Skip to main content
Log in

Specific inherent optical properties of highly turbid productive water for retrieval of water quality after optical classification

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Assessments of specific inherent optical properties (SIOPs) and their variability in highly turbid and productive inland waters are essential for the accurate estimation of water quality. A new optical classification method including two classification criteria [i.e., normalized remote sensing reflectance slope (NS), and normalized remote sensing reflectance depth (ND)] was developed to divide remote sensing reflectance into four classes, i.e., class 1 (NS < −0.0017 and ND < 0.21) is low turbid and productive water; class 2 (NS < −0.0017 and ND > 0.21) is low turbid and high productive water; class 3 (NS > −0.0017 and ND < 0.09) is high turbid and low productive water; and class 4 (NS > −0.0017 and ND > 0.009) is high turbid and high productive water. The relationships between phytoplankton absorption at 440 nm [a ph(440)] and chlorophyll-a concentration [C chla] as well as between particle backscattering coefficient at 440 nm [b bp(440)] and total suspended matter concentration (C TSM) after classification were obtained from a large number of in situ data in Lake Taihu. The measured specific phytoplankton absorption \([ {a_{\text{ph}}^{*} \left( \lambda \right)} ]\) and particle backscattering coefficient \([ {b_{\text{bp}}^{*} \left( \lambda \right)} ]\) show significant variations even within the same class. The mean values of \(a_{\text{ph}}^{*} \left( \lambda \right)\) at 440 nm \([ {a_{\text{ph}}^{*} \left( {440} \right)} ]\) for each class are 0.048 ± 0.013, 0.060 ± 0.012, 0.083 ± 0.021, and 0.056 ± 0.017 m2/mg, respectively. The mean values of \(b_{\text{bp}}^{*} \left( \lambda \right)\) at 440 nm \([ {b_{\text{bp}}^{*} \left( {440} \right)} ]\) for each class are 0.035 ± 0.01, 0.024 ± 0.004, 0.041 ± 0.009, and 0.038 ± 0.009 m2/g, respectively. The power functions of SIOPs and water constituents’ concentration indicate that \(a_{\text{ph}}^{*} \left( {440} \right)\) and \(b_{\text{bp}}^{*} \left( {440} \right)\) vary with C chla and C TSM. The validation results show that our proposed values for \(a_{\text{ph}}^{*} \left( {440} \right)\) and \(b_{\text{bp}}^{*} \left( {440} \right)\) cover a very wide range of water optical properties, which are characterized from clear water to highly turbid productive water. The validation results also suggest that the retrieval accuracy of C chla and C TSM bio-optical model was improved after classification. The root mean square error (RMSE) of C chla was improved from 14.18 to 7.43 μg/L (mean value of all classes) and RMSE of C TSM was improved from 32.98 to 26.10 mg/L (mean value of all classes). Thus, the temporal and spatial variation of \(a_{\text{ph}}^{*} \left( {440} \right)\) and \(b_{\text{bp}}^{*} \left( {440} \right)\) should be considered in the bio-optical retrieval model of water quality.

Graphical Abstract

In complex optical properties of inland water, retrieving the water constituents with high accuracy needs to classify the water optical properties from the remote sensing spectrum by optical classification method. The figure shows the water color examples of each class

.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ambarwulan W, Salama MS, Mannaerts CM, Verhoef W (2011) Estimating specific inherent optical properties of tropical coastal waters using bio-optical model inversion and in situ measurements: case of the Berau estuary, East Kalimantan, Indonesia. Hydrobiologia 658:197–211

    Article  Google Scholar 

  • Bachmann RW, Hoyer MV, Canfield DE (2000) The potential for wave disturbance in shallow Florida lakes. J Lake Reserv Manag 16:281–291

    Article  Google Scholar 

  • Blondeau-Patissier D, Brando VE, Oubelkheir K, Dekker AG, Clementson LA, Daniel P (2009) Bio-optical variability of the absorption and scattering properties of the Queensland inshore and reef waters, Australia. J Geophys Res 114:C05003. doi:10.1029/2008JC005039

    Google Scholar 

  • Bricaud A, Babin M, Morel A, Claustre H (1995) Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: analysis and parameterization. J Geophys Res 100(C7):13321–13332

