[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R 2 = 0.94, 0.91) than their performance on Chl-a concentration (R 2 = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R 2 = 0.91, 0.82) and TSM (R 2 = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R 2 = 0.75 and 0.90 for Chl-a; R 2 = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adler-Golden, S. M., Matthew, M. W., Bernstein, L. S., Levine, R. Y., Berk, A., et al. (1999). Atmospheric correction for short-wave imagery based on MODTRAN 4. Proceedings of SPIE, 3753, 61–69.

    Article  Google Scholar 

  • APHA/AWWA/WEF (1998). Standard methods for the examination of water and wastewater. Washington, DC.

  • Binding, C. E., John, H. J., Bukata, R. P., & William, G. B. (2008). Spectral absorption properties of dissolved and particulate matter in Lake Erie. Remote Sensing of Environment, 112, 1702–1711.

    Article  Google Scholar 

  • Brando, V. E., & Dekker, A. G. (2003). Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing, 41, 1378–1387.

    Article  Google Scholar 

  • Bricaud, A., Mejia, C., Blondeau-Patissier, D., Claustre, H., Crepon, M., & Thiria, S. (2007). Retrieval of pigment concentrations and size structure of algal populations from their absorption spectra using multilayered perceptrons. Applied Optics, 46(8), 1251–1260.

    Article  Google Scholar 

  • Bryant, R., Moran, M. S., McElroy, S., Holifield, C., Thome, K., Miura, T., et al. (2003). Data continuity of Earth Observing 1 (EO-1) Advanced Land Imager (ALI) and Landsat TM and ETM+. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1204–1214.

    Article  Google Scholar 

  • Cacuci, D. G. (2003). Sensitivity & uncertainty analysis, volume 1: Theory (p. 304). Chapman & Hall/CRC.

  • Chen, Z. M., Curran, P. J., & Hanson, J. D. (1992). Derivative reflectance spectroscopy to estimate suspended sediment concentration. Remote Sensing of Environment, 40, 67–77.

    Article  Google Scholar 

  • Dall’Olmo, G., Gitelson, A., & Rundquist, D. (2003). Towards a unified approach for remote estimation of chlorophyll a in both terrestrial vegetation and turbid productive waters. Geophysical Research Letters, 30, 1938–1942.

    Article  Google Scholar 

  • Dekker, A. G., Malthus, T. J., & Seyhan, E. (1991). Quantitative modeling of inland water quality for high resolution MSS system. IEEE Transactions on Geoscience and Remote Sensing, 29, 89–95.

    Article  Google Scholar 

  • Dekker, A. G., & Peters, S. W. (1993). The use of the thematic mapper for the analysis of eutrophic lakes: A case study in the Netherlands. International Journal of Remote Sensing, 14(5), 799–821.

    Article  Google Scholar 

  • Dekker, A. G., Vos, R. J., & Peters, S. W. M. (2001). Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. The Science of the Total Environment, 268(1–3), 197–214.

    CAS  Google Scholar 

  • Dekker, A. G., Vos, R. J., & Peters, S. W. M. (2002). Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. International Journal of Remote Sensing, 23(1), 15–35.

    Article  Google Scholar 

  • Doxaran, D., Cherukuru, N. R. C., & Lavender, S. J. (2004). Estimation of surface reflection effects on upwelling radiance field measurements in turbid waters. Journal of Optics A: Pure Applied Optics, 6, 690–697.

    Article  Google Scholar 

  • Doxaran, D., Cherukuru, R. C. N., & Lavender, S. J. (2006). Inherent and apparent optical properties of turbid estuarine waters: measurements, modelling and application to remote sensing. Applied Optics, 45, 2310–2324.

    Article  Google Scholar 

  • Doxaran, D., Froidefond, J. M., & Castaing, P. (2003). Remote sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects using reflectance ratios. Applied Optics, 42, 2623–2634.

    Article  Google Scholar 

  • Doxaran, D., Froidefond, J. M., Lavender, S., & Castaing, P. (2002). Spectral signature of highly turbid waters. Application with SPOT data to quantify suspended particulate matter concentrations. Remote Sensing of Environment, 81, 149–161.

    Article  Google Scholar 

  • Duan, H. T., Ma, R. H., Zhang, Y. Z., & Zhang, B. (2009). Remote-sensing assessment of regional inland lake water clarity in northeast China. Limnology, 10, 135–141.

    Article  Google Scholar 

  • Ebenhoeh, W., Bekker, J. G. B., & Baretta, J. W. (1997). The primary production module in the marine ecosystem model ERSEM II, with emphasis on the light forcing. Journal of Sea Research, 38, 173–193.

