Précédent|Prochain

Research progress of remote sensing monitoring of case II water environmental parameters based on water optical classification

  • role： First author第一作者
  • Affiliation：

    Nanjing Normal University, Nanjing 210023, China

  • Email：liyunmei@njnu.edu.cn
  • Introduction：李云梅，1966年生，女，教授，研究方向为水环境遥感。E-mail： liyunmei@njnu.edu.cn
LI Yunmei1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Ministry of Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China

  • Email：zhaohuan900817@163.com
  • Introduction：赵焕，1990年生，女，工程师，研究方向为水环境遥感。E-mail： zhaohuan900817@163.com
ZHAO Huan2*,  
  • Affiliation：

    Nanjing Normal University, Nanjing 210023, China

BI Shun1,  
  • Affiliation：

    Nanjing Normal University, Nanjing 210023, China

LYU Heng1

résumé

Case II waters, including inland and inshore waters, are affected by many factors, such as phytoplankton, suspended particles, and colored dissolved organic matter, leading to complex and changeable optical characteristics of the water body. Hence, establishing a unified remote sensing quantitative estimation model for retrieving water environmental parameters is difficult. According to the optical characteristics of water, the method of water optical classification and water environmental parameter inversion can not only improve the accuracy of parameter estimation but also facilitate the model to be popularized in similar waters. This study aims to review the state-of-the-art concepts and methods of water optical classification on remote sensing technology for case II water monitoring. The classification-based applications on retrieving environmental parameters as well as the limitations and prospects are discussed.The criteria for considering studies for this review are based on the general development of water optical classification technology and ongoing studies from authors and their collaborators. The selection of studies is classified by different methods and applications for parameter retrieval. The main concept and advantages of water optical classification are illustrated with several examples presented in this study.According to the optical characteristics of water, the method of water optical classification and water environmental parameter inversion can not only improve the accuracy of parameter estimation but also facilitate the model to be popularized in similar waters. Water optical classification methods mainly include optical classification based on inherent optical characteristics, remote sensing reflectance waveform characteristics, and parameter inversion. The classification inversion strategy includes the fusion of classification and model algorithm, the optimization algorithm based on water optical type, and the hybrid calculation based on optimization of multi-model.Water optical classification is an effective tool for remotely recording the water quality and improving the estimation of the parameters especially in optically complex case II waters. The water retrieval of one predominated optical type should be based on its optimal model. However, accurate estimation of water composed of various types with spatiotemporal dynamics requires the determination of optimal models for each type and the blending strategy. The fuzzy-logic-based blending supports the production of seamless contiguities by considering weight factors. However, different classification methods and parameter estimation strategies should be reconsidered according to the complexity of water optical characteristics and research purposes.

mots-clés

Water optical classification;case II water;water environmental parameters;remote sensing monitoring;remote sensing quantitative estimation model

