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Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China

Author

Listed:
  • Ze-Lin Na

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

  • Huan-Mei Yao

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

  • Hua-Quan Chen

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

  • Yi-Ming Wei

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

  • Ke Wen

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

  • Yi Huang

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

  • Peng-Ren Liao

    (School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China)

Abstract

Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R 2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8).

Suggested Citation

  • Ze-Lin Na & Huan-Mei Yao & Hua-Quan Chen & Yi-Ming Wei & Ke Wen & Yi Huang & Peng-Ren Liao, 2021. "Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China," Sustainability, MDPI, vol. 13(9), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4649-:d:540958
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    Cited by:

    1. Yun Xue & Yi-Min Wen & Zhong-Man Duan & Wei Zhang & Fen-Liang Liu, 2021. "Retrieval of Chlorophyll a Concentration in Water Considering High-Concentration Samples and Spectral Absorption Characteristics," Sustainability, MDPI, vol. 13(21), pages 1-14, November.

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