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Identification of soybean in Argentina using Sentinel-2 composite images

International Journal of Agricultural and Biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google earth Engine

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Trends and drivers of anthropogenic NOx emissions in China since 2020

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Environmental Science and Ecotechnology 2024年第5期21卷 287-293页

作者： Hui Li Bo Zheng Yu Lei Didier Hauglustaine Cuihong Chen Xin Lin Yi Zhang Qiang Zhang Kebin He Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and EcologyTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084China State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation and Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing 100041China Laboratoire des Sciences du Climat et de l’Environnement LSCE/IPSLCEA-CNRS-UVSQUniversite Paris-SaclayGif-sur-YvetteFrance Center for Satellite Application on Ecology and Environment Ministry of Ecology and Environment of ChinaBeijing 100094China Institute of Future Human Habitats Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Nitrogen oxides(NOx),significant contributors to air pollution and climate change,form aerosols and ozone in the ***,timely,and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone ***,a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from Chinadthe world's largest emitterdhas been lacking since 2020 due to delays in emissions *** we show a consistent decline in China's NOx emissions from 2020 to 2022,despite increased fossil fuel consumption,utilizing satellite observations as constraints for NOx emission estimates through atmospheric *** reduction is corroborated by data from two independent spaceborne instruments:the TROPOspheric Monitoring Instrument(TROPOMI)and the Ozone Monitoring Instrument(OMI).Notably,a reduction in transport emissions,largely due to the COVID-19 lockdowns,slightly decreased China's NOx emissions in *** subsequent years,2021 and 2022,reductions in NOx emissions were driven by the industry and transport sectors,influenced by stringent air pollution *** satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

关键词： China's NOx emissions Pollution control Socio-economic drivers Atmospheric inversion

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Trends in extreme high temperature at different altitudes of Southwest China during 1961–2014

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Atmospheric and Oceanic Science Letters 2020年第5期13卷 417-425页

作者： XUE Yuting CHEN Quanliang ZHANG Jiayu HUANG Ping College of Atmospheric Sciences Chengdu University of Information TechnologyChengduChina Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Earth System Modeling Center and Climate Dynamics Research Center Nanjing University of Information Science and TechnologyNanjingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Chinese Academy of SciencesBeijingChina

This study analyzed the trends in extreme high temperature in Southwest China based on the observed daily maximum temperature and average temperature data from 410 Chinese stations recently released by the China Meteorological *** authors found that the trends in extreme high temperature at different altitudes of Southwest China exhibit staged variations during a recent 50-year period(1961–2014).The trends in mean temperature and maximum temperature also exhibit phase *** temperature-related variables increase gently during the period 1975–94,whereas they increase dramatically during the recent period of 1995–2014,with a rate that is approximately two to ten times more than that during 1975–*** addition,the trends in mean temperature,maximum temperature,and the frequency of extreme high temperature in the low altitudes transit from negative to positive in the two periods,while they increase dramatically in the mid-and high-altitude areas during 1995–2014,the well-known global warming hiatus *** particular,the maximum temperature increases much faster than that of average *** result implies that the regional temperature trend could be apparently different from the global mean temperature change.

关键词： Southwest China extreme high temperature trend change altitude

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Dominant atmospheric circulation patterns associated with the rapid intensification of summer flash droughts in Eastern China

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Science of the Total Environment 2024年 957卷 177416页

作者： Ma, Feng Yuan, Xing Li, Hua Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing210044 China School of Hydrology and Water Resources Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China

Flash droughts (FDs), which are characterized by rapid intensification, occurred frequently over Eastern China, posing great challenges for drought forecasting and preparation on subseasonal timescale. However, the drivers of the rapid development of FDs are not well understood. By comparing with slow droughts (SDs), this study investigates the dominant physical processes responsible for FDs in four different regions over Eastern China through diagnosing moisture budgets and further linking them to large-scale atmospheric circulation patterns. Results reveal that the rapid intensification of FDs in the four regions are dominantly driven by stronger and more persistent subsidence than those for SDs, which suppress precipitation and heat the atmosphere, leading to rapid soil drying. Further analyses indicate that stronger and more stable atmospheric circulation systems are conductive to the rapid intensification of FDs. Specifically, in Northeast China, the persistent subsidence is strongly related to the maintenance of high over area south to Lake Baikal, which is associated with enhanced subtropical jet stream. In North China Plain, the upper-level cyclonic anomalies associated with the negative phase of the circumglobal teleconnection, together with more southward East Asia summer monsoon (EASM) rain belt, are responsible for the stronger sinking motions. In southern China, droughts can be attributed to simultaneous expansions of the South Asia high (SAH) and western Pacific subtropical high (WPSH). While the persistently joint control of the SAH and WPSH and larger area under the control of the two systems are crucial for FDs in Yangtze River basin and South China, respectively. The SDs are often related to the less organized circulation patterns and inconsistent shift of the EASM rain belt during the development phase. Our findings suggest potential source of predictability for summer FDs in Eastern China. © 2024 Elsevier B.V.

