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Light from space illuminating the polar silk road

International Journal of Digital Earth 2022年第1期15卷 2028-2046页

作者： Xiao-Ming Li Yujia Qiu Yacheng Wang Bingqing Huang Haiming Lu Min Chu Han Fu Fengming Hui International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Space Star Technology CO. LTDBeijingPeople’s Republic of China COSCO Shipping Specialized Carriers CO. LTDGuangzhouPeople’s Republic of China CMA Earth System Modeling and Prediction Centre (CEMC) BeijingPeople’s Republic of China State Key Laboratory of Severe Weather (LaSW) BeijingPeople’s Republic of China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information EngineeringBeijingPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen Universityand Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)ZhuhaiPeople’s Republic of China

The rapid decline of Arctic sea ice has been reminding us the significant impacts caused by global ***,the other side of the coin is that this opens a window to utilize the Arctic sea routes in the summer seasons,bringing remarkable economic benefits for ocean transportation between Asia and ***,commercial vessels with low ice classes must tackle substantial environmental challenges in the Arctic sea routes,particularly those caused by variable sea ice,even in the melting ***,the science-based support for shipping safety in the Arctic sea routes is being given more *** satellite remote sensing technology plays a critical role in environmental monitoring in the *** paper reviews state-of-the-art satellite observations on monitoring sea ice and potential applications on supporting shipping activities in the Arctic ***,we introduced a recently developed system based on satellite observations to support the safe transportation of Chinese cargo vessels in the Arctic northern sea route,demonstrating the efforts by both the science and business communities to promote the development of the polar silk road.

关键词： Arctic northern sea route earth observation digital earth sustainable development

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FuXi Weather: A data-to-forecast machine learning system for global weather

arXiv

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arXiv 2024年

作者： Sun, Xiuyu Zhong, Xiaohui Xu, Xiaoze Huang, Yuanqing Li, Hao Neelin, J. David Chen, Deliang Feng, Jie Han, Wei Wu, Libo Qi, Yuan Shanghai Academy of Artificial Intelligence for Science Shanghai200232 China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai200433 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Earth System Modeling and Prediction Centre China Meteorological Administration Beijing100081 China Department of Atmospheric and Oceanic Sciences University of California Los Angeles90095 United States Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing100084 China University of Gothenburg Gothenburg Sweden Department of Atmospheric and Oceanic Sciences Institute of Atmospheric Sciences Fudan University Shanghai200433 China School of Data Science Fudan University Shanghai200433 China Institute for Big Data Fudan University Shanghai200433 China MOE Laboratory for National Development and Intelligent Governance Fudan University Shanghai200433 China

Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrates global observational systems, data assimilation (DA), and forecasting models. Despite steady improvements in forecast accuracy over recent decades, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and the challenges of obtaining finer resolution. These limitations, alongside the uneven distribution of observational networks, result in global disparities in forecast accuracy, leaving some regions vulnerable to extreme weather. Recent advances in machine learning present a promising alternative, providing more efficient and accurate forecasts using the same initial conditions as NWP. However, current machine learning models still depend on the initial conditions generated by NWP systems, which require extensive computational resources and expertise. Here we introduce FuXi Weather, a machine learning weather forecasting system that assimilates data from multiple satellites. Operating on a 6-hourly DA and forecast cycle, FuXi Weather generates reliable and accurate 10-day global weather forecasts at a spatial resolution of 0.25◦. FuXi Weather is the first system to achieve all-grid, all-surface, all-channel, and all-sky DA and forecasting, extending skillful forecast lead times beyond those of the European Centre for Medium-range Weather Forecasts (ECMWF) high-resolution forecasts (HRES) while using significantly fewer observations. FuXi Weather consistently outperforms ECMWF HRES in observation-sparse regions, such as central Africa, demonstrating its potential to improve forecasts where observational infrastructure is limited. © 2024, CC BY.