    Article  Google Scholar 

  • Bricaud A, Claustre H, Ras J, Oubelkheir K (2004) Natural variability of phytoplanktonic absorption in oceanic waters: Influence of the size structure of algal populations. J Geophys Res 109(C11010). doi:10.1029/2004JC002419

  • Campbell G, Phinn SR, Daniel P (2011) The specific inherent optical properties of three sub-tropical and tropical water reservoirs in Queensland, Australia. Hydrobiologia 658:233–252

    Article  Google Scholar 

  • Chen WM, Chen KN, Hu YH (2006) Discussion on possible error for phytoplankton chlorophyll-a concentration analysis using hot-ethanol extraction method. J Lake Sci 18:550–552 (In Chinese)

    Google Scholar 

  • Cleveland JS, Weidemann AD (1993) Quantifying absorption by aquatic particles: a multiple scattering correction for glass-fiber filters. Limnol Oceanogr 38:1321–1327

    Article  Google Scholar 

  • Dekker A (1993) Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing. Ph.D. thesis, Free University, Amsterdam, The Netherlands

  • Doron M, Babin M, Mangin A, Hembise O (2007) Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. J Geophys Res 112:C06003. doi:10.1029/2006JC004007

    Google Scholar 

  • Doron M, Bélanger S, Doxaran D, Babin M (2011) Spectral variations in the near-infrared ocean reflectance. Remote Sens Environ 115(7):1617–1631. doi:10.1016/j.rse.2011.01.015

    Article  Google Scholar 

  • Doxaran D, Cherukuru N, Lavender SJ (2006) Apparent and inherent optical properties of turbid estuarine water: measurements, empirical quantification relationships, and modeling. Appl Opt 45(1):2310–2324

    Article  Google Scholar 

  • Duan HT, Ma RH, Zhang YZ, Loiselle S A, Xu JP, Zhao CL, Zhou L, Shang LL (2010) A new three-band algorithm for estimating chlorophyll concentrations in turbid inland lakes. Environ Res Lett 5:044009 (p 6). doi:10.1088/1748-9326/5/4/044009

  • Gallegos CL, Correll DL, Pierce JW (1990) Modeling spectral diffuse attenuation, absorption, and scattering coefficients in a turbid estuary. Limnol Oceanogr 35(7):1486–1502

    Article  Google Scholar 

  • Gitelson A, Doll’olom G, Moses W, Rundquist DC, Barrow T, Fisher T, Gurlin D, Holz J (2008) A simple semi-analytical model for remote estimation of chlorophyll-a in turbid water: validation. Remote Sens Environ 112(9):3582–3593

    Article  Google Scholar 

  • Gitelson A, Gurlin D, Moses W, Barrow T (2009) A bio-optical algorithm for the remote estimation of chlorophyll-a concentration in case 2 water. Environ Res Lett 4(045003):5

    Google Scholar 

  • Gitelson A, Gao BC, Li RR, Berdnikov S, Saprygin V (2011) Estimation of chlorophyll-a concentration in productive turbid water using hyper spectral image for coastal ocean: the Azov sea case study. Environ Res Lett 6(024023):6

    Google Scholar 

  • Gordon HR, Brown OB, Jacobs MM (1975) Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Appl Opt 14(2):417–427

    Article  Google Scholar 

  • Gordon HR, Brown OB, Evans RH, Brown JW, Smith RC, Baker KS, Clark DK (1988) A semi-analytic radiance model of ocean color. J Geophys Res 93(D3):10909–10924

    Article  Google Scholar 

  • Heege T, Fischer J (2004) Mapping of water constituents in Lake Constance using multispectral airborne scanner data and a physically based processing scheme. Can J Remote Sens 30(1):77–86

    Article  Google Scholar 

  • Hoge FE, Wright CW, Lyon PE, Swift RN, Yungel JK (1999) Satellite retrieval of inherent optical properties by inversion of an oceanic radiance model: a preliminary algorithm. Appl Opt 38:495–504

    Article  Google Scholar 

  • Huang CC, Li YM, Wang Q, Sun DY, Le CF (2011) Retrieval of Microcystis aentginosa percentage from high turbid and eutrophia inland water: a case Study in Taihu Lake. IEEE Trans Geosci Remote Sens 49(10):4090–4100

