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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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Retraction Note: Using hardware counter-based performance model to diagnose scaling issues of HPC applications

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Neural Computing and applications 2024年第23期36卷 14615-14615页

作者： Ding, Nan Xu, Shiming Song, Zhenya Zhang, Baoquan Li, Jingmei Zheng, Zhigao Department of Computer Science and Technology Tsinghua University Beijing China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology Qingdao China Department of Earth System Science Tsinghua University Beijing China First Institute of Oceanography State Oceanic Administration Qingdao China Department of Computer Science and Technology University of Minnesota Minneapolis USA Department of Computer Science and Technology Harbin Engineering University Harbin China Department of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

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Author Correction: Submesoscale inverse energy cascade enhances Southern Ocean eddy heat transport

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Nature communications 2023年第1期14卷 8403页

作者： Zhiwei Zhang Yuelin Liu Bo Qiu Yiyong Luo Wenju Cai Qingguo Yuan Yinxing Liu Hong Zhang Hailong Liu Mingfang Miao Jinchao Zhang Wei Zhao Jiwei Tian Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory/Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Ocean University of China Qingdao/Sanya China. zzw330@***. Laoshan Laboratory Qingdao China. zzw330@***. Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory/Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Ocean University of China Qingdao/Sanya China. Department of Oceanography University of Hawaii at Manoa Honolulu HI USA. Laoshan Laboratory Qingdao China. Center for Southern Hemisphere Oceans Research (CSHOR) CSIRO Oceans and Atmosphere Hobart Australia. Joint Institute for Regional Earth System Science and Engineering University of California Los Angeles CA USA. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory/Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Ocean University of China Qingdao/Sanya China. weizhao@***. Laoshan Laboratory Qingdao China. weizhao@***. Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory/Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Ocean University of China Qingdao/Sanya China. tianjw@***. Laoshan Laboratory Qingdao China. tianjw@***.

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Erratum to: Could the Recent Taal Volcano Eruption Trigger an El Niño and Lead to Eurasian Warming?

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Advances in Atmospheric Sciences 2020年第11期37卷 1295-1295页

作者： Liu, Fei Xing, Chen Li, Jinbao Wang, Bin Chai, Jing Gao, Chaochao Huang, Gang Liu, Jian Chen, Deliang School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University Zhuhai China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China Earth System Modeling and Climate Dynamics Research Center Nanjing University of Information Science and Technology Nanjing China Department of Geography University of Hong Kong Pokfulam Hong Kong Department of Atmospheric Sciences and International Pacific Research Center University of Hawaii at Manoa Honolulu USA Department of Environmental Science Zhejiang University Hangzhou China State key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Key Laboratory for Virtual Geographic Environment Ministry of Education Jiangsu Provincial State Key Laboratory Cultivation Base of Geographical Environment Evolution School of Geography Science Nanjing Normal University Nanjing China Department of Earth Sciences University of Gothenburg Gothenburg Sweden

In the third column of the five row of Table 1, “120.4°W” should be “20.4°E”.

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Erratum to: Vol. 35 No. 1 Publisher’s Erratum

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Chinese Journal of Oceanology and Limnology 2017年第2期35卷 466-467页