References

  1. 1.
    Bezdek J C. 1973. Cluster validity with fuzzy sets. Journal of Cybernetics, 3(3): 58-73
  2. 2.
    Bezdek J C, Ehrlich R and Full W. 1984. FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2/3): 191-203
  3. 3.
    Bi S. 2021. Remote Sensing of Algal Column-Integrated Biomass for Inland Waters based on Soft Classification. Nanjing: Nanjing Normal University
  4. 4.
    Bi S, Li Y M, Li Y M, Xu J, Liu G, Song K S, Mu M, Lyu H, Miao S and Xu J F. 2019. Optical classification of inland waters based on an improved Fuzzy C-Means method. Optics Express, 27(24): 34838-34856
  5. 5.
    Bi S, Li Y M, Wang Q, Lyu H, Liu G, Zheng Z B, Du C G, Mu M, Xu J, Lei S H and Miao S. 2018. Inland water atmospheric correction based on turbidity classification using OLCI and SLSTR synergistic observations. Remote Sensing, 10(7): 1002
  6. 6.
    Brewin R J W, Sathyendranath S, Müller D, Brockmann C, Deschamps P Y, Devred E, Doerffer R, Fomferra N, Franz B, Grant M, Groom S, Horseman A, Hu C M, Krasemann H, Lee Z, Maritorena S, Mélin F, Peters M, Platt T, Regner P, Smyth T, Steinmetz F, Swinton J, Werdell J and White G N III. 2015. The ocean colour climate change initiative: III. A round-robin comparison on in-water bio-optical algorithms. Remote Sensing of Environment, 162: 271-294
  7. 7.
    Candiani G, Giardino C and Brando V E. 2007. Adjacency effects and bio-optical model regionalisation: MERIS data to assess lake water quality in the Subalpine ecoregion//Proceedings of the ENVISAT Symposium. Montreux, Switzerland: [s.n.]
  8. 8.
    Chen J, Li Y, Li D P, Huang Y and Zhu P Y. 2016. Distribution of phosphorus forms in the overlying water under disturbance with the addition of algae. Environmental Science, 37(4): 1413-1421
  9. 9.
    Cui T W, Zhang J, Wang K, Wei J W, Mu B, Ma Y, Zhu J H, Liu R J and Chen X Y. 2020. Remote sensing of chlorophyll a concentration in turbid coastal waters based on a global optical water classification system. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 187-201
  10. 10.
    Dembélé D and Kastner P. 2003. Fuzzy C-means method for clustering microarray data. Bioinformatics, 19(8): 973-980
  11. 11.
    Du C G. 2018. Temporal and Spatial Variation of Total Phosphorus Concentration and its Influencing Factors in Taihu Lake. Nanjing: Nanjing Normal University
  12. 12.
    Du C G, Wang Q, Li Y M, Lyu H, Zhu L, Zheng Z B, Wen S, Liu G and Guo Y L. 2018. Estimation of total phosphorus concentration using a water classification method in inland water. International Journal of Applied Earth Observation and Geoinformation, 71: 29-42
  13. 13.
    Futschik M E and Carlisle B. 2005. Noise-robust soft clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology, 3(4): 965-988
  14. 14.
    Gitelson A. 1992. The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13(17): 3367-3373
  15. 15.
    Gitelson A A, Dall’Olmo G, Moses W, Rundquist D C, Barrow T, Fisher T R, Gurlin D and Holz J. 2008. A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: validation. Remote Sensing of Environment, 112(9): 3582-3593
  16. 16.
    Gitelson A A, Gao B C, Li R R, Berdnikov S and Saprygin V. 2011. Estimation of chlorophyll-a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean—the Azov Sea case study. Environmental Research Letters, 6(2): 024023
  17. 17.
    Gilerson A A, Gitelson A A, Zhou J, Gurlin D, Moses W, Ioannou I and Ahmed S A. 2010. Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Optics Express, 18(23): 24109-24125
  18. 18.
    González Vilas L, Spyrakos E and Torres Palenzuela J M. 2011. Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain). Remote Sensing of Environment, 115(2): 524-535
  19. 19.
    Gower J, King S, Borstad G and Brown L. 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26(9): 2005-2012
  20. 20.
    Gower J, King S and Goncalves P. 2008. Global monitoring of plankton blooms using MERIS MCI. International Journal of Remote Sensing, 29(21): 6209-6216
  21. 21.