关键词： Weather forecasting

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

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Innovation 2025年第6期6卷

作者： Hu, Jinkang Peng, Dailiang Chen, Jing M. Huete, Alfredo R. Yu, Le Lou, Zihang Cheng, Enhui Yang, Xuan Zhang, Bing Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China College of Resource and Environment University of Chinese Academy of Sciences Beijing100094 China School of Geographical Sciences Fujian Normal University Fuzhou350117 China Department of Geography and Planning University of Toronto TorontoONM5S 3G3 Canada School of Life Sciences University of Technology Sydney UltimoNSW2007 Australia Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Institute of Agricultural Remote Sensing and Information Technology Application College of Environmental and Resource Sciences Zhejiang University Hangzhou310058 China China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

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A data driven time-dependent transmission rate for tracking an epidemic:a case study of 2019-nCoV

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Science Bulletin 2020年第6期65卷 425-427,M0003页

作者： Norden EHuang Fangli Qiao First Institute of Oceanography Ministry of Natural Resources of ChinaQingdao 266061China Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural Resources of ChinaQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao 266061China

Ever since the first report of the discovery of an unknown novel coronavirus that had caused pneumonia in Wuhan,China,had filed to WHO by China,on December 31,2019,the government had instituted drastic measures to contain the outbreak of the *** keep transparency,the China's National Health Commission reports confirmed,suspected,recovered and death case numbers daily,yet the epidemiological community is still calling for more ***,they want to have the critical"Basic Reproduction,Number"*** to Jones[1]and van den Driessche and Watmough[2],Ro,is defined as the expected number of secondary cases produced by a single(typical)infection in a completely susceptible population.

关键词：数据驱动 filed transparency

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A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

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Journal of Oceanology and Limnology 2020年第4期38卷 930-961页

作者： ZHANG Rong-Hua YU Yongqiang SONG Zhenya REN Hong-Li TANG Youmin QIAO Fangli WU Tongwen GAO Chuan HU Junya TIAN Feng ZHU Yuchao CHEN Lin LIU Hailong LIN Pengfei WU Fanghua WANG Lin Key Laboratory of Ocean Circulation and Waves Institute of Oceanologyand Center for Ocean Mega-ScienceChinese Academy of SciencesQingdao 266071China Laboratory for Ocean and Climate Dynamics Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China Center for Excellence in Quaternary Science and Global Change Chinese Academy of SciencesXi’an 710061China University of Chinese Academy of Sciences Beijing 100049China LASG Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100049China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China Key Laboratory of Marine Science and Numerical Modeling(MASNUM) Ministry of Natural ResourcesQingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao 266061China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Laboratory for Climate Studies and CMA-NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China Environmental Science and Engineering University of Northern British ColumbiaBritish Columbia V2N 4Z9Canada State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China Key Laboratory of Meteorological Disaster Ministry of Education(KLME)and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science and TechnologyNanjing 210044China Beijing Climate Center China Meteorological AdministrationBeijing 100081China

El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate *** the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in ***,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO *** primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made *** ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and ***,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and ***,model performances of ENSO simulations are assessed in terms of advantages and disadvantage

关键词： El Niño-Southern Oscillation(ENSO) coupled ocean-atmosphere models simulations and predictions model biases and uncertainties

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

引用

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Significant greening of China's mangrove forests during the last three decades

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Ecological Indicators 2025年 176卷

作者： Wang, Xinzhi Zhang, Rong Jia, Mingming Wang, Zongming Zhao, Chuanpeng Guo, Xianxian Li, Huiying Xue, Zhaohui Zhou, Chao Xiao, Xiangming State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun130102 China National Marine Environmental Monitoring Center Ministry of Ecology Environment Dalian116023 China School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266520 China College of Geography and Remote Sensing Hohai University Nanjing211100 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Department of Microbiology and Plant Biology Center for Earth Observation and Modeling University of Oklahoma NormanOK United States College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China

