-393 [DOI: 10. 18307 /2018. 0210]
  88. 88.
    Zhou B T, Shang M S, Wang G Y, Zhang S, Feng L, Liu X N, Wu L and Shan K. 2018. Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI). Science of the Total Environment, 628-629: 848-857
  89. 89.
    Zimba P V. 2012. An improved phycobilin extraction method. Harmful Algae, 17: 35-39

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website