Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-4331-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-4331-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
05 Sep 2025
Data description article |
| 05 Sep 2025
| 05 Sep 2025
A typology of global relief classes derived from digital elevation models at 1 arcsec resolution
Xin Yang
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Junfei Ma
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Yang Chen
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Xingyu Zhou
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Fayuan Li
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Liyang Xiong
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Chenghu Zhou
Institute of Geographical Information Science and Natural Resources, Chinese Academy of Science, Beijing 100101, China
Guoan Tang
CORRESPONDING AUTHOR
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing 210023, China
School of Geography, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Michael E. Meadows
CORRESPONDING AUTHOR
School of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China
Department of Environmental & Geographical Science, University of Cape Town, Rondebosch 7701, South Africa
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Cited
6 citations as recorded by crossref.
- Evaluating Scale Transferability and Observation-Based Calibration in High-Resolution PM2.5 Downscaling Y. Jiang et al. https://doi.org/10.3390/rs18172876
- Cartographic modeling of the spatial structural organization of landscapes of the Dnipropetrovsk region O. Syzenko https://doi.org/10.17721/phgg.2026.1/131.03
- A transformer-based landform mapping method combining topographic knowledge and digital elevation data H. Wang et al. https://doi.org/10.1016/j.jag.2025.104974
- TIDA-SR: A time-conditioned deformable attention network for DEM super-resolution in cloud-covered mountainous regions K. Chen et al. https://doi.org/10.1016/j.rse.2026.115254
- Representation of relief by geomorphometric archetypes: a ‘new’ vision of geomorphological mapping in Brazil G. Soares Neto et al. https://doi.org/10.1080/17445647.2026.2626113
- An explainable correction and fusion framework for global bare-earth DTM generation in mountain areas J. Chen et al. https://doi.org/10.1016/j.rse.2026.115364
6 citations as recorded by crossref.
- Evaluating Scale Transferability and Observation-Based Calibration in High-Resolution PM2.5 Downscaling Y. Jiang et al. https://doi.org/10.3390/rs18172876
- Cartographic modeling of the spatial structural organization of landscapes of the Dnipropetrovsk region O. Syzenko https://doi.org/10.17721/phgg.2026.1/131.03
- A transformer-based landform mapping method combining topographic knowledge and digital elevation data H. Wang et al. https://doi.org/10.1016/j.jag.2025.104974
- TIDA-SR: A time-conditioned deformable attention network for DEM super-resolution in cloud-covered mountainous regions K. Chen et al. https://doi.org/10.1016/j.rse.2026.115254
- Representation of relief by geomorphometric archetypes: a ‘new’ vision of geomorphological mapping in Brazil G. Soares Neto et al. https://doi.org/10.1080/17445647.2026.2626113
- An explainable correction and fusion framework for global bare-earth DTM generation in mountain areas J. Chen et al. https://doi.org/10.1016/j.rse.2026.115364
Saved (final revised paper)
Latest update: 15 Sep 2026
Short summary
Surveys of global relief classes are important for understanding the morphology and land processes of Earth's surface. This study proposes a novel framework integrating the accumulated slope and a new surface relief index for global relief classification (GRC) with 1 arcsec resolution. This dataset can provide abundant and detailed land surface information for the field of Earth sciences.
Surveys of global relief classes are important for understanding the morphology and land...
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