Machine learning analysis of global LAI trends and their relationship with climate variability (1982–2022)

Understanding vegetation responses to climate variability is essential for assessing long-term ecosystem dynamics. Leaf Area Index (LAI) is a widely used variable to characterise vegetation state and productivity. However, attributing observed global LAI trends to specific climatic drivers remains challenging due to non-linear interactions, strong spatial heterogeneity, and scale-dependent processes.

This study is conducted within the framework of the PROFECIA project, which aims to improve the monitoring and interpretation of vegetation responses to climate change by combining remote sensing observations and artificial intelligence techniques. We analyse global LAI trends over the period 1982–2022 using the GEOV2-AVHRR long-term satellite record and examine their relationship with trends in key climatic variables obtained from the ERA5 reanalysis, including temperature, precipitation, radiation, and several indicators of water availability and drought conditions. All trends are computed consistently over the 1982-2022 temporal record to ensure a homogeneous assessment of long-term vegetation–climate relationships at the global scale.

The vegetation–climate relationships are modelled using a suite of machine learning approaches, including tree-based methods and neural networks, designed to capture non-linear responses across diverse climatic and ecological conditions. Particular emphasis is placed on the role of the training strategy: different spatio-temporal sampling schemes are evaluated to assess their impact on model performance, robustness, and generalisation capability when analysing long-term trends at the global scale.

To move beyond purely predictive modelling, the study systematically applies explainable artificial intelligence (XAI) techniques to interpret the trained models. Methods such as SHAP-based attribution and partial dependence analyses are used to quantify the relative contribution of individual climatic drivers to observed LAI trends and to examine how these contributions vary across regions and time periods.

Overall, this work highlights the importance of combining robust machine learning training strategies with interpretability tools to improve the attribution of long-term vegetation trends to climatic drivers, providing new insights into global vegetation–climate interactions over the last four decades.