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<p>Irregular temporal sampling and limited data availability restrict the performance of remote sensing–based chlorophyll-a prediction for water eutrophication assessment. Meanwhile, atmospheric pollutant deposition represents an important pathway for nutrient input into lakes, yet such information is rarely incorporated into data-driven prediction frameworks.To address these challenges, this study proposes a multimodal fusion framework integrating atmospheric pollutant data and chlorophyll remote sensing imagery. First, a transfer learning-based generative adversarial network (TL-MCAS-CSG-GAN) is developed to reconstruct missing chlorophyll remote sensing images and enhance temporal continuity. Second, a cross-attention Conv2D-LSTM GAN model is designed to fuse atmospheric six-parameter features with chlorophyll image sequences for eutrophication prediction.Experiments conducted on Taihu Lake datasets demonstrate that the proposed model improves SSIM, PSNR, and COSIN by 6.51% compared with baseline models without atmospheric feature fusion. The results indicate that atmospheric pollutant information provides complementary spatiotemporal signals for chlorophyll prediction and improves eutrophication assessment accuracy.The proposed framework offers a data-driven paradigm for multimodal environmental modeling and contributes to intelligent water quality monitoring and management.</p>

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Remote sensing image enhancement and water eutrophication prediction based on atmospheric-water multimodal information fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月10日

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  • Li Wang,
  • Yihan Sun,
  • Xiaoyi Wang,
  • Jiping Xu,
  • Zhiyao Zhao,
  • Jiabin Yu,
  • Huiyan Zhang,
  • Qian Sun,
  • Yuting Bai

摘要

Irregular temporal sampling and limited data availability restrict the performance of remote sensing–based chlorophyll-a prediction for water eutrophication assessment. Meanwhile, atmospheric pollutant deposition represents an important pathway for nutrient input into lakes, yet such information is rarely incorporated into data-driven prediction frameworks.To address these challenges, this study proposes a multimodal fusion framework integrating atmospheric pollutant data and chlorophyll remote sensing imagery. First, a transfer learning-based generative adversarial network (TL-MCAS-CSG-GAN) is developed to reconstruct missing chlorophyll remote sensing images and enhance temporal continuity. Second, a cross-attention Conv2D-LSTM GAN model is designed to fuse atmospheric six-parameter features with chlorophyll image sequences for eutrophication prediction.Experiments conducted on Taihu Lake datasets demonstrate that the proposed model improves SSIM, PSNR, and COSIN by 6.51% compared with baseline models without atmospheric feature fusion. The results indicate that atmospheric pollutant information provides complementary spatiotemporal signals for chlorophyll prediction and improves eutrophication assessment accuracy.The proposed framework offers a data-driven paradigm for multimodal environmental modeling and contributes to intelligent water quality monitoring and management.