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remote sensing of liquid cloud profiles based on an analytical cloud profiling model

science China Earth sciences 2025年第4期68卷 998-1012页

作者： Huazhe SHANG Husi LETU Lesi WEI Run MA Yutong WANG Zhaoxin CAI Shuai YIN Chong SHI State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing100101China University of Chinese Academy of Sciences Beijing100049China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science&EngineeringYibin644005China Meteorological Disasters Defense Technology Center Meteorological Bureau of Shanxi ProvinceTaiyuan030032China

The characteristics of microphysical profiles of liquid clouds(i.e.,the cloud effective radius(ER)and liquid water content(LWC))are important factors in understanding the aerosol-cloud-precipitation process and improving the evaluation of cloud radiative effects on a global ***,the profiling of clouds is limited to measurements such as radars or lidars,and retrievals from active sensors are influenced by the attenuation of signals and surface *** when vast amounts of cloud property retrievals that express column or cloud-top/base characteristics from passive sensors are generated,these two-dimensional cloud products are scarcely ever used to further produce three-dimensional cloud profiles because of the lack of a reasonable analytical model to bridge the ***,this study developed a cloud profile reconstruction model(CPRM)based on our previous analytical cloud profile model and evaluated the sensitivities of visible-to-infrared bands of passive cloud observation sensors to each parameter of the *** results indicated that the optical thickness,cloud-top ER,and geometrical thickness can be derived from multiwavelength measurements,whereas the slope of the cloud droplet number concentration(CDNC)and the cloud-base ER could hardly be directly ***,the analytical model was utilized to rebuild cloud profiles seen by CloudSat and *** analytical model could capture the shape and the vertical variations of the cloud ER and LWC.

关键词： Analytical cloud profiling model Cloud effective radius Cloud liquid water content CloudSat Airborne mea-surements

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Analysis and Optimization of Ecological Network Change in Guangxi Beibu Gulf Economic Zone

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Journal of Geodesy and Geoinformation science 2024年第4期7卷 56-74页

作者： LI Yiyun LING Ziyan JIANG Weiguo WU Juncheng School of Geography and Planning Nanning Normal UniversityNanning 530001China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520China State Key Laboratory of Remote Sensing Science Beijing Normal UniversityBeijing 100875China School of Remote Sensing and Information Engineering Wuhan UniversityWuhan 430070China

With the continuous acceleration of urbanization,the resulting habitat fragmentation poses a serious threat to regional ecological *** an ecological network not only facilitates the positive circulation of regional materials and energy but also holds significant importance for achieving regional sustainable *** the Guangxi Beibu Gulf Economic Zone as the study area,and supported by GIS and other technologies,this study uses 10-meter high-resolution land use data from 2017 and 2021 to select ecological sources based on MSPA and landscape *** the MCR-gravity model,a higher precision ecological network for the Guangxi Beibu Gulf Economic Zone is *** network is then optimized and comparatively analyzed using circuit theory and network analysis *** results showed that:① Although the core area of ecological sources accounts for 95.16%of the total ecological source area,a comparison of the two-year analysis results shows an increasing degree of landscape fragmentation and a decline in landscape connectivity.② The optimized ecological network of the Beibu Gulf Economic Zone of Guangxi comprises 11 ecological sources,91 ecological corridors,and 71 ecological nodes.③ The indices of the optimized network structure have all improved,enhancing the degree of ecological network connectivity within the study area.

关键词： ecological network MSPA landscape connectivity MCR gravity model circuit theory

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Sea ice drift vector extraction based on feature matching using CS-1 Images

Sea ice drift vector extraction based on feature matching us...

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2023 International Conference on Frontiers of Ocean science and technology, ICFOST 2023

作者： Yang, Y.L. Xie, T. School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing210044 China Laboratory for Regional Oceanography and Numerical Modelling Qingdao National Laboratory for Marine Science and Technology Qingdao266000 China

The study emphasizes the crucial role of sea ice drift in Arctic climate research and the protection of human activities. Employing C-SAR/01 (CS-1) imagery as the primary data source, the study assesses the effectiveness of the ORB feature matching algorithm for extracting Arctic Sea ice drift vectors. Additionally, it investigates the spatial distribution variations in the drift vectors extracted from the HH and HV polarization channels. The accuracy of the extracted drift vectors is validated using manually extracted sea ice drift data. Experimental results reveal a significant disparity in the number of drift vectors extracted from CS-1 HV polarization images compared to HH polarization images. The sea ice drift vectors extracted using the Oriented fast and Roasted Bried (ORB) operator demonstrate an average velocity error of less than 0.27 cm/s (0.31 km/d) and an average direction error of less than 5.66° in HV polarization images. © Published under licence by IOP Publishing Ltd.

