Accurate estimation of forest aboveground biomass (AGB) is vital for informing ecosystem and carbon management. The Global Ecosystem Dynamics Investigation (GEDI) instrument—a new-generation spaceborne lidar system from NASA—provides the first global coverage of high-resolution 3D altimetry data aimed specifically for mapping Earth’s forests, but its performance is yet to be tested for large parts of the world. Here, our goal is to evaluate the accuracies of GEDI in measuring terrain, forest vertical structures, and AGB in reference to independent airborne lidar data over temperate and tropical forests in North America. We compared GEDI-derived elevations and canopy heights (e.g., relative height percentiles such as RH50 and RH100) with those from the Shuttle Radar Topography Mission (SRTM) or from two airborne lidar systems: the Laser Vegetation Imaging Sensor (LVIS) and Goddard’s Lidar, Hyperspectral and Thermal system (G-LiHT). We also estimated GEDI’s geolocation errors by matching GEDI waveforms and G-LiHT pseudo-waveforms. We assessed the predictive power of GEDI metrics in estimating AGB using Random Forests regression. Results showed that GEDI-derived ground elevations correlated strongly those from LVIS, G-LiHT, and LVIS (R² > 0.91), but with nonnegligible RMSEs of 5.7 m (G-LiHT), 3.1 m (LVIS), and 10.9 m (SRTM). GEDI canopy heights had poorer correlation with LVIS (e.g., R² = 0.44 for RH100) than with G-LiHT (e.g., R² = 0.60 for RH100). The estimated horizontal geolocation errors of GEDI footprints averaged 6.5 meters, comparable to the nominal accuracy of 9 m. Correction for the locational errors improved the correlation of GEDI vs G-LiHT canopy heights significantly, on average by 53% (e.g., R² from 0.57 to 0.82 for RH50). GEDI canopy metrics were useful for predicting AGB (R² = 0.82 and RMSE = 19.1 Mg/Ha), with the maximum canopy height RH100 being the most useful predictor. Our results highlight the importance of accommodating or correcting for GEDI geolocation errors for estimating forest characteristics and provide empirical evidence on the utility of GEDI for monitoring global biomass dynamics from space.

Multi-angle optical reflectance measurements such as those from the NASA moderate resolution imaging spectroradiometer (MODIS) are sensitive to forest 3D structures, potentially serving as a useful proxy to estimate forest structural variables such as aboveground biomass (AGB)—a potential theoretically recognized but rarely explored. In this paper, we examined the effectiveness of the reconstructed MODIS typical-angle reflectances—reflectances observed from the hotspot, darkspot, and nadir directions—for estimating forest AGB from both theoretical and practical perspectives. To gain theoretical insights, we first tested the sensitivities of typical-angle reflectances to forest AGB through simulations using the 4-scale bidirectional reflectance distribution function (BRDF) model. We then built statistical models to fit the relationship between MODIS multi-angle observations and field-measured deciduous-broadleaf/mixed-temperate forest AGB at five sites in the eastern USA, assisted by a semivariogram analysis to determine the effect of pixel heterogeneity on the MODIS–AGB relationship. We also determined the effects of terrain and season on the predictive relationships. Our results indicated that multi-angle reflectances with fewer visible shadows yielded better AGB estimates (hotspot: R² = 0.63, RMSE = 54.28 Mg/ha; nadir: R² = 0.55, RMSE = 59.95 Mg/ha; darkspot: R² = 0.46, RMSE = 65.66 Mg/ha) after filtering out the effects of complex terrain and pixel heterogeneity; the MODIS typical-angle reflectances in the NIR band were the most sensitive to forest AGB. We also found strong sensitivities of estimated accuracies to MODIS image acquisition dates or season. Overall, our results suggest that the current practice of leveraging only single-angle MODIS data can be a suboptimal strategy for AGB estimation. We advocate the use of MODIS multi-angle reflectances for optical remote sensing of forest AGB or potentially other ecological applications requiring forest structure information.