Monitoring deforestation-driven carbon emissions requires both spatially explicit and temporally continuous estimates of aboveground biomass density (AGBD) with calibrated uncertainty. NASA's Global Ecosystem Dynamics Investigation (GEDI) provides reliable LIDAR-derived AGBD, but its orbital sampling causes irregular spatiotemporal coverage, and occasional operational interruptions, including a 13-month hibernation from March 2023 to April 2024, leave extended gaps in the observational record. Prior work has used machine learning approaches to fill GEDI's spatial gaps using satellite-derived features, but temporal interpolation of biomass through unobserved periods, particularly across active disturbance events, remains largely unaddressed. Moreover, standard ensemble methods for biomass mapping have been shown to produce systematically miscalibrated prediction intervals. To address these gaps, we extend the Attentive Neural Process (ANP) framework, previously applied to spatial biomass interpolation, to jointly sparse spatiotemporal settings using geospatial foundation model embeddings. We treat space and time symmetrically, empirically validating a form of space-for-time substitution in which observations from nearby locations at other times inform predictions at held-out periods. Our results demonstrate that the ANP produces well-calibrated uncertainty estimates across disturbance regimes, supporting its use in Measurement, Reporting, and Verification (MRV) applications that require reliable uncertainty quantification for forest carbon accounting.

翻译：监测森林砍伐驱动的碳排放需要同时对地上生物量密度（AGBD）进行空间显式且时间连续的估算，并具有校准的不确定性。NASA的全球生态系统动态调查（GEDI）提供了可靠的基于激光雷达的AGBD数据，但其轨道采样导致时空覆盖不规则，且偶尔的运行中断（包括2023年3月至2024年4月长达13个月的休眠期）在观测记录中留下了持续缺口。以往研究采用机器学习方法利用卫星衍生特征填补GEDI的空间缺口，但通过未观测时段（尤其是活跃干扰事件期间）对生物量进行时间插补仍未得到充分解决。此外，用于生物量制图的标准集成方法已被证明会产生系统性校准偏差的预测区间。为解决这些问题，我们将先前应用于空间生物量插补的注意力神经过程（ANP）框架扩展至联合稀疏时空场景，利用地理空间基础模型嵌入。我们对空间和时间进行对称处理，实证验证了一种空间换时间的替代形式，即利用其他时段邻近位置的观测值来推断保留时段的预测结果。结果表明，ANP能在不同干扰机制下生成良好校准的不确定性估计，支持其在需要可靠不确定性量化进行森林碳核算的测量、报告与核查（MRV）应用中的使用。