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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG school of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China college of global change and earth system science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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Advances in Atmospheric sciences 2024年第7期41卷 1326-1341页

作者： Lu LI Yongjiu DAI Zhongwang WEI Wei SHANGGUAN Nan WEI Yonggen ZHANG Qingliang LI Xian-Xiang LI school of Atmospheric sciences Sun Yat-sen UniversityGuangzhou 510275China Southern Marine science and Engineering Guangdong Laboratory(Zhuhai) Guangzhou 510275China Guangdong Province Key Laboratory for Climate change and Natural Disaster Studies Guangzhou 510275China Institute of Surface-earth system science School of Earth System ScienceTianjin UniversityTianjin 300072China college of Computer science and Technology Changchun Normal UniversityChangchun 130123China

Accurate soil moisture(SM)prediction is critical for understanding hydrological ***-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface *** addition to PB models,deep learning(DL)models have been widely used in SM predictions ***,few pure DL models have notably high success rates due to lacking physical ***,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM *** this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the global Forecast system to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM *** performances of the proposed hybrid models were investigated and compared with two existing hybrid *** results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid ***,the ensemble model performed best among all hybrid models at all forecast time scales and different soil *** is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day *** findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

关键词： soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism

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changes in albedo and its radiative forcing of grasslands in East Asia drylands

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Ecological Processes 2024年第1期13卷 237-251页

作者： Qingsong Zhu Jiquan Chen Liangxu Wu Yuting Huang Changliang Shao Gang Dong Zhe Xu Xianglan Li State Key Laboratory of Remote Sensing science College of Global Change and Earth System Science Faculty of Geographical ScienceBeijing Normal University Department of Geography Environment and Spatial Sciences Michigan State University State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China National Hulunber Grassland Ecosystem Observation and Research Station Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences school of Life science Shanxi University

Background Grasslands in drylands are increasingly influenced by human activities and climate change, leading to alterations in albedo and radiative energy balance among others. Surface biophysical properties and their interactions change greatly following disturbances. However, our understanding of these processes and their climatic impacts remains limited. In this study, we used multi-year observations from satellites and eddy-covariance towers to investigate the response of albedo to variables closely associated with human disturbances, including vegetation greenness（EVI） and surface soil volumetric water content（VWC）, as well as snow cover and clearness index（Ta） for their potential *** EVI and VWC during the growing season were the primary factors influencing albedo. EVI and VWC were negatively correlated with albedo, with VWC's total direct and indirect impacts being slightly smaller than those of EVI. During the non-growing season, snow cover was the most influential factor on albedo. VWC and Ta negatively affected albedo throughout the year. We estimated the impact of variations in EVI and VWC on climate to be in the range of 0.004 to 0.113 kg CO2m-2yr-1in *** This study indicates the significant impacts of climate change and human disturbances on vulnerable grassland ecosystems from the perspective of altered albedo. changes in vegetation greenness and soil properties induced by climate change and human activities may have a substantial impact on albedo, which in turn feedback on climate change, indicating that future climate policies should take this factor into consideration.

关键词： Albedo global warming potential Grassland Disturbance

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Implementation of a particle-in-cell method for the energy solver in 3D spherical geodynamic modeling

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earth and Planetary Physics 2024年第3期8卷 549-563页

作者： Hao Dong ZeBin Cao LiJun Liu YanChong Li SanZhong Li LiMing Dai XinYu Li Frontiers science Center for Deep Ocean Multispheres and earth system Key Laboratory of Submarine Geosciences and Prospecting TechniquesMinistry of Education and College of Marine GeosciencesOcean University of ChinaQingdao 266100China State Key Laboratory of Lithospheric Evolution Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Laboratory for Marine Geology Laoshan LaboratoryQingdao 266237China Department of earth science&Environmental change University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Laboratory of Seismology and Physics of earth’s Interior School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei 230026China

The thermal evolution of the earth’s interior and its dynamic effects are the focus of earth ***,the commonly adopted grid-based temperature solver is usually prone to numerical oscillations,especially in the presence of sharp thermal gradients,such as when modeling subducting slabs and rising *** phenomenon prohibits the correct representation of thermal evolution and may cause incorrect implications of geodynamic *** examining several approaches for removing these numerical oscillations,we show that the Lagrangian method provides an ideal way to solve this *** this study,we propose a particle-in-cell method as a strategy for improving the solution to the energy equation and demonstrate its effectiveness in both one-dimensional and three-dimensional thermal problems,as well as in a global spherical simulation with data *** have implemented this method in the open-source finite-element code CitcomS,which features a spherical coordinate system,distributed memory parallel computing,and data assimilation algorithms.

