Changes in climate can greatly affect the phenology of plants, which can have important feedback effects, such as altering the carbon cycle. These phenological feedback effects are often induced by a shift in the start or end dates of the growing season of plants. The normalized difference vegetation index (NDVI) serves as a straightforward indicator for assessing the presence of green vegetation and can also provide an estimation of the plants' growing season. In this study, we investigated the effect of soil temperature on the timing of the start of the season (SOS), timing of the peak of the season (POS), and the maximum annual NDVI value (PEAK) in subarctic grassland ecosystems between 2014 and 2019. We also explored the impact of other meteorological variables, including air temperature, precipitation, and irradiance, on the inter-annual variation in vegetation phenology. Using machine learning (ML) techniques and SHapley Additive exPlanations (SHAP) values, we analyzed the relative importance and contribution of each variable to the phenological predictions. Our results reveal a significant relationship between soil temperature and SOS and POS, indicating that higher soil temperatures lead to an earlier start and peak of the growing season. However, the Peak NDVI values showed just a slight increase with higher soil temperatures. The analysis of other meteorological variables demonstrated their impacts on the inter-annual variation of the vegetation phenology. Ultimately, this study contributes to our knowledge of the relationships between soil temperature, meteorological variables, and vegetation phenology, providing valuable insights for predicting vegetation phenology characteristics and managing subarctic grasslands in the face of climate change. Additionally, this work provides a solid foundation for future ML-based vegetation phenology studies.

翻译：气候变化会显著影响植物的物候特征，并可能产生重要的反馈效应，例如改变碳循环。这些物候反馈效应通常由植物生长季起始或结束日期的变化引发。归一化植被指数作为评估绿色植被覆盖度的直接指标，也可用于估算植物的生长季。本研究基于2014-2019年亚北极草原生态系统数据，探究土壤温度对生长季开始日期、生长季峰值日期及年最大NDVI值的影响。同时，我们评估了气温、降水量、辐射量等其他气象变量对植被物候年际变化的作用。通过运用机器学习技术与夏普利加法解释值，我们分析了各变量对物候预测的相对重要性与贡献度。结果表明，土壤温度与生长季开始日期及峰值日期存在显著关联：更高的土壤温度会促使生长季提前开始并提前达到峰值。但峰值NDVI值随土壤温度升高仅呈现轻微增长。对其他气象变量的分析揭示了其对植被物候年际变化的影响机制。本研究深化了对土壤温度、气象变量与植被物候间关系的认知，为预测气候变化背景下亚北极草原的植被物候特征提供了重要参考，同时为未来基于机器学习的植被物候研究奠定了坚实基础。