用稳定区域做隐式监督，减少遥感土地覆盖变化追踪的标注负担。

Reliable classification of Earth Observation data depends on consistent, up-to-date reference labels. However, collecting new labelled data at each time step remains expensive and logistically difficult, especially in dynamic or remote ecological systems. As a response to this challenge, we demonstrate that a model with access to reference data solely from time step t0 can perform competitively on both t0 and a future time step t1, outperforming models trained separately on time-specific reference data (the gold standard). This finding suggests that effective temporal generalization can be achieved without requiring manual updates to reference labels beyond the initial time step t0. Drawing on concepts from change detection and semi-supervised learning (SSL), the most performant approach, "Common Ground", uses a semi-supervised framework that leverages temporally stable regions-areas with little to no change in spectral or semantic characteristics between time steps-as a source of implicit supervision for dynamic regions. We evaluate this strategy across multiple classifiers, sensors (Landsat-8, Sentinel-2 satellite multispectral and airborne imaging spectroscopy), and ecological use cases. For invasive tree species mapping, we observed a 21-40% improvement in classification accuracy using Common Ground compared to naive temporal transfer, where models trained at a single time step are directly applied to a future time step. We also observe a 10 -16% higher accuracy for the introduced approach compared to a gold-standard approach. In contrast, when broad land cover categories were mapped across Europe, we observed a more modest 2% increase in accuracy compared to both the naive and gold-standard approaches. These results underscore the effectiveness of combining stable reference screening with SSL for scalable and label-efficient multi-temporal remote sensing classification.