    Gurlin D, Gitelson A A and Moses W J. 2011. Remote estimation of chl-a concentration in turbid productive waters—Return to a simple two-band NIR-red model?. Remote Sensing of Environment, 115(12): 3479-3490
  22. 22.
    Hu C M, Lee Z and Franz B. 2012. Chlorophyll a algorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117: C01011
  23. 23.
    Huang C C, Li Y M, Sun D Y, Le C F and Jin X. 2011. Research of scattering spectrum characteristic and formative mechanism of Taihu Lake waters. Acta Optica Sinica, 31(5): 0501003
  24. 24.
    Jackson T, Sathyendranath S and Mélin F. 2017. An improved optical classification scheme for the Ocean Colour Essential Climate Variable and its applications. Remote Sensing of Environment, 203: 152-161
  25. 25.
    Jerlov N G and Koczy F F. 1951. Photographic measurements of daylight in deep water. Elanders Boktryckeri Aktiebolag, Stockholm
  26. 26.
    Jiang G J, Duan G Q, Huang Z X, Cai W S, Lu C Q, Su W, Yang J and Zhang C C. 2016. Remote sensing classification of the dominant optically active components and its variations in the Pearl River estuary. Haiyang Xuebao, 38(9): 64-75
  27. 27.
    Jiang G J, Loiselle S A, Yang D T, Ma R H, Su W and Gao C J. 2020. Remote estimation of chlorophyll a concentrations over a wide range of optical conditions based on water classification from VIIRS observations. Remote Sensing of Environment, 241: 111735
  28. 28.
    Le C F, Li Y M, Sun D Y and Wang H J. 2007. Research on chlorophyll concentration retrieval models of Taihu Lake based on seasonal difference. Journal of Remote Sensing, 11(4): 473-480
  29. 29.
    Le C F, Li Y M, Zha Y and Sun D Y. 2009. Specific absorption coefficient and the phytoplankton package effect in Lake Taihu, China. Hydrobiologia, 619: 27-37
  30. 30.
    Le C F, Li Y M, Zha Y, Sun D Y, Huang C C and Zhang H. 2011. Remote estimation of chlorophyll a in optically complex waters based on optical classification. Remote Sensing of Environment, 115(2): 725-737
  31. 31.
    Lee Z, 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
  32. 32.
    Lei S H, Xu J, Li Y M, Lyu H, Liu G, Zheng Z B, Xu Y F, Du C G, Zeng S, Wang H J, Dong X Z, Cai X L and Li J D. 2020. Temporal and spatial distribution of Kd (490) and its response to precipitation and wind in lake Hongze based on MODIS data. Ecological Indicators, 108: 105684
  33. 33.
    Li J S, Gao M, Feng L, Zhao H L, Shen Q, Zhang F F, Wang S L and Zhang B. 2019. Estimation of chlorophyll-a concentrations in a highly turbid eutrophic lake using a classification-based MODIS land-band algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10): 3769-3783
  34. 34.
    Li L L, Li Y M, Lü H, Xu J, Yang Z Q, Bi S and Xu J F. 2020. Remote sensing classification of urban black-odor water based on decision tree. Environmental Science, 41(11): 5060-5072
  35. 35.
    Li Y M, Huang J Z, Wei Y C and Lu W N. 2006. Inversing chlorophyll concentration of Taihu Lake by analytic model. Journal of Remote Sensing, 10(2): 169-175
  36. 36.
    Li Y M, Wang Q, Wu C Q, Zhao S H, Xu X, Wang Y F and Huang C C. 2012. Estimation of Chlorophyll a concentration using NIR/Red bands of MERIS and classification procedure in inland turbid water. IEEE Transactions on Geoscience and Remote Sensing, 50(3): 988-997
  37. 37.
    Liu G, Li L, Song K S, Li Y M, Lyu H, Wen Z D, Fang C, Bi S, Sun X P, Wang Z M, Cao Z G, Shang Y X, Yu G L, Zheng Z B, Huang C C, Xu Y F and Shi K. 2020. An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters. Remote Sensing of Environment, 239: 111648
  38. 38.
    Liu Z H, Li Y M, Lü H, Xu Y F, Xu X and Huang J Z. 2011. Estimating of backscattering rate in Lake Chaohu based on bio-optical model. Environmental Science, 32(2): 464-471
  39. 39.
    Lu C P, Lü H and Li Y M. 2012. Backscattering properties and parametric model of Taihu Lake based on spectral classification. Journal of Remote Sensing, 16(2): 417-434
  40. 40.
    Maritorena S, Siegel D A and Peterson A R. 2002. Optimization of a semianalytical ocean color model for global-scale applications. Applied Optics, 41(15): 2705-2714
  41. 41.