    Article  Google Scholar 

  • Felde G. W., Anderson, G. P., Adler-Golden, S. M., Matthew, M. W., & Berk, A. (2003). Analysis of hyperion data with the FLAASH atmospheric correction algorithm: Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery. In Proceedings of SPIE, IX aerosense conference, Orlando (pp. 21–25).

  • Fettweis, M., Francken, F., Pison, V., & Eynde, V. D. D. (2006). Suspended particulate matter dynamics and aggregate sizes in a high turbidity area. Marine Geology, 235, 63–74.

    Article  Google Scholar 

  • Giardino, C., Pepe, M., Brivio, P. A., Ghezzi, P., & Zilioli, E. (2001). Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery. Science of the Total Environment, 268, 19–29.

    Article  CAS  Google Scholar 

  • Gilerson, A. A., Gitelsonm, A. A., Zhou, J., Gurlin, D., Moses, W., Ioannou, I., & Ahmed, S.A. (2010). Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Optical Express, 18(23), 24109–24125.

    Article  CAS  Google Scholar 

  • Gitelson, A. A., Dall’Olmo, G., Moses, W., Rundquist, D. C., Barrow, T., Fisher, T. R., et al. (2008). A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation. Remote Sensing of Environment, 112, 3582–3593.

    Article  Google Scholar 

  • Gitelson, A. A., Garbuzov, G., Szilagyi, F., Mittenzway, K. H., Karnieli, A., & Kaiser, A. (1993). Quantitative remote sensing methods for real-time monitoring of inland waters quality. International Journal of Remote Sensing, 14(7), 1269–1295.

    Article  Google Scholar 

  • Gitelson, A. A., Laorawat, S., Keydan, G. P., & Vonshank, A. (1995). Optical properties of dense algal culture outdoors and their application to remote estimation of biomass and pigment concentration in Spirulina platensis (Cyanobacteria). Journal of Phycology, 31, 828–834.

    Article  Google Scholar 

  • Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, 1–4.

    Article  Google Scholar 

  • Gitelson, A. A., Yacobi, Y. Z., Schalles, J. F., Rundquist, D. C., Han, L., Stark, R., et al. (2000). Remote estimation of phytoplankton density in productive waters. Archiv fuer Hydrobiologie-Special Issues Advancements in Limnology, 55, 121–136.

    CAS  Google Scholar 

  • Goetz, A. F. H., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147–11537.

    Article  CAS  Google Scholar 

  • Gons, H. J. (1999). Optical teledetection of chlorophyll a in turbid inland waters. Environmental Sciences & Technology, 33, 1127–1132.

    Article  CAS  Google Scholar 

  • Gons, H. J., Rijkeboer, M., & Ruddick, K. G. (2002). A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters. Journal of Plankton Research, 24, 947–951.

    Article  CAS  Google Scholar 

  • Gordon, H., & Morel, A. (1983). Remote assessment of ocean color for interpretation of satellite visible imagery: A review (pp. 3–33). New York: Springer.

    Google Scholar 

  • Gordon, H. R., Brown, O. B., & Jacobs, M. M. (1975). Computed relationship between the inherent and apparent optical properties of a flat homogeneous ocean. Applied Optics, 14, 417–427.

    Article  CAS  Google Scholar 

  • Hans, H., Haan, J., Jordans, R., Vos, R., Peters, S., & Rijkeboer, M. (2002). Towards airborne remote sensing of water quality in The Netherlands—validation and error analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 57(3), 171–183.

    Article  Google Scholar 

  • Han, L., & Rundquist, D. C. (1997). Comparison of NIR/red ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir. Remote Sensing of Environment, 62, 253–261.

    Article  Google Scholar 

  • Han, L., Rundquist, D. C., Liu, L. L., Fraser, R. N., & Schalles, J. F. (1994). The spectral responses of algal chlorophyll in water with varying levels of suspended sediment. International Journal of Remote Sensing, 15, 3707–3718.

    Article  Google Scholar 

  • Huret, M., Dadou, I., Dumas, F., Lazure P., & Garçon, V. (2005). Coupling physical and biogeochemical processes in the Río de la Plata plume. Continental Shelf Research, 25, 629–653.

    Article  Google Scholar 

  • James, L. M., & Giulietta, S. F. (2002). Ocean optical protocols for satellite ocean color sensor validation. Revision 3, Part II, NASA. Maryland: Goddard Space Flight Space Center.

