Reducing uncertainty in the estimation of aboveground biomass (AGB) stocks is required to map global aboveground carbon stocks at high spatial resolution (< 1 km) and monitor patterns of woody vegetation growth and mortality to assess the impacts of natural and anthropogenic perturbations to ecosystem dynamics. The NASA Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018 that has now been collecting science data since April 2019 and is expected to continue to at least January 2023. These observations underpin efforts by the NASA Carbon Monitoring System (CMS) to advance pantropical mapping of forest and woodland AGB and AGB change through fusion of GEDI with interferometric Synthetic Aperture Radar (InSAR) observations from current and upcoming missions. These aim to facilitate much needed improvements to national-scale carbon accounting and other monitoring, reporting and verification (MRV) activities across forest and woodland ecosystems in pantropical countries. Here we present a novel fusion approach that combines billions of GEDI measurements with high resolution InSAR data acquired between 2010 and 2019 by TanDEM-X, resulting in wall-to-wall canopy height and AGB estimates at 1 ha spatial resolution across the pantropics, including Brazil, Gabon, Mexico, Australia. We first present AGB prediction models that use GEDI measurements of canopy height and cover at the scale of field plots typically used for calibration and validation of satellite mapping of AGB. These include the footprint scale (0.0625 ha) and, through aggregation at International Space Station (ISS) orbital crossovers, the 1 and 4 ha scales specified by upcoming spaceborne InSAR missions designed for global mapping of AGB (NASA/ISRO NISAR, ESA BIOMASS). We show that the addition of GEDI measurements improved 1 ha TanDEM-X canopy height RMSE by 16.6-38.2% over pilot countries and reduced the magnitude of systematic deviations observed using TanDEM-X alone. Finally, using new models that link GEDI plot scale estimates of AGB with vertical and horizontal canopy structure metrics from TanDEM-X, and Generalized Hierarchical Model-Based inference (GHMB) to propagate uncertainty, we compare the precision of estimates achieved through our fusion approach to those achieved using GEDI or TanDEM-X alone. This study defines good practices for linking GEDI observations with those from satellite imaging SAR that are based on refined measures of quality and geolocation, and their impact on estimates of AGB uncertainty achieved through fusion of GEDI with satellite InSAR. Our approach takes full advantage of more direct estimates of structure and AGB from GEDI, and further highlights the importance of a formal and transparent framework to estimate uncertainty and enable the separation of true and spurious change in the monitoring of AGB across pantropical forest and woodland ecosystems.