Global Ensemble Digital Terrain modeling and parametrization at 30 m resolution (GEDTM30): a data fusion approach based on ICESat-2, GEDI and multisource data

Global Ensemble Digital Terrain modeling and parametrization at 30 m resolution (GEDTM30): a data fusion approach based on ICESat-2, GEDI and multisource data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Ensemble Digital Terrain modeling and parametrization at 30 m resolution (GEDTM30): a data fusion approach based on ICESat-2, GEDI and multisource data Yu-Feng Ho, Carlos H Grohmann, John Lindsay, Hannes I Reuter, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6280607/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jul, 2025 Read the published version in PeerJ → Version 1 posted You are reading this latest preprint version Abstract Production and validation of an open global ensemble digital terrain model (GEDTM30) and derived land surface parameters at ∼30 m spatial resolution is described. Copernicus DEM, ALOS World3D, and object height models were combined in a data fusion approach to generate a globally consistent DTM. This DTM was then used to compute 15 standard land surface parameters across six scales (30, 60, 120, 240, 480 and 960 m). A global-to-local transfer learning model framework with 5°×5° tiling leveraged globally distributed lidar datasets: ICESat-2 ATL08 (best-fit terrain height) and GEDI02 (lowest mode elevation), totaling over 30 billion training points. A global model was initially fitted using ICESat-2 and GEDI, followed by locally optimized models per tile, ensuring both global consistency and local accuracy. Independent validation shows that GEDTM30 reduces Copernicus DEM RMSE by about 25.4% in built-up areas, 10.0% in regions with 10–50% tree cover, and 27.3% in areas with over 50% tree cover. Compared to state-of-the-art DTMs (MERIT DEM, FABDEM and FathomDEM), GEDTM30 achieves the lowest vertical errors when assessed with GNSS station records, yielding a standard deviation of 7.77 m, an RMSE of 10.69 m, and a mean error of 7.34 m. FathomDEM exhibited the lowest vertical RMSE when validated against independent reference DTMs. GEDTM30 was further used to generate multiscale land surface parameters of topography and hydrology through an optimized tiling workflow (∼800 tiles of 600×600 km with ∼16% overlap) based on the Equi7 grid system. The entire workflow was implemented in Python using GDAL and Whitebox Workflows. Visual inspection confirmed the absence of boundary artifacts and the preservation of hydrologic connectivity. The tiling-based implementation significantly reduces computational costs of generating large-scale DTMs and derived geomorphometric variables. The GEDTM30 dataset and code are publicly available as Cloud-Optimized GeoTIFFs via Zenodo and the OpenLandMap STAC. Further fusion with local lidar-based DTMs and national DTMs is recommended to enhance local accuracy and level of detail. Geomorphology Artificial Intelligence and Machine Learning Geographic Information Systems digital elevation model digital terrain model land surface parameter hydrology topography geomorphometry open data data fusion machine learning transfer learning Full Text Additional Declarations The authors declare potential competing interests as follows: Yu-Feng Ho, Leandro Parente, Martijn Witjes, and Tomislav Hengl are employed by OpenGeoHub. Cite Share Download PDF Status: Published Journal Publication published 23 Jul, 2025 Read the published version in PeerJ → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6280607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432307743,"identity":"879d9b9e-7ace-4327-9733-00941eb8445e","order_by":0,"name":"Yu-Feng 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Copernicus DEM, ALOS World3D, and object height models were combined in a data fusion approach to generate a globally consistent DTM. This DTM was then used to compute 15 standard land surface parameters across six scales (30, 60, 120, 240, 480 and 960 m). A global-to-local transfer learning model framework with 5°×5° tiling leveraged globally distributed lidar datasets: ICESat-2 ATL08 (best-fit terrain height) and GEDI02 (lowest mode elevation), totaling over 30 billion training points. A global model was initially fitted using ICESat-2 and GEDI, followed by locally optimized models per tile, ensuring both global consistency and local accuracy. Independent validation shows that GEDTM30 reduces Copernicus DEM RMSE by about 25.4% in built-up areas, 10.0% in regions with 10–50% tree cover, and 27.3% in areas with over 50% tree cover. 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The GEDTM30 dataset and code are publicly available as Cloud-Optimized GeoTIFFs via Zenodo and the OpenLandMap STAC. 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