Environmental Monitoring and Assessment

, Volume 170, Issue 1–4, pp 231–244

Comparison of different semi-empirical algorithms to estimate chlorophyll-a concentration in inland lake water

  • Hongtao Duan
  • Ronghua Ma
  • Jingping Xu
  • Yuanzhi Zhang
  • Bai Zhang
Article

Abstract

Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, a comparison of several existing semi-empirical algorithms to determine chlorophyll-a (Chl-a) content was made by applying them to the field spectra and in situ chlorophyll measurements. Results indicated that the first derivative of reflectance was well correlated with Chl-a. The highest correlation between the first derivative and Chl-a was at 680 nm. The two-band model, NIR/red ratio of R710/670, was also an effective predictor of Chl-a concentration. Since the two-band ratios model is a special case of the three-band model developed recently, three-band model in Lake Chagan showed a higher resolution. The new algorithm named reverse continuum removal relies on the reflectance peak at 700 nm whose shape and position depend strongly upon chlorophyll concentration: The depth and area of the peak above a baseline showed a linear relationship to Chl-a concentration. All of the algorithms mentioned proved to be of value and can be used to predict Chl-a concentration. Best results were obtained by using the algorithms of the first derivative, which yielded R2 around 0.74 and RMSE around 6.39 μg/l. The two-band and three-band algorithms were further applied to MERIS when filed spectral were resampled with regard to their center wavelengths. Both algorithms showed an adequate precision, and the differences on the outcome were small with R2 = 0.70 and 0.71.

