及时监测玉米生长早期阶段对保障粮食安全和优化农业管理至关重要，但现有大多数制图方法依赖于成熟期的光谱特征，导致业务化应用滞后。本研究提出TCBA-ViT——一种融合卷积神经网络（CNN）与视觉Transformer（ViT）的混合框架，该框架引入双路径卷积块注意力模块（CBAM）与时间注意力机制，协同提取多时相Sentinel-2影像中的局部光谱细节与全局时间动态特征。基于2019–2024年美国玉米带六年数据，TCBA-ViT可在6月（V7生育期，播种后约四周）即可靠识别玉米，并于7月下旬实现稳定准确率超90%，较生理成熟期提前近两个月。跨年度实验表明其对年际变异与轮作模式具有鲁棒性；跨区域测试证实其具备强空间泛化能力，在250 km范围内F1分数保持在0.85以上，450 km范围内仍高于0.80。相较于现有基线模型，TCBA-ViT在不同年份与区域均持续实现更早、更准确的分类。消融分析进一步凸显CBAM与时间注意力对性能提升的不可或缺作用。本研究通过回答‘玉米最早可在何时被分类’及‘模型泛化能力边界何在’两个关键问题，提供了一种经验证的早季动态作物分类与大尺度农业监测框架，支撑可持续决策制定。

Timely monitoring of corn growth at early stages is essential for food security and agricultural management, yet most existing mapping approaches depend on mature-stage spectral features, delaying operational applications. This study introduces TCBA-ViT, a hybrid framework that integrates convolutional neural networks and Vision Transformers, enhanced with dual-path Convolutional Block Attention Module (CBAM) and temporal attention, to jointly capture local spectral details and global temporal dynamics from multi-temporal Sentinel-2 imagery. Using six years (2019-2024) of data from the U.S. Corn Belt, TCBA-ViT reliably identified corn as early as June (V7 stage, four weeks after seeding) and achieved stable accuracies above 90% by late July, nearly two months before physiological maturity. Cross-year experiments demonstrated robustness to interannual variability and crop rotation, while cross-regional tests confirmed strong spatial generalization, maintaining F1-scores above 0.85 within 250 km and above 0.80 within 450 km. Compared with existing baseline models, TCBA-ViT consistently delivered earlier and more accurate classification across years and regions. Ablation analyses further highlighted the indispensable contributions of CBAM and temporal attention to performance gains. By addressing the questions of how early corn can be classified and how far models can generalize, this study provides a validated framework for early-season dynamic crop classification and large-scale agricultural monitoring, supporting sustainable decision-making.