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Spatiotemporal dynamics of chlorophyll-a in a large reservoir as derived from Landsat 8 OLI data: understanding its driving and restrictive factors

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Abstract

Chlorophyll-a (Chla) is an important indicator of water quality and eutrophication status. Monitoring Chla concentration (C Chla ) and understanding the interactions between C Chla and related environmental factors (hydrological and meteorological conditions, nutrients enrichment, etc.) are necessary for assessing and managing water quality and eutrophication. An acceptable Landsat 8 OLI-based empirical algorithm for C Chla has been developed and validated, with a mean absolute percentage error of 14.05% and a root mean square error of 1.10 μg L−1. A time series of remotely estimated C Chla was developed from 2013 to 2015 and examined the relationship of C Chla to inflow rate, rainfall, temperature, and sunshine duration. Spatially, C Chla values in the riverine zone were higher than in the transition and lacustrine zones. Temporally, mean C Chla value were ranked as spring > summer > autumn > winter. A significant positive correlation [Pearson correlation coefficient (r) = 0.88, p < 0.001] was observed between the inflow rate and mean C Chla in the northwest segment of the Xin’anjiang Reservoir. However, no significant relation was observed between mean C Chla and meteorological conditions. Mean (± standard deviation) value for the ratio of total nitrogen concentration to total phosphorus concentration in our in situ dataset is 75.75 ± 55.72. This result supports that phosphorus is the restrictive factor to algal growth in Xin’anjiang Reservoir. In addition, the response of nutrients to Chla has spatial variabilities. Current results show the potential of Landsat 8 OLI data for estimating Chla in slight turbid reservoir and indicate that external pollution loading is an important driving force for the Chla spatiotemporal variability.

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Acknowledgements

This study was jointly funded by the National Natural Science Foundation of China (grants 41325001, 41621002, and 41501374) and the Zhejiang Provincial Natural Science Foundation of China (LQ16D010001). The authors would like to thank Zhixu Wu, Mingliang Liu, Gang Liu, Yan Yin, and Yang Bai for their participation in the field samples collection and experimental analysis. We are grateful to the two anonymous reviewers and the editor Philippe Garrigues for their constructive comments and suggestions to improve the quality of this work.

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Authors and Affiliations

  1. Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing, 210008, People’s Republic of China

    Yuan Li, Yunlin Zhang, Kun Shi, Yongqiang Zhou, Yibo Zhang & Xiaohan Liu

  2. School of Tourism and City Management, Zhejiang Gongshang University, Hangzhou, 310018, China

    Yuan Li

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yongqiang Zhou, Yibo Zhang & Xiaohan Liu

  4. College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou, 450002, China

    Yulong Guo

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  1. Yuan Li
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  2. Yunlin Zhang
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  3. Kun Shi
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Correspondence to Yunlin Zhang.

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Responsible editor: Philippe Garrigues

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Li, Y., Zhang, Y., Shi, K. et al. Spatiotemporal dynamics of chlorophyll-a in a large reservoir as derived from Landsat 8 OLI data: understanding its driving and restrictive factors. Environ Sci Pollut Res 25, 1359–1374 (2018). https://doi.org/10.1007/s11356-017-0536-7

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