对比两种模型在气候区外的产量预测表现，发现空间时间对齐比模型复杂度更重要。
Out-of-Distribution Generalization in Climate-Aware Yield Prediction with Earth Observation Data
- 用地理与年份隔离的交叉验证测试模型跨区域泛化能力
- GNN-RNN在跨区域预测中误差低于10蒲式耳/英亩，而MMST-ViT性能骤降
- 训练速度差135倍，GNN-RNN更适合可持续部署
气候变化正日益扰乱农业系统，精准作物产量预测对粮食安全至关重要。尽管深度学习模型在利用卫星和气象数据预测产量方面展现出潜力，其在地理区域和年份间的泛化能力——真实部署的关键——仍缺乏充分检验。我们基于涵盖2017-2022年美国1200多个县的CropNet数据集，对两种前沿模型GNN-RNN和MMST-ViT在真实分布外（OOD）条件下进行基准测试。通过在七个美国农业资源区执行留一区交叉验证及提前一年预测场景，发现跨区域迁移性能存在显著差异：GNN-RNN在地理迁移下表现良好，相关性为正；而MMST-ViT在域内表现佳，但在分布外条件下性能急剧下降。如心腹地带和北大平原地区对迁移保持稳定（大豆RMSE小于10蒲式耳/英亩），但草原门户区无论模型或作物均持续表现不佳（RMSE大于20蒲式耳/英亩），可能由半干旱气候、灌溉模式及光谱覆盖不全导致。除精度差异外，GNN-RNN训练速度达MMST-ViT的135倍（14分钟对31.5小时），更利于可持续部署。研究强调，时空对齐比模型复杂度或数据规模更为关键，并呼吁建立透明的分布外评估协议，以确保气候感知农业预测的公平与可靠。
原文摘要 · Abstract (English)
Climate change is increasingly disrupting agricultural systems, making accurate crop yield forecasting essential for food security. While deep learning models have shown promise in yield prediction using satellite and weather data, their ability to generalize across geographic regions and years - critical for real-world deployment - remains largely untested. We benchmark two state-of-the-art models, GNN-RNN and MMST-ViT, under realistic out-of-distribution (OOD) conditions using the large-scale CropNet dataset spanning 1,200+ U.S. counties from 2017-2022. Through leave-one-cluster-out cross-validation across seven USDA Farm Resource Regions and year-ahead prediction scenarios, we identify substantial variability in cross-region transferability. GNN-RNN demonstrates superior generalization with positive correlations under geographic shifts, while MMST-ViT performs well in-domain but degrades sharply under OOD conditions. Regions like Heartland and Northern Great Plains show stable transfer dynamics (RMSE less than 10 bu/acre for soybean), whereas Prairie Gateway exhibits persistent underperformance (RMSE greater than 20 bu/acre) across both models and crops, revealing structural dissimilarities likely driven by semi-arid climate, irrigation patterns, and incomplete spectral coverage. Beyond accuracy differences, GNN-RNN achieves 135x faster training than MMST-ViT (14 minutes vs. 31.5 hours), making it more viable for sustainable deployment. Our findings underscore that spatial-temporal alignment - not merely model complexity or data scale - is key to robust generalization, and highlight the need for transparent OOD evaluation protocols to ensure equitable and reliable climate-aware agricultural forecasting.
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