Data Sheet 1_Leveraging Sentinel-2 and Sentinel-1 imagery for retrieving key wheat phenology stages in a dryland agricultural setting.docx

To achieve this, we used 85 cloud-free Sentinel-2 and 94 Sentinel-1 images, along with in situ phenology observations. From Sentinel-2 imagery, we derived the normalized difference vegetation index (NDVI), green-red vegetation index (GRVI), and plant senescence reflectance index (PSRI), while volume backscattering (VV-VH), i.e., difference between vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH), was derived from Sentinel-1 imagery.

Field-level time-series of each Sentinel-2 and Sentinel-1 metric were extracted and fitted with Gaussian functions. Next, the five phenology stages were retrieved by applying stage-specific curve-based rules to Gaussian-fitted field-level time-series of each metric. The accuracy of retrievals was assessed against in situ observations.

Results showed that curve-based rules applied to NDVI, GRVI, and PSRI provided relative root mean square error (rRMSE) values of 2.64%–14.17% and bias magnitudes of 7.34–20.60 days across all stages without requiring rule refinement. Rules applied to VV-VH produced rRMSE values of 4.60%–26.22% and bias magnitudes of 7.11–43.73 days. Targeted refinement of flowering and harvesting rules reduced these errors.

Overall, this work demonstrates the effectiveness of applying curve-based rules to each of Sentinel-2 and Sentinel-1 data separately in the dryland setting examined here, providing a foundation for evaluation across the broader range of drylands.</p>