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Geolocation error in spaceborne sampling light detection and ranging (LiDAR)measurements of forest structure can compromise forest attribute estimates anddegrade integration with georeferenced field measurements or other remotelysensed data. Data integration is especially problematic when geolocation erroris not well quantified. We propose a general model that uses airborne laserscanning (ALS) data to quantify and correct geolocation error in spacebornesampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDARHyperspectral & Thermal Imager (G-LiHT) was used with a subset of LiDAR datafrom NASA's Global Ecosystem Dynamics Investigation (GEDI). The modelaccommodates multiple canopy height metrics derived from a simulated GEDIfootprint kernel using spatially coincident G-LiHT, and incorporates bothadditive and multiplicative mapping between the canopy height metrics generatedfrom both datasets. A Bayesian implementation provides probabilisticuncertainty quantification in both parameter and geolocation error estimates.Results show a systematic geolocation error of 9.62 m in the southwestdirection. In addition, estimated geolocation errors within GEDI footprintswere highly variable, with results showing a ~0.45 probability the truefootprint center is within 20 m. Estimating and correcting geolocation errorvia the model outlined here can help inform subsequent efforts to integratespaceborne LiDAR data, like GEDI, with other georeferenced data.

Quantifying and correcting geolocation error in spaceborne LiDAR forest  canopy observations using high spatial accuracy ALS: A Bayesian model  approach