Skip to main content
Log in

Remote-sensing inversion model of surface water suspended sediment concentration based on in situ measured spectrum in Hangzhou Bay, China

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

Abstract

Suspended sediment concentration (SSC) is an important parameter for monitoring coastal water quality. Suspended particles are also the main optically active substances for ocean color remote sensing. It is important to study the surface reflectance spectra features of coastal turbid water, as it can be the basis for establishing more accurate remote-sensing inversion models. In this study, Hangzhou Bay, China, was selected as the study area. Two in situ measurement and sampling stations in the estuary of the Qiantang River which flows into Hangzhou Bay were set up separately. Above-water spectrum observation method, which the NASA recommended, was adopted to measure the reflection spectrum of turbid waters. Surface water samples were simultaneously collected to obtain the corresponding SSC data. The results showed that the total suspended particle concentrations in the Hangzhou Bay were typically high, and the inorganic suspended particle concentrations were far greater than the phytoplankton concentrations, which averages 705 mg/L and 1.16 mg/m3. The SSC at two sampling stations both showed significant temporal variability, particularly appearing short-period rapid fluctuations accompanying the tidal cycle. The measured surface water reflectance spectra all showed typical curve characteristics of high turbid water, and as the SSC increased, the corresponding reflectivity of surface water also increased. The increments at different wavelengths were variational, with two reflectance peaks appearing at 650–700-nm and near the 800-nm wavelength of spectral curves, respectively. The first derivative of spectral curves showed that the first reflectance peak location appeared to be a “red shift” phenomenon with the SSC increasing. The correlation coefficients between the SSC of surface water and the remote-sensing reflectance according to moderate resolution imaging spectra-radiometer (MODIS) channels’ central wavelength were different significantly, which were larger at MODIS long-wavelength channels (>650 nm) and smaller at MODIS short-wavelength channels (400–550 nm). The value of determination coefficient R 2 was 0.82 when the reflectance ratio of MODIS band 2 to band 1 was selected as the SSC sensitive bands combination and exponential regression analysis was employed. Therefore, the reflectance ratio of MODIS band 2 to band 1 can be adopted as the main band combination for establishing surface water SSC remote-sensing inversion model in the Hangzhou Bay.

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

Similar content being viewed by others

References

  • Bricaud A, Morel A, Babin M, Allali K, Claustre H (1998) Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: analysis and implications for bio-optical models. J Geophys Res 103:31033–31044

    Article  Google Scholar 

  • Carder KL, Chen FR, Cannizzaro JP, Campbell JW (2004) Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a. Adv Space Res 33(7):1152–1159

    Article  Google Scholar 

  • Chen SL, Zhang GA, Yang SL, John ZS (2006) Temporal variations of fine suspended sediment concentration in the Changjiang River estuary and adjacent coastal waters, China. J Hydrol 133:137–145

    Article  Google Scholar 

  • Curran PJ, Novo EMM (1988) The relationship between suspended sediment concentration and remotely sensed spectral radiance: a review. J Coast Res 4:351–368

    Google Scholar 

  • Devred E, Sathyendranath S, Stuart V, Maass H, Ulloa O, Platt T (2006) A two component model of phytoplankton absorption in the open ocean: theory and applications. J Geophys Res 111:C03011

    Article  Google Scholar 

  • Doerffer R, Fischer J (1994) Concentrations of chlorophyll, suspended matter, and gelbstoff in case 2 waters derived from satellite coastal zone color scanner data with inverse modeling methods. J Geophys Res 99:7457–7466

    Article  Google Scholar 

  • Doerffer R, Fischer J, Stossel M, Brockmann C (1989) Analysis of Thematic Mapper data for studying the suspended matter distribution in the coastal area of the Germany Bight (North Sea). Remote Sens Environ 28:61–73

    Article  Google Scholar 

  • Doxaran D, Cherukuru N, Lavender SJ (2006) Apparent and inherent optical properties of turbid estuarine waters: measurements, empirical quantification relationships, and modeling. Appl Opt 45(10):2310–2324

    Article  Google Scholar 

  • Gao SQ, Yu GH, Wang YH (1993) Distributional features and fluxes of dissolved nitrogen, phosphorus and silicon in the Hangzhou Bay. Mar Chem 43:65–81

    Article  Google Scholar 

  • Gordon HR, Brown OB, Evans RH et al (1988) A semianalytic radiance model of ocean color. J Geophys Res 93:10909–10924

    Article  Google Scholar 

  • IOCCG (2000). Remote sensing of ocean color in coastal and other optically complex waters, vol 3 International Ocean-Color Coordinating Group, Dartmouth, CanadaTech. Report

