Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5357-2024
© Author(s) 2024. 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-16-5357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
25 Nov 2024
Data description article |
| 25 Nov 2024
| 25 Nov 2024
3D-GloBFP: the first global three-dimensional building footprint dataset
Yangzi Che
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Xuecao Li
College of Land Science and Technology, China Agricultural University, Beijing, 100083, China
Xiaoping Liu
CORRESPONDING AUTHOR
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Yuhao Wang
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Weilin Liao
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Xianwei Zheng
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China
Xucai Zhang
Department of Geography, Ghent University, 9000 Ghent, Belgium
Xiaocong Xu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Qian Shi
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Jiajun Zhu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Honghui Zhang
School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou, 510006, China
Guangdong Engineering Center for Intelligent Spatial Planning, Guangdong Guodi Planning Science Technology Co. Ltd, Guangzhou, 510651, China
Hua Yuan
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, 510275, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, 510275, China
Related authors
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
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.
Hongbin Liang, Shulei Zhang, Omarjan Obulkasim, Qiancheng Ye, Linyi Song, Zhongwang Wei, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-694, https://doi.org/10.5194/essd-2026-694, 2026
Preprint under review for ESSD
Short summary
Short summary
China has built the world’s largest reservoir system, yet modeling of its climate and environmental impacts lacks attribute values. We developed a database of 5,142 reservoirs across China with over 20 key attributes, covering 87.5 % of total storage and 62 % of hydropower capacity. It enables more realistic identification of reservoir-served irrigation areas and improves streamflow simulations, offering a valuable resource for hydrology, agriculture, energy, climate, and environmental research.
Xiaocong Xu, Shiyu He, Jinpei Ou, Yan Zhou, and Xiaoping Liu
Earth Syst. Sci. Data, 18, 6995–7019, https://doi.org/10.5194/essd-18-6995-2026, https://doi.org/10.5194/essd-18-6995-2026, 2026
Short summary
Short summary
We developed annual population maps for mainland China from 1990 to 2020 at 500-metre detail. A key advance is the use of changing three-dimensional building volume, allowing the maps to distinguish densely populated areas with extensive high-rise development that conventional maps based mainly on two-dimensional built-up land may overlook. Combined with census and other data, the maps show high accuracy and can support studies of urban growth, environmental change, and disaster risk.
Zhilong Fan, Lu Li, Zhongwang Wei, Shupeng Zhang, Sisi Chen, Xingjie Lu, Wei Shangguan, Nan Wei, Hua Yuan, Pan Zhang, Jiaxin Tian, Jianhong Zhou, Baoxiang Pan, Wei Han, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-5358, https://doi.org/10.5194/egusphere-2026-5358, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a new land data assimilation framework based on deep generative models. Compared with existing methods, it better handles non-Gaussian model errors and noisy observations. Tests in both an idealized system and a realistic land data assimilation experiment showed more accurate and consistent estimates, demonstrating its potential for improving land-state estimation with complex land surface models.
Xiaoying Shi, Daniel M. Ricciuto, Yaoping Wang, Joe R. Melton, Fortunat Joos, Yiqi Luo, Xiaofeng Xu, Dafeng Hui, Qianlai Zhuang, Chunjing Qiu, Hongxing He, Siya Shao, Xingjie Lu, Yongjiu Dai, Akihiko Ito, Qing Sun, Nitin Chaudhary, Ayesha Hussain, Mousong Wu, Jian Zhou, Jingwei Zhang, Ye Yuan, Rachael H. Turton, Eleanor J. Burke, Jiafu Mao, Yuefeng Hao, Natalie A. Griffiths, Verity Salmon, Paul J. Hanson, Melanie A. Mayes, and Peter E. Thornton
EGUsphere, https://doi.org/10.5194/egusphere-2026-5193, https://doi.org/10.5194/egusphere-2026-5193, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Northern peatlands store vast amounts of carbon, but their response to climate change remains uncertain. We compared 15 ecosystem models with observations from a long-term warming and elevated carbon dioxide experiment in Minnesota. Models differed widely in their carbon responses, reflecting differences in ecosystem processes and model parameters. The results identify priorities for improving peatland models and projections of future carbon–climate feedbacks.