    Article  Google Scholar 

  • Huang CC, Li YM, Wang Q, Sun DY, Le CF, Shi K (2012) Scattering spectrum properties and their relationship to biogeochemical parameters: a case study in Taihu Lake. Limnology 13:1–11. doi:10.1007/s10201-011-0346-4

    Article  Google Scholar 

  • Ibelings B, Vos R, Boderie P, Hakvoort H, Hoogenboom E (2001) The RALLY Project: Remote Sensing of Algal Blooms in Lake IJssel: Integration with in Situ Data and Computational Modelling. Netherlands Remote Sensing Board (BCRS), Programme Bureau, Rijkswaterstaat Survey Department

  • Kirk JTO (1984) Dependence of relationship between inherent and apparent optical properties of water on solar altitude. Limnol Oceanogr 29(2):350–356

    Article  Google Scholar 

  • Kirk JTO (1991) Volume scattering function, average cosines, and the underwater light field. Limnol Oceanogr 36(3):455–467

    Article  Google Scholar 

  • Le CF, Li YM, Zha Y, Sun DY, Huang CC, Lu H (2009a) A four-band semi analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China. Remote Sens Environ 113(6):1175–1182

    Article  Google Scholar 

  • Le CF, Li YM, Zha Y, Sun DY (2009b) Specific absorption coefficient and the phytoplankton package effect in Lake Taihu, China. Hydrobiologia 619(1):27–37

    Article  Google Scholar 

  • Le CF, Li YM, Zha Y, Sun DY, Huang CC, Zhang H (2011) Remote estimation of chlorophyll a in optically complex waters based on optical classification. Remote Sens Environ 115:725–737

    Article  Google Scholar 

  • Lee ZP, Carder KL, Mobley CD, Steward RG, Patch JS (1999) Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Appl Opt 38:3831–3843

    Article  Google Scholar 

  • Lee ZP, Carder KL, Arnone RA (2002) Deriving inherent optical properties from water color: a multi-band quasi-analytical algorithm for optically deep waters. Appl Opt 41:5755–5772

    Article  Google Scholar 

  • Lee ZP, Darecki M, Carder KL, Davis CO, Stramski D, Rhea WJ (2005) Diffuse attenuation coefficient of downwelling irradiance: an evaluation of remote sensing methods. J Geophys Res 110:1–9

    Google Scholar 

  • Lee ZP, Ahn YH, Mobley CD, Arnone R (2010) Removal of surface-reflected light for the measurement of remote-sensing reflectance from an above-surface platform. Opt Express 18(25):26313–26324

    Article  Google Scholar 

  • Lubac B, Loisel H (2007) Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea. Remote Sens Environ 110(1):45–58

    Article  Google Scholar 

  • Maritorena S, Siegel DA, Peterson AR (2002) Optimization of a semi analytical ocean color model for global-scale applications. Appl Opt 41:2705–2714

    Article  Google Scholar 

  • Matsuoka A, Hill V, Huot Y, Babin M, Bricaud A (2011) Seasonal variability in the light absorption properties of western Arctic waters: parameterization of the individual components of absorption for ocean color applications. J Geophys Res 116:C02007. doi:10.1029/2009JC005594

    Google Scholar 

  • Matthews MW, Bernard S, Robertson L (2012) An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sens Environ 124:637–652

    Article  Google Scholar 

  • Mitchell BG, Kiefer DA (1988) Chlorophyll a specific absorption and fluorescence excitation spectra for light limited phytoplankton. Deep Sea Res 35:639–663

    Article  Google Scholar 

  • Mobley CD (1994) Light and water: radiative transfer in natural waters. Academic Press, New York

    Google Scholar 

  • Moore C, Barnard A, Hankins D (2004) Spectral absorption and attenuation meter (ac-s): user’s guide, revision A. WET Labs Inc., Philomath pp 5–20

  • Morel A, Gentili B (1993) Diffuse reflectance of oceanic waters. II. Bidirectional aspects. Appl Opt 32:6864–6879