关键词： Drought

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Influence of the North Pacific Victoria mode on the tropical Pacific summer ITCZ

Influence of the North Pacific Victoria mode on the tropical...

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第一届中国大地测量和地球物理学学术大会

作者： Ruiqiang Ding Jianping Li Yu-heng Tseng State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(L College of Global Change and Earth System Sciences(GCESS) Beijing Normal UniversityBeijingChina Climate and Global Dynamics Division NCARBoulderColorado

In this paper,we demonstrate the close connection between the second dominant mode of spring SST anomalies (SSTAs) in the North Pacific poleward of 20°N,referred to as the Victoria mode (VM),and the interannual variability of the Pacific ITCZ precipitation during the following *** analysis shows that strong positive VM cases are followed by positive precipitation anomalies over the central–eastern Pacific ITCZ region,in association with negative precipitation anomalies over the ITCZ regions of the tropical western Pacific and eastern North *** influence of the spring VM on the Pacific ITCZ summer precipitation is independent of the effect of ENSO from the previous winter,and they may together act as effective predictors of Pacific ITCZ summer *** hypothesized physical mechanism through which the spring VM induces the Pacific ITCZ summer precipitation anomalies is generally consistent with the seasonal footprinting mechanism (SFM).During strong positive VM cases,positive SSTAs associated with the VM persist until summer in the subtropics,where low-level convergence and divergence caused by SST gradients give rise to enhanced precipitation over the central–eastern Pacific ITCZ region,and to reduced precipitation over the ITCZ regions of the tropical western Pacific and eastern North *** thermodynamic ocean–atmosphere coupling between the ITCZ and SSTAs associated with the VM in the subtropical Pacific may play a vital role in the initiation of ENSO events.

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Contribution of El Nino amplitude change to tropical Pacific precipitation decline in the late 1990s

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Atmospheric and Oceanic Science Letters 2019年第5期12卷 355-360页

作者： GUO Suqi WU Renguang Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

Equatorial central Pacific precipitation experienced a prominent decline in the late 1990 *** change was previously attributed to a La Nina-like mean sea surface temperature(SST)change in the Pacific Ocean associated with a phase switch of the Interdecadal Pacific ***,using a series of model experiments,the authors reveal that the El Nino-related interannual SST anomalies contributed largely to the precipitation decrease over the equatorial central *** El Nino SST effect was due to the change in the amplitude of El Nino events in the late 1990 *** 1980-98 decade had more large-amplitude El Nino events than the 1999-2014 *** nonlinear precipitation response to SST anomalies resulted in a larger decadal mean precipitation in the 1980-98 decade than in the 1999-2014 *** results highlight the importance of El Nino amplitude change in future climate change related to global warming.

关键词： Tropical Pacific precipitation decline Interdecadal Pacific Oscillation phase switch interannual SST effect large-amplitude El Nino events

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Boosting weather forecast via generative superensemble

arXiv

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arXiv 2024年

作者： Nai, Congyi Chen, Xi Yang, Shangshang Liang, Yuan Xiao, Ziniu Pan, Baoxiang Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Engineering and Design Technical University of Munich Munich Germany Potsdam Institute for Climate Impact Research Potsdam Germany TianJi Weather Science and Technology Company Beijing China

Accurate weather forecasting is essential for socioeconomic activities. While data-driven forecasting demonstrates superior predictive capabilities over traditional numerical Weather Prediction (NWP) with reduced computational demands, its deterministic nature and limited advantages over physics-based ensemble predictions restrict operational applications. We introduce the generative ensemble prediction system (GenEPS) framework to address these limitations by randomizing and mitigating both random errors and systematic biases. GenEPS provides a plug-and-play ensemble forecasting capability for deterministic models to eliminate random errors, while incorporating cross-model integration for cross-model ensembles to address systematic biases. The framework culminates in a superensemble approach utilizing all available data-driven models to further minimize systematic biases. GenEPS achieves an Anomaly Correlation Coefficient (ACC) of 0.679 for 500hPa geopotential (Z500), exceeding the ECMWF Ensemble Prediction system’s (ENS) ACC of 0.646. Integration of the ECMWF ensemble mean further improves the ACC to 0.683. The framework also enhances extreme event representation and produces energy spectra more consistent with ERA5 reanalysis. GenEPS establishes a new paradigm in ensemble forecasting by enabling the integration of multiple data-driven models into a high-performing super-ensemble system. © 2024, CC BY-NC-SA.

