Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3623-2023
© Author(s) 2023. 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-15-3623-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
11 Aug 2023
Data description article |
| 11 Aug 2023
| 11 Aug 2023
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
Wanru He
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Xuecao Li
CORRESPONDING AUTHOR
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Yuyu Zhou
CORRESPONDING AUTHOR
Department of Geography, The University of Hong Kong, Hong Kong SAR 999077, China
Urban Systems Institute, The University of Hong Kong, Hong Kong SAR 999077, China
Zitong Shi
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Guojiang Yu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Tengyun Hu
Beijing Municipal Institute of City Planning and Design, Beijing 100045, China
Yixuan Wang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Jianxi Huang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Tiecheng Bai
School of Information Engineering, Tarim University, Alaer 843300, China
Zhongchang Sun
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100101, China
Xiaoping Liu
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Peng Gong
Department of Geography, The University of Hong Kong, Hong Kong SAR 999077, China
Urban Systems Institute, The University of Hong Kong, Hong Kong SAR 999077, China
Related authors
No articles found.
Hongquan Cheng, Mengqing Geng, Xuecao Li, Shijie Li, Min Zhao, Chen Lin, Jie Wang, Peng Gong, and Yuyu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-129, https://doi.org/10.5194/essd-2026-129, 2026
Preprint under review for ESSD
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 the first global high-resolution monthly nighttime light dataset from 1992 to 2024. By combining DMSP 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.
Yuanhong Liao, Shuang Chen, Yuqi Bai, Jie Wang, and Peng Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-838, https://doi.org/10.5194/essd-2025-838, 2026
Preprint under review for ESSD
Short summary
Short summary
To capture true farming dynamics, we created the Global 30-meter Annual Cropland Extent Dynamics (GACED30) dataset, providing a highly accurate and reliable historical baseline. Our Structural Evolution Indicator distinguishes real change from temporary planting, revealing that agricultural expansion is driven by Global South countries while the Global North remains stable. This work provides a vital baseline for monitoring food security and environmental sustainability.
Shuang Chen, Jie Wang, Shuai Yuan, Jiayang Li, Yu Xia, Yuanhong Liao, Junbo Wei, Jincheng Yuan, Xiaoqing Xu, Xiaolin Zhu, Peng Zhu, Hongsheng Zhang, Yuyu Zhou, Haohuan Fu, Huabing Huang, Bin Chen, Fan Dai, and Peng Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-57, https://doi.org/10.5194/essd-2026-57, 2026
Preprint under review for ESSD
Short summary
Short summary
Monitoring our planet with satellites produces massive datasets too large for researchers to handle. We created a new global database that condenses 25 years of Landsat and MODIS observations into a highly efficient format (Analysis-ready embedding vectors) using AI. By reducing data size by over 340 times while maintaining high accuracy, we allow global studies to be run on standard PC. Makes planetary research accessible to everyone and helps us better track environmental changes over time.
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.
Xiujuan He, Jiyong Eom, Sha Yu, Shu Liu, Wenru Xu, and Yuyu Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-709, https://doi.org/10.5194/essd-2025-709, 2025
Preprint under review for ESSD
Short summary
Short summary
Buildings consume significant energy for heating and cooling. To reduce carbon emissions, we need accurate predictions of energy demand, which depend on knowing the outdoor temperature at which buildings start heating or cooling. We used artificial intelligence to create the first global dataset of these temperature thresholds for regions worldwide. Our dataset improves energy demand prediction accuracy by 10%, supporting better energy planning and climate policy decisions.
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.
Yizhi Zhang, Yi Wang, Quanhua Dong, Xiao-Jian Chen, Fan Zhang, Xuecao Li, and Yu Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-632, https://doi.org/10.5194/essd-2025-632, 2025
Preprint under review for ESSD
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 mapped 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.
