Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning ...

• Main Authors: Mortier, Steven, Hamedpour, Amir, Bussmann, Bart, Wandji, Ruth Phoebe Tchana, Latré, Steven, Sigurdsson, Bjarni D., De Schepper, Tom, Verdonck, Tim
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Machine Learning cs.LG; FOS Computer and information sciences; Subarctic
• Online Access: https://dx.doi.org/10.48550/arxiv.2312.12258; https://arxiv.org/abs/2312.12258

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 ... : 31 pages, 7 figures, 5 tables ...