    Matsushita B, Yang W, Yu G L, Oyama Y, Yoshimura K and Fukushima T. 2015. A hybrid algorithm for estimating the chlorophyll-a concentration across different trophic states in Asian inland waters. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 28-37
  42. 42.
    Matthews M W, Bernard S and Robertson L. 2012. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment, 124: 637-652
  43. 43.
    Matthews M W and Odermatt D. 2015. Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sensing of Environment, 156: 374-382
  44. 44.
    Mélin F, Vantrepotte V, Clerici M, D’Alimonte D, Zibordi G, Berthon J F and Canuti E. 2011. Multi-sensor satellite time series of optical properties and chlorophyll-a concentration in the Adriatic Sea. Progress in Oceanography, 91(3): 229-244
  45. 45.
    Mobley C D, Stramski D, Bissett W P and Boss E. 2004. Optical modeling of ocean waters: is the case 1-case 2 classification still useful?. Oceanography, 17(2): 60-67
  46. 46.
    Mobley C D and Sundman L K. 2003. Effects of optically shallow bottoms on upwelling radiances: inhomogeneous and sloping bottoms. Limnology and Oceanography, 48: 329-336
  47. 47.
    Moore T S, Dowell M D, Bradt S and Verdu A R. 2014. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote Sensing of Environment, 143: 97-111
  48. 48.
    Morel A and Maritorena S. 2001. Bio-optical properties of oceanic waters: a reappraisal. Journal of Geophysical Research: Oceans, 106(C4): 7163-7180
  49. 49.
    Morel A and Prieur L. 1977. Analysis of variations in ocean color. Limnology and Oceanography, 22(4): 709-722
  50. 50.
    Mu M, Wu C Q, Li Y M, Lyu H, Fang S Z, Yan X, Liu G, Zheng Z B, Du C G and Bi S. 2019. Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake. Environmental Science and Pollution Research, 26(11): 11012-11028
  51. 51.
    Neil C, Spyrakos E, Hunter P D and Tyler A N. 2019. A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types. Remote Sensing of Environment, 229: 159-178
  52. 52.
    O’Reilly J E, Maritorena S, Mitchell B G, Siegel D A, Carder K L, Garver S A, Kahru M and McClain C. 1998. Ocean color chlorophyll algorithms for SeaWIFS. Journal of Geophysical Research: Oceans, 103(C11): 24937-24953
  53. 53.
    O’Reilly J E, Maritorena S, Siegel D A, O’Brien M C, Toole D, Mitchell B G, Kahru M, Ghavez F P, Strutton P, Cota G F, Hooker S B, McClain C R, Carder K L, Müller-Karger F, Harding L, Magnuson A, Phinney D, Moore G F, Aiken J, Arrigo K R, Letelier R and Culver M. 2000. Ocean Color Chlorophyll a Algorithms for SeaWiFS, OC2, and OC4: version 4//SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. [s.l.]: [s.n.]: 9-23
  54. 54.
    O’Reilly J E and Werdell P J. 2019. Chlorophyll algorithms for ocean color sensors-OC4, OC5 and OC6. Remote sensing of Environment, 229: 32-47
  55. 55.
    Palacios S L, Peterson T D and Kudela R M. 2012. Optical characterization of water masses within the Columbia River plume. Journal of Geophysical Research: Oceans, 117: C11020
  56. 56.
    Prieur L and Sathyendranath S. 1981. An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter, and other particulate materials. Limnology and Oceanography, 26(4): 671-689
  57. 57.
    Schwämmle V and Jensen O N. 2010. A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics, 26(22): 2841-2848
  58. 58.
    Shen F, Zhou Y X, Li D J, Zhu W J and Suhyb Salama M. 2010. Medium resolution imaging spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary. International Journal of Remote Sensing, 31(17/18): 4635-4650
  59. 59.
    Shi K, Li Y M, Li L and Lu H. 2013. Absorption characteristics of optically complex inland waters: implications for water optical classification. Journal of Geophysical Research: Biogeosciences, 118(2): 860-874
  60. 60.
    Shi K, Li Y M, Zhang Y L, Li L, Lv H and Song K S. 2014. Classification of inland waters based on bio-optical properties. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2): 543-561
  61. 61.
    Shi K, Zhang Y L, Zhu G W, Liu X H, Zhou Y Q, Xu H, Qin B Q, Liu G and Li Y M. 2015. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250m MODIS-Aqua data. Remote Sensing of Environment, 164: 43-56