  • Keiner, L. E., & Yan, X. H. (1998). Neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery. Remote Sensing of Environment, 66, 153–165.

    Article  Google Scholar 

  • Krasnopolsky, V. M. (2007). Neural network emulations for complex multidimensional geophysical mappings: Applications of neural network techniques to atmospheric and oceanic satellite retrievals and numerical modeling. Review of Geophysics, 45, RG3009.

    Article  Google Scholar 

  • Lacroix, G., Ruddick, K., Park, Y., Gypens, N., & Lancelot, C. (2007). Validation of the 3D biogeochemical model MIRO&CO with field nutrient and phytoplankton data and MERIS-derived surface chlorophyll a images. Journal of Marine Systems, 64(1–4), 66–88.

    Article  Google Scholar 

  • Lazure, P., Garnier, V., Dumas, F., Herry, C., & Chifflet, M. (2009). Development of a hydrodynamic model of the Bay of Biscay. Validation of hydrology. Continental Shelf Research, 29(8), 985–997.

    Article  Google Scholar 

  • Lee, Z. P., & Carder, K. L. (2004). Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance. Remote Sensing of Environment, 89, 361–368.

    Article  Google Scholar 

  • Mittenzwey, K. H., Ullrich, S., Gitelson, A. A., & Kondratiev, K. Y. (1992). Determination of chlorophyll a of inland waters on the basis of spectral reflectance. Limnology and Oceanography, 37(1), 147–149.

    Article  Google Scholar 

  • Miehle, P., Livesley, S. J., Liw, C., Feikemaz, P. M., Adams, M. A., & Arndt, S. K. (2006). Quantifying uncertainty from large-scale model predictions of forest carbon dynamics. Global Change Biology, 12, 1421–1434.

    Article  Google Scholar 

  • Mobley, C. D. (1999). Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38, 7442–7455.

    Article  CAS  Google Scholar 

  • Morel, A. (2001). Bio-optical models. In J. H. Steele, K. K. Turekian, & S. A. Thorpe (Eds.), Encyclopedia of ocean sciences (pp. 317–326). New York: Academic.

    Chapter  Google Scholar 

  • Morel, A., & Prieur, L. (1977). Analysis of variations in ocean color. Limnology and Oceanography, 22, 709–722.

    Article  Google Scholar 

  • Nechad, B., Ruddick, K. G., & Park, Y. (2010). Calibration and validation of a generic multisensory algorithm for mapping of total suspended matter in turbid waters. Remote Sensing of Environment, 114, 854–866.

    Article  Google Scholar 

  • Paerl, H. W., & Huisman, J. (2008). Climate—blooms like it hot. Science, 320, 57–58.

    Article  CAS  Google Scholar 

  • Panigrahi, S., Wikner, J., Panigrahy, R. C., Satapathy, K. K., & Acharya, B. C. (2009). Variability of nutrients and phytoplankton biomass in a shallow brackish water ecosystem (Chilika Lagoon, India). Limnology, 10, 73–85.

    Article  CAS  Google Scholar 

  • Pattiaratchi, C., Avery, P., & Wyllie, A. (1994). Estimates of water quality in coastal waters using multi-date Landsat Thematic Mapper data. International Journal of Remote Sensing, 15, 1571–1584.

    Article  Google Scholar 

  • Rundquist, D. C., Han, L., Schalles, J. F., & Peake, J. S. (1996). Remote measurement of algal chlorophyll in surface waters: The case for the first derivative of reflectance near 690 nm. Photogrammetric Engineering & Remote Sensing, 62, 195–200.

    Google Scholar 

  • Sabine, T., & Kaufmann, H. (2002). Lake water quality monitoring using hyperspectral airborne data—a semi-empirical multisensor and multitemporal approach for the Mechlenburg Kake District, Germany. Remote Sensing of Environment, 81, 228–237.

    Article  Google Scholar 

  • Schalles, J. F., & Yacobi, Y. Z. (2000). Remote detection and seasonal patterns of phycocynin, carotenoid and chlorophyll pigments in eutrophic waters. Archiv fuer Hydrobiologie-Special Issues Advancements in Limnology, 55, 153–168.

    CAS  Google Scholar 

  • Simis, S. G., Peters, S. W., & Gons, H. (2005). Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnology and Oceanography, 50, 237–245.

    Article  CAS  Google Scholar 

  • Song, K. S., Liu, D. W., Li, L., Wang, Z. M., Wang, Y. D., & Jiang, G. J. (2010). Spectral absorption properties of colored dissolved organic matter (CDOM) and total suspended matter (TSM) of inland waters. Proceedings of SPIE, 7811, 78110B–78110B-13.

