Springtime

Phenology is the study of the timing of seasonal events in plants, such as flowering, leafing, and fruiting, which are influenced by climate conditions. Understanding plant phenology is critical for detecting the effects of climate change on ecosystems, agriculture, and even human health. As climate change causes more extreme weather events, like droughts, floods, and wildfires, it disrupts plant growth and distribution, making it essential to better understand how plants respond to these changing conditions.

The “Springtime” project aimed to create innovative and efficient tools for plant phenological analysis and modelling using machine learning (ML) techniques. The project sought to assist researchers by providing tools that facilitate the access, pre-processing, and integration of various datasets and modelling techniques. The phenological datasets come from multiple sources, including national and continent-scale observatories, remote sensing data, and environmental factors such as temperature, precipitation, and air humidity. These data sources are essential for developing accurate models of plant phenology. Phenological datasets are typically clustered in distinct regions and separate time intervals. This introduces an additional source of random variation and complexity that needs to be considered in modelling. This project aimed to develop an efficient ML-based modelling solution that captures the complex correlations across both space and time present in multi-source large scale phenological datasets, and that provides accurate, consistent, and interpretable predictions.

One of the major outcomes of the project was the creation of the Springtime Python package, an open-source tool designed to support plant phenological research. The package streamlines the process of accessing and pre-processing phenological data and integrates not only various ML methods but also the common mechanistic models. Traditionally, phenological research has been dominated by mechanistic models. By enabling researchers to seamlessly execute data processing and modelling workflows with a single command, Springtime has significantly reduced the barriers to using these advanced techniques. The project also tested the integration of statistical methods, such as mixed effects models, with ML algorithms, helping preserve the spatial and temporal relationships within the phenological datasets while improving model accuracy. Particularly, it exploited the flexible linear-based structure of Explainable Boosting Machines (EBM), which is an advanced interpretable regression technique, to develop a mixed effect hybrid approach.

This project was important because it filled a gap in the tools available to researchers studying phenological modelling who come from various disciplines (e.g., from data science and machine learning, remote sensing up to different environmental sciences such as agronomy, biology, or ecology just to mention a few involved). The impact of the project was significant in changing how researchers approach phenological modelling, providing them with more powerful tools for their work. It provided a user-friendly set of tools that allow researchers to better analyse plant responses to climate change using various modelling strategies and in a reproducible manner.

The project largely met its initial objectives and, in some cases, expanded its scope to include additional tools and integrations. Moving forward, the project’s results could be expanded to further support phenological modellers in developing innovative approaches. For example, further exploration of integrating mixed-effects techniques and EBM holds promise for improving model performance and interpretability. Additionally, there is a critical need for a reliable, multidisciplinary open benchmark study that includes diverse phenological datasets and compares various mechanistic and ML models for phenology prediction. The Springtime tools could facilitate such an initiative by providing a standardized framework for data integration and model testing. Moreover, exploring hybrid approaches that combine mechanistic and ML methods deserves special attention, and the Springtime tools are well-positioned to support such studies.