The potential of Sentinel-1 time series for large-scale assessment of maize and wheat phenology across Germany

Monitoring crop phenometrics is crucial for understanding crop conditions and dynamics. Dense time series are needed for accurate information. Recent work has shown that remote sensing data can be used effectively for many crops but commonly used optical data often suffer from cloud cover and atmospheric effects. Synthetic Aperture Radar (SAR) sensors provide cloud-free dense time series. This study evaluates the potential of SAR data by analyzing Sentinel-1 VH and VV signals and cross-ratio (CR, VH/VV) time series from 2017 to 2020 for wheat and maize in South and East Germany. Phenometrics were derived using two techniques over smoothed time series: inflection point detection and curvature change rate analysis for specific crops and regions during the growing season. Results were compared with field-level crop growth observations, the Copernicus High-Resolution Vegetation Phenology and Productivity (HR-VPP) product, and phenological data from the German Weather Service (DWD). Spatially explicit maps for Brandenburg, Saxony, and Bavaria were derived, showing the Start, Shooting/Tassel, Maximum, Ripeness, and End of the Season based on thresholds from field-level analysis. SAR data effectively captured growth stages with a novel slope-based approach for detecting curve fluctuations. Wheat phenometrics showed a 4–6 day difference from reference data for most stages, while maize exhibited a 2–10 day difference for emergence, growth, and harvest. Regional results indicated homogeneous spatial distributions for both crops. In conclusion, this study highlights the potential of SAR data for spatially explicit retrieval of crop phenometrics, offering improved results compared to optical data. This approach can also support precision agriculture practices, optimize resource use, and improve yield predictions, contributing to more sustainable agricultural systems.