Aoqi Sun, Wenjie Xu, Enze Ma, Hua Yuan, and Chen Yang
Hydrol. Earth Syst. Sci., 30, 5215–5227, https://doi.org/10.5194/hess-30-5215-2026, https://doi.org/10.5194/hess-30-5215-2026, 2026
Short summary
Short summary
Groundwater is often viewed as a hidden reserve that supports evapotranspiration and streamflow during dry periods. We show that sustained warming and greening can weaken this buffering role. As groundwater levels decline, links between shallow and deeper stores reorganize, reducing older groundwater inputs to streams and evapotranspiration. Over time, water cycling shifts toward shallower, faster pathways, potentially lowering system resilience and predictability under long-term climate stress.
Yizhi Zhang, Yi Wang, Quanhua Dong, Xiao-Jian Chen, Fan Zhang, Xuecao Li, and Yu Liu
Earth Syst. Sci. Data, 18, 5329–5355, https://doi.org/10.5194/essd-18-5329-2026, https://doi.org/10.5194/essd-18-5329-2026, 2026
Short summary
Short summary
China’s cities have transformed dramatically over the past 30 years. Using multi-source satellite data and machine learning, this study maps annual building heights at 30-meter detail from 1990 to 2019, revealing both horizontal and vertical urban growth. The open dataset offers new insights into how Chinese cities expand and renew, supporting research and planning for urban development.
Xueqi Cao, Nan Wei, Hua Yuan, Shupeng Zhang, Zhongwang Wei, Xingjie Lu, Lu Li, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-4210, https://doi.org/10.5194/egusphere-2026-4210, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Lakes influence weather by storing and releasing heat, but models often oversimplify how sunlight travels through water, snow, and ice. We improved how a model handles light entering a lake. Tests across nine diverse lakes show surface waters warm more, deep waters warm less, and under-ice heating is better captured. Benefits vary among lakes, so better light treatment must pair with better mixing and heat storage.
Yanan Wen, Tuo Chen, Xuecao Li, Tiecheng Bai, Ke Yao, Liheng Zhong, Han Chen, Meiling Liu, Xieqin Huang, Shunlin Liang, Shuangxi Miao, and Jianxi Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-347, https://doi.org/10.5194/essd-2026-347, 2026
Revised manuscript accepted for ESSD
Short summary
Short summary
We developed a new method to track winter wheat growth across the world from 2001 to 2020. Using satellite data, we produced high-resolution maps (1 km) that show exactly where and how much winter wheat was planted over the last two decades. The results proved to be highly accurate when compared against international agricultural records. As the first long-term, global view of its kind, this map series is a vital tool for scientists and policymakers working to eliminate hunger.
Hongquan Cheng, Mengqing Geng, Xuecao Li, Shijie Li, Min Zhao, Chen Lin, Jie Wang, Peng Gong, and Yuyu Zhou
Earth Syst. Sci. Data, 18, 3449–3479, https://doi.org/10.5194/essd-18-3449-2026, https://doi.org/10.5194/essd-18-3449-2026, 2026
Short summary
Short summary
Monthly records of nighttime light are scarce, especially over long periods, yet they are vital for tracking short-term economic shifts and seasonal urban change. This study provides a temporally consistent global 500 m-resolution monthly VIIRS (Visible Infrared Imaging Radiometer Suite)-like nighttime light dataset (1992–2024). By combining DMSP (Defense Meteorological Satellite Program) and VIIRS through reconstruction and correction, a consistent long-term record is created. The dataset supports improved analysis of urban growth and economic activity worldwide.
Chen Yang, Hui Huang, Zeyu Tang, Yongjiu Dai, and Xiaobin Chang
EGUsphere, https://doi.org/10.31223/X5PR3B, https://doi.org/10.31223/X5PR3B, 2026
Short summary
Short summary
Groundwater plays an important role in how water and energy move between land and atmosphere, but simulating it in detail is very slow. We developed a faster method that keeps the key processes while reducing computing cost. Tested in a large river basin in China, it ran about twenty times faster and still closely matched detailed simulations. This approach makes it easier to include groundwater in large-scale and long-term environmental modeling.
Zezhen Wu, Zhongwang Wei, Xingjie Lu, Nan Wei, Lu Li, Shupeng Zhang, Hua Yuan, Shaofeng Liu, and Yongjiu Dai
Hydrol. Earth Syst. Sci., 30, 2651–2666, https://doi.org/10.5194/hess-30-2651-2026, https://doi.org/10.5194/hess-30-2651-2026, 2026
Short summary
Short summary
Land surface models simulate exchanges at the Earth's surface. Traditional evaluation methods can be misleading because they may hide errors or be overly sensitive to outliers. We introduce the Model Fidelity Metric, which measures model performance in terms of accuracy, variability and distribution similarity. Tests with synthetic data and real streamflow observations show that this metric provides more stable and informative assessments of model performance.
