DAWM: A Dynamic and Adaptive Winter Wheat Mapping Framework During the Growing Season

Timely and accurate access to the spatial distribution of crops is critical for precision agriculture. However, large-scale automated in-season crop mapping often faces the challenge of limited training data. In this study, we proposed a Dynamic and Adapted in-season winter Wheat Mapping (DAWM) framework, which leverages temporal-spectral similarity to minimize reliance on ground truth labels. The DAWM framework starts by utilizing historical prior knowledge from target domains to construct a temporal- spectral library for winter wheat, which integrates the phenological curves of winter wheat throughout the entire growing season and the spectral curves of winter wheat at different times. In this part, two modules – confidence learning (CL) and re-clustering is used to refine historical crop maps and generate a reference temporal-spectral curve library. Then, a novel Dynamic Temporal-Spectral Similarity (DTSS) metric, proposed in this study, is used to produce training data for the random forest (RF) model by dynamically calculating the temporal-spectral similarity of each unlabeled objects to each cluster in the reference library.The proposed DAWM framework was evaluated in three global sites (10,000 km2 each) and compared with two existing phenology-based methods: time- weighted Dynamic Time Warping (twDTW) and phenology-time weighted Dynamic Time Warping (ptDTW). Results demonstrated that DAWM outperforms both methods, achieving F1-scores above 0.90 across all study sites, respectively. Notably, DAWM accurately identifies winter wheat around the heading stage, showcasing its potential for dynamic in-season crop mapping. Extensive testing in the Huang-Huai-Hai Plain region of China achieved an F1 accuracy of 86.37%, while additional experiments in Heilongjiang Province demonstrated the method’s applicability for mapping maize and rice, with F1 accuracies of 90.35% and 96.31%, respectively. The proposed approach offers a robust solution for dynamic, high-quality crop mapping without relying on local ground labels, providing valuable data support for agricultural production management. The code is open-source and available on GitHub (https://github.com/gactyxc/DTLS.git).