Hao Jiang, Mengjun Ku, Xia Zhou, Qiong Zheng, Yangxiaoyue Liu, Jianhui Xu, Dan Li, Chongyang Wang, Jiayi Wei, Jing Zhang, Shuisen Chen, and Jianxi Huang
Earth Syst. Sci. Data, 17, 6703–6729, https://doi.org/10.5194/essd-17-6703-2025, https://doi.org/10.5194/essd-17-6703-2025, 2025
Short summary
Short summary
Existing cropland datasets in China show significant discrepancies. We created a high-resolution cropland map of China for 2020, using imagery from Mapbox and Google. By combining image quality assessments, active learning for semantic segmentation, and results integration. The accuracy achieved to 88.73 %, with 30 out of 32 provincial units reporting area estimates within ±10 % of official statistics. In contrast, only 9 to 1 provinces from 7 existing datasets meet the same accuracy standard.
Fengxiang Guo, Fan Dai, Peng Gong, and Yuyu Zhou
Earth Syst. Sci. Data, 17, 4799–4819, https://doi.org/10.5194/essd-17-4799-2025, https://doi.org/10.5194/essd-17-4799-2025, 2025
Short summary
Short summary
China, the world’s largest methane emitter, faces challenges in accurately tracking. CHN-CH4, a map of anthropogenic methane emissions was created by combining satellite data, national statistics, and climate guidelines. Over 30 years, China emitted about 1157 Tg of methane, peaking in the 2010s. Shanxi province had the highest emissions. CHN-CH4 helps improve tracking, informs global climate models, and strengthens collaboration between science and policy to combat climate change.
Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu
Earth Syst. Sci. Data, 17, 4005–4022, https://doi.org/10.5194/essd-17-4005-2025, https://doi.org/10.5194/essd-17-4005-2025, 2025
Short summary
Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycles. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982–2020 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
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.
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.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, https://doi.org/10.5194/essd-16-5449-2024, 2024
Short summary
Short summary
The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
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.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
Short summary
Short summary
The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
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.
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
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
Acuto, M., Parnell, S., and Seto, K. C.:
Building a global urban science, Nat. Sustain., 1, 2–4, https://doi.org/10.1038/s41893-017-0013-9, 2018.
Alberti, M., Correa, C., Marzluff John, M., Hendry Andrew, P., Palkovacs Eric, P., Gotanda Kiyoko, M., Hunt Victoria, M., Apgar Travis, M., and Zhou, Y.:
Global urban signatures of phenotypic change in animal and plant populations, P. Natl. Acad. Sci. USA, 114, 8951–8956, https://doi.org/10.1073/pnas.1606034114, 2017.
Borrelli, P., Robinson, D. A., Panagos, P., Lugato, E., Yang, J. E., Alewell, C., Wuepper, D., Montanarella, L., and Ballabio, C.:
Land use and climate change impacts on global soil erosion by water (2015-2070), P. Natl. Acad. Sci. USA, 117, 21994–22001, https://doi.org/10.1073/pnas.2001403117, 2020.
Brown de Colstoun, E. C., Huang, C., Wang, P., Tilton, J. C., Tan, B., Phillips, J., Niemczura, S., Ling, P.-Y., and Wolfe, R. E.: Global Man-made Impervious Surface (GMIS) Dataset From Landsat, NASA Socioeconomic Data and Applications Center (SEDAC) [data set], https://doi.org/10.7927/H4P55KKF, 2017.
Castán Broto, V. and Bulkeley, H.:
A survey of urban climate change experiments in 100 cities, Global Environ. Chang., 23, 92–102, https://doi.org/10.1016/j.gloenvcha.2012.07.005, 2013.
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, J., Gong, P., He, C., Luo, W., Tamura, M., and Shi, P. J.:
Assessment of the urban development plan of Beijing by using a CA-based urban growth model, Photogramm. Eng. Rem. S., 68, 1063–1071, 2002.
Chen, Y., Li, X., Liu, X., and Ai, B.:
Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy, Int. J. Geogr. Inf. Sci., 28, 234–255, https://doi.org/10.1080/13658816.2013.831868, 2014.
Chen, Y., Li, X., Huang, K., Luo, M., and Gao, M.:
High-Resolution Gridded Population Projections for China Under the Shared Socioeconomic Pathways, Earths Future, 8, e2020EF001491, https://doi.org/10.1029/2020EF001491, 2020.
