Spatiotemporal Interpolation of GEDI Biomass with Calibrated Uncertainty

arXiv cs.LG / 4/7/2026

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Key Points

The paper addresses the need for spatially explicit and temporally continuous GEDI aboveground biomass density (AGBD) estimates, including periods of missing data caused by irregular sampling and a 13-month GEDI hibernation from March 2023 to April 2024.
It argues that prior machine-learning gap-filling has not adequately handled temporal interpolation across unobserved intervals, especially during active disturbance events.
The authors extend the Attentive Neural Process (ANP) framework to a jointly sparse spatiotemporal interpolation setting, using geospatial foundation model embeddings and treating space and time symmetrically.
They validate a space-for-time substitution strategy, showing that observations from nearby locations at other times can inform held-out temporal predictions.
The approach produces well-calibrated uncertainty estimates across disturbance regimes, aiming to support MRV workflows that require reliable uncertainty quantification for forest carbon accounting.

Abstract

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.