IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16874-d1005082.html
   My bibliography  Save this article

Long-Term Dynamics of Chlorophyll-a Concentration and Its Response to Human and Natural Factors in Lake Taihu Based on MODIS Data

Author

Listed:
  • Zihong Qin

    (South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China
    School of Geography and Planning, Nanning Normal University, Nanning 530001, China
    These authors contributed equally to this work.)

  • Baozhen Ruan

    (School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Jian Yang

    (South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China)

  • Zushuai Wei

    (South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China)

  • Weiwei Song

    (South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China)

  • Qiang Sun

    (South China Institute of Environmental Science, Ministry of Ecology and Environment, NO.18 Ruihe RD., Guangzhou 510535, China)

Abstract

Chlorophyll-a plays an essential biochemical role in the eutrophication process, and is widely considered an important water quality indicator for assessing human activity’s effects on aquatic ecosystems. Herein, 20 years of moderate resolution imaging spectroradiometer (MODIS) data were applied to investigate the spatiotemporal patterns and trends of chlorophyll-a concentration (Chla) in the eutrophic Lake Taihu, based on a new empirical model. The validated results suggested that our developed model presented appreciable performance in estimating Chla, with a root mean square error (MAPE) of 12.95 μg/L and mean absolute percentage error (RMSE) of 29.98%. Long-term MODIS observations suggested that the Chla of Lake Taihu experienced an overall increasing trend and significant spatiotemporal heterogeneity during 2002–2021. A driving factor analysis indicated that precipitation and air temperature had a significant impact on the monthly dynamics of Chla, while chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were important driving factors and together explained more than 81% of the long-term dynamics of Chla. This study provides a 20 year recorded dataset of Chla for inland waters, offering new insights for future precise eutrophication control and efficient water resource management.

Suggested Citation

  • Zihong Qin & Baozhen Ruan & Jian Yang & Zushuai Wei & Weiwei Song & Qiang Sun, 2022. "Long-Term Dynamics of Chlorophyll-a Concentration and Its Response to Human and Natural Factors in Lake Taihu Based on MODIS Data," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16874-:d:1005082
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16874/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/24/16874/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernanda Sayuri Yoshino Watanabe & Enner Alcântara & Thanan Walesza Pequeno Rodrigues & Nilton Nobuhiro Imai & Cláudio Clemente Faria Barbosa & Luiz Henrique da Silva Rotta, 2015. "Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images," IJERPH, MDPI, vol. 12(9), pages 1-27, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haobin Meng & Jing Zhang & Zhen Zheng, 2022. "Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression," IJERPH, MDPI, vol. 19(13), pages 1-26, June.
    2. Changchun Peng & Zhijun Xie & Xing Jin, 2024. "Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    3. Weidong Zhu & Fei Yang & Zhenge Qiu & Naiying He & Xiaolong Zhu & Yaqin Li & Yuelin Xu & Zhigang Lu, 2023. "Enhancing Forest Canopy Height Retrieval: Insights from Integrated GEDI and Landsat Data Analysis," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    4. Liang Zhang & Hugo A. Loáiciga & Meng Xu & Chao Du & Yun Du, 2015. "Kinetics and Mechanisms of Phosphorus Adsorption in Soils from Diverse Ecological Zones in the Source Area of a Drinking-Water Reservoir," IJERPH, MDPI, vol. 12(11), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16874-:d:1005082. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.