关键词： numerical oscillation overshooting and undershooting particle-in-cell method three-dimensional spherical geodynamic modeling energy solver finite element method

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CAS-ESM2.0 Successfully Reproduces Historical Atmospheric CO_(2) in a Coupled Carbon−Climate Simulation

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Advances in Atmospheric sciences 2024年第4期41卷 572-580页

作者： Jiawen ZHU Juanxiong HE Duoying JI Yangchun LI He ZHANG Minghua ZHANG Xiaodong ZENG Kece FEI Jiangbo JIN International Center for Climate and Environment sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China college of global change and earth system science Beijing Normal UniversityBeijing 100875China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China school of Marine and Atmospheric sciences Stony Brook UniversityNY 11790USA University of Chinese Academy of sciences Beijing 100049China

The atmospheric carbon dioxide(CO_(2))concentration has been increasing rapidly since the Industrial Revolution,which has led to unequivocal global warming and crucial environmental *** is extremely important to investigate the interactions among atmospheric CO_(2),the physical climate system,and the carbon cycle of the underlying surface for a better understanding of the earth *** system models are widely used to investigate these interactions via coupled carbon-climate *** Chinese Academy of sciences earth system Model version 2(CAS-ESM2.0)has successfully fixed a two-way coupling of atmospheric CO_(2)with the climate and carbon cycle on land and in the *** CAS-ESM2.0,we conducted a coupled carbon-climate simulation by following the CMIP6 proposal of a historical emissions-driven *** paper examines the modeled CO_(2)by comparison with observed CO_(2)at the sites of Mauna Loa and Barrow,and the Greenhouse Gases Observing Satellite(GOSAT)CO_(2)*** results showed that CAS-ESM2.0 agrees very well with observations in reproducing the increasing trend of annual CO_(2)during the period 1850-2014,and in capturing the seasonal cycle of CO_(2)at the two baseline sites,as well as over northern high *** agreements illustrate a good ability of CAS-ESM2.0 in simulating carbon-climate interactions,even though uncertainties remain in the processes *** paper reports an important stage of the development of CAS-ESM with the coupling of carbon and climate,which will provide significant scientific support for climate research and China’s goal of carbon neutrality.

关键词： CAS-ESM atmospheric CO_(2) coupled carbon-climate simulation emissions-driven CMIP6 experiment

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Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

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Advances in Atmospheric sciences 2024年第7期41卷 1299-1312页

作者： Ya WANG Gang HUANG Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO LIU Haijie LI State Key Laboratory of Numerical Modeling for Atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China college of earth and Planetary sciences University of Chinese Academy of SciencesBeijing 100049China earth system Modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam Institute for Climate Impact Research Potsdam 14412Germany Department of Mathematics and global systems Institute University of ExeterExeter EX44QEUK CMA earth system Modeling and Prediction Centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated ***,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future *** these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning ***,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community earth system Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST ***,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to ***,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to *** enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales *** study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

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Progress of vegetation modelling and future research prospects

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science China earth sciences 2024年第9期67卷 2718-2738页

作者： Siqi LI Xu ZHANG Zhengyao LU Jian NI Jianhua LU State Key Laboratory of Tibetan Plateau earth system Resources and Environment (TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of Sciences college of Resources and Environment University of Chinese Academy of Sciences British Antarctic Survey Department of Physical Geography and Ecosystem science Lund University college of Life sciences Zhejiang Normal University school of Atmospheric sciences Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Terrestrial vegetation is a crucial component of the earth system, and its changes not only represent one of the most distinct aspects of climate change but also exert significant feedback within the climate system by exchanging energy, moisture,and carbon dioxide. To quantitatively and mechanistically study climate-vegetation feedback, numerical vegetation models have been developed on the theory of ecophysiological constraints on plant functional types. The models eventually can simulate vegetation distribution and succession across different spatial and temporal scales, and associated terrestrial carbon cycle processes by categorizing vegetation into biomes according different plant functional types and their associated environmental factors. Here we review the developing history of vegetation models and provide recent advances and future directions. Before 21st century, static vegetation models, as developed statistical models, can only simulate equilibrated characteristics of vegetation distribution. In last several decades, Dynamic global Vegetation Models(DGVMs) have been developed to simulate instantaneous responses of vegetation to climate change and associated dynamics, and can be coupled with earth system models to investigate interactions among atmosphere, ocean, and land. DGVMs are also widely applied to investigate the dynamics accounting for changes in the geographic distribution patterns of land surface vegetation at different spatial and temporal scales and to assess the impacts of terrestrial carbon and water fluxes and land use changes. We suggest that future vegetation modeling could integrate with machine learning, and explore vegetation transient response and feedback as well as impacts of process hierarchies and human activities on climate and ecosystem.

