ICESat-2 Controlled Integration of GEDI and SRTM Data for Large-Scale Digital Elevation Model Generation

Recent developments in spaceborne laser altimetry have revolutionized the way DEMs can be created with unprecedented productivity and accuracy. This paper aims to develop a holistic mathematic framework that is able to combine current multiple space-based terrain data sources for large-scale DEM generation. To achieve this goal, the developed framework can accommodate the heterogeneity of the involved multi-sensor data. Under the context model estimation or regression, the ICESat-2 ATL08 terrain dataset is treated as the observations for the target variable, while the predictor variables are derived from the GEDI Level 2A terrain data and the SRTM DEM. Three different models are then applied to determine their performance and identify the most accurate and robust one. Comparative evaluation for areas of over 11,000 square kilometers demonstrates that the support vector regression approach consistently yields superior and satisfactory results, surpassing the quality of current SRTM 30 m and 90 m DEMs. Using the 3DEP DEM as independent reference, the corrected SRTM DEMs exhibit a substantial reduction in the mean of the DEM error by one order of magnitude (~10 times), and a 27% to 36% significant improvement in RMSE. As for the corrected GEDI L2A terrain data, it achieves an exceptional accuracy of -0.4±1.4 m for plain suburban area and -0.6±9.3 m for mountain area. This study underscores the benefits of integrating multiple spaceborne altimeter data and the necessity of adopting holistic data integration models for such purpose.