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Satellite SAR observation of the sea surface wind field caused by rain cells

Acta Oceanologica Sinica 2016年第9期35卷 80-85页

作者： YE Xiaomin LIN Mingsen yuan Xinzhe DING Jing XIE Xuetong ZHANG Yi XU Ying College of information Science and engineering Ocean University of China National Satellite Ocean Application Service State Oceanic Administration Key Laboratory of Space Ocean remote sensing and Application State Oceanic Administration school of Geographical Sciences Guangzhou University

Rain cells or convective rain,the dominant form of rain in the tropics and subtropics,can be easy detected by satellite Synthetic Aperture Radar（SAR） images with high horizontal *** footprints of rain cells on SAR images are caused by the scattering and attenuation of the rain drops,as well as the downward *** this study,we extract sea surface wind field and its structure caused by rain cells by using a RADARSAT-2 SAR image with a spatial resolution of 100 m for case *** extract the sea surface wind speeds from SAR image by using CMOD4 geophysical model function with outside wind directions of NCEP final operational global analysis data,Advance Scatterometer（ASCAT） onboard European Met Op-A satellite and microwave scatterometer onboard Chinese HY-2 satellite,*** root-mean-square errors（RMSE） of these SAR wind speeds,validated against NCEP,ASCAT and HY-2,are 1.48 m/s,1.64 m/s and 2.14 m/s,*** signature patterns with brighter on one side and darker on the opposite side on SAR image are interpreted as the sea surface wind speed（or sea surface roughness） variety caused by downdraft associated with rain *** wind speeds taken from the transect profile which superposes to the wind ambient vectors and goes through the center of the circular footprint of rain cell can be fitted as a cosine or sine curve in high linear correlation with the values of no less than *** background wind speed,the wind speed caused by rain cell and the diameter of footprint of the rain cell with kilometers or tens of kilometers can be acquired by fitting *** cases interpreted and analyzed in this study all show the same conclusion.

关键词： rain cells Synthetic Aperture Radar (SAR) sea surface wind field downdraft

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Impacts of landscape and climatic factors on snow cover in the Altai Mountains,china

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Advances in Climate Change Research 2021年第1期12卷 95-107页

作者： ZHONG Xin-Yue ZHANG Tingjun SU Hang XIAO Xiong-Xin WANG Shu-Fa HU yuan-Tao WANG Hui-Juan ZHENG Lei ZHANG Wei XU Min WANG Jian Key Laboratory of remote sensing of Gansu Province Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou730000China Key Laboratory of Western china's Environmental Systems(Ministiy of Education) College of Earth and Environmental SciencesLanzhou UniversityLanzhou730000China State Key Laboratory of Water Resources and Hydropower engineering Science Wuhan UniversityWuhan430072China school of remote sensing and information engineering Wuhan UniversityWuhan430072China Institute of Atmospheric Sciences Fudan UniversityShanghai200433China Gansu Tianshui Power Supply Company Tianshui741000China school of Geospatial engineering and Science Sun Yat-Sen UniversityGuangzhou510275China State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou730000China

Snow properties and their changes are crucial to better understanding of hydrological processes,soil thermal regimes,and surface energy *** data and information on snow depth and snow water equivalent(SWE)are also crucial for water resource assessments and socio-economic development at local and regional ***,these data are extremely limited and unreliable in northern Xinjiang,*** study thus aims to investigate spatial variations of snow depth,SWE,and snow density based on winter snowfield surveys during 2015 through 2017 in the Altai Mountains,northwestern *** results indicated that snow depth(25-114 cm)and SWE(40-290 mm)were greater in the alpine Kanas-Hemu region,and shallow snow accumulated(9-42 cm for snow depth,26-106 mm for SWE)on the piedmont sloping *** there was no remarkable regional difference in the distribution of snow *** property distributions were strongly controlled by topography and *** and latitude were the most important factors affecting snow depth and SWE,while snow density was strongly affected by longitude across the Altai Mountains in *** influence of topography on snow property distributions was spatially *** snow depth increased from 13.7 to 31.2 cm and SWE from 28.5 to 79.9 mm,respectively,with elevation increased from 400 to 1000 m *** the piedmont sloping *** depth decreased to about 15.1 cm and SWE to about 28.5 mm from 1000 to 1800 m a.s.l.,then again increased to about 98.1 cm and 271.7 mm on peaks(-2000 m a.s.l.)in the alpine *** slopes were easier to accumulate snow cover,especially on north-,east-,and southeast-facing *** interception was also the cause of the difference in snow *** depth,SWE,and snow density in forests were reduced by 8%-53%,2%-67%and-4%to+48%,respectively,compared with surrounding open *** when snow depth was less than 40 cm,snow depth and SWE differences in

关键词： Altai Mountains Snow cover Topography Vegetation Climate factor

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Disciplinary structure and development strategy of information geography in china

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Journal of Geographical Sciences 2022年第9期32卷 1670-1682页

