A global–hierarchical–categorical alignment framework to address sample scarcity and domain shift in crop mapping

The scarcity and high acquisition cost of field crop samples remain a major bottleneck for applying Artificial Intelligence (AI)–driven supervised learning methods in large-scale geoscientific applications such as crop type mapping. Meanwhile, crop phenology and, consequently, spectra-temporal characteristics of the same crop type present significant interannual and regional variations due to the differences in local conditions and human activities, such as climatic, soil properties and farming practices. This causes the “domain shift” challenge. Therefore, directly applying a classification model trained in a specific region and year to a new region or year inevitably leads to poor prediction performance. The gap between the abundant availability of Earth Observations imagery and the limited accessibility of training crop samples hider efficient mapping of varied crop types across large regions. To address training sample scarcity and cross-region/year domain shift in large-scale crop type mapping, we propose a transferable crop mapping method named Global-Hierarchical-Categorical feature Alignment (GHCA). GHCA integrates unsupervised domain adaptation, contrastive learning, and pseudo-labeling to achieve multi-dimensional alignment between source domain and target domain at global, hierarchical and categorical levels. The developed method enables accurate and transferable crop mapping across diverse agricultural landscapes with minimum field survey requirements. The main contributions of our study can be summarized as follows: (1) A global feature pre-alignment mechanism is introduced by calculating the Multi-Kernel Maximum Mean Discrepancy (MK-MMD) metric across different hierarchical features to align source and target domains in global and hierarchical feature spaces. This mechanism substantially improves the initial reliability of pseudo-labels generated for the target domain, providing a reliable foundation for subsequent fine-grained categorical level feature alignment; (2) A robust pseudo-label generation strategy is developed by jointly considering prediction confidence, prediction certainty, and prediction stability. Reliable pseudo-labels for target domain are selected by calculating model prediction probabilities and predictive uncertainty estimates through teacher-student model. Moreover, the Exponential Moving Average (EMA) strategy is adopted to updated model parameters in the teacher path to enable the acquisition of obtaining more stable pseudo-labels; (3) Category-wise feature alignment is achieved by integrating pseudo-labeling with contrastive learning, which explicitly pulls intra-class feature closer for the same crop types across source and target domains, while pushing inter-class feature apart for different crop types. The effectiveness of the proposed GHCA method for both cross-region and cross-year crop mapping was evaluated across five regions in China and the U.S. over a two-year timeframe. GHCA was compared with a machine learning method (RF), supervised deep learning models (DCM, Transformer, and PhenoCropNet), and transfer learning methods (DACCN, PAN, and CSTN) for cross‑year and cross‑region crop mapping. Experimental results showed that GHCA outperformed other models in most transfer cases, with OA ranging from 0.82 to 0.95 (cross-region) and 0.89 to 0.98 (cross-year), achieving an average OA increase of 6.2% and 3.5% in cross-region and cross-year experiments, respectively. These results highlight the strong potential of advanced AI methodologies to deliver robust, quantitative, and transferable solutions for complex geoscientific problems using large Earth observation datasets.