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Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China

Author

Listed:
  • Qiaozhen Guo

    (School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China)

  • Xiaoxu Wu

    (College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China)

  • Qixuan Bing

    (School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China)

  • Yingyang Pan

    (School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China)

  • Zhiheng Wang

    (School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China)

  • Ying Fu

    (School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China)

  • Dongchuan Wang

    (School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China)

  • Jianing Liu

    (College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China)

Abstract

The optical complexity of urban waters makes the remote retrieval of chlorophyll-a (Chl-a) concentration a challenging task. In this study, Chl-a concentration was retrieved using reflectance data of Landsat OLI images. Chl-a concentration in the Haihe River of China was obtained using mathematical regression analysis (MRA) and an artificial neural network (ANN). A regression model was built based on an analysis of the spectral reflectance and water quality sampling data. Remote sensing inversion results of Chl-a concentration were obtained and analyzed based on a verification of the algorithm and application of the models to the images. The analysis results revealed that the two models satisfactorily reproduced the temporal variation based on the input variables. In particular, the ANN model showed better performance than the MRA model, which was reflected in its higher accuracy in the validation. This study demonstrated that Landsat Operational Land Imager (OLI) images are suitable for remote sensing monitoring of water quality and that they can produce high-accuracy inversion results.

Suggested Citation

  • Qiaozhen Guo & Xiaoxu Wu & Qixuan Bing & Yingyang Pan & Zhiheng Wang & Ying Fu & Dongchuan Wang & Jianing Liu, 2016. "Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China," Sustainability, MDPI, vol. 8(8), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:8:p:758-:d:75490
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    References listed on IDEAS

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    1. Luciana Cavalcanti Maia Santos & Humberto Reis Matos & Yara Schaeffer-Novelli & Marília Cunha-Lignon & Marisa Dantas Bitencourt & Nico Koedam & Farid Dahdouh-Guebas, 2014. "Anthropogenic activities on mangrove areas (são francisco river estuary, brazil northeast): A gis-based analysis of cbers and spot images to aid in local management," ULB Institutional Repository 2013/168498, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    1. Hanhu Liu & Xiangqi Lei & Hui Liang & Xiao Wang, 2023. "Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    2. Miguel Ángel Matus-Hernández & Norma Yolanda Hernández-Saavedra & Raúl Octavio Martínez-Rincón, 2018. "Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
    3. Tainá T. Guimarães & Maurício R. Veronez & Emilie C. Koste & Eniuce M. Souza & Diego Brum & Luiz Gonzaga & Frederico F. Mauad, 2019. "Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
    4. Xi Zhu & Yansha Wen & Xiang Li & Feng Yan & Shuhe Zhao, 2023. "Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

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