Tingting Wu, Shupeng Zhang, Xiaofan Yang, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-2275, https://doi.org/10.5194/egusphere-2026-2275, 2026
Short summary
Short summary
We coupled a land surface model (CoLM) with an analysis tool (PSUADE) to evaluate processes controlling water table depth (WTD) through sensitivity analysis and parameter optimization. A small set of sensitive parameters governing WTD was identified, showing subsurface runoff controls average groundwater levels, while soil properties govern variability. A stepwise calibration was then suggested to improve accuracy. This framework offers a transferable pathway to enhance groundwater simulation.
Chen Yang, Aoqi Sun, Shupeng Zhang, Yongjiu Dai, Stefan Kollet, and Reed Maxwell
Geosci. Model Dev., 19, 1849–1866, https://doi.org/10.5194/gmd-19-1849-2026, https://doi.org/10.5194/gmd-19-1849-2026, 2026
Short summary
Short summary
Groundwater plays a key role in land–atmosphere water and energy exchange, yet it is often simplified in large-scale Earth system models. We review 20 years of efforts to couple the groundwater model ParFlow with land surface and atmospheric models, showing how groundwater dynamics shape terrestrial fluxes. We also present an updated coupling framework that enhances model performance and flexibility, and outline a modular strategy to guide future development.
Yaotong Cai, Baoni Li, Xiaoye Liu, Xin Jiang, Yakun Zhu, Shuxin Luo, Yingzuo Qin, Shudi Xie, Jianhuai Ye, Huizhong Shen, Zhilin Guo, Xiaoping Liu, and Zhenzhong Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-78, https://doi.org/10.5194/essd-2026-78, 2026
Preprint under review for ESSD
Short summary
Short summary
Persistent clouds and small farm sizes in Southeast Asia have historically hindered accurate mapping. We combined satellite imagery with advanced artificial intelligence to produce the first 10-meter annual cropland map for the region (2019–2024). Achieving over 92 % accuracy, our dataset reveals three to four times more farming on steep slopes than previously reported, providing a vital baseline for monitoring food security and deforestation risks.
Wanyi Lin, Hua Yuan, Wenzong Dong, Zhuo Liu, Jiayi Xiang, Xinran Yu, Shupeng Zhang, Zhongwang Wei, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-21, https://doi.org/10.5194/essd-2026-21, 2026
Manuscript not accepted for further review
Short summary
Short summary
Land surface models require information on vegetation composition and seasonal leaf area dynamics, yet long-term high-resolution datasets remain limited. We present a global dataset of plant functional types and corresponding leaf area index for 1985–2020, derived from multiple satellite observations. The dataset reduces uncertainties in vegetation representation and improves the description of phenological dynamics, supporting more realistic land surface and climate modeling.
Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang
Earth Syst. Sci. Data, 17, 6993–7018, https://doi.org/10.5194/essd-17-6993-2025, https://doi.org/10.5194/essd-17-6993-2025, 2025
Short summary
Short summary
China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term high-resolution data on their biomass have been limited. We developed a 30 m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibao Liu, and Yongjiu Dai
Geosci. Model Dev., 18, 6517–6540, https://doi.org/10.5194/gmd-18-6517-2025, https://doi.org/10.5194/gmd-18-6517-2025, 2025
Short summary
Short summary
Land surface models are used for simulating how Earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes the evaluation process more comprehensive and efficient, stands out by incorporating various factors and working with data at any scale, enabling scientists to incorporate new types of models and measurements as our understanding of Earth's systems evolves.
Shuyang Guo, Yongjiu Dai, Hua Yuan, and Hongbin Liang
The Cryosphere, 19, 3553–3570, https://doi.org/10.5194/tc-19-3553-2025, https://doi.org/10.5194/tc-19-3553-2025, 2025
Short summary
Short summary
The Snow, Ice, and Aerosol Radiation Model version 4 has only been used to evaluate bare-ice albedo in land surface models, with necessary ice property data lacking quality control. We integrated this model into our land surface model and improved bare-ice properties using quality-controlled satellite data. Our findings show regional warming and reduced snow cover in Greenland’s bare-ice region, driven by changes in bare-ice properties through bare-ice–snow albedo feedback.
