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<p>Gross primary production (GPP), the total carbon fixed through plant photosynthesis, is a central component of the global energy and carbon cycles. While substantial divergences persist among inter-model GPP estimates, especially in their seasonal and interannual behaviours, more precise estimation approaches are urgently required. The present study introduces TRAX GPP, a new global GPP dataset with 0.05° spatial resolution and monthly coverage from 2001 to 2024. The dataset is generated using a hybrid Long Short–Term Memory (LSTM) framework that integrates Robust Seasonal–Trend decomposition (RSTL) with a loss function optimized for temporal learning. The model integrates satellite-derived vegetation and meteorological drivers, further enhanced by atmospheric CO<sub>2</sub> assimilation constraints to better capture CO<sub>2</sub> fertilization effects. TRAX GPP dataset aligns well with tower flux measurements and outperforms standard LSTM models in capturing both seasonal dynamics and interannual fluctuations. Compared with previous GPP products, TRAX GPP alleviates the common biases in global GPP totals and interannual trends. This dataset provides a new pathway for global GPP estimation and supports refined monitoring of terrestrial carbon dynamics.</p>

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A global 0.05° gross primary production dataset from 2001 to 2024 generated using a hybrid LSTM framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Shaoyang Chen,
  • Xinjie Liu,
  • Qizhi Han,
  • Yanhong Wu,
  • Liangyun Liu

摘要

Gross primary production (GPP), the total carbon fixed through plant photosynthesis, is a central component of the global energy and carbon cycles. While substantial divergences persist among inter-model GPP estimates, especially in their seasonal and interannual behaviours, more precise estimation approaches are urgently required. The present study introduces TRAX GPP, a new global GPP dataset with 0.05° spatial resolution and monthly coverage from 2001 to 2024. The dataset is generated using a hybrid Long Short–Term Memory (LSTM) framework that integrates Robust Seasonal–Trend decomposition (RSTL) with a loss function optimized for temporal learning. The model integrates satellite-derived vegetation and meteorological drivers, further enhanced by atmospheric CO2 assimilation constraints to better capture CO2 fertilization effects. TRAX GPP dataset aligns well with tower flux measurements and outperforms standard LSTM models in capturing both seasonal dynamics and interannual fluctuations. Compared with previous GPP products, TRAX GPP alleviates the common biases in global GPP totals and interannual trends. This dataset provides a new pathway for global GPP estimation and supports refined monitoring of terrestrial carbon dynamics.