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A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

Author

Listed:
  • Banglong Pan

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

  • Hanming Yu

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

  • Hongwei Cheng

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

  • Shuhua Du

    (Institute of Geological Experiments of Anhui Province, Heifei 230000, China)

  • Shutong Cai

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

  • Minle Zhao

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

  • Juan Du

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

  • Fazhi Xie

    (The School of Environmental and Energy Engineering, Anhui Jianzhu University, Heifei 230000, China)

Abstract

As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and carbon cycle, the estimation of its concentration using satellite remote sensing is of great interest. However, the high complexity and variability of lake water composition pose major challenges to the estimation algorithm of POC concentration in Class II water. This study aimed to formulate a machine-learning algorithm to predict POC concentration and compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) algorithm based on spectral and time sequences was proposed to construct an estimation model using the Sentinel 2 satellite images and water surface sample data of Chaohu Lake in China. As a comparison, the performances of the Backpropagation Neural Network (BP), Generalized Regression Neural Network (GRNN), and Convolutional Neural Network (CNN) models were evaluated for remote sensing inversion of POC concentration. The results show that the CNN–LSTM model obtained higher prediction precision than the BP, GRNN, and CNN models, with a coefficient of determination (R 2 ) of 0.88, a root mean square error (RMSE) of 3.66, and residual prediction deviation (RPD) of 3.03, which are 6.02%, 22.13%, and 28.4% better than the CNN model, respectively. This indicates that CNN–LSTM effectively combines spatial and temporal information, quickly captures time-series features, strengthens the learning ability of multi-scale features, is conducive to improving estimation precision of remote sensing models, and offers good support for carbon source monitoring and assessment in lakes.

Suggested Citation

  • Banglong Pan & Hanming Yu & Hongwei Cheng & Shuhua Du & Shutong Cai & Minle Zhao & Juan Du & Fazhi Xie, 2023. "A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13043-:d:1228451
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    References listed on IDEAS

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    1. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    2. Jiangtao Sun & Wei Dang & Fengqin Wang & Haikuan Nie & Xiaoliang Wei & Pei Li & Shaohua Zhang & Yubo Feng & Fei Li, 2023. "Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost," Energies, MDPI, vol. 16(10), pages 1-26, May.
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    1. Jianjun Huang & Jindong Xu & Weiqing Yan & Peng Wu & Haihua Xing, 2023. "Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model," Sustainability, MDPI, vol. 16(1), pages 1-21, December.

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