Harmonizing Machine Learning Based Phenological Modeling: A Unified Workflow for Comparative Analyses

Undoubtedly, climate change is influencing plant distribution and growth. Phenology, the study of events such as leafing and flowering timing, plays a crucial role in understanding and monitoring climate change impacts. Phenological modeling stands as a vital tool to quantify the influence of environmental changes on plants. Nevertheless, a lack of standardization among phenology prediction models hinders comparisons between different approaches. This paper introduces a workflow utilizing existing data, tools and software packages, thereby easing comparisons among various process-based phenology prediction models and machine learning methods (including classification and regression). The primary objective of this article is to promote consistency in phenological modeling and model evaluation.