Shulei Zhang, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai
Hydrol. Earth Syst. Sci., 29, 3119–3143, https://doi.org/10.5194/hess-29-3119-2025, https://doi.org/10.5194/hess-29-3119-2025, 2025
Short summary
Short summary
This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
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.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
Short summary
Short summary
We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
Short summary
Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Kaiqi Du, Guilong Xiao, Jianxi Huang, Xiaoyan Kang, Xuecao Li, Yelu Zeng, Quandi Niu, Haixiang Guan, and Jianjian Song
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-432, https://doi.org/10.5194/essd-2024-432, 2025
Manuscript not accepted for further review
Short summary
Short summary
In this manuscript, we developed a 500-m spatial resolution monthly SIF dataset for the China region (CNSIF) from 2003 to 2022 based on high-resolution apparent reflectance and thermal infrared data. The comparison of CNSIF with tower-based SIF observations, tower-based GPP observations, MODIS GPP products, and other SIF datasets has validated CNSIF's ability to capture photosynthetic activity across different vegetation types and its potential for estimating carbon fluxes.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
Short summary
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
Short summary
Short summary
We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Y. Cai, Q. Shi, and X. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-W1-2022, 1–6, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, 2022
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
Short summary
Short summary
Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2851–2864, https://doi.org/10.5194/essd-14-2851-2022, https://doi.org/10.5194/essd-14-2851-2022, 2022
Short summary
Short summary
In this paper we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.
Min Zhao, Changxiu Cheng, Yuyu Zhou, Xuecao Li, Shi Shen, and Changqing Song
Earth Syst. Sci. Data, 14, 517–534, https://doi.org/10.5194/essd-14-517-2022, https://doi.org/10.5194/essd-14-517-2022, 2022
Short summary
Short summary
We generated a unique dataset of global annual urban extents (1992–2020) using consistent nighttime light observations and analyzed global urban dynamics over the past 3 decades. Evaluations using other urbanization-related ancillary data indicate that the derived urban areas are reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. This dataset can provide unique information for studying urbanization and its impacts.
Bowen Cao, Le Yu, Xuecao Li, Min Chen, Xia Li, Pengyu Hao, and Peng Gong
Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, https://doi.org/10.5194/essd-13-5403-2021, 2021
Short summary
Short summary
In the study, the first 1 km global cropland proportion dataset for 10 000 BCE–2100 CE was produced through the harmonization and downscaling framework. The mapping result coincides well with widely used datasets at present. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The dataset will be valuable for long-term simulations and precise analyses. The framework can be extended to specific regions or other land use types.
Cited articles
Arehart, J., Pomponi, F., D'Amico, B., and Srubar III, W.: A new estimate of building floor space in North America, Environ. Sci. Technol., 55, 5161–5170, https://doi.org/10.1021/acs.est.0c05081, 2021.
Arehart, J. H., Pomponi, F., D'Amico, B., and Srubar, W. V.: Structural material demand and associated embodied carbon emissions of the United States building stock: 2020–2100, Resour. Conserv. Recy., 186, 106583, https://doi.org/10.1016/j.resconrec.2022.106583, 2022.
Basaraner, M. and Cetinkaya, S.: Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS, Int. J. Geogr. Inf. Sci., 31, 1952–1977, https://doi.org/10.1080/13658816.2017.1346257, 2017.
Cai, B., Shao, Z., Huang, X., Zhou, X., and Fang, S.: Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data, Int. J. Appl. Earth Obs., 122, 103399, https://doi.org/10.1016/j.jag.2023.103399, 2023.
Cao, Y. and Huang, X.: A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities, Remote Sens. Environ., 264, 112590, https://doi.org/10.1016/j.rse.2021.112590, 2021.
Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y.: Building height of Asia in 3D-GloBFP [data set], https://doi.org/10.5281/zenodo.11397014, 2024a.
Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y.: Building height of Europe in 3D-GloBFP [data set], https://doi.org/10.5281/zenodo.11391076, 2024b.
Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Zhang, H., Yuan, H., and Dai, Y.: Building height of the Americas, Africa, and Oceania in 3D-GloBFP [data set], https://doi.org/10.5281/zenodo.11319912, 2024c.
Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., Xu, X., Liao, W., Qiu, Y. A., Wu, Q., and Huang, K.: Global projections of future urban land expansion under shared socioeconomic pathways, Nat. Commun., 11, 537, https://doi.org/10.1038/s41467-020-14386-x, 2020.
Chen, G., Zhou, Y., Voogt, J. A., and Stokes, E. C.: Remote sensing of diverse urban environments: From the single city to multiple cities, Remote Sens. Environ., 305, 114108, https://doi.org/10.1016/j.rse.2024.114108, 2024.
Chen, P., Huang, H., Liu, J., Wang, J., Liu, C., Zhang, N., Su, M., and Zhang, D.: Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities, Remote Sens. Environ., 298, 113802, https://doi.org/10.1016/j.rse.2023.113802, 2023.
Chen, W., Zhou, Y., Stokes, E. C., and Zhang, X.: Large-scale urban building function mapping by integrating multi-source web-based geospatial data, Geo-spatial Information Science, 26, 1–15, https://doi.org/10.1080/10095020.2023.2264342, 2023.
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B.: A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, 2022.
Ding, G., Guo, J., Pueppke, S. G., Yi, J., Ou, M., Ou, W., and Tao, Y.: The influence of urban form compactness on CO2 emissions and its threshold effect: Evidence from cities in China, J. Environ. Manage., 322, 116032, 2022.
Esch, T., Brzoska, E., Dech, S., Leutner, B., Palacios-Lopez, D., Metz-Marconcini, A., Marconcini, M., Roth, A., and Zeidler, J.: World Settlement Footprint 3D – A first three-dimensional survey of the global building stock, Remote Sens. Environ., 270, 112877, https://doi.org/10.1016/j.rse.2021.112877, 2022.
Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., and Hostert, P.: National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series, Remote Sens. Environ., 252, 112128, https://doi.org/10.1016/j.rse.2020.112128, 2021.
Frantz, D., Schug, F., Wiedenhofer, D., Baumgart, A., Virág, D., Cooper, S., Gómez-Medina, C., Lehmann, F., Udelhoven, T., van der Linden, S., Hostert, P., and Haberl, H.: Unveiling patterns in human dominated landscapes through mapping the mass of US built structures, Nat. Commun., 14, 8014, https://doi.org/10.1038/s41467-023-43755-5, 2023.
Geiß, C., Leichtle, T., Wurm, M., Pelizari, P. A., Standfuß, I., Zhu, X. X., So, E., Siedentop, S., Esch, T., and Taubenböck, H.: Large-Area Characterization of Urban Morphology – Mapping of Built-Up Height and Density Using TanDEM-X and Sentinel-2 Data, IEEE J. Sel. Top. Appl., 12, 2912–2927, https://doi.org/10.1109/JSTARS.2019.2917755, 2019.
Güneralp, B., Zhou, Y., Ürge-Vorsatz, D., Gupta, M., Yu, S., Patel, P. L., Fragkias, M., Li, X., and Seto, K. C.: Global scenarios of urban density and its impacts on building energy use through 2050, P. Natl. Acad. Sci. USA, 114, 8945–8950, https://doi.org/10.1073/pnas.1606035114, 2017.
He, X., Li, Y., Wang, X., Chen, L., Yu, B., Zhang, Y., and Miao, S.: High-resolution dataset of urban canopy parameters for Beijing and its application to the integrated WRF/Urban modelling system, J. Clean. Prod., 208, 373–383, https://doi.org/10.1016/j.jclepro.2018.10.086, 2019.
Hossain, M. K. and Meng, Q.: A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood, Land Use Policy, 99, 104829, https://doi.org/10.1016/j.landusepol.2020.104829, 2020.
Huang, H., Chen, P., Xu, X., Liu, C., Wang, J., Liu, C., Clinton, N., and Gong, P.: Estimating building height in China from ALOS AW3D30, ISPRS J. Photogramm., 185, 146–157, https://doi.org/10.1016/j.isprsjprs.2022.01.022, 2022.
Koppel, K., Zalite, K., Voormansik, K., and Jagdhuber, T.: Sensitivity of Sentinel-1 backscatter to characteristics of buildings, Int. J. Remote Sens., 38, 6298–6318, https://doi.org/10.1080/01431161.2017.1353160, 2017.
Kouskoulas, V. and Koehn, E.: Predesign Cost-Estimation Function for Buildings, J. Construct. Div.-ASCE, 100, 589–604, https://doi.org/10.1061/JCCEAZ.0000461, 1974.