关键词： Vegetation models DGVMs Ecosystems Plant functional types Plant functional traits

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中国碳收支:1980～2021年（英文）

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science Bulletin 2024年第1期 114-124页

作者：夏晓圣任培阳王旭辉刘丹陈修治丹利何斌何洪林居为民梁敏琪陆星劼彭静覃章才夏江周郑博魏静乐旭于贵瑞朴世龙袁文平 school of Atmospheric sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Sino-French Institute for earth system science College of Urban and Environmental Sciences Peking University State Key Laboratory of Tibetan Plateau earth system Resources and Environment Institute of Tibetan Plateau Research Chinese Academy of Sciences Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Sciences State Key Laboratory of earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Synthesis Research Center of China's Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences International Institute for earth system science Nanjing University Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology

As one of the world’s largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon ***,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO2),and to monitor China’s progress toward these *** state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO2emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO2for all of China during *** recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China’s National Greenhouse Gas Inventories(NGHGIs) for 1994,2005,2010,2012,and *** CO2emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2a-1in *** benefiting from ecological projects(e.g., Three Norths Shelter Forest system Project),the land carbon sink in China has reached 1.65 Gt CO2a-1averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the *** average,China’s terrestrial ecosystems offset 14.69%±2.49% of anthropogenic CO2emissions through *** provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic *** study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2emissions,but found notable differences for land carbon *** estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

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global convergence in terrestrial gross primary production response to atmospheric vapor pressure deficit

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science China Life sciences 2024年第9期67卷 2016-2025页

作者： Chao Huang Jingfeng Huang Jingfeng Xiao Xing Li Hong S.He Yu Liang Fusheng Chen Hanqin Tian Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed College of Forestry Jiangxi Agricultural University CAS Key Laboratory of Forest Ecology and Management Institute of Applied Ecology Chinese Academy of Sciences Institute of Applied Remote Sensing & Information Technology College of Environmental and Resource Sciences Zhejiang University Key Laboratory of Agricultural Remote Sensing and Information systems Zhejiang Province Zhejiang University earth systems Research Center Institute for the Study of Earth Oceans and Space University of New Hampshire Research Institute of Agriculture and Life sciences Seoul National University school of Natural Resources University of Missouri Schiller Institute for Integrated science and Society Department of Earth and Environmental Sciences Boston College

Atmospheric vapor pressure deficit（VPD） increases with climate warming and may limit plant growth. However, gross primary production（GPP） responses to VPD remain a mystery, offering a significant source of uncertainty in the estimation of global terrestrial ecosystems carbon dynamics. In this study, in-situ measurements, satellite-derived data, and earth system Models（ESMs） simulations were analysed to show that the GPP of most ecosystems has a similar threshold in response to VPD: first increasing and then declining. When VPD exceeds these thresholds, atmospheric drought stress reduces soil moisture and stomatal conductance, thereby decreasing the productivity of terrestrial ecosystems. Current ESMs underscore CO2fertilization effects but predict significant GPP decline in low-latitude ecosystems when VPD exceeds the thresholds. These results emphasize the impacts of climate warming on VPD and propose limitations to future ecosystems productivity caused by increased atmospheric water demand. Incorporating VPD, soil moisture, and canopy conductance interactions into ESMs enhances the prediction of terrestrial ecosystem responses to climate change.

关键词： atmospheric water demands soil moisture stomatal conductance photosynthesis climate warming.

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中国碳收支:1980~2021年

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science Bulletin 2024年第1期69卷 114-124页

作者：夏晓圣任培阳王旭辉刘丹陈修治丹利何斌何洪林居为民梁敏琪陆星劼彭静覃章才夏江周郑博魏静乐旭于贵瑞朴世龙袁文平 school of Atmospheric sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon CycleSun Yat-sen UniversityZhuhai 510245China Sino-French Institute for earth system science College of Urban and Environmental SciencesPeking UniversityBeijing 100091China State Key Laboratory of Tibetan Plateau earth system Resources and EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of earth Surface Processes and Resource Ecology College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Synthesis Research Center of China’s Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Institute for earth system science Nanjing UniversityNanjing 210023China Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal UniversityTianjin 300387China Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China

As one of the world's largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon ***,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO_(2)),and to monitor China's progress toward these *** state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO_(2)emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO_(2)for all of China during *** recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China's National Greenhouse Gas Inventories(NGHGIs)for 1994,2005,2010,2012,and *** CO_(2)emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO_(2)a^(-1)in *** benefiting from ecological projects(e.g.,Three Norths Shelter Forest system Project),the land carbon sink in China has reached 1.65 Gt CO_(2)a^(-1)averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the *** average,China's terrestrial ecosystems offset 14.69%±2.49%of anthropogenic CO_(2)emissions through *** provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10%of anthropogenic CO_(2)*** study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO_(2)emissions,but found notable differences for land carbon *** estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

关键词： CO_(2)emission Land carbon sink China Carbon neutrality Carbon budget

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