Phenological Alignment and Divergence in Agricultural Systems Derived from Sentinel-1

This study presents a novel framework for monitoring crop phenology using Sentinel-1 (S1) time series data. The proposed approach establishes explicit links between landscape-scale vegetation patterns and field-level phenological developments to address three key objectives: evaluating the agreement between field and landscape phenological signals, identifying dominant phenological tendencies at the field scale, and detecting phenological outliers. Two core indicators were developed—Average Agreement (AVA), which quantifies the correspondence between individual field dynamics and overall landscape development, and Dominance of Tendency (DoT), which characterizes whether fields are phenologically ahead or behind the broader landscape trend, while assessing the consistency of these tendencies across multiple S1 features and orbits.

Environmental descriptors, including soil organic carbon, topographic wetness index, and elevation, were found to shape the spatial and temporal variability of both indicators. Although no single dominant driver was identified, random forest analyses achieved an R² of 0.8, highlighting the complex, multifactorial nature of phenological processes. By integrating growing degree day (GDD) information and S1 time series metrics, the framework reduces reliance on extensive in situ measurements while enabling robust field-scale characterization of phenological progression.

Results show that combining outlier detection with cross-scale comparisons provides valuable insights into typical and atypical crop behavior, supporting assessments of climate vulnerability, resilience, and adaptive management strategies. The flexibility of the method allows seamless application across various S1 features, acquisition geometries, and crop types, demonstrating strong potential for upscaling to regional or national monitoring as well as for broader studies of phenological dynamics.

This work establishes a data-driven pathway toward advanced agricultural management by linking temporal S1 observations with crop performance indicators, thereby enhancing informed decision-making in a sector increasingly challenged by climate change.