Remote sensing estimation of chlorophyll-a concentration in Taihu Lake considering spatial and temporal variations

  • Chunmei Cheng
  • Yuchun WeiEmail author
  • Guonian Lv
  • Ning Xu


The estimation of chlorophyll-a concentrations (Chla) in lakes using remote sensing is convenient, but its use remains challenging in large eutrophic water bodies due to the great spatial and temporal variations of its optical properties. Combining the sampling location and date information with Chla data, this study divided the lake water into three types, I, II and III, and then built an optimal Chla estimation model for each type based on 11 datasets collected from 2004 to 2012 in Taihu Lake, China. The resultant model expression is Chla = exp (ax2 + bx + c), where x is R701/R677, (1/R686–1/R695) × R710 and (R690/R550–R675/R700) / (R690/R550 + R675/R700). For the Chla ranging from 2 to 192 mg/m3, the root-mean-square error (RMSE) of the new model decreased up to 5.1 mg/m3 compared to that of previous band combination models, such as band ratio, three-band and four-band models when directly validated. The RMSE of the re-parametrization model (the lowest RMSE < 12 mg/m3) is also lower than for those models (the lowest RMSE > 16 mg/m3), indicating that the Chla estimation model that considers the spatial and temporal variations has a better performance and validation accuracy and therefore is more effective for remote sensing monitoring of water quality.


Water quality Hyperspectral remote sensing Data partition Estimation model Case II water 


Funding information

The study was supported by the National Natural Science Foundation of China (No. 41471283). We would like to express our gratitude to Wang Lei, Zhang Xiaowei, Zhou Yu and Sun Xiaopeng for field work and to Zhang Jing for laboratory work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chunmei Cheng
    • 1
    • 2
  • Yuchun Wei
    • 2
    • 3
    Email author
  • Guonian Lv
    • 2
    • 3
  • Ning Xu
    • 4
  1. 1.School of Geomatics and Municipal EngineeringZhejiang University of Water Resources and Electric PowerHangzhouPeople’s Republic of China
  2. 2.Key Laboratory of Virtual Geographic Environment, Ministry of EducationNanjing Normal UniversityNanjingPeople’s Republic of China
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingPeople’s Republic of China
  4. 4.Water Resource Protection Research Institute of Haihe River Water Conservancy CommissionTianjinPeople’s Republic of China

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