Zonal Estimation of the Earliest Winter Wheat Identification Time in Shandong Province Considering Phenological and Environmental Factors

Early-season crop mapping plays a critical role in yield estimation, agricultural management, and policy-making. However, most existing methods assign a uniform earliest identification time across provincial or broader extents, overlooking spatial heterogeneity in crop phenology and environmental conditions. This often results in delayed detection or reduced mapping accuracy. To address this issue, we proposed a zonal-based early-season mapping framework for winter wheat by integrating phenological and environmental factors. Aggregation zones across Shandong Province were delineated using Principal Component Analysis (PCA) based on factors such as start of season, end of season, temperature, slope, and others. On this basis, early-season winter wheat identification was conducted for each zone individually. Training samples were generated using the Time-Weighted Dynamic Time Warping (TWDTW) method. Time-series datasets derived from Sentinel-1/2 imagery (2021–2022) were processed on the Google Earth Engine (GEE) platform, followed by feature selection and classification using the Random Forest (RF) algorithm. Results indicated that Shandong Province was divided into four zones (A–D), with Zone D (southwestern Shandong) achieving the earliest mapping by early December with an overall accuracy (OA) of 97.0%. Other zones reached optimal timing between late December and late January, all with OA above 95%. The zonal strategy improved OA by 3.6% compared to the non-zonal approach, demonstrated a high correlation with official municipal-level statistics (R2 = 0.97), and surpassed the ChinaWheat10 and ChinaWheatMap10 datasets in terms of crop differentiation and boundary delineation. Historical validation using 2017–2018 data from Liaocheng City, a prefecture-level city in Shandong Province, achieved an OA of 0.98 and an F1 score of 0.96, further confirming the temporal robustness of the proposed approach. This zonal strategy significantly enhances the accuracy and timeliness of early-season winter wheat mapping at a large scale.