    Article  Google Scholar 

  • Mueller JL, Fargion GS, Zaneveld JRV (2003) Ocean optics protocols for satellite ocean color sensor validation. Revision 4, vol IV, NASA

  • O’Donnell DM, Effler SW, Perkins MG, Strait C, Lee ZP, Greb S (2013a) Resolution of optical gradients and pursuit of optical closure for Green Bay, Lake Michigan. J Great Lakes Res 39(1):161–172

    Article  Google Scholar 

  • O’Donnell DM, Effler SW, Perkins MG, Strait C (2013b) Optical characterization of Lake Champlain: spatial heterogeneity and closure. J Great Lakes Res 39(2):247–258

    Article  Google Scholar 

  • Oubelkheir K, Clementson LA, Webster IT, Ford PW, Dekker AG, Radke LC, Daniel P (2006) Using inherent optical properties to investigate biogeochemical dynamics in a tropical macrotidal coastal system. J Geophys Res 111:C07021. doi:10.1029/2005JC003113

    Google Scholar 

  • Perkins MG, Effler SW, Peng F, O’Donnell DM, Strait C (2013) Light-absorbing components in the Great Lakes. J Great Lakes Res 39(1):123–136

    Article  Google Scholar 

  • Qin BQ, Hu WP, Chen WM (2004) The process and mechanism of water environment evolvement in Lake Taihu. Beijing Science Press, Beijing

  • Sasaki H, Saitoh SI, Kishino M (2001) Bio-optical properties of seawater in the Western subarctic gyre and Alaskan gyre in the subarctic north pacific and the southern Bering sea during the Summer of 1997. J Oceanogr 57(3):275–284

    Article  Google Scholar 

  • Stambler N (2005) Bio-optical properties of the northern Red Sea and the Gulf of Eilat (Aqaba) during winter 1999. J Sea Res 54:186–203

    Article  Google Scholar 

  • Sun DY, Li YM, Wang Q, Le CF, Huang CC, Shi K (2011) Development of optical criteria to discriminate various types of highly turbid lake waters. Hydrobiologia 669:83–104

    Article  Google Scholar 

  • Sun DY, Li YM, Wang Q, Le CF, Lv H, Huang CC, Gong SQ (2012) Specific inherent optical quantities of complex turbid inland waters, from the perspective of water classification. Photochem Photobiol Sci. doi:10.1039/c2pp25061f

    Google Scholar 

  • Wang B (2011) A study of New York/New Jersey coastal water: bio-optical characteristics of the harbor estuary and effects of heavy metals on brown tide alga of the bight, New Jersey Institute of Technology, PHD

  • Westberry TK, Siegel DA, Subramaniam A (2005) An improved bio-optical model for the remote sensing of Trichodesmium spp. blooms. J Geophys Res 110:C06012. doi:10.1029/2004JC002517

    Google Scholar 

  • Yang W, Matsushita B, Chen J, Fukushima T, Ma RH (2010) An enhanced three-band index for estimating chlorophyll-a in turbid case-II waters: case studies of Lake Kasumigaura, Japan, and Lake Dianchi, China. IEEE Trans Geosci Remote Sens 7(4):655–659

    Article  Google Scholar 

  • Yentsch CS (1962) Measurement of visible light absorption by particulate matter in the ocean. Limnol Oceanogr 7:207–217

    Article  Google Scholar 

  • Zhang YL, Liu ML, Wang X, Zhu GW, Chen WM (2009) Bio-optical properties and estimation of the optically active substances in Lake Tianmuhu in summer. Int J Remote Sens 30:2837–2857

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by National Science and Technology Support Project of China (No. 41201325; No. 41271343; No. 41030751; No. 41103047), Open Research Fund of Key Laboratory of Digital Earth, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences (No. 2012LDE009), The Research Fund for the Doctoral Program of Higher Education (20123207120017), and Natural Science Funds of Provincial Universities (12KJB170005). The project is funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. We deeply thank the two anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liangcheng Zhou.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, C., Chen, X., Li, Y. et al. Specific inherent optical properties of highly turbid productive water for retrieval of water quality after optical classification. Environ Earth Sci 73, 1961–1973 (2015). https://doi.org/10.1007/s12665-014-3548-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-014-3548-3

Keywords

Navigation