作者： LI Xin yuan Linwang PEI Tao HUANG Xin LIU Guang ZHENG Donghai State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchCASBeijing100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing100049China Ministry of Education Key Laboratory of Virtual Geographic Environment Nanjing Normal UniversityNanjing210023China State Key Laboratory of Resources and Environment information System Institute of Geographic Sciences and Natural Resources ResearchCASBeijing100101China school of remote sensing and information engineering Wuhan UniversityWuhan430079China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteCASBeijing100094China

The advent of the information era has resulted in exceptional advances in geographic *** domain of geographic science has expanded from traditional physical and human geography to physical,human,and information geography,resulting in the emergence of the field of information *** subdisciplines have gradually formed,i.e.,geographic remote sensing science,geographic information science,and geographic data *** a view towards preparing a description of the disciplinary structure of geographic science for the“Development Strategy of Discipline and Frontier Research in china(2021-2035)”,this article summarizes the history,definition,and disciplinary structure of information *** strategic layout of the discipline,along with the goals and key directions of priority development fields,are also *** insights into this new discipline provided in this paper will promote the development and application of remote sensing and geographic information within the framework of geographic science,advancing the synthesis of geographic research and the integrated development of geographic science.

关键词： information geography geographic remote sensing science geographic information science geographic data science disciplinary structure

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A fully automatic and high-accuracy surface water mapping framework on Google Earth Engine using Landsat time-series

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International Journal of Digital Earth 2023年第1期16卷 210-233页

作者： Linwei Yue Baoguang Li Shuang Zhu Qiangqiang yuan Huanfeng Shen school of Geography and information engineering China University of GeosciencesWuhanPeople’s Republic of China National engineering Research Center of Geographic information System China University of GeosciencesWuhanPeople’s Republic of China school of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China school of Resources and Environmental Science Wuhan UniversityWuhanPeople’s Republic of China

Efficient and continuous monitoring of surface water is essential for water resource *** effort has been devoted to the task of water mapping based on remote sensing ***,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic ***,water area statistics are sensitive to the satellite image observation *** study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence *** samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample *** reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid *** experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping *** tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.

关键词： Water mapping automatic training samples temporal correction Google Earth Engine

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remote sensing-based Spatiotemporal Distribution of Grassland Aboveground Biomass and Its Response to Climate Change in the Hindu Kush Himalayan Region

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Chinese Geographical Science 2022年第5期32卷 759-775页

作者： XU Cong LIU Wenjun ZHAO Dan HAO Yanbin XIA Anquan YAN Nana ZENG yuan State Key Laboratory of remote sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101China university of Chinese Academy of Sciences Beijing 100049China school of Ecology and Environmental Sci-ences&Yunnan Key Laboratory for Plateau Mountain Ecology and Restoration of Degraded Environments Yunnan UniversityKun-ming 650091China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China CAS Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of SciencesBeijing 100101China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China College of Resources and Envir-onment University of Chinese Academy of SciencesBeijing 100049China

The grassland in the Hindu Kush Himalayan(HKH) region is one of the large st and most biodiverse mountain grassland types in the world,and its ecosystem service functions have profound impacts on the sustainable development of the HKH *** the spatiotemporal distribution of grassland aboveground biomass(AGB) accurately and quantifying its response to climate change are indispensable sources of information for sustainably managing grassland ecosystems in the HKH *** this study,a pure vegetation index model(PVIM) was applied to estimate the long-term dynamics of grassland AGB in the HKH region during *** further quantified the response of grassland AGB to climate change(temperature and precipitation) by partial correlation and variance partitioning analyses and then compared their differences with *** results demonstrated that the grassland AGB predicted by the PVIM had a good linear relationship with the ground sampling *** grassland AGB distribution pattern showed a decreasing trend from east to west across the HKH region except in the southern *** 2000 to 2018,the mean AGB of the HKH region increased at a rate of 1.57 g/(m~2·yr) and ranged from 252.9(2000) to 307.8 g/m~2(2018).AGB had a positive correlation with precipitation in more than 80% of the grassland,and temperature was positively correlated with AGB in approximately half of the *** change in grassland AGB was more responsive to the cumulative effect of annual precipitation,while it was more sensitive to the change in temperature in the growing season;in addition,the influence of climate varied at different ***,compared with that of temperature,the contribution of precipitation to grassland AGB change was greater in approximately 60% of the grassland,but the differences in the contribution for each climate factor were small between the two temporal scales at elevations over 2000 *** accurate assessment of the temporal and spatial

关键词： grassland aboveground biomass(AGB) climate change elevation spatiotemporal distribution Hindu Kush Himalayan(HKH)region

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Laser-induced breakdown spectroscopy in china

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Frontiers of physics 2014年第4期9卷 419-438页