  62. 62.
    Smith M E, Lain L R and Bernard S. 2018. An optimized chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters. Remote Sensing of Environment, 215: 217-227
  63. 63.
    Spyrakos E, O’Donnell R, Hunter P D, Miller C, Scott M, Simis S G H, Neil C, Barbosa C C F, Binding C E, Bradt S, Bresciani M, Dall’Olmo G, Giardino C, Gitelson A A, Kutser T, Li L, Matsushita B, Martinez-Vicente V, Matthews M W, Ogashawara I, Ruiz-Verdú A, Schalles J F, Tebbs E, Zhang Y L and Tyler A N. 2018. Optical types of inland and coastal waters. Limnology and Oceanography, 63(2): 846-870
  64. 64.
    Sun D Y, Li Y M, Le C F, Gong S Q, Wang H J, Wu L and Huang C C. 2007. Scattering characteristics of Taihu Lake and its relationship models with suspended particle concentration. Environmental Science, 28(12): 2688-2694
  65. 65.
    Sun D Y, Li Y M, Wang Q, Le C F, Huang C C and Shi K. 2011. Development of optical criteria to discriminate various types of highly turbid lake waters. Hydrobiologia, 669(1): 83-104
  66. 66.
    Sun D Y, Li Y M, Wang Q, Le C F, Lv H, Huang C C and Gong S Q. 2012. Specific inherent optical quantities of complex turbid inland waters, from the perspective of water classification. Photochemical and Photobiological Sciences, 11(8): 1299-1312
  67. 67.
    Vantrepotte V, Loisel H, Dessailly D and Mériaux X. 2012. Optical classification of contrasted coastal waters. Remote Sensing of Environment, 123: 306-323
  68. 68.
    Wei J W, Lee Z and Shang S L. 2016. A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments. Journal of Geophysical Research: Oceans, 121(11): 8189-8207
  69. 69.
    Xu J, Lei S H, Bi S, Li Y M, Lyu H, Xu J F, Xu X G, Mu M, Miao S, Zeng S and Zheng Z B. 2020. Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing. Water Research, 168: 115162
  70. 70.
    Xu J, Li Y M, Lyu H, Lei S H, Mu M, Bi S, Xu J F, Xu X G, Miao S, Li L L and Yan X C. 2021. Simultaneous inversion of concentrations of POC and its endmembers in lakes: a novel remote sensing strategy. Science of the Total Environment, 770: 145249
  71. 71.
    Xu Y F, Li Y M, Wang Q, Lü H, Liu Z H, Xu X, Tan J, Guo Y L and Wu C Q. 2011. Optical properties and retrieving of diffuse attenuation coefficient of the Three Gorges Reservoir by remote sensing. Journal of Lake Sciences, 23(1): 95-103
  72. 72.
    Xue K, Ma R H, Wang D and Shen M. 2019. Optical classification of the remote sensing reflectance and its application in deriving the specific phytoplankton absorption in optically complex lakes. Remote Sensing, 11(2): 184
  73. 73.
    Yu X L, Lee Z, Shen F, Wang M H, Wei J W, Jiang L D and Shang Z H. 2019. An empirical algorithm to seamlessly retrieve the concentration of suspended particulate matter from water color across ocean to turbid river mouths. Remote Sensing of Environment, 235: 111491
  74. 74.
    Zeng S, Li Y M, Lyu H, Xu J F, Dong X Z, Wang R, Yang Z Q and Li J C. 2020. Mapping spatio-temporal dynamics of main water parameters and understanding their relationships with driving factors using GF-1 images in a clear reservoir. Environmental Science and Pollution Research, 27: 33929-33950
  75. 75.
    Zhang F F, Li J S, Shen Q, Zhang B, Tian L Q, Ye H P, Wang S L and Lu Z Y. 2019. A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake. International Journal of Applied Earth Observation and Geoinformation, 74: 138-149
  76. 76.
    Zhang Y L, Zhang B, Ma R H, Feng S and Le C F. 2007. Optically active substances and their contributions to the underwater light climate in Lake Taihu, a large shallow lake in China. Fundamental and Applied Limnology, 170(1): 11-19
  77. 77.
    Zheng Z B, Ren J L, Li Y M, Huang C C, Liu G, Du C G and Lyu H. 2016. Remote sensing of diffuse attenuation coefficient patterns from Landsat 8 OLI imagery of turbid inland waters: a case study of Dongting Lake. Science of the Total Environment, 573: 39-54

Lire l'article complet

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