关键词： Vectors

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Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images

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International Journal of Agricultural and Biological Engineering 2024年第2期17卷 260-267页

作者： Xiaobin Xu Cong Teng Hongchun Zhu Haikuan Feng Yu Zhao Zhenhai Li College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China

Predicting crop yield timely can considerably accelerate agricultural production management and food policy-making,which are also important requirements for precise agricultural *** the development of hyperspectral imaging technology,a simple and efficient modeling method is convenient for predicting crop yield by using airborne hyperspectral *** this study,the Unmanned Aerial Vehicle(UAV)hyperspectral and maturity yield data in 2014-2015 and 2017-2018 were *** winter wheat yield prediction model was established by optimizing Vegetation Indices(VIs)feature scales and sample scales,incorporating Partial Least Squares Regression(PLSR),Random Forest algorithm(RF),and Back Propagation Neural Network algorithm(BPN).Results showed that PLSR stands out as the optimal wheat yield prediction model considering stability and accuracy(RMSE=948.88 kg/hm2).Contrary to the belief that more input features result in higher accuracy,PLSR,RF,and BPN models performed best when trained with the top 3,8,and 4 VIs with the highest correlation,*** an increase in training samples,model accuracy improves,reaching stability when the training samples reach *** PLSR and optimal feature scales,UAV yield prediction maps were generated,holding significant value for field management in precision agriculture.

关键词： hyperspectral imagery unmanned aerial vehicle winter wheat yield prediction model remote sensing

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The present situation and shifts observed in wetlands within the *** Seaway region of Canada,utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine

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Big Earth Data 2025年第1期9卷 47-71页

作者： Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh Digital Environmental Group WSP Canada Inc.OttawaONCanada Department of Computer Science Chalmers University of TechnologyGothenburgSweden Digital Environmental Group WSP Canada Inc.EdmontonABCanada dIndependent Researcher Department of Photogrammetry and Remote Sensing Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina

This study examined wetland trends in the *** Seaway(~500,000 km^(2))in Canada over the past four *** this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were *** samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged *** spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to ***,a change analysis was conducted to calculate the loss and gain of different wetland ***,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,*** was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 ***,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an ***,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),*** study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.

关键词： remote sensing Google Earth Engine(GEE) cloud computing satellite change detection continuous change detection and classification(CCDC) wetlands

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A spatial-spectral classification framework for multispectral LiDAR

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地球空间信息科学学报（英文版） 2024年第5期27卷中插4,1460-1474页

作者： Shuo Shi Biwu Chen Sifu Bi Junkai Li Wei Gong Jia Sun Bowen Chen Lin Du Jian Yang Qian Xu Fei Wang Shalei Song State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Scientific Laboratory Shanghai Radio Equipment Research InstituteShanghaiChina Industry Development and Planning Institute NFGABeijingChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Electronic Information School Wuhan UniversityWuhanChina Faculty of Information Engineering China University of GeosciencesWuhanChina Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Innovation Academy for Precision Measurement Science and Technology Chinese Academy of SciencesWuhanChina

Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous *** to the lack of spectral information,the existing research on single wavelength LiDAR classification is *** information from images could address this limitation,but data fusion suffers from varying illumination conditions and the registration problem.A novel multi-spectral LiDAR successfully obtains spatial and spectral information as a brand-new data type,namely,multispectral point cloud,thereby improving classification ***,spa-tial and spectral information of multispectral LiDAR has been processed separately in previous studies,thereby possibly limiting the classification performance of multispectral *** explore the potential of this new data type,the current spatial-spectral classification framework for multispectral LiDAR that includes four steps:(1)neighborhood selection,(2)feature extraction and selection,(3)classification,and(4)label *** novel highlights were proposed in this spatial-spectral classification framework.(1)We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood.(2)We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy.(3)We conducted spatial label smoothing by a conditional random field,accounting for the spatial and spectral informa-tion of the neighborhood *** proposed method demonstrated by a multispectral LiDAR with three channels:466 nm(blue),527 nm(green),and 628 nm(red).Experimental results demonstrate the effectiveness of the proposed spatial-spectral classification ***,this research takes advantages of the complementation of spatial and spectral infor-mation,which could benefit more precise neighbor

关键词： multispectral Light Detection and Ranging(LiDAR) point cloud classification neighborhood selection feature selection condition random field

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Multimodal Electromagnetic Vortex SAR Image Fusion using CNN

Multimodal Electromagnetic Vortex SAR Image Fusion using CNN

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2024 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2024

作者： Lan, Kun Sun, Xiyuan He, Zi Ding, Da-Zhi Jiang, Xinrui Yang, Bixia Nanjing University of Science and Technology Nanjing China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (纸本)9798331520717

Due to the hollow characteristics of vortex electromagnetic beams, target information may be missing to varying degrees in electromagnetic vortex SAR images under different modes. The target information can be restored using image fusion methods. Traditional pixel-level fusion methods require precise setting of weights, which may result in loss of information from the source image. This paper proposes a Multimodal Electromagnetic Vortex SAR Image Fusion using CNN. First, training is conducted using a large-scale RGB-D dataset (NYU-D2), and then fusion tests are performed using electromagnetic vortex SAR images of different modalities. The comparative experimental results show that this method can integrate electromagnetic vortex SAR images of different modes and recover target information, thereby producing high-quality SAR images. © 2024 IEEE.

