Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO$_2$ flux, especially significant for forests. Eddy Covariance (EC) measurements are widely used for ecosystem-scale GPP quantification but are globally sparse. In areas lacking local EC measurements, remote sensing (RS) data are typically utilised to estimate GPP after statistically relating them to in-situ data. Deep learning offers novel perspectives, and the potential of recurrent neural network architectures for estimating daily GPP remains underexplored. This study presents a comparative analysis of three architectures: Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long-Short Term Memory (LSTMs). Our findings reveal comparable performance across all models for full-year and growing season predictions. Notably, LSTMs outperform in predicting climate-induced GPP extremes. Furthermore, our analysis highlights the importance of incorporating radiation and RS inputs (optical, temperature, and radar) for accurate GPP predictions, particularly during climate extremes.

翻译：准确量化总初级生产力（GPP）对于理解陆地碳动态至关重要。GPP代表了最大的大气-陆地CO₂通量，尤其对森林具有重要意义。涡度协方差（EC）测量被广泛用于生态系统尺度的GPP量化，但在全球范围内分布稀疏。在缺乏本地EC测量的地区，通常根据遥感（RS）数据与现场数据的统计关系来估算GPP。深度学习提供了新的视角，而循环神经网络架构在估算日GPP方面的潜力尚未得到充分探索。本研究对三种架构进行了比较分析：循环神经网络（RNN）、门控循环单元（GRU）和长短期记忆网络（LSTM）。我们的发现表明，在全年来和生长季预测中，所有模型的性能相当。值得注意的是，LSTM在预测气候引发的GPP极端值方面表现更优。此外，我们的分析强调了将辐射和RS输入（光学、温度和雷达数据）纳入模型对准确预测GPP的重要性，尤其是在气候极端事件期间。