In recent decades, China's mangroves have experienced severe degradation, followed by significant initiatives aimed at conservation and recovery. These initiatives have brought about significant changes in the extent of mangrove areas and their health conditions. Previous research has primarily concentrated on the dynamics of mangrove areas. Few studies have addressed the health condition, limited by lower spatial resolution, short time spans, and saturation effects of traditional vegetation indices. To overcome these challenges, we leveraged 37,144 Landsat scenes from 1993 to 2022 to generate kernel normalized difference vegetation index (kNDVI) images, monitoring spatial and temporal progression of mangrove greenness by kNDVI dynamics. Additionally, we analyzed the driving forces of kNDVI changes and environmental factors. The results showed that: (1) China's mangroves’ kNDVI values were increasing obviously during the last three decades, which indicated significant greening trends;(2) mangroves with greening tendencies were predominantly found in densely distributed regions, whereas degradation occurred in dispersed and isolated mangroves;(3) in general, mangrove greenness was positively correlated with sea surface salinity (r = 0.540, p © 2025 The Authors

关键词： Mineral resources

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Daily CO_(2) Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China

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The Innovation 2020年第3期1卷 24-36页

作者： Rong Wang Yuankang Xiong Xiaofan Xing Ruipu Yang Jiarong Li Yijing Wang Junji Cao Yves Balkanski Josep Peñuelas Philippe Ciais Didier Hauglustaine Jordi Sardans Jianmin Chen Jianmin Ma Tang Xu Haidong Kan Yan Zhang Tomohiro Oda Lidia Morawska Renhe Zhang Shu Tao Department of Environmental Science and Engineering Fudan UniversityShanghai 200438P.R.China Institute of Atmospheric Sciences Fudan UniversityShanghai 200438P.R.China Big Data Institute for Carbon Emission and Environmental Pollution Fudan UniversityShanghai 200438P.R.China Key Laboratory of Aerosol Chemistry and Physics Institute of Earth EnvironmentChinese Academy of SciencesXi'an 710075P.R.China Laboratoire des Sciences du Climat et de l’Environnement CEA CNRS UVSQ91190 Gif-sur-YvetteFrance CREAF Cerdanyola del Vallès08193 CataloniaSpain CSIC Global Ecology Unit CREAF-CSIC-UABBellaterra08193 CataloniaSpain College of Urban and Environmental Sciences Laboratory for Earth Surface ProcessesPeking University100871 BeijingP.R.China School of Public Health Key Laboratory of Public Health Safety of the Ministry of Education and National Health Commission Key Laboratory of Health Technology AssessmentFudan UniversityShanghai 200438P.R.China Universities Space Research Association ColumbiaMDUSA Global Modeling and Assimilation Office NASA Goddard Space Flight CenterGreenbeltMDUSA Queensland University of Technology 2 George StreetBrisbaneQLD 4001Australia Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092P.R.China

Lockdown measures are essential to containing the spread of coronavirus disease 2019(COVID-19),but they will slow down economic growth by reducing industrial and commercial ***,the benefits of activity control from containing the pandemic have not been examined and *** we use daily carbon dioxide(CO_(2))emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide(NO_(2))measured by the Ozone Monitoring Instrument(OMI)as an indicator for reduced activities consecutive to a *** perform a correlation analysis to show that a 1%day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO_(2) emissions of 0.22%±0.02%using statistical data for energy consumption relative to emissions without COVID-19,or 0.20%±0.02%using satellite data for atmospheric column NO_(2).We estimate that swift action in China is effective in limiting the number of COVID-19 cases<100,000 with a reduction in CO_(2) emissions of up to 23%by the end of February 2020,whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same *** analyzing the costs of health care and fatalities,we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic *** findings suggest an unprecedentedly high cost of maintaining activities and CO_(2) emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

关键词： COVID-19 PANDEMIC CARBON EMISSION ENERGY CONSUMPTION SOCIAL CARBON COST CONTAINMENT EFFICACY PUBLIC HEALTH CORRELATION ANALYSIS INTEGRATED MODEL

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Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2018

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Science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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