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Development of China's spaceborne SAR satellite,processing strategy,and application:take Gaofen-3 series as an example

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地球空间信息科学学报（英文版） 2024年第1期27卷 221-236页

作者： Guo Zhang Shunyao Wang Zhenwei Chen Yuzhi Zheng Ruishan Zhao Taoyang Wang Yu Zhu Xinzhe Yuan Wei Wu Weitao Chen State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Law Wuhan UniversityWuhanChina School of Geomatics Liaoning Technical UniversityFuxinChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Institute of Spacecraft System Engineering China Academy of Space TechnologyBeijingChina National Disaster Reduction Center of China Ministry of Emergency ManagementBeijingChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources of the People's Republic of ChinaBeijingChina

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar(SAR),China's SAR satellites have been successfully launched into *** the only civil microwave satellite listed in the"National High-resolution Earth Observation System Major Project,"the Gaofen-3(GF-3)01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 ***-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution,wide swath,high geometric radiation quality,multiple imaging modes,and long operation *** GF-3 series satellites as an example,this study introduces the development of China's civil SAR satellites,as well as their processing strategies and *** success of the GF-3 series satellites shows that China's SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

关键词： High-resolution Synthetic Aperture Radar(SAR) GaoFen-3(GF-3)satellites earth observation

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Multisource Data Integration of Sentinel-1 and Sentinel-2 for Above Ground Biomass Inversion 2

Multisource Data Integration of Sentinel-1 and Sentinel-2 fo...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Xing, Yaxuan Lin, Huiping Zhu, Jingwen Wang, Feng Xu, Feng Jiang, Wen School of Information Science and Technology Shanghai China Chinese Academy of Sciences Aerial Remote Sensing Center Aerospace Information Research Institute Beijing China

ISBN: (纸本)9798331515669

remote sensing data provides several advantages for vegetation parameter inversion, including non-invasive measurement, high spatiotemporal resolution, and extensive coverage. However, as multisource remote sensing data continues to advance, challenges emerge in data fusion, particularly in ensuring consistency across various data sources and addressing physical differences among them. To address such challenges, this paper employs multisource re mote sensing data as inputs for Above Ground Biomass (AGB) inversion. As a scattering model, the Water Cloud Model (WCM) is widely adopted for vegetation parameter inversion due to its simplicity and ability to describe both vegetation and soil scattering mechanisms. In this study, Sentinel-1 data is utilized to extract backscatter coefficients and incidence angle information, while Sentinel-2 multispectral data is used to calculate soil moisture through the tasseled cap transformation. These variables are employed to calibrate the coefficients of the WCM, facilitating the bio mass inversion process. The approach effectively integrates multiple sources of remote sensing data, overcoming limitation of data saturation typically associated with single-source data, and achieving accurate biomass inversion across different polarization modes. © 2024 IEEE.

关键词： Abiotic

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Understanding the spatio-temporal dynamics of ecosystem services under multiple future scenarios to assess the progress of Sustainable Development Goals implementation

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Journal of Geographical sciences 2025年第4期35卷 745-762页

作者： ZHANG Ze JIANG Weiguo LING Ziyan PENG Kaifeng WU Zhifeng LI Zhuo State Key Laboratory of Remote Sensing and Digital Earth Science Faculty of Geographical SciencesBeijing Normal UniversityBeijing 100875China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520ShandongChina College of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin 300387China School of Geography and Remote Sensing Guangzhou UniversityGuangzhou 510006China National Marine Data and Information Service Tianjin 300171China

Ecosystem services in urban agglomerations are the environmental conditions under which human survival and development are *** assessment of ecosystem services and complex interactions can contribute positively to the achievement of the Sustainable Development Goals(SDGs)for urban ***,studies on the future contribution of multi-scenario ecosystem services to the SDGS are *** pronovel integrated modeling framework that integrates the CLUES,InVEST,SOM,and GWR approaches to address the complex relationship between ecosystem services over a long“past-present-future”time *** construct a novel ecosystem service bundle-based approach for measuring urban agglomerations progress towards achieving ecologically relevant sustainable development goals at multiple *** the future scenario,the water yield(WY),habitat quality(HQ),and soil conservation(SC)show similar spatial patterns,with comparable spatial grids,while carbon stock(CS)remains predominantly unchanged and the ecological protection scenario(EPS)improves more *** high-synergy regions are mainly distributed in bundle 4,and most of the trade-off regions appear in bundles 1 and *** the last 30 years,all but the water-related SDGs are declining in bundle 1 of the two urban agglomerations,which are 15%higher in the Guangxi Beibu Gulf(GBG)than in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA).From 2020 to 2035,the three scenarios demonstrate that the optimization of the SDGs progresses most effectively under the future ecological protection scenario(EPS).In particular,bundles 3 and 4 are significantly *** critical new knowledge can be used in sustainable ecosystem management and decision-making in urban agglomerations.

关键词： Sustainable Development Goals(SDGs) ecosystem service bundle trade-offs and synergies InVEST model self-organizing mapping(SOM)algorithm urban agglomeration in south China

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