Abstract
AbstractLand surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.
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