Articles | Volume 17, issue 5
https://doi.org/10.5194/essd-17-2147-2025
© 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-17-2147-2025
© 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
| 
21 May 2025
Data description article |
| 21 May 2025
| 21 May 2025
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
Yifan Cheng
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Institute for Sustainability, Energy, and Environment (iSEE), University of Illinois Urbana-Champaign, Urbana, IL, USA
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL, USA
Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Keith Oleson
Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA
Matthias Demuzere
B-Kode VOF, Ghent, Belgium
Xiaoping Liu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yangzi Che
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Yuyu Zhou
Department of Geography, The University of Hong Kong, 999077, Hong Kong SAR, China
Xinchang “Cathy” Li
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
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- Optimized LCZ mapping with automated machine learning reveals thermal disparities during a heatwave event J. Xia et al. https://doi.org/10.1016/j.uclim.2026.103001
- Divergent surface urban heat island responses to background aerosol mitigation S. Liu et al. https://doi.org/10.1038/s44284-026-00463-7
- Dynamic parameterization of global land surface albedo components: Bare soil, non-photosynthetic vegetation, and photosynthetic vegetation A. Jia et al. https://doi.org/10.1016/j.rse.2025.114943
- Linking urban population exposure to heatwaves with land cover change across the UK Y. Sun et al. https://doi.org/10.1016/j.scs.2026.107349
- Wind tunnel study of wind flow over low- to high-rise building arrays using PIV and POD analysis Y. Zhang et al. https://doi.org/10.1016/j.buildenv.2026.114474
- Systematic evaluation of atmospheric forcing, surface datasets, and mesh effects on kilometer-scale land surface and river modeling L. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135500
- Analytical view factors in urban canopy models with trees: Model development and case study in Brooklyn, New York City C. Wang et al. https://doi.org/10.1016/j.buildenv.2026.114710
- Unraveling the intractable trilemma in urban weather and climate modeling P. Li et al. https://doi.org/10.1038/s42949-026-00388-z
- GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model W. Liao et al. https://doi.org/10.5194/essd-17-2535-2025
- Filtering non-urban signals reveals strong urbanization impacts on long-term near-surface wind speed changes over eastern China S. Fu & D. Chen https://doi.org/10.1016/j.accre.2026.06.014
- Leveraging automated machine learning (AutoML) for urban climate emulation J. Yu et al. https://doi.org/10.1016/j.bdes.2025.100040
- Divergent humidity-driven growth in urban air-conditioning energy demand under climate change X. Li et al. https://doi.org/10.1038/s44284-026-00474-4
- A Comprehensive Assessment of Global 10-m Impervious Surface Products: Accuracy, Spatial Consistency, and Variability Under a Unified Evaluation Framework Y. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3697409
- Interactions between background climate, urban morphology, and rain garden cooling potential L. Gray et al. https://doi.org/10.1088/2634-4505/ae7fb4
- Correcting angular effects on MODIS LST in urban areas using an enhanced time-evolving parametric geometric model J. Qin et al. https://doi.org/10.1016/j.rse.2025.115103
- Study on parameter calibration of urban land surface models based on multi-objective bayesian optimization Q. Li et al. https://doi.org/10.1016/j.buildenv.2025.114121
17 citations as recorded by crossref.
- Urban population exposure to apparent temperature whiplash is projected to increase with global warming Y. Yang & R. Zhao https://doi.org/10.1038/s43247-025-03066-7
- Optimized LCZ mapping with automated machine learning reveals thermal disparities during a heatwave event J. Xia et al. https://doi.org/10.1016/j.uclim.2026.103001
- Divergent surface urban heat island responses to background aerosol mitigation S. Liu et al. https://doi.org/10.1038/s44284-026-00463-7
- Dynamic parameterization of global land surface albedo components: Bare soil, non-photosynthetic vegetation, and photosynthetic vegetation A. Jia et al. https://doi.org/10.1016/j.rse.2025.114943
- Linking urban population exposure to heatwaves with land cover change across the UK Y. Sun et al. https://doi.org/10.1016/j.scs.2026.107349
- Wind tunnel study of wind flow over low- to high-rise building arrays using PIV and POD analysis Y. Zhang et al. https://doi.org/10.1016/j.buildenv.2026.114474
- Systematic evaluation of atmospheric forcing, surface datasets, and mesh effects on kilometer-scale land surface and river modeling L. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135500
- Analytical view factors in urban canopy models with trees: Model development and case study in Brooklyn, New York City C. Wang et al. https://doi.org/10.1016/j.buildenv.2026.114710
- Unraveling the intractable trilemma in urban weather and climate modeling P. Li et al. https://doi.org/10.1038/s42949-026-00388-z
- GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model W. Liao et al. https://doi.org/10.5194/essd-17-2535-2025
- Filtering non-urban signals reveals strong urbanization impacts on long-term near-surface wind speed changes over eastern China S. Fu & D. Chen https://doi.org/10.1016/j.accre.2026.06.014
- Leveraging automated machine learning (AutoML) for urban climate emulation J. Yu et al. https://doi.org/10.1016/j.bdes.2025.100040
- Divergent humidity-driven growth in urban air-conditioning energy demand under climate change X. Li et al. https://doi.org/10.1038/s44284-026-00474-4
- A Comprehensive Assessment of Global 10-m Impervious Surface Products: Accuracy, Spatial Consistency, and Variability Under a Unified Evaluation Framework Y. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3697409
- Interactions between background climate, urban morphology, and rain garden cooling potential L. Gray et al. https://doi.org/10.1088/2634-4505/ae7fb4
- Correcting angular effects on MODIS LST in urban areas using an enhanced time-evolving parametric geometric model J. Qin et al. https://doi.org/10.1016/j.rse.2025.115103
- Study on parameter calibration of urban land surface models based on multi-objective bayesian optimization Q. Li et al. https://doi.org/10.1016/j.buildenv.2025.114121
Saved (final revised paper)
Latest update: 07 Sep 2026
Short summary
The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
The absence of globally consistent and spatially continuous urban surface input has long...
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