Reliable wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While machine learning approaches like Random Forest and XGBoost are widely used, they treat spatial predictions of GEDI observations from multispectral or SAR remote sensing data as independent without adapting to the varying difficulty of heterogeneous landscapes. We demonstrate these approaches generally fail to produce calibrated prediction intervals. We show that this stems from conflating ensemble variance with aleatoric uncertainty and ignoring local spatial context. To resolve this, we introduce Attentive Neural Processes (ANPs), a probabilistic meta-learning architecture that explicitly conditions predictions on local observation sets and exploits geospatial foundation model embeddings. Unlike static ensembles, ANPs learn a flexible spatial covariance function, allowing estimates to be more uncertain in complex landscapes and less in homogeneous areas. We validate this approach across five distinct biomes ranging from tropical Amazonian forests to boreal, temperate, and alpine ecosystems, demonstrating that ANPs achieve competitive accuracy while maintaining near-ideal uncertainty calibration. We demonstrate the operational utility of the method through few-shot adaptation, where the model recovers most of the performance gap in cross-region transfer using minimal local data. This work provides a scalable, theoretically rigorous alternative to ensemble variance for continental scale earth observation.

翻译：从NASA的GEDI任务中实现可靠的全域生物量密度估算，需要将稀疏的LIDAR观测数据插值到异质性地表景观中。虽然随机森林和XGBoost等机器学习方法被广泛使用，但它们将多光谱或SAR遥感数据对GEDI观测的空间预测视为独立过程，未能适应异质性地表的不同复杂程度。我们证明这些方法通常无法产生校准的预测区间。研究表明，这源于将集成方差与偶然不确定性混为一谈，并忽略了局部空间上下文。为解决这一问题，我们引入了注意力神经过程（ANPs）——一种概率元学习架构，该架构显式地将预测条件建立在局部观测集上，并利用地理空间基础模型嵌入。与静态集成方法不同，ANPs学习灵活的空间协方差函数，使得估算结果在复杂景观中具有更高不确定性，在均质区域则较低。我们在从热带亚马逊森林到北方、温带及高山生态系统的五种不同生物群落中验证了该方法，证明ANPs在保持接近理想的不确定性校准的同时，获得了具有竞争力的精度。我们通过少样本适应展示了该方法的实际应用价值：模型仅需少量本地数据即可在跨区域迁移中恢复大部分性能差距。这项工作为大陆尺度地球观测提供了可扩展、理论严谨的集成方差替代方案。