面向地球观测的地理空间基础模型在预训练数据中未充分覆盖的环境中往往表现不可靠。我们提出SHRUG-FM框架，实现可靠性感知的预测，该框架整合了三种互补信号：输入空间中的分布外（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。应用于烧毁迹地分割任务时，SHRUG-FM表明OOD评分与特定环境条件下的性能下降相关，而基于不确定性的标记有助于剔除大量表现不佳的预测结果。将这些标记与HydroATLAS提供的土地覆盖属性关联分析发现，模型失败并非随机分布，而是集中于某些地理区域，如低海拔地带和大型河流区域，这很可能是由于预训练数据中对此类区域的代表性不足所致。SHRUG-FM为气候敏感应用中基础地理模型（GFM）的安全且可解释的部署提供了可行路径，有助于弥合基准性能与实际可靠性之间的差距。

Geospatial foundation models for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for reliability-aware prediction that integrates three complementary signals: out-of-distribution (OOD) detection in the input space, OOD detection in the embedding space and task-specific predictive uncertainty. Applied to burn scar segmentation, SHRUG-FM shows that OOD scores correlate with lower performance in specific environmental conditions, while uncertainty-based flags help discard many poorly performing predictions. Linking these flags to land cover attributes from HydroATLAS shows that failures are not random but concentrated in certain geographies, such as low-elevation zones and large river areas, likely due to underrepresentation in pretraining data. SHRUG-FM provides a pathway toward safer and more interpretable deployment of GFMs in climate-sensitive applications, helping bridge the gap between benchmark performance and real-world reliability.