关键词： Prediction models

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A Time-Scale Decomposition Statistical Downscaling Model for Seasonal Prediction of East Asian Summer Monsoon

A Time-Scale Decomposition Statistical Downscaling Model for...

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第一届中国大地测量和地球物理学学术大会

作者： Jianping Li Chengqing Ruan Yan Guo Yun Li College of Global Change and Earth System Science(GCESS)Beijing Normal University Beijing 100875 C State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics CSIRO Mathematics Informatics and Statistics Wembley Western Australia 6913 Australia

A new statistical downscaling model based on the time-scale decomposition (TSD) approach is *** makes use of distinct downscaling models respectively corresponding to different time-scale variability with distinct different *** employ this model to statistically downscale East Asian summer monsoon (EASM),especially,summer (July-August) rainfall over North China as example.

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CMIP5模式对大尺度年平均地面气温异常的多年代际趋势的模拟评估和未来预估

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Engineering 2017年第1期 289-305页

作者：邢楠李建平王兰宁 State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology

基于观测和第五次耦合模式比较计划（CMIP5）模式的模拟结果,本文对全球、半球、半球陆地及海洋尺度的年平均地面气温异常在过去一百多年及两个代表性浓度路径（RCPs）情景下的多年代际变化及趋势进行了评估分析。根据模式对全球平均地... 详细信息

基于观测和第五次耦合模式比较计划（CMIP5）模式的模拟结果,本文对全球、半球、半球陆地及海洋尺度的年平均地面气温异常在过去一百多年及两个代表性浓度路径（RCPs）情景下的多年代际变化及趋势进行了评估分析。根据模式对全球平均地面气温异常的时间变率、长期趋势、多年代际变化及趋势的模拟能力,筛选出15个模式进行分析。观测结果表明,北半球陆地、北半球海洋和南半球海洋平均地面气温异常与全球平均地面气温异常具有相似的多年代际变化特征:在1900—1944年及1971—2000年呈现增暖趋势,并在1945—1970年和2001—2013年呈现增暖停滞甚至变冷趋势。模式能够基本再现以上观测特征。然而,与以上变化不同的是,南半球陆地的平均地面气温在1945—1970年呈现增暖趋势,并且模式不能很好模拟该特征。对于近期的增暖停滞阶段（2001—2013年）,BCC-CSM1-1-m模式、CMCC-CM模式、GFDL-ESM2M模式及NorESM1-ME模式在RCP4.5和RCP8.5情景下预估的全球及半球尺度的地面气温异常趋势值最接近观测值,表明它们具有较好的预估能力。由于这四个模式在地面气温异常的多年代际趋势上具有较好的模拟及预估性能,故选择它们来预估2006—2099年的地面气温异常在全球及半球尺度上的变化特征。结果显示在RCP4.5（RCP8.5）情景下,所选四个模式集合平均的全球、北半球及南半球年平均地面气温异常趋势值分别为0.17（0.29）、0.22（0.36）及0.11（0.23）℃·decade;,其趋势值明显小于未经过模式筛选的CMIP5模式集合的结果。

关键词：地面气温异常多年代际趋势第五次耦合模式比较计划(CMIP5) 预估

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Insights from the Second Tibetan Plateau Scientific Expedition: Unveiling the westerly–monsoon synergy and hydroclimate changes

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Atmospheric and Oceanic Science Letters 2024年第5期17卷 1-2页

作者： Chaofan Li Yaoming Ma Tianjun Zhou Riyu Lu Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Land-Atmosphere Interaction and Its Climatic Effects Group State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Hydraulic&Environmental Engineering China Three Gorges UniversityYichangChina College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina College of Atmospheric Sciences Lanzhou UniversityLanzhouChina Qomolangma Atmospheric and Environmental Observation and Research Station TingriChina Kathmandu Center for Research and Education Chinese Academy of Sciences-Tribhuvan UniversityBeijingChina China-Pakistan Joint Research Center on Earth Sciences IslamabadPakistan

The Tibetan Plateau(TP),often referred to as the“Asian Water Tower”,holds vast reserves of glaciers,snow,and permafrost,serving as the crucial source for major rivers that support billions of people across *** TP’s unique geographical positioning fosters significant interplay between the westerly and monsoon systems,the hydroclimate changes on the TP and its interactions with these two major atmospheric circulation systems through both the thermodynamic and dynamic processes,as well as the atmospheric water cycle of the *** interactions have far-reaching impacts on the weather and climate of China,Asia,and even the global atmospheric circulation.

关键词： Tibetan Plateau holds

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