, Volume 661, Issue 1, pp 235–250 | Cite as

Temporal and spatial variability of chlorophyll a concentration in Lake Taihu using MODIS time-series data

  • Yuchao Zhang
  • Shan Lin
  • Xin Qian
  • Qin’geng Wang
  • Yu Qian
  • Jianping Liu
  • Yi Ge
Primary research paper


In order to predict the distribution of chlorophyll a synoptically in Lake Taihu from 2006 to 2008, a common empirical algorithm was developed to relate time series chlorophyll a concentrations in the lake to reflectance derived as a function of band 2 MODIS data (r 2 = 0.907, n = 145) using time series from 2005. The empirical model was further validated with chlorophyll a data from a 2008 to 2009 dataset, with RMSE < 7.48 μg l−1 and r 2 = 0.978. The seasonal and inter-annual variability of the surface chlorophyll a concentration from 2006 to 2008 was then examined using Empirical Orthogonal Function (EOF) analysis. The results revealed that the first four modes were significant, explaining 54.0% of the total chlorophyll a variance, and indicated that during the summer, algal blooms always occur in the northern bays, Meiliang Bay and Gonghu Bay, while they occur along the southwestern lakeshore during early summer, fall, and even early winter. The inter-annual variance analysis showed that the duration of algal blooms was from April to December of 2007, which was different from the bloom periods in 2006 and 2008. The results of EOF analysis show its potential for long-term integrated lake monitoring, not only in Lake Taihu but also in other large lakes threatened by accelerating eutrophication.


Algal blooms Chlorophyll a Empirical algorithms EOF analysis 



We are grateful to NASA for providing the MODIS-Terra surface reflectance daily level 2 products (MOD09), and to the Taihu Ecosystem Research and Field Observation Station, Chinese Academy of Sciences, for providing field measurement data. This study was funded by the National Basic Research Program of China (“973” Program), 2008CB418003, the National High Technology Research and Development Program of China, 2007AA06A405, and the Chinese National Science Foundation, 40701005. We thank Associate Prof. Heng Lu from Nanjing Normal University for his helpful advice and we thank two anonymous reviewers for their careful reviews and constructive suggestions.


