A novel approach to assessing the tracking accuracy of crop phenology for multi-orbit and multi-feature Sentinel-1 time series

Abstract

This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90 % of GDD progression of in situ reported phenological stages, while systematic differences of around 5 % by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.