Dahal, K. R. and Chow, T. E.:
Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth, Int. J. Geogr. Inf. Sci., 29, 475–497, 2015.
DeFries, R. S., Rudel, T., Uriarte, M., and Hansen, M.:
Deforestation driven by urban population growth and agricultural trade in the twenty-first century, Nat. Geosci., 3, 178–181, https://doi.org/10.1038/ngeo756, 2010.
Doelman, J. C., Stehfest, E., Tabeau, A., van Meijl, H., Lassaletta, L., Gernaat, D. E. H. J., Hermans, K., Harmsen, M., Daioglou, V., Biemans, H., van der Sluis, S., and van Vuuren, D. P.:
Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation, Global Environ. Chang., 48, 119–135, https://doi.org/10.1016/j.gloenvcha.2017.11.014, 2018.
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., and Snyder, P. K.:
Global Consequences of Land Use, Science, 309, 570–574, https://doi.org/10.1126/science.1111772, 2005.
Gao, J. and O'Neill, B. C.:
Data-driven spatial modeling of global long-term urban land development: The SELECT model, Environ. Modell. Softw., 119, 458–471, https://doi.org/10.1016/j.envsoft.2019.06.015, 2019.
Gao, J. and O'Neill, B. C.:
Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways, Nat. Commun., 11, 2302, https://doi.org/10.1038/s41467-020-15788-7, 2020.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Gong, P., Liang, S., Carlton, E. J., Jiang, Q., Wu, J., Wang, L., and Remais, J. V.:
Urbanisation and health in China, Lancet, 379, 843–852, https://doi.org/10.1016/S0140-6736(11)61878-3, 2012.
Gong, P., Li, X., and Zhang, W.:
40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing, Sci. Bull., 64, 756–763, https://doi.org/10.1016/j.scib.2019.04.024, 2019.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang, J., Zhang, W., and Zhou, Y.:
Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Güneralp, B., Lwasa, S., Masundire, H., Parnell, S., and Seto, K. C.:
Urbanization in Africa: challenges and opportunities for conservation, Environ. Res. Lett., 13, 015002, https://doi.org/10.1088/1748-9326/aa94fe, 2017.
He, W., Li, X., Zhou, Y., Shi, Z., Yu, G., Hu, T., Wang, Y., Huang, J., Bai, T., Wang, Y., Liu, X., and Gong, P.:
Global urban fractional changes at 1 km resolution under diverse SSP-RCP scenarios throughout 2100, figshare [data set], https://doi.org/10.6084/m9.figshare.20391117, 2022.
He, W., Li, X., Zhou, Y., Liu, X., Gong, P., Hu, T., Yin, P., Huang, J., Yang, J., Miao, S., Wang, X., and Wu, T.:
Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model, Cities, 133, 104146, https://doi.org/10.1016/j.cities.2022.104146, 2023.
Herold, M., Goldstein, N. C., and Clarke, K. C.:
The spatiotemporal form of urban growth: measurement, analysis and modeling, Remote Sens. Environ., 86, 286–302, https://doi.org/10.1016/S0034-4257(03)00075-0, 2003.
Hong, C., Burney, J. A., Pongratz, J., Nabel, J. E. M. S., Mueller, N. D., Jackson, R. B., and Davis, S. J.:
Global and regional drivers of land-use emissions in 1961–2017, Nature, 589, 554–561, https://doi.org/10.1038/s41586-020-03138-y, 2021.
Hosmer Jr., D. W., Lemeshow, S., and Sturdivant, R. X.: Applied logistic regression, John Wiley & Sons, https://doi.org/10.1002/9781118548387, 2013.
Hu, Z. and Lo, C. P.:
Modeling urban growth in Atlanta using logistic regression, Computers, Environment and Urban Systems, 31, 667–688, https://doi.org/10.1016/j.compenvurbsys.2006.11.001, 2007.
Huang, X., Li, J., Yang, J., Zhang, Z., Li, D., and Liu, X.:
30 m global impervious surface area dynamics and urban expansion pattern observed by Landsat satellites: From 1972 to 2019, Science China Earth Sciences, 64, 1922–1933, https://doi.org/10.1007/s11430-020-9797-9, 2021.