Li, C. Z., Tam, V. W. Y., Lai, X., Zhou, Y., and Guo, S.: Carbon footprint accounting of prefabricated buildings: A circular economy perspective, Build. Environ., 258, 111602, https://doi.org/10.1016/j.buildenv.2024.111602, 2024.
Li, L., Bisht, G., Hao, D., and Leung, L. R.: Global 1 km land surface parameters for kilometer-scale Earth system modeling, Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, 2024.
Li, M., Koks, E., Taubenböck, H., and van Vliet, J.: Continental-scale mapping and analysis of 3D building structure, Remote Sens. Environ., 245, 111859, https://doi.org/10.1016/j.rse.2020.111859, 2020.
Li, M., Wang, Y., Rosier, J. F., Verburg, P. H., and van Vliet, J.: Global maps of 3D built-up patterns for urban morphological analysis, Int. J. Appl. Earth Obs., 114, 103048, https://doi.org/10.1016/j.jag.2022.103048, 2022.
Li, W., Goodchild, M. F., and Church, R.: An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems, Int. J. Geogr. Inf. Sci., 27, 1227–1250, https://doi.org/10.1080/13658816.2012.752093, 2013.
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu, B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X., Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping global urban boundaries from the global artificial impervious area (GAIA) data, Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3, 2020a.
Li, X., Zhou, Y., Gong, P., Seto, K. C., and Clinton, N.: Developing a method to estimate building height from Sentinel-1 data, Remote Sens. Environ., 240, 111705, https://doi.org/10.1016/j.rse.2020.111705, 2020b.
Li, Y., Schubert, S., Kropp, J. P., and Rybski, D.: On the influence of density and morphology on the Urban Heat Island intensity, Nat. Commun., 11, 2647, https://doi.org/10.1038/s41467-020-16461-9, 2020.
Liasis, G. and Stavrou, S.: Satellite images analysis for shadow detection and building height estimation, ISPRS J. Photogramm., 119, 437–450, https://doi.org/10.1016/j.isprsjprs.2016.07.006, 2016.
Liu, M., Ma, J., Zhou, R., Li, C., Li, D., and Hu, Y.: High-resolution mapping of mainland China's urban floor area, Landscape Urban Plan., 214, 104187, https://doi.org/10.1016/j.landurbplan.2021.104187, 2021.
Liu, X., Wu, X., Li, X., Xu, X., Liao, W., Jiao, L., Zeng, Z., Chen, G., and Li, X.: Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality, Engineering, in press, https://doi.org/10.1016/j.eng.2024.01.025, 2024.
Lyu, S., Ji, C., Liu, Z., Tang, H., Zhang, L., and Yang, X.: Four seasonal composite Sentinel-2 images for the large-scale estimation of the number of stories in each individual building, Remote Sens. Environ., 303, 114017, https://doi.org/10.1016/j.rse.2024.114017, 2024.
Ma, X., Zheng, G., Chi, X., Yang, L., Geng, Q., Li, J., and Qiao, Y.: Mapping fine-scale building heights in urban agglomeration with spaceborne lidar, Remote Sens. Environ., 285, 113392, https://doi.org/10.1016/j.rse.2022.113392, 2023.
Microsoft: US Building Footprints, https://wiki.openstreetmap.org/wiki/Microsoft_Building_Footprint_Data#March_2017_Release (last access: May 2021), 2018.
Microsoft: Worldwide building footprints derived from satellite imagery, GitHub, https://github.com/microsoft/GlobalMLBuildingFootprints/tree/main (last access: April 2023), 2020.
Pappaccogli, G., Giovannini, L., Zardi, D., and Martilli, A.: Sensitivity analysis of urban microclimatic conditions and building energy consumption on urban parameters by means of idealized numerical simulations, Urban Climate, 34, 100677, https://doi.org/10.1016/j.uclim.2020.100677, 2020.
Park, Y. and Guldmann, J.-M.: Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach, Comput. Environ. Urban, 75, 76–89, https://doi.org/10.1016/j.compenvurbsys.2019.01.004, 2019.
Pesaresi, M., Corbane, C., Ren, C., and Edward, N.: Generalized Vertical Components of built-up areas from global Digital Elevation Models by multi-scale linear regression modelling, PLOS ONE, 16, e0244478, https://doi.org/10.1371/journal.pone.0244478, 2021.