  1. Chen, Y. W. & X. Y. Gao, 2000. Comparison of two methods for phytoplankton chlorophyll-a concentration measurement. Hupo Kexue 12: 185–188 (in Chinese).Google Scholar
  2. Chen, Y. W., C. X. Fan, K. Teubner & M. Dokullil, 2003. Changes of nutrients and phytoplankton chlorophyll-a in a large shallowlake, Taihu, China: an 8-year investigation. Hydrobiologia 506: 273–279.CrossRefGoogle Scholar
  3. Dall’Olmo, G., A. A. Gitelson, D. C. Rundquist, B. Leavitt, T. Barrow & J. C. Holz, 2005. Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands. Remote Sensing of Environment 96: 176–187.CrossRefGoogle Scholar
  4. Duan, H., S. Mang & Y. Mang, 2008. Cyanobacteria bloom monitoring with remote sensing in Lake Taihu. Hupo Kexue 20: 145–152 (in Chinese).Google Scholar
  5. 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: 3367–3373.CrossRefGoogle Scholar
  6. Gitelson, A. A., D. Gurlin, W. J. Moses & T. Barrow, 2009. A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environmental Research Letters 4: 045003.CrossRefGoogle Scholar
  7. Han, L. H. & D. C. Rundquist, 1994. The response of both surface reflectance and the underwater light field to various levels of suspended sediments – preliminary results. Photogrammetric Engineering and Remote Sensing 60: 1463–1471.Google Scholar
  8. Hu, C. M., Z. Q. Chen, T. D. Clayton, P. Swarzenski, J. C. Brock & F. E. Muller-Karger, 2004. Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL. Remote Sensing of Environment 93: 423–441.CrossRefGoogle Scholar
  9. Hu, W. P., S. E. Jorgensen & F. B. Zhang, 2006. A vertical-compressed three-dimensional ecological model in Lake Taihu, China. Ecological Modelling 190: 367–398.CrossRefGoogle Scholar
  10. IOCCG, 2000. Remote sensing of ocean colour in coastal, and other optically-complex, waters. In Sathyendranath, S. (ed.), Reports of the International Ocean-Colour Coordinating Group. IOCCG, Dartmouth, Canada.Google Scholar
  11. Jiao, H. B., Y. Zha, Y. M. Li, J. Z. Huang & Y. C. Wei, 2006. Modelling chlorophyll-a concentration in Lake Taihu from hyperspectral reflectance data. Journal of Remote Sensing 10: 242–248 (in Chinese).Google Scholar
  12. Kong, F. X. & G. Gao, 2005. Hypothesis on cyanobacteria bloom-forming mechanism in large shallow eutrophic lakes. Acta Ecologica Sinica 25: 589–595 (in Chinese).Google Scholar
  13. Le, C. F., Y. M. Li, D. Y. Sun, H. J. Wang & C. C. Huang, 2008. Spatio-temporal distribution of chlorophyll a concentration and its estimation in Lake Taihu. Huanjing Kexue 29: 619–626 (in Chinese).PubMedGoogle Scholar
  14. Le, C. F., Y. M. Li & Y. Zha, 2009a. Specific absorption coefficient and the phytoplankton package effect in Lake Taihu, China. Hydrobiologia 619: 27–37.CrossRefGoogle Scholar
  15. Le, C. F., Y. M. Li, Y. Zha, D. Y. Sun, C. C. Huang & H. Lu, 2009b. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Lake Taihu, China. Remote Sensing of Environment 113: 1175–1182.CrossRefGoogle Scholar
  16. Li, Y. M., J. Z. Huang, Y. C. Wei, W. N. Lu & J. Z. Shi, 2006. Evaluating eutrophic state of Lake Taihu by in situ hyperspectra. Huanjing Kexue 27: 1770–1775 (in Chinese).PubMedGoogle Scholar
  17. Luo, L. & B. Qin, 2004. Numerical simulation based on a three-dimensional shallow-water hydrodynamic model-current circulations in Lake Taihu with prevailing wind-forcing. Journal of Hydrodynamics (Serial B) 16: 341–349.Google Scholar
  18. Luo, L., B. Qin & G. Zhu, 2004a. Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow LakeTaihu, China. Journal of Environmental Sciences 16: 908–911 (in Chinese).Google Scholar
  19. Luo, L., B. Qin, G. Zhu & Y. Zhang, 2004b. The current pattern of Meiliang Bay in the winter time. Hupo Kexue 16: 73–76 (in Chinese).Google Scholar
  20. Ma, R. H. & J. Dai, 2005a. Chlorophyll-a concentration estimation with field spectra of water body near Meiliang Bay in Lake Taihu. Journal of Remote Sensing 9: 78–86 (in Chinese).Google Scholar
  21. Ma, R. H. & J. F. Dai, 2005b. Investigation of chlorophyll-a and total suspended matter concentrations using Landsat ETM and field spectral measurement in Lake Taihu, China. International Journal of Remote Sensing 26: 2779–2795.CrossRefGoogle Scholar
  22. Ma, R., J. Tang & J. Dai, 2006. Bio-optical model with optimal parameter suitable for Lake Taihu in water colour remote sensing. International Journal of Remote Sensing 27: 4305–4328.CrossRefGoogle Scholar
  23. Ma, R., F. Kong, H. Duan, S. Zhang, W. Kong & J. Hao, 2008. Spatio-temporal distribution of cyanobacteria blooms based on satellite imageries in Lake Taihu, China. Hupo Kexue 20: 687–694 (in Chinese).Google Scholar
  24. Miller, R. L. & B. A. McKee, 2004. Using MODIS terra 250 imagery to map concentrations of total suspended matter in coastal waters. Remote Sensing of Environment 93: 259–266.CrossRefGoogle Scholar