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones, C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P., and Wang, Y. P.:
Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands, Climatic Change, 109, 117, https://doi.org/10.1007/s10584-011-0153-2, 2011.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.:
Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Klein Goldewijk, K., Beusen, A., and Janssen, P.:
Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1, Holocene, 20, 565–573, https://doi.org/10.1177/0959683609356587, 2010.
Kocabas, V. and Dragicevic, S.:
Assessing cellular automata model behaviour using a sensitivity analysis approach, Computers, Environment and Urban Systems, 30, 921–953, https://doi.org/10.1016/j.compenvurbsys.2006.01.001, 2006.
Li, X. and Gong, P.:
Urban growth models: progress and perspective, Sci. Bull., 61, 1637–1650, https://doi.org/10.1007/s11434-016-1111-1, 2016.
Li, X. and Liu, X.:
Defining agents' behaviors to simulate complex residential development using multicriteria evaluation, J. Environ. Manage., 85, 1063–1075, https://doi.org/10.1016/j.jenvman.2006.11.006, 2007.
Li, X., Liu, X., and Yu, L.:
A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules, Int. J. Geogr. Inf. Sci., 28, 1317–1335, https://doi.org/10.1080/13658816.2014.883079, 2014.
Li, X., Gong, P., and Liang, L.:
A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data, Remote Sens. Environ., 166, 78–90, https://doi.org/10.1016/j.rse.2015.06.007, 2015.
Li, X., Yu, L., Sohl, T., Clinton, N., Li, W., Zhu, Z., Liu, X., and Gong, P.:
A cellular automata downscaling based 1 km global land use datasets (2010–2100), Sci. Bull., 61, 1651–1661, https://doi.org/10.1007/s11434-016-1148-1, 2016.
Li, X., Chen, G., Liu, X., Liang, X., Wang, S., Chen, Y., Pei, F., and Xu, X.:
A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions, Ann. Am. Assoc. Geogr., 107, 1040–1059, https://doi.org/10.1080/24694452.2017.1303357, 2017.
Li, X., Zhou, Y., Eom, J., Yu, S., and Asrar, G. R.:
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways, Earths Future, 7, 351–362, https://doi.org/10.1029/2019EF001152, 2019a.
Li, X., Zhou, Y., Yu, S., Jia, G., Li, H., and Li, W.:
Urban heat island impacts on building energy consumption: A review of approaches and findings, Energy, 174, 407–419, https://doi.org/10.1016/j.energy.2019.02.183, 2019b.
Li, X., Zhou, Y., and Chen, W.:
An improved urban cellular automata model by using the trend-adjusted neighborhood, Ecol. Process., 9, 28, https://doi.org/10.1186/s13717-020-00234-9, 2020.
Li, X., Zhou, Y., Hejazi, M., Wise, M., Vernon, C., Iyer, G., and Chen, W.:
Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling, Communications Earth & Environment, 2, 201, https://doi.org/10.1038/s43247-021-00273-w, 2021.
Liao, J., Tang, L., Shao, G., Su, X., Chen, D., and Xu, T.:
Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations, Environ. Modell. Softw., 75, 163–175, 2016.
Liu, X., Li, X., Liu, L., He, J., and Ai, B.:
A bottom-up approach to discover transition rules of cellular automata using ant intelligence, Int. J. Geogr. Inf. Sci., 22, 1247–1269, https://doi.org/10.1080/13658810701757510, 2008.
Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., and Pei, F.:
A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects, Landscape Urban Plan., 168, 94–116, https://doi.org/10.1016/j.landurbplan.2017.09.019, 2017.
Liu, X., Hu, G., Ai, B., Li, X., Tian, G., Chen, Y., and Li, S.:
Simulating urban dynamics in China using a gradient cellular automata model based on S-shaped curve evolution characteristics, Int. J. Geogr. Inf. Sci., 32, 73–101, https://doi.org/10.1080/13658816.2017.1376065, 2018.
