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：监测毁林导致的碳排放需要具备空间明确且时间连续的地上生物量密度（AGBD）估算，并附有校准后的不确定性。NASA的全球生态系统动力学调查（GEDI）提供了可靠的激光雷达（LIDAR）衍生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）在观测记录中留下长期空白。先前研究利用机器学习方法，借助卫星衍生特征填补GEDI的空间缺失，但通过未观测时段（尤其是活跃扰动事件期间）对生物量进行时间插值仍有待解决。此外，用于生物量绘图的集成方法已被证明会产生系统性失准的预测区间。为解决这些问题，我们扩展了此前用于空间生物量插值的注意力神经过程（ANP）框架，利用地理空间基础模型嵌入将其应用于稀疏联合时空场景。我们对称处理空间与时间，通过实证验证了"以时间换空间"的替代形式：即利用其他时间邻近位置观测值推断保留时段预测。结果表明，ANP能在扰动事件间生成校准良好的不确定性估算，支持其在需要可靠森林碳核算不确定性量化的测量、报告与核查（MRV）应用中投入使用。