Abstract. The spectral signatures of vegetation are indicative of ecosystem states and health. Spectral indices used to monitor vegetation are characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the potential of neural networks in learning and predicting vegetation response, including extreme behavior from meteorological data. 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 recurrent-based learning models, which differ in their training and architecture for predicting spectral indices at different forest sites in Europe: (1) recurrent neural networks (RNNs), (2) long short-term memory networks (LSTMs), (3) gated recurrent unit 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.

Francesco Martinuzzi

Miguel D. Mahecha

Gustau Camps-Valls

David Montero

Tristan Williams

Karin Mora

Nonlinear processes in geophysics

NONLINEAR PROCESSES IN GEOPHYSICS (NPG) 是一本国际性、跨学科/跨学科的非营利性期刊，致力于打破地球和空间科学标准方法经常面临的僵局。因此，它寻求颠覆性和创新的概念和方法，以及这些概念和方法的原始应用，以解决地球科学系统以及相互作用的社会和生物系统中普遍存在的复杂性。这样的系统是非线性的，响应与扰动非常不成比例，并显示出跨尺度的相关极端可变性。

Nonlinear Processes in Geophysics (NPG) is an international, interdisciplinary, non-profit journal dedicated to overcoming the limitations often faced by standard methods in Earth and space sciences. It seeks to explore disruptive and innovative concepts and methods, as well as their original applications in addressing the pervasive complexities present in Earth science systems and their interactions with social and biological systems. Such systems are typically nonlinear, exhibiting disproportionately large responses to disturbances and showing extreme variability across scales.

《Nonlinear Processes in Geophysics》（NPG）是一本国际性、跨学科的非营利性期刊，旨在突破地球与空间科学标准方法所面临的局限。期刊致力于探索颠覆性和创新的概念与方法，以及这些概念和方法在解决地球科学系统及其与社会和生物系统相互作用中普遍存在的复杂性方面的原始应用。这些系统通常是非线性的，对扰动的响应往往不成比例，并且表现出跨尺度的极端可变性。

Category	Quartile
地球科学	4
地球科学, 地球科学综合	4
地球科学, 数学跨学科应用	4
地球科学, 气象与大气科学	4
地球科学, 物理流体与等离子体	4

Learning extreme vegetation response to climate drivers with recurrent neural networks

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Journal Info

Category	Quartile
PHYSICS, FLUIDS & PLASMAS	3