Liu, Y.:
Modelling sustainable urban growth in a rapidly urbanising region using a fuzzy-constrained cellular automata approach, Int. J. Geogr. Inf. Sci., 26, 151–167, https://doi.org/10.1080/13658816.2011.577434, 2012.
Liu, Y. and Phinn, S. R.:
Modelling urban development with cellular automata incorporating fuzzy-set approaches, Computers, Environment and Urban Systems, 27, 637–658, https://doi.org/10.1016/S0198-9715(02)00069-8, 2003.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W.:
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000.
Mu, H., Li, X., Zhou, Y., Gong, P., Huang, J., Du, X., Guo, J., Cao, W., Sun, Z., Xu, C., and Liu, D.:
Identifying discrepant regions in urban mapping from historical and projected global urban extents, All Earth, 34, 167–178, https://doi.org/10.1080/27669645.2022.2104990, 2022.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.:
The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Global Environ. Chang., 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Potere, D., Schneider, A., Angel, S., and Civco, D. L.:
Mapping urban areas on a global scale: which of the eight maps now available is more accurate?, Int. J. Remote Sens., 30, 6531–6558, https://doi.org/10.1080/01431160903121134, 2009.
Santé, I., García, A. M., Miranda, D., and Crecente, R.:
Cellular automata models for the simulation of real-world urban processes: A review and analysis, Landscape Urban Plan., 96, 108–122, https://doi.org/10.1016/j.landurbplan.2010.03.001, 2010.
Seto, K. C. and Ramankutty, N.:
Hidden linkages between urbanization and food systems, Science, 352, 943–945, https://doi.org/10.1126/science.aaf7439, 2016.
Seto, K. C., Güneralp, B., and Hutyra, L. R.:
Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools, P. Natl. Acad. Sci. USA, 109, 16083–16088, https://doi.org/10.1073/pnas.1211658109, 2012.
Shi, L., Ling, F., Ge, Y., Foody, G. M., Li, X., Wang, L., Zhang, Y., and Du, Y.: Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016, Remote Sens., 9, 1148, https://doi.org/10.3390/rs9111148, 2017.
Song, X.-P., Sexton, J. O., Huang, C., Channan, S., and Townshend, J. R.: Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover, Remote Sens. Environ., 175, 1–13, https://doi.org/10.1016/j.rse.2015.12.027, 2016.
Sunde, M. G., He, H. S., Zhou, B., Hubbart, J. A., and Spicci, A.:
Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth, Landscape Urban Plan., 124, 104–108, https://doi.org/10.1016/j.landurbplan.2014.01.007, 2014.
United Nations:
World urbanization prospects: the 2018 revision, Department of Economic and Social Affairs, Population Division, New York, USA, 2019.
Verburg, P. H., Schulp, C. J. E., Witte, N., and Veldkamp, A.:
Downscaling of land use change scenarios to assess the dynamics of European landscapes, Agr. Ecosyst. Environ., 114, 39–56, https://doi.org/10.1016/j.agee.2005.11.024, 2006.
Wu, F.:
Calibration of stochastic cellular automata: the application to rural-urban land conversions, Int. J. Geogr. Inf. Sci., 16, 795–818, https://doi.org/10.1080/13658810210157769, 2002.
Wu, H., Zhou, L., Chi, X., Li, Y., and Sun, Y.:
Quantifying and analyzing neighborhood configuration characteristics to cellular automata for land use simulation considering data source error, Earth Sci. Inform., 5, 77–86, https://doi.org/10.1007/s12145-012-0097-8, 2012.
Wu, H., Li, Z., Clarke, K. C., Shi, W., Fang, L., Lin, A., and Zhou, J.:
Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change, Int. J. Geogr. Inf. Sci., 33, 1040–1061, 2019.
Zhou, Y., Varquez, A. C. G., and Kanda, M.:
High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model, Scientific Data, 6, 34, https://doi.org/10.1038/s41597-019-0048-z, 2019.
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 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).
Most existing global urban products with future projections were developed in urban and...
Altmetrics
Final-revised paper
Preprint