作者： Shuangyan He Mingxia He Jürgen Fischer Dongliang Yuan Peng Xu Tengfei Xu Xianping Yang Leonid Sokoletsky Xiaodao Wei Fang Shen Juhong Zou Maohua Guo Songxue Cui Wu Zhou Dalu Gao Guangzhen Jin Xianqing Lü Fuwen Qiu Wendong Fang Aijun Pan Jing Cha Shanwu Zhang Jiang Huang Tao Wang Yongzhou Cheng Xiaoyan Chen Zhaopu Liu Xiaohua Long Zhishuai Hou Haishen Wen Jifang Li Feng He Qun Liu Jinhuan Wang Biao Guan Qinglong Wang Md. Shahjahan Md. Farajul Kabir Kizar Ahmed Sumon Lipi Rani Bhowmik Harunur Rashid Shu Li Kefu Yu Jianxin Zhao Yuexing Feng Tianran Chen Shun Zhou Yichao Ren Christopher M. Pearce Shuanglin Dong Xiangli Tian Qinfeng Gao Fang Wang Liming Liu Rongbin Du Xiaoling Zhang Shichun Sun Song Feng Jianing Lin Song Sun Fang Zhang Zhipeng Zhang Xuexi Tang Haitian Tang Jingjing Song Jian Zhou Hongjun Liu Qixiang Wang Kuimei Qian Xia Liu Yuwei Chen Chengjun Sun Fenghua Jiang Wei Gao Xiaoyun Li Yanzhen Yu Xiaofei Yin Yong Wang Haibing Ding Zhongmin Sun Yongqiang Wang Pengcheng Yan Hui Guo Jianting Yao Jiro Tanaka Hiroshi Kawai Na Song Muyan Chen Tianxiang Gao Takashi Yanagimoto Xia Lu Sheng Luan Jie Kong Longyang Hu Yong Mao Shengping Zhong Yan Liu Weihong Zhao Caiyan Li Hui Miao Ocean Remote Sensing Institute Ocean University of China Qingdao China Institute for Space Sciences Freie Universität Berlin Berlin Germany Ocean College Zhejiang University Hangzhou China Key Laboratory of Ocean Circulation and Waves (KLOCAW) Institute of Oceanology Chinese Academy of Sciences Qingdao China Qingdao Collaborative Innovation Center of Marine Science and Technology Qingdao China University of Chinese Academy of Sciences Beijing China First Institute of Oceanography State Oceanic Administration Qingdao China State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai China National Satellite Ocean Application Service Beijing China Key Laboratory of Space Ocean Remote Sensing and Application SOA Beijing China Key Laboratory of Physical Oceanography (Ocean University of China) Ministry of Education Qingdao China Key Lab of Marine Science and Numerical Modeling First Institute of Oceanography SOA Qingdao China Third Institute of Oceanography State Oceanic Administration Xiamen China State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou China Jiangsu Provincial Key Laboratory of Marine Biology College of Resources and Environmental Sciences Nanjing Agricultural University Nanjing China Fisheries College Ocean University of China Qingdao China Key Laboratory of Mariculture Ministry of Education Ocean University of China Qingdao China Department of Fisheries Management Bangladesh Agricultural University Mymensingh Bangladesh Sado Marine Biological Station Faculty of Science Niigata University Sado Niigata Japan Department of Aquatic Ecology and Water Quality Management Wageningen University Wageningen University and Research Centre Amsterdam The Netherlands Science and Math Program Asian University for Women Chittagong Bangladesh Key laboratory of Marginal Sea Geology South China Sea Institute of Oceanology Chinese Academy of Sciences

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State of the climate in 2019 global oceans

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Bulletin of the American Meteorological Society 2020年第8期101卷 S129-S184页

