Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (10): 2062-2077.doi: 10.12082/dqxxkx.2020.190547

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A Remote Sensing Method for Retrieving Chlorophyll-a Concentration from River Water Body

LIU Weihua1,2(), WANG Siyuan1,*(), MA Yuanxu1,2, SHEN Ming1,2, YOU Yongfa1,2, HAI Kai3, WU Linlin4   

  1. 1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Fuzhou University, Fuzhou 350002, China
    4. East China University of Technology, Nanchang 330013, China
  • Received:2019-09-25 Revised:2019-12-11 Online:2020-10-25 Published:2020-12-25
  • Contact: WANG Siyuan;
  • Supported by:
    The Strategic Priority Research Program of the Chinese Academy of Sciences: CAS Earth Big Data Science Project(XDA19030501);The Second Comprehensive Scientific Investigation of the Tibetan Plateau(2019QZKK0204)


Chlorophyll-a (Chl-a) is an important indicator to evaluate water quality security. The accurate estimation of its concentration is of major significance to aquaculture development, aquatic ecosystem sustainability, and human drinking water safety. With the enhancement of the spatial and spectral resolution of earth-observed satellite sensor, remote sensing technology is exerting a growing important effect on monitoring the spatiotemporal changes of water quality in rivers. In this study, we synchronously measured water spectrum and collected water samples along the upper and middle reaches of the Kaidu River and around some small lakes in the Bayanbulak Wetland. Chlorophyll-a concentration and turbidity were measured for each sample in the laboratory. Based on the water reflectance spectrum and measured chlorophyll-a, we initially performed the sensitivity analysis of spectrum band to the concentration of chlorophyll-a, and then established various spectral index models, including band differences, ratios, and difference-sum ratios. Then Chl-a=4.50 mg/m3 was proposed as a hierarchical threshold for dividing waters samples into two groups and 11 empirical and semi-analytical chlorophyll-a retrieval models after calibration were applied to all sample datasets and the two separate datasets with relatively high and low chlorophyll-a concentrations to evaluate their accuracy. The optimal linear relationship between the independent variable (D3B) of three-band semi-analytical model and chlorophyll-a determined that D3B=-0.051 could be regarded as an indicator to classify waters with different chlorophyll-a concentrations. According to the performance of all the models, we ultimately selected the D3B model for high chlorophyll-a concentration waters and the blue-green band ratio model for low chlorophyll-a concentration waters, resulting in the hierarchical retrieval algorithm OC2-D3B. Its accuracy (R2=0.96, RMSE=0.32 mg/m3, MAE=0.24 mg/m3, and MRE=5.71%) was greatly improved compared with other single algorithms. Finally, we analyzed the spatial distribution and seasonal pattern of chlorophyll-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicate that the chlorophyll-a concentration in lake was higher than that in river, and the highest chlorophyll-a concentration usually appeared in summer, followed by spring and autumn, while the lowest chlorophyll-a concentration occurred in winter. Based on observational data from the Bayanbulak meteorological station, we further analyzed the effects of three environmental factors of temperature, precipitation, and sunshine duration on the chlorophyll-a concentration in the wetland river. The results show that the correlation coefficient between temperature and chlorophyll-a concentration reached 0.88, which was much higher than the other two factors. Thus, it seems that temperature was the main factor affecting chlorophyll-a concentration to some extent. In addition, this study could provide technical support for water environmental protection and water resource regulation in Bayanbulak.

Key words: remote sensing, spectrum analysis, wetland river, model evaluation, chlorophyll-a retrieval, spatiotemporal distribution, meteorological factor, Bayanbulak