Foundation models are transforming Earth observation, but their potential for hyperspectral crop mapping remains underexplored. This study benchmarks three foundation models for cereal crop mapping using hyperspectral imagery: HyperSigma, DOFA, and Vision Transformers pre-trained on the SpectralEarth dataset (a large multitemporal hyperspectral archive). Models were fine-tuned on manually labeled data from a training region and evaluated on an independent test region. Performance was measured with overall accuracy (OA), average accuracy (AA), and F1-score. HyperSigma achieved an OA of 34.5% (+/- 1.8%), DOFA reached 62.6% (+/- 3.5%), and the SpectralEarth model achieved an OA of 93.5% (+/- 0.8%). A compact SpectralEarth variant trained from scratch achieved 91%, highlighting the importance of model architecture for strong generalization across geographic regions and sensor platforms. These results provide a systematic evaluation of foundation models for operational hyperspectral crop mapping and outline directions for future model development.

翻译：基础模型正在变革地球观测领域，但其在高光谱作物制图方面的潜力仍未得到充分探索。本研究针对利用高光谱影像进行谷物作物制图，对三种基础模型进行了基准测试：HyperSigma、DOFA以及在SpectralEarth数据集（一个大型多时相高光谱档案库）上预训练的Vision Transformers。模型在训练区域的手动标注数据上进行微调，并在独立的测试区域进行评估。性能通过总体准确率（OA）、平均准确率（AA）和F1分数进行衡量。HyperSigma的OA为34.5%（+/- 1.8%），DOFA达到62.6%（+/- 3.5%），而SpectralEarth模型的OA达到93.5%（+/- 0.8%）。一个从头开始训练的紧凑型SpectralEarth变体取得了91%的准确率，凸显了模型架构对于实现跨地理区域和传感器平台的强泛化能力的重要性。这些结果为业务化高光谱作物制图的基础模型提供了系统性评估，并指明了未来模型开发的方向。