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<p>The integration of deep learning with remote sensing techniques has emerged as a transformative approach in analyzing carbon flux patterns to mitigate climate change impacts on environmental health. This study introduces a Spatiotemporal Attention Network (SATN) that leverages satellite-derived data and ground-based observations to predict and understand carbon flux variations. Key features such as vegetation indices, soil moisture, and land surface temperature are prioritized, contributing over 60% to the model’s predictive accuracy. The proposed SATN achieved a remarkable <i>R</i><sup>2</sup> score of 0.95, outperforming traditional models like ConvLSTM (0.87) and Transformers (0.90). With a low RMSE of 0.045 and MAE of 0.031, the SATN demonstrated robust accuracy and efficiency, achieving a balanced F1-score of 0.91. The results highlight spatial hotspots like the Amazon Rainforest and temporal trends indicating seasonal vegetation growth and human-induced emissions as critical contributors to carbon flux dynamics. By identifying regions and periods of high carbon activity, this study provides actionable insights for reforestation efforts, urban emissions control, and climate resilience planning. The SATN framework sets a new benchmark for integrating machine learning with environmental science, paving the way for innovative climate mitigation strategies.</p>

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Deep Learning in Analyzing Carbon Flux Patterns for Environmental Health: Remote Sensing Insights for Climate Mitigation Strategies

  • 期刊论文

  • 发表日期：

    2025年2月26日

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  • Hariharan Subramani,
  • M. Neela Harish,
  • Selva kumar S,
  • P. Radhakrishnan,
  • L. N. K. Sai Madupu,
  • Kasturi Sai Sandeep,
  • Divya Nimma

摘要

The integration of deep learning with remote sensing techniques has emerged as a transformative approach in analyzing carbon flux patterns to mitigate climate change impacts on environmental health. This study introduces a Spatiotemporal Attention Network (SATN) that leverages satellite-derived data and ground-based observations to predict and understand carbon flux variations. Key features such as vegetation indices, soil moisture, and land surface temperature are prioritized, contributing over 60% to the model’s predictive accuracy. The proposed SATN achieved a remarkable R2 score of 0.95, outperforming traditional models like ConvLSTM (0.87) and Transformers (0.90). With a low RMSE of 0.045 and MAE of 0.031, the SATN demonstrated robust accuracy and efficiency, achieving a balanced F1-score of 0.91. The results highlight spatial hotspots like the Amazon Rainforest and temporal trends indicating seasonal vegetation growth and human-induced emissions as critical contributors to carbon flux dynamics. By identifying regions and periods of high carbon activity, this study provides actionable insights for reforestation efforts, urban emissions control, and climate resilience planning. The SATN framework sets a new benchmark for integrating machine learning with environmental science, paving the way for innovative climate mitigation strategies.