  • IOCCG (2006). Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications, vol 5 International Ocean-Color Coordinating Group, Dartmouth, CanadaTech. Report

  • Jerome J, Bukata R, Miller J (1996) Remote sensing reflectance and its relationship to optical properties of natural waters. Int J Remote Sens 17:3135–3155

    Article  Google Scholar 

  • Lavery P, Pattiaratchi C, Wyllie A, Hick P (1993) Water quality monitoring in estuarine water using the Landsat Thematic Mapper. Remote Sens Environ 46:268–280

    Article  Google Scholar 

  • Lee ZP, Carder KL, Du KP (2004) Effects of molecular and particle scatterings on model parameters for remote-sensing reflectance. Appl Opt 43:4957–4964

    Article  Google Scholar 

  • Li Y, Wei H, Ming F (1998) An algorithm for the retrieval of suspended sediment in coastal waters of China from AVHRR data. Cont Shelf Res 18:487–500

    Article  Google Scholar 

  • Michael S, Robert AA (1997) Effect of suspended particulate and dissolved organic matter on remote sensing of coastal and riverine waters. Appl Opt 27:6905–6912

    Google Scholar 

  • Miller RL, Brent AM (2004) Using MODIS Terra 250 m imagery to map concentrations of total suspended matter in coastal waters. Remote Sens Environ 93:259–266

    Article  Google Scholar 

  • Mobley CD (1999) Estimation of the remote-sensing reflectance from above-surface measurements. Appl Opt 38:7442–7455

    Article  Google Scholar 

  • Mueller JL et al (2003) Ocean optics protocols for satellite ocean color sensor validation, revision 4, vol 3: Radiometric measurements and data analysis protocols, NASA/TM-2003-211621

  • O’ Reilly J, Maritorena S, Mitchell BG, Siegel D, Carder KL, Garver S et al (1998) Ocean color chlorophyll algorithms for SeaWiFS. J Geophys Res 103:24937–24953

    Article  Google Scholar 

  • Pattiaratchi C, Lavery P, Wyllie A, Hick P (1994) Estimates of water quality in coastal waters using multi date Landsat Thematic Mapper data. Int J Remote Sens 15:1571–1584

    Article  Google Scholar 

  • Pope RM, Fry ES (1997) Absorption spectrum (380–700 nm) of pure water.2. Integrating cavity measurements. Appl Opt 36(33):8710–8723

    Article  Google Scholar 

  • Su JL, Wang KS (1989) Changjiang River Plume and suspended sediment transport in Hangzhou Bay. Cont Shelf Res 9:93–111

    Article  Google Scholar 

  • Tassan S (1994) Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. Appl Opt 33(12):2369–2378

    Article  Google Scholar 

  • Wang BC, Eisma D (1990) Supply and deposition of sediment along the north bank of Hangzhou Bay China. Neth J Sea Res 25(3):377–390

    Article  Google Scholar 

  • Wang F, Zhou B, Xu JM (2009) Application of neural network and MODIS 250 m imagery for estimating suspended sediments concentration in Hangzhou Bay, China. Environ Geol 56:1093–1101

    Article  Google Scholar 

  • Warrick JA, Mertes KA, Siegel DA, Mackenzie C (2004) Estimating suspended sediment concentrations in turbid coastal waters of the Santa Barbara Channel with SeaWiFS. Int J Remote Sens 25(10):1995–2002

    Article  Google Scholar 

  • Woodruff DL, Stumpf RP, Scope JA, Paerl HW (1999) Remote estimation of water clarity in optically complex estuarine waters. Remote Sens Environ 68:41–52

    Article  Google Scholar 

  • Zhang MW, Tang JW, Dong Q, Song QT, Ding J (2010) Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery. Remote Sens Environ 114:392–403

    Article  Google Scholar 

Download references

Acknowledgments

This research was sponsored by the National Natural Science Foundation of China (40901254, 40971193) and the Project of Zhejiang Key Scientific and Technological Innovation Team (2010R50039). The authors would like to thank the Institute of Hydraulics and Estuary of Zhejiang Province for providing much useful tide information about the study area. The authors also thank Professor Earl Bossard from San Jose State University for providing precious suggestions and English presentation revisions. We also wish to thank the anonymous reviewers and editor-in-chief for reviewing the paper.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xingmei Liu or Gendi Zhou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, F., Zhou, B., Liu, X. et al. Remote-sensing inversion model of surface water suspended sediment concentration based on in situ measured spectrum in Hangzhou Bay, China. Environ Earth Sci 67, 1669–1677 (2012). https://doi.org/10.1007/s12665-012-1608-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12665-012-1608-0

Keywords

Navigation