Keywords

Field spectral Lake Chagan Continuum removal Three-band model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arenz, R. F., Lewis, W. M., & Saunders, J. F. (1996). Determination of chlorophyll and dissolved organic carbon from reflectance data for Colorado reservoirs. International Journal of Remote Sensing, 17, 1547–1566. doi:10.1080/01431169608948723.CrossRefGoogle Scholar
  2. Dall’Olmo, G., & Gitelson, A. A. (2006). Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters: Modeling results. Apply Optics, 45, 3577–3592. doi:10.1364/AO.45.003577.CrossRefGoogle Scholar
  3. Dekker, A. G., Malthus, T. J., & Seyhan, E. (1991). Quantitative modeling of inland water quality for high resolution MSS systems. IEEE Transaction on Geoscience and Remote Sensing, 29, 89–95. doi:10.1109/36.103296.CrossRefGoogle Scholar
  4. Doerffer, R., & Schiller, H. (2007). The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28, 517–535. doi:10.1080/01431160600821127.CrossRefGoogle Scholar
  5. Duan, H., Zhang, Y., Zhang, B., Song, K., Wang, Z., Liu, D., et al. (2008). Estimation of chlorophyll-a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements. International Journal of Remote Sensing, 29, 767–786. doi:10.1080/01431160701355249.CrossRefGoogle Scholar
  6. Feng, H., Campbell, J. W., Dowell, M. D., & Moore, T. S. (2005). Modeling spectral reflectance of optically complex waters using bio-optical measurements from Tokyo Bay. Remote Sensing of Environment, 99, 232–243. doi:10.1016/j.rse.2005.08.015.Google Scholar
  7. Fraser, R. N. (1998). Hyperspectral remote sensing of turbidity and chlorophyll a among Nebraska Sand Hills lakes. International Journal of Remote Sensing, 19, 1579–1589. doi:10.1080/014311698215360.CrossRefGoogle Scholar
  8. 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. The Science of the Total Environment, 268, 19–29. doi:10.1016/S0048-9697(00)00692-6.CrossRefGoogle Scholar
  9. Gitelson, A. (1992). The peak near 700 nm on radiance spectra of algae and water: Relationship of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13, 3367–3373. doi:10.1080/01431169208904125.CrossRefGoogle Scholar
  10. Gitelson, A., Garbuzov, G., Szilagyi, F., Mittenzwey, K.-H., & Karnieli, A. (1993). Quantitative remote sensing methods for real-time monitoring of inland waters quality. International Journal of Remote Sensing, 14, 1269–1295. doi:10.1080/01431169308953956.CrossRefGoogle Scholar
  11. Gitelson, A., Laorawat, S., Keydan, G., & Vonshak, A. (1995). Optical properties of dense algal cultures outdoors and its application to remote estimation of biomass and pigment concentration in Spirulina platensis. Journal of Phycology, 31, 828–834. doi:10.1111/j.0022-3646.1995.00828.x.CrossRefGoogle Scholar
  12. 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. doi:10.1016/j.rse.2008.04.015.CrossRefGoogle Scholar
  13. Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. (2003). Relationship between leaf chlorophyll content and spectral reflectance and algorithms for nondestructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271–282. doi:10.1078/0176-1617-00887.CrossRefGoogle Scholar
  14. Gordon, H. (1979). Diffusive reflectance of the ocean: The theory of its augmentation by chlorophyll-2a fluorescence at 685 nm. Applied Optics, 18, 1161–1166.CrossRefGoogle Scholar
  15. Gower, J. F. R., & Borstad, G. A. (2004). On the potential of MODIS and MERIS for imaging chlorophyll fluorescence from space. International Journal of Remote Sensing, 25, 1459–1464. doi:10.1080/01431160310001592445.CrossRefGoogle Scholar
  16. Han, L. (2005). Estimating chlorophyll-a concentration using first-derivative spectra in coastal water. International Journal of Remote Sensing, 26, 5235–5244. doi:10.1080/01431160500219133.CrossRefGoogle Scholar
  17. 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. doi:10.1016/S0034-4257(97)00106-5.CrossRefGoogle Scholar
  18. Han, L., Rundquist, D. C., Liu, L. L., & Fraser, L. N. (1994). The spectral responses of algal chlorophyll in water with varying levels of suspended sediment. International Journal of Remote Sensing, 15, 3707–3718. doi:10.1080/01431169408954353.CrossRefGoogle Scholar
  19. Ingle, J. D., & Crouch, S. R. (1998). Spectrochemical analysis. Englewood Cliffs, New Jersey: Prentice Hall.Google Scholar
  20. Kokaly, R. F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75, 153–161. doi:10.1016/S0034-4257(00)00163-2.CrossRefGoogle Scholar
  21. Kokaly, R. F., & Clark, R. N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67, 267–287. doi:10.1016/S0034-4257(98)00084-4.CrossRefGoogle Scholar
  22. Koponen, S., Pulliainen, J., Kallio, K., & Hallikainen, M. (2002). Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment, 79, 51–59. doi:10.1016/S0034-4257(01)00238-3.CrossRefGoogle Scholar
  23. Koponen, S., Pulliainen, J., Servomaa, H., Zhang, Y., Hallikainen, M., Kallio, K., et al. (2001). Analysis on the feasibility of multisource remote sensing observations for Chl-a monitoring in Finish lakes. The Science of the Total Environment, 268, 95–106. doi:10.1016/S0048-9697(00)00689-6.CrossRefGoogle Scholar
  24. Kutser, T., Herlevi, A., Kallio, K., & Arst, H. (2001). A hyperspectral model for interpretation of passive optical remote sensing data from turbid lakes. The Science of the Total Environment, 268, 47–58. doi:10.1016/S0048-9697(00)00682-3.CrossRefGoogle Scholar
  25. Lee, Z. P., & Carder, K. L. (2000). Band-ratio or spectral-curvature algorithms for satellite remote sensing. Applied Optics, 39, 4377–4380. doi:10.1364/AO.39.004377.CrossRefGoogle Scholar
  26. Mittenzwey, K. H., Gitelson, A., Ullrich, S., & Kondratyev, K. Y. (1992). Determination of chlorophyll a of inland waters on the basis of spectral reflectance. Limnology and Oceanography, 37, 147–149.CrossRefGoogle Scholar
  27. Mutanga, O., Skidmore, A. K., & Van Wieren, S. (2003). Discriminating tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. ISPRS Journal of Photogrammetry and Remote Sensing, 57, 263–272. doi:10.1016/S0924-2716(02)00158-2.CrossRefGoogle Scholar
  28. Pulliainen, J., Kallio, K., Eloheimo, K., Koponen, S., Servomaa, H., Hannonen, T., et al. (2001). A semi-operative approach to lake water quality retrieval from remote sensing data. The Science of the Total Environment, 268, 79–93. doi:10.1016/S0048-9697(00)00687-2.CrossRefGoogle Scholar
  29. Richardson, L. (1996). Remote sensing of algal bloom dynamics. BioScience, 46, 492–501.CrossRefGoogle Scholar
  30. Ritchie, J. C., Cooper, C. M., & Schiebe, F. R. (1990). The relationship of MSS and TM digital data with suspended sediments, chlorophyll, and temperature in Moon Lake, Mississippi. Remote Sensing of Environment, 33, 137–148. doi:10.1016/0034-4257(90)90039-O.CrossRefGoogle Scholar
  31. Schalles, J. F., Gitelson, A., Yacobi, Y. Z., & Kroenke, A. E. (1998). Estimation of chlorophyll from time series measurements of high spectral resolution reflectance in an eutrophic lake. Journal of Phycology, 34, 383–390. doi:10.1046/j.1529-8817.1998.340383.x.CrossRefGoogle Scholar
  32. Schmidt, K. S., & Skidmore, A. K. (2001). Exploring spectral discrimination of grass species in African rangelands. International Journal of Remote Sensing, 22, 3421–3434. doi:10.1080/01431160152609245.CrossRefGoogle Scholar
  33. Sørensen, K., Aas, E., & Høkedal, J. (2007). Validation of MERIS water products and bio-optical relationships in the Skagerrak. International Journal of Remote Sensing, 28, 555–568. doi:10.1080/01431160600815566.CrossRefGoogle Scholar
  34. Wernand, M. R., Shimwell, S. J., Boxall, S., & Van Aken, H. M. (1998). Evaluation of specific semi-empirical coastal colour algorithms using historic data sets. Aquatic Ecology, 32, 73–91. doi:10.1023/A:1009946501534.CrossRefGoogle Scholar
  35. Xu, J. P., Li, F., Zhang, B., Song, K. S., Wang, Z. M., Liu, D. W., et al. (2009). Estimation of chlorophyll-a concentration using field spectral data: A case study in inland Case-II waters, North China. Environmental Monitoring and Assessment. doi:10.1007/s10661-008-0568-z.Google Scholar
  36. Zhang, Y., Koponen, S., Pulliainen, J., & 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. doi:10.1016/S0034-4257(02)00009-3.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Hongtao Duan
    • 1
  • Ronghua Ma
    • 1
  • Jingping Xu
    • 2
  • Yuanzhi Zhang
    • 3
  • Bai Zhang
    • 4
  1. 1.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina
  2. 2.State Key Laboratory of Remote Sensing ScienceJointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal UniversityBeijingChina
  3. 3.Institute of Space and Earth Information ScienceThe Chinese University of Hong KongShatinHong Kong
  4. 4.Northeast Institute of Geography and Agricultural EcologyChinese Academy of SciencesChangchunChina

Personalised recommendations