    Google Scholar 

  • Sun, D., Li, Y. M., & Wang, Q. (2009). A unified model for remotely estimating chlorophyll a in Lake Taihu, China, based on SVM and in situ hyperspectral data. IEEE Transaction on Geoscience and Remote Sensing, 47(8), 2957–2965.

    Article  Google Scholar 

  • Tang, Y. L., Zhang, G. X., Yang, Y. S., & Gao, Y. Z. (2009). Identifying key environmental factors influencing spatial variation of water quality in upper Shitoukoumen Reservoir Basin in Jilin Province, China. Chinese Geographical Science, 19(4), 365–374.

    Article  Google Scholar 

  • Taylor, J. R. (1997). An introduction to error analysis: The study of uncertainties in physical measurements (2nd ed.). Sausalito, California: University Science Books.

    Google Scholar 

  • Tyler, A. N., Svab, E., Preston, T., Présing, M., & Kovács, W. A. (2006). Remote sensing of the water quality of shallow lakes: A mixture modelling approach to quantifying phytoplankton in water characterized by high-suspended sediment. International Journal of Remote Sensing, 27(8), 1521–1537.

    Article  Google Scholar 

  • Wang, Q. J., & Tian, J. J. (2007). Quality evaluation of LISS3 image from IRS-P6 Satellite. Geography and Geo-Information Science, 23(3), 11–14.

    Google Scholar 

  • Wang, Y. P., Xia, H., Fu, J. & Sheng, G. Y. (2004). Water quality change in reservoirs of Shenzhen, China: Detection using LANDSAT/TM data. Science of the Total Environment, 328, 195–206.

    Article  CAS  Google Scholar 

  • Williams, P. C. (2001). Implementation of near infrared technology. In P. C. Williams, & K. H. Norris (Eds.), Near infrared technology in the agricultural and food industries (pp. 145–171). St Paul, Minnesota: American Association of Cereal Chemists.

    Google Scholar 

  • Woodruff, D. L., Stumpf, R. P., Scope, J. A., & Paerl, H. W. (1999). Remote estimation of water clarity in optically complex estuarine waters. Remote Sensing of Environment, 68, 41–52.

    Article  Google Scholar 

  • Xing, K. X., Guo, H. C., Sun, Y. F., & Huang, Y. T. (2005). Assessment of the spatial-temporal eutrophic character in the Lake Dianchi. Journal of Geographical Sciences, 15(1), 37–43.

    Google Scholar 

  • Xu, J. P., Zhang, B., Li, F., Song, K. S., Wang, Z. M., Liu, D. W., et al. (2009a). Estimation of Chlorophyll-a concentration using field spectral data: A case study in inland Case-II waters, North China. Environmental Monitoring and Assessment, 158, 105–116.

    Article  CAS  Google Scholar 

  • Xu, J. P., Zhang, B., Li, F., Song, K. S., Wang, Z. M., Liu, D. W., et al. (2009b). Retrieval of total suspended matters using field spectral data in Shitoukoumen Reservoir, China. Chinese Geographic Sciences, 19(1), 77–82.

    Article  Google Scholar 

  • Yang, W., Matsushit, B., Chen, J., & Fukushima, T. (2011). Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables. Remote Sensing of Environment, 115, 1247–1259.

    Article  Google Scholar 

  • Zhang, Y., Pulliainen, J., Koponen, S., & Hallikainen, M. (2000). Water quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland. IEEE Transactions on Geoscience and Remote Sensing, 41(3), 622–629.

    Article  Google Scholar 

  • Zhang, Y., Pulliainen, J., Koponen, S., & Hallikainen, M. (2002). Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sensing of Environment, 81, 327–336.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China, 130012

    Kaishan Song, Zongming Wang, Dianwei Liu, Bai Zhang, Jia Du & Yuandong Wang

  2. Department of Earth Sciences, Indiana University–Purdue University, Indianapolis, IN, 46246, USA

    Kaishan Song, Lin Li, Linhai Li & Shuai Li

  3. Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, P.O. Box 9718, China, 100101

    Jingping Xu

Authors
  1. Kaishan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zongming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dianwei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jingping Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jia Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Linhai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shuai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yuandong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaishan Song.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, K., Li, L., Wang, Z. et al. Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources. Environ Monit Assess 184, 1449–1470 (2012). https://doi.org/10.1007/s10661-011-2053-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-011-2053-3

Keywords