面向地球观测的地理空间基础模型（GFMs）在预训练阶段未充分表征的环境中往往表现不可靠。本文提出 SHRUG-FM，一种可靠性感知预测框架，使 GFMs 能够识别并主动规避可能失败的预测。该方法融合三种互补信号：输入空间中的地球物理分布外（OOD）检测、嵌入空间中的 OOD 检测，以及任务特定的预测不确定性。我们在三项高风险快速制图任务上评估 SHRUG-FM：火烧迹地分割、洪水制图与滑坡检测。结果表明，SHRUG-FM 在保留样本上持续降低预测风险，性能优于预测熵等经典单信号基线方法。关键在于，SHRUG-FM 采用浅层“透明盒”决策树实现信号融合，从而提供可解释的拒判阈值。该工作为 GFMs 在气候敏感型应用中的更安全、更可解释部署提供了可行路径，弥合了基准测试性能与真实世界可靠性之间的鸿沟。

Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for reliability-aware prediction that enables GFMs to identify and abstain from likely failures. Our approach integrates three complementary signals: geophysical out-of-distribution (OOD) detection in the input space, OOD detection in the embedding space, and task-specific predictive uncertainty. We evaluate SHRUG-FM across three high-stakes rapid-mapping tasks: burn scar segmentation, flood mapping, and landslide detection. Our results show that SHRUG-FM consistently reduces prediction risk on retained samples, outperforming established single-signal baselines like predictive entropy. Crucially, by utilizing a shallow "glass-box" decision tree for signal fusion, SHRUG-FM provides interpretable abstention thresholds. It builds a pathway toward safer and more interpretable deployment of GFMs in climate-sensitive applications, bridging the gap between benchmark performance and real-world reliability.