Rodriguez Mendez, Q., Fuss, S., Lück, S., and Creutzig, F.: Assessing global urban CO2 removal, Nature Cities, 1, 413–423, https://doi.org/10.1038/s44284-024-00069-x, 2024.
Shang, S., Du, S., Du, S., and Zhu, S.: Estimating building-scale population using multi-source spatial data, Cities, 111, 103002, https://doi.org/10.1016/j.cities.2020.103002, 2020.
Shao, L., Liao, W., Li, P., Luo, M., Xiong, X., and Liu, X.: Drivers of global surface urban heat islands: Surface property, climate background, and 2D/3D urban morphologies, Build. Environ., 242, 110581, https://doi.org/10.1016/j.buildenv.2023.110581, 2023.
Shi, Q., Zhu, J., Liu, Z., Guo, H., Gao, S., Liu, M., Liu, Z., and Liu, X.: The Last Puzzle of Global Building Footprints – Mapping 280 Million Buildings in East Asia Based on VHR Images, Journal of Remote Sensing, 4, 0138, https://doi.org/10.34133/remotesensing.0138, 2024.
Stilla, U., Soergel, U., and Thoennessen, U.: Potential and limits of InSAR data for building reconstruction in built-up areas, ISPRS J. Photogramm., 58, 113–123, https://doi.org/10.1016/S0924-2716(03)00021-2, 2003.
Sun, Y., Zhang, N., Miao, S., Kong, F., Zhang, Y., and Li, N.: Urban Morphological Parameters of the Main Cities in China and Their Application in the WRF Model, J. Adv. Model. Earth Sy., 13, e2020MS002382, https://doi.org/10.1029/2020MS002382, 2021.
United Nations Human Settlements Programme: World Cities Report 2022: Envisaging the Future of Cities, Nairobi, ISBN 978-92-1-132894-3, 2022.
Watanabe, S., Nagano, K., Ishii, J., and Horikoshi, T.: Evaluation of outdoor thermal comfort in sunlight, building shade, and pergola shade during summer in a humid subtropical region, Build. Environ., 82, 556-565, https://doi.org/10.1016/j.buildenv.2014.10.002, 2014.
Wu, W.-B., Ma, J., Banzhaf, E., Meadows, M. E., Yu, Z.-W., Guo, F.-X., Sengupta, D., Cai, X.-X., and Zhao, B.: A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning, Remote Sens. Environ., 291, 113578, https://doi.org/10.1016/j.rse.2023.113578, 2023.
Xu, X., Ou, J., Liu, P., Liu, X., and Zhang, H.: Investigating the impacts of three-dimensional spatial structures on CO2 emissions at the urban scale, Sci. Total Environ., 762, 143096, https://doi.org/10.1016/j.scitotenv.2020.143096, 2021.
Yu, G., Xie, Z., Xuecao, L., Wang, Y., Huang, J., and Yao, X.: The Potential of 3D Building Height Data to Characterize Socioeconomic, Remote Sens., 14, 2087, https://doi.org/10.3390/rs14092087, 2022.
Yuan, F. and Bauer, M. E.: Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote Sens. Environ., 106, 375–386, https://doi.org/10.1016/j.rse.2006.09.003, 2007.
Zhao, X., Zhou, Y., Chen, W., Li, X., Li, X., and Li, D.: Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China, GISci. Remote Sens., 58, 717–732, https://doi.org/10.1080/15481603.2021.1935128, 2021.
Zheng, Y., Zhang, X., Ou, J., and Liu, X.: Identifying building function using multisource data: A case study of China's three major urban agglomerations, Sustain. Cities Soc., 108, 105498, https://doi.org/10.1016/j.scs.2024.105498, 2024.
Zhong, X., Hu, M., Deetman, S., Steubing, B., Lin, H. X., Hernandez, G. A., Harpprecht, C., Zhang, C., Tukker, A., and Behrens, P.: Global greenhouse gas emissions from residential and commercial building materials and mitigation strategies to 2060, Nat. Commun., 12, 6126, https://doi.org/10.1038/s41467-021-26212-z, 2021.
Zhou, Y., Li, X., Chen, W., Meng, L., Wu, Q., Gong, P., and Seto, K. C.: Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South, P. Natl. Acad. Sci. USA, 119, e2214813119, https://doi.org/10.1073/pnas.2214813119, 2022.
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Most existing building height products are limited with respect to either spatial resolution or...
Altmetrics
Final-revised paper
Preprint