Inferring the relationship between soil temperature and the normalized
  difference vegetation index with machine learning
Steven Mortier Amir Hamedpour Bart Bussmann Ruth Phoebe Tchana Wandji Steven Latré Bjarni D. Sigurdsson Tom De Schepper Tim Verdonck
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.