GEDICorrect: A Scalable Python Tool for Orbit-, Beam-, and Footprint-Level GEDI Geolocation Correction

TL;DR

GEDICorrect is a scalable Python tool that significantly improves the geolocation accuracy of GEDI LiDAR data at multiple levels, enhancing vegetation and terrain metrics while being computationally efficient and compatible with existing data products.

Contribution

The paper introduces GEDICorrect, a flexible and efficient framework that extends existing GEDI modules for improved geolocation correction at orbit, beam, and footprint levels.

Findings

Improved canopy height accuracy with R^2 up to 0.78.

Reduced RMSE of terrain elevation by approximately 0.35 meters.

Achieved a 19.5-fold speedup over previous methods.

Abstract

Accurate geolocation is essential for the reliable use of GEDI LiDAR data in footprint-scale applications such as aboveground biomass modeling, data fusion, and ecosystem monitoring. However, residual geolocation errors arising from both systematic biases and random ISS-induced jitter can significantly affect the accuracy of derived vegetation and terrain metrics. The main goal of this study is to develop and evaluate a flexible, computationally efficient framework (GEDICorrect) that enables geolocation correction of GEDI data at the orbit, beam, and footprint levels. The framework integrates existing GEDI Simulator modules (gediRat and gediMetrics) and extends their functionality with flexible correction logic, multiple similarity metrics, adaptive footprint clustering, and optimized I/O handling. Using the Kullback--Leibler divergence as the waveform similarity metric, GEDICorrect improved canopy height (RH95) accuracy from $R^2 = 0.61$ (uncorrected) to 0.74 with the orbit-level correction, and up to $R^2 = 0.78$ with the footprint-level correction, reducing RMSE from 2.62~m ($rRMSE = 43.13\%$) to 2.12~m ($rRMSE = 34.97\%$) at the orbit level, and 2.01~m ($rRMSE = 33.05\%$) at the footprint level. Terrain elevation accuracy also improved, decreasing RMSE by 0.34~m relative to uncorrected data and by 0.37~m compared to the GEDI Simulator baseline. In terms of computational efficiency, GEDICorrect achieved a $\sim2.4\times$ speedup over the GEDI Simulator in single-process mode (reducing runtime from $\sim84$~h to $\sim35$~h) and scaled efficiently to 24 cores, completing the same task in $\sim4.3$~h -- an overall $\sim19.5\times$ improvement. GEDICorrect offers a robust and scalable solution for improving GEDI geolocation accuracy while maintaining full compatibility with standard GEDI data products.