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Deep learning–based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images

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Abstract

Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used to construct a new retrieval model, namely, point-centered regression convolutional neural network (PSRCNN) suitable for Sentinel 2 and Landsat 8 images. The impact of input feature variables on the accuracy of the inversion model was examined, and the performance of an optimized PSRCNN model was also assessed. This model was applied to remote sensing images of three shallow lakes in the eastern China plain acquired in summer. The PSRCNN model, constructed using five identical bands from Landsat 8 and Sentinel 2 images and 20 band combinations as the input variables, the input window size of 5 × 5 pixels, proves a good predictive ability, with a verification accuracy of R2 = 0.85, the root mean squared error (RMSE) = 13.0 cm, and the relative predictive deviation (RPD) = 2.58. After the sensitive spectral analysis of water transparency, the band combinations that had correlation coefficients higher than 0.6 were selected as the new input feature variables to construct an optimized PSRCNN model (PSRCNNopt) for water transparency. The PSRCNNopt model has an excellent predictive ability, with a verification accuracy of R2 = 0.89, RMSE = 11.48 cm, and RPD =3.0. It outperforms the commonly retrieval models (band ratios, random forest, support vector machine, etc.), with higher accuracy and robustness. Spatial variations in water transparency of three lakes from the retrieval results by PSRCNNopt model are consistent with the field observations.

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Funding

This research was funded by the National Natural Science Foundation of China, grant numbers 41401022, 41861002, and 41801332.

Author information

Authors and Affiliations

  1. School of Science, Anhui Agricultural University, Hefei, 230036, China

    Yuhuan Cui & Shuang Hao

  2. School of Resources and Environmental Engineering, Anhui University, Hefei, 230601, China

    Zhongnan Yan & Jie Wang

  3. School of Geographical Sciences and Tourism, Jiaying University, Meizhou, 341000, China

    Youcun Liu

Authors
  1. Yuhuan Cui
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  2. Zhongnan Yan
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  3. Jie Wang
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  4. Shuang Hao
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  5. Youcun Liu
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Contributions

Conceptualization: Yuhuan Cui and Jie Wang

Methodology: Zhongnan Yan

Software: Zhongnan Yan and Shuang Hao

Validation: Yuhuan Cui and Youcun Liu

Formal analysis: Yuhuan Cui

Investigation: Shuang Hao, Jie Wang, and Youcun Liu

Resources: Jie Wang and Youcun Liu

Data curation: Shuang Hao

Writing—original draft preparation: Yuhuan Cui

Writing—review and editing: Jie Wang

Visualization: Jie Wang

Supervision: Yuhuan Cui

Project administration: Jie Wang

Funding acquisition: Yuhuan Cui and Shuang Hao

All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jie Wang.

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Responsible Editor: Philippe Garrigues

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Appendix

Appendix

The information of input feature variables used in the PSRCNNopt model

Feature types

Feature variables

Correlation coefficient

Original Bands

Rrs (442)

0.140

Rrs (559)

−0.183

Rrs (665)

−0.598

Rrs (492)

−0.485

Rrs (864)

−0.015

Band ratios

Rrs (442)/Rrs (492)

0.76

Rrs (442)/Rrs (559)

0.839

Rrs (442)/Rrs (665)

0.683

Rrs (492)/Rrs (442)

−0.731

Rrs (492)/Rrs (559)

0.807

Rrs (559)/Rrs (442)

−0.788

Rrs (559)/Rrs (492)

−0.763

Rrs (665)/Rrs (442)

−0.615

Band subtraction

Rrs (442)-Rrs (492)

0.731

Rrs (442)-Rrs (559)

0.83

Rrs (442)-Rrs (665)

0.619

Rrs (492)-Rrs (559)

0.821

  1. Note: Rrs (442), Rrs (559), Rrs (665), Rrs (492), and Rrs (864) represent the remote sensing reflectance of five original bands in OLI and MSI image

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Cui, Y., Yan, Z., Wang, J. et al. Deep learning–based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images. Environ Sci Pollut Res 29, 4401–4413 (2022). https://doi.org/10.1007/s11356-021-16004-9

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