作者： Zhe Wang Ting-Bi yuan Zong-Yu Hou Wei-Dong Zhou Ji-Dong Lu Hong-Bin Ding Xiao-Yan Zeng State Key Lab of Power Systems Department of Thermal Engineering Tsinghua-BP Clean Energy Center Tsinghua University Beijing 100084 China Institute of information Optics Zhejiang Normal University Jinhua 321004 China school of Electric Power South China University of Technology Guangzhou 510640 China school of Physics and Optoelectronic engineering Dalian University of Technology Dalian 116024 China school of Optical and Electronic information Huazhong University of Science Technology Wuhan 430074 China

Laser-induced breakdown spectroscopy （LIBS） has been regarded as a future superstar for chemical analysis for years due to its unique features such as little or no sample preparation, remote sensing, and fast and multi-element analysis. Chinese LIBS comnmnity is one of the most dynamically developing communities in the World. The aim of the work is to inspect what have been done in china for LIBS development and, based on the understanding of the overall status, to identify the challenges and opportunities for the future development. In this paper, the scientific contributions from Chinese LIBS comlnunity are reviewed for the following four aspects： fimdamentals, instrumentation, data processing and modeling, and applications; and the driving force of LIBS development in china is analyzed, the critical issues for successful LIBS application are discussed, and in our opinion, the potential direction to improve the technology and to realize large scale commercialization in china is proposed.

关键词： laser-induced breakdown spectroscopy laser-induced breakdown spectroscopy (LIBS),quantitative analysis signal enhancement application coal metal environment energy

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin school of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological university SingaporeSingapore school of Computer Science Wuhan UniversityWuhanChina Department of Electrical engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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A new method of pseudo absence data generation in landslide susceptibility mapping with a case study of Shenzhen

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Science china(Technological Sciences) 2010年第S1期53卷 75-84页

作者： XIAO ChenChao TIAN yuan SHI WenZhong GUO QingHua WU Lun Institute of remote sensing and Geographic information System Peking UniversityBeijing 100871China university of Salzburg Z_GISCenter for GeoinformaticsSchillerstraβe 305020 SalzburgAustria Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong KongChina school of engineering University of California MercedMerced 95343USA

Landslide susceptibility mapping is a typical two-class classification problem where generating pseudo absence (non-slide) data plays an important *** this paper,a new method,target space exteriorization sampling method (TSES),is presented to generate pseudo absence data based on presence data directly in feature *** exteriorizes a presence sample to become a pseudo absence one by replacing the value of one of its features with a new one outside the value range of this feature of all presence *** method is compared with two existing methods,buffer controlled sampling (BCS) and iteratively refined sampling (IRS),in a study area of Shenzhen *** pseudo absence data generated by each of these three methods are organized into 20 subsets with increasing data sizes to study the effects of the proportion of pseudo absence data to presence *** landslide susceptibility maps of the study area are calculated with all these datasets by general additive model (GAM).It can be concluded that,through a 10-fold validation,TSES and IRS-based models have similar AUC values that are both greater than that of BCS,but TSES outperforms BCS and IRS in prediction *** results also have more reasonable spatial and histogram distributions than BCS and IRS,which can support categorization of an area into more susceptibility ranks,while IRS shows a tendency to separate the whole study area into two susceptibility *** can be also concluded that when using BCS,the pseudo absence data proportion to the presence data would be about 50% to get a considerable result,while for IRS or TSES the minimum proportion is 40%.

关键词： pseudo absence data landslide susceptibility mapping GIS GAM

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Marine information technology:the best is yet to come

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Frontiers of information Technology & Electronic engineering 2018年第8期19卷 947-950页

作者： Wen XU yuan-liang MA Fumin ZHANG Daniel ROUSEFF Fei JI Jun-hong CUI Hussein YAHIA College of lnformation Science and Electrontc Engmeermg Zhejiang UniversityHangzhou 310027China school of Marine Science and Technology Northwestern Polytechnical UniversityXi'an 710072China school of Electncal and Computer engineering Georgia Institute of TechnologyAtlantaGA 30332USA Applied Physics Laboratory University of WashingtonSeattleWA 98105USA school of Electronic and lnformation engineering South China University of TechnologyGuangzhou 510641China Smart Ocean Research Center Jilin UniversityChangchun 130012China Research Center INRIA Bordeaux-South West Talence 33405France

Marine information technology(MarineIT)involves marine information gathering,transmission,processing,and ***,this topic has been referred to in the context of acoustic,optical,and electromagnetic sensing of the ocean environment,most notably sonar/radar processing and satellite remote *** its embodiment becomes enriched and its scope extends,particularly accompanied

关键词：海洋环境信息技术卫星遥感上下文

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Feature Selection with Data Field

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Chinese Journal of Electronics 2014年第4期23卷 661-665页

作者： yuan Hanning WANG Shuliang LI Ying FAN Jinghua school of Software Beijing Institute of Technology State Key Laboratory of information engineering in Surveying Mapping and Remote Sensing International school of Software Wuhan University

A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.

关键词： Potential entropy Data field Feature selection Dimension reduction High-dimensional objects

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