  25. Navarro, G. & J. Ruiz, 2006. Spatial and temporal variability of phytoplankton in the Gulf of Cadiz through remote sensing images. Deep-Sea Research Part II – Topical Studies in Oceanography 53: 1241–1260.CrossRefGoogle Scholar
  26. North, G. R., T. L. Bell, R. F. Cahalan & F. J. Moeng, 1982. Sampling errors in the estimation of empirical orthogonal functions. Monthly Weather Review 110: 699–706.CrossRefGoogle Scholar
  27. Oyama, Y., B. Matsushita, T. Fukushima, K. Matsushige & A. Imai, 2009. Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data. ISPRS Journal of Photogrammetry and Remote Sensing 64: 73–85.CrossRefGoogle Scholar
  28. Qin, B., W. Hu, W. Chen, J. Ji, C. Fan, Y. Chen, X. Gao, L. Yang, G. Gao, W. Huang, J. Jiang, S. Zhang, Y. Liu & Z. Zhou, 2000. Studies on the hydrodynamic processes and related factors in Meiliang Bay, Northern Taihu. Hupo Kexue 12: 327–334 (in Chinese).Google Scholar
  29. Qin, B. Q., P. Z. Xu, Q. L. Wu, L. C. Luo & Y. L. Zhang, 2007. Environmental issues of Lake Taihu, China. Hydrobiologia 581: 3–14.CrossRefGoogle Scholar
  30. Qin, B. Q., G. W. Zhu, G. Gao, Y. L. Zhang, W. Li, H. W. Paerl & W. W. Carmichael, 2010. A drinking water crisis in Lake Taihu, China: linkage to climatic variability and lake management. Environmental Management 45: 105–112.CrossRefPubMedGoogle Scholar
  31. Shu, X. Z., Q. Yin & D. B. Kuang, 2000. Relationship between algal chlorophyll concentration and spectral reflectance of inland waters. Journal of Remote Sensing 4: 41–45 (in Chinese)Google Scholar
  32. Svab, E., A. N. Tyler, T. Preston, M. Presing & K. V. Balogh, 2005. Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations. International Journal of Remote Sensing 26: 919–928.CrossRefGoogle Scholar
  33. Tyler, A. N., E. Svab, M. Preston & W. A. Kovacs, 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: 1521–1537.CrossRefGoogle Scholar
  34. Vermote, E. F. & A. Vermeulen, 1999. Atmospheric correction algorithm: spectral reflectance (MOD09). Algorithm Technical Background Document Version 4.0, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.Google Scholar
  35. Vermote, E. F., N. Z. El Saleous & C. O. Justice, 2002. Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sensing of Environment 83: 97–111.CrossRefGoogle Scholar
  36. Wang, Q. G., G. Gu & Y. Higano, 2006. Toward integrated environmental management for challenges in water environmental protection of Lake Taihu basin in China. Environmental Management 37: 579–588.CrossRefPubMedGoogle Scholar
  37. Wei, Y. C., J. Z. Huang, Y. M. Li & J. Guang, 2007. The hyperspectral data monitoring model of chlorophyll-a of summer in Lake Taihu, China. Journal of Remote Sensing 11: 756–762 (in Chinese).Google Scholar
  38. Yang, D. T., D. L. Pan & X. Y. Zhang, 2005. Retrieval of water quality parameters by hyperspectral remote sensing in Lake Taihu, China, Spie-Int Society Optical Engineering. In: sings of the Society of Photo-Optical Instrumentation Engineers, Bellingham: 431–439.Google Scholar
  39. Yang, M., J. W. Yu, Z. L. Li, Z. H. Guo, M. Burch & T. F. Lin, 2008. Lake Taihu not to blame for Wuxi’s woes. Science 319: 158–158.CrossRefPubMedGoogle Scholar
  40. Yentsch, S. C., 1984. Remote assessment of ocean color for interpretation of satellite visible imagery: a review. The Quarterly Review of Biology 59: 348.CrossRefGoogle Scholar
  41. Yoder, J. A., S. E. Schollaert & J. E. O’Reilly, 2002. Climatological phytoplankton chlorophyll and sea surface temperature patterns in continental shelf and slope waters off the northeast US coast. Limnology and Oceanography 47: 672–682.CrossRefGoogle Scholar
  42. Yu, H., Q. M. Cai & J. L. Wu, 2003. Study on characteristic of the absorption and scattering coefficients of Lake Taihu waters. Advances in Water Science 14: 46–49 (in Chinese).Google Scholar
  43. Yuan, D. & C. D. Elvidge, 1996. Comparison of relative radiometric normalization techniques. ISPRS Journal of Photogrammetry and Remote Sensing 51: 117–126.CrossRefGoogle Scholar
  44. Zhang, Y. L., M. L. Liu, B. Q. Qin, H. J. van der Woerd, J. S. Li & Y. L. Li, 2009. Modeling remote-sensing reflectance and retrieving chlorophyll-a concentration in extremely turbid case-2 waters (Lake Taihu, China). IEEE Transactions on Geoscience and Remote Sensing 47: 1937–1948.CrossRefGoogle Scholar
  45. Zhu, G. W., 2008. Eutrophic status and causing factors for a large, shallow and subtropical Lake Taihu, China. Hupo Kexue 20: 21–26 (in Chinese).Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Yuchao Zhang
    • 1
  • Shan Lin
    • 1
  • Xin Qian
    • 1
  • Qin’geng Wang
    • 1
  • Yu Qian
    • 1
  • Jianping Liu
    • 1
  • Yi Ge
    • 1
  1. 1.State Key Laboratory of Pollution Control and Resource ReuseSchool of the Environment, Nanjing UniversityNanjingPeople’s Republic of China

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