Abstract. Vegetation state variables are key indicators of land-atmosphere interactions characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in capturing vegetation state responses, including extreme behavior driven by atmospheric conditions. While machine learning methods, particularly neural networks, have significantly advanced in modeling nonlinear dynamics, it has become standard practice to approach the problem using recurrent architectures capable of capturing nonlinear effects and accommodating both long and short-term memory. We compare four recurrence-based learning models, which differ in their training and architecture: 1) recurrent neural networks (RNNs), 2) long short-term memory-based networks (LSTMs), 3) gated recurrent unit-based networks (GRUs), and 4) echo state networks (ESNs). While our results show minimal quantitative differences in their performances, ESNs exhibit slightly superior results across various metrics. Overall, we show that recurrent network architectures prove generally suitable for vegetation state prediction yet exhibit limitations under extreme conditions. This study highlights the potential of recurrent network architectures for vegetation state prediction, emphasizing the need for further research to address limitations in modeling extreme conditions within ecosystem dynamics.

中文翻译：

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学习极端植被对气候强迫的响应：循环神经网络架构的比较

摘要。植被状态变量是陆地与大气相互作用的关键指标，其特征是长期趋势、季节性波动和对天气异常的响应。这项研究调查了神经网络在捕捉植被状态反应方面的潜力，包括大气条件驱动的极端行为。虽然机器学习方法，特别是神经网络，在非线性动力学建模方面取得了显着进步，但使用能够捕获非线性效应并适应长期和短期记忆的循环架构来解决问题已成为标准做法。我们比较了四种基于循环的学习模型，它们的训练和架构有所不同：1）循环神经网络（RNN），2）基于长短期记忆的网络（LSTM），3）基于门控循环单元的网络（GRU） ）和 4）回声状态网络（ESN）。虽然我们的结果显示它们的性能在数量上差异很小，但 ESN 在各种指标上都表现出稍微优越的结果。总的来说，我们表明循环网络架构通常适用于植被状态预测，但在极端条件下表现出局限性。这项研究强调了循环网络架构在植被状态预测方面的潜力，强调需要进一步研究以解决生态系统动态中极端条件建模的局限性。