作者： Lumpkin, Rick Baringer, Molly Bif, Mariana B. Boyer, Tim Bushinsky, Seth M. Carter, Brendan R. Cetinić, Ivona Chambers, Don P. Cheng, Lijing Chiba, Sanai Dai, Minhan Domingues, Catia M. Dong, Shenfu Fassbender, Andrea J. Feely, Richard A. Frajka-Williams, Eleanor Franz, Bryan A. Gilson, John Goni, Gustavo Hamlington, Benjamin D. Hu, Zeng-Zhen Huang, Boyin Ishii, Masayoshi Jevrejeva, Svetlana Johns, William E. Johnson, Gregory C. Johnson, Kenneth S. Kennedy, John Kersalé, Marion Killick, Rachel E. Landschützer, Peter Lankhorst, Matthias Lee, Tong Leuliette, Eric Li, Feili Lindstrom, Eric Locarnini, Ricardo Lozier, Susan Lyman, John M. Marra, John J. Meinen, Christopher S. Merrifield, Mark A. Mitchum, Gary T. Moat, Ben Monselesan, Didier Nerem, R. Steven Perez, Renellys C. Purkey, Sarah G. Rayner, Darren Reagan, James Rome, Nicholas Sanchez-Franks, Alejandra Schmid, Claudia Scott, Joel P. Send, Uwe Siegel, David A. Smeed, David A. Speich, Sabrina Stackhouse, Paul W. Sweet, William Takeshita, Yuichiro Thompson, Philip R. Triñanes, Joaquin A. Visbeck, Martin Volkov, Denis L. Wanninkhof, Rik Weller, Robert A. Westberry, Toby K. Widlansky, Matthew J. Wijffels, Susan E. Wilber, Anne C. Yu, Lisan Yu, Weidong Zhang, Huai-Min Andersen, Andrea Griffin, Jessicca Hammer, Gregory Love-Brotak, S. Elizabeth Misch, Deborah J. Riddle, Deborah B. Veasey, Sara W. NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory MiamiFL United States Monterey Bay Aquarium Research Institute Moss LandingCA United States NOAA/NESDIS National Centers for Environmental Information Silver SpringMD United States University of Hawai'i at Mānoa HonoluluHI United States Joint Institute for the Study of the Atmosphere and Ocean University of Washington NOAA/OAR Pacific Marine Environmental Laboratory SeattleWA United States NASA Goddard Space Flight Center GreenbeltMD United States Universities Space Research Association ColumbiaMD United States College of Marine Science University of South Florida St. PetersburgFL United States International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Japan Agency for Marine-Earth Science and Technology Yokosuka Japan Xiamen University Xiamen China Institute for Marine and Antarctic Studies University of Tasmania Antarctic Climate and Ecosystems Cooperative Research Centre Australian Research Council's Centre of Excellence for Climate System Science HobartTAS Australia NOAA/OAR Pacific Marine Environmental Laboratory SeattleWA United States National Oceanography Centre Southampton United Kingdom Scripps Institution of Oceanography University of California at San Diego San diegoCA United States NASA Jet Propulsion Laboratory PasadenaCA United States NOAA/NCEP Climate Prediction Center College ParkMD United States NOAA/NESDIS National Centers for Environmental Information AshevilleNC United States Department of Atmosphere Ocean and Earth System Modeling Research Meteorological Research Institute Japan Meteorological Agency Tsukuba Japan National Oceanography Centre Liverpool United Kingdom Rosenstiel School of Marine and Atmospheric Science University of Miami MiamiFL United States Met Office Hadley Centre Exeter United Kingdom Cooperative Institute for Marine and Atmospheric Studies University of Miami Miam

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Near surface camera informed agricultural land monitoring for climate smart agriculture

Climate Smart Agriculture

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Climate Smart Agriculture 2024年第1期1卷

作者： Le Yu Zhenrong Du Xiyu Li Qiang Zhao Hui Wu Duoji weise Xinqun Yuan Yuanzheng Yang Wenhua Cai Weimin Song Pei Wang Zhicong Zhao Ying Long Yongguang Zhang Jinbang Peng Xiaoping Xin Fei Xu Miaogen Shen Hui Wang Yuanmei Jiao Yong Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing China Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing China School of Information and Communication Engineering Dalian University of Technology Dalian China Geothermal Geological Brigade Tibet Autonomous Region Geological and Mineral Exploration and Development Bureau Lhasa China International Department Beijing No. 35 High School Beijing China Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education Nanning Normal University Nanning China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning China Key Laboratory of Coastal Environmental Processes and Ecological Restoration Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China Yellow River Delta Field Observation and Research Station of Coastal Wetland Ecosystem Chinese Academy of Sciences Dongying China Department of Landscape Architecture School of Architecture Tsinghua University Beijing China Institute for National Parks Tsinghua University Beijing China School of Architecture and Hang Lung Center for Real Estate Key Laboratory of Eco Planning & Green Building Ministry of Education Tsinghua University Beijing China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Nanjing China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing Univer

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

关键词： Near surface camera (NSCam) Climate-smart agriculture (CSA) Agricultural monitoring Data fusion Sentinel-2

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