Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance

TL;DR

This paper extends Neural Processes to jointly interpolate biomass data across space and time, providing well-calibrated uncertainty estimates crucial for forest carbon monitoring amid data gaps and disturbances.

Contribution

It introduces a spatiotemporal Neural Process framework that leverages foundation model embeddings to improve biomass estimation during observational gaps and disturbances.

Findings

ANP achieves well-calibrated uncertainty across disturbance regimes.

The model effectively uses space-for-time substitution with nearby observations.

Results support ANP's application in forest carbon MRV tasks.

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.