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remote sensing and its applications using GNSS reflected signals:advances and prospects

Satellite Navigation 2024年第1期5卷 10-51页

作者： Shuanggen Jin Adriano Camps Yan Jia Feng Wang Manuel Martin-Neira Feixiong Huang Qingyun Yan Shuangcheng Zhang Zhongyu Li Komi Edokossi Dongkai Yang Zhiyu Xiao Zhongmin Ma Weihua Bai Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai200030China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo454003China Department of Signal Theory and Communications Technical University of CataloniaBarcelona08034Spain Department of Surveying and Geoinformatics Nanjing University of Posts and TelecommunicationsNanjing210023China School of Electronics and Information Engineering Beihang UniversityBeijing100191China European Space Research and Technology Centre(ESTEC) European Space Agency2200-AGNoordwijkThe Netherlands National Space Science Center Chinese Academy of SciencesBeijing100190China School of remote sensing and Geomatics Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Geological Engineering and Geomatics Chang’an UniversityXi’an710054China University of Electronic Science and Technology of China Chengdu611731China

The Global Navigation Satellite Systems(GNSS),including the US’s GPS,China’s BDS,the European Union’s Galileo,and Russia’s GLONASS,offer real-time,all-weather,any-time,anywhere and high precision observations by transmitting L band signals continuously,which have been widely used for positioning,navigation and timing.With the development of GNSS technology,it has been found that GNSS-reflected signals can be used to detect Earth’s surface characteristics together with other signals of opportunity.In this paper,the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry(GNSS-R)in theory,methods,techniques and observations.New developments and progresses in GNSS-R instruments,theoretical modeling,and signal processing,ground and space-/air-borne experiments,parameters retrieval(e.***.wind speed,sea surface height,soil moisture,ice thickness),sea surface altimetry and applications in the atmosphere,oceans,land,vegetation,and cryosphere are given and reviewed in details.Meanwhile,the challenges in the GNSS-R development of each field are also given.Finally,the future applications and prospects of GNSS-R are discussed,including multi-GNSS reflectometry,new GNSS-R receivers,GNSS-R missions,and emerging applications,such as mesoscale ocean eddies,ocean phytoplankton blooms,microplastics detection,target recognition,river flow,desert studies,natural hazards and landslides monitoring.

关键词： GNSS Reflectometry remote sensing Environmental change

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remote sensing image encryption algorithm utilizing 2D Logistic memristive hyperchaotic map and SHA-512

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Science China(Technological Sciences) 2024年第5期67卷 1553-1566页

作者： LAI Qiang LIU Yuan YANG Liang School of Electrical and Automation Engineering East China Jiaotong UniversityNanchang330013China

The two-dimensional Logistic memristive hyperchaotic map(2D-LMHM)and the secure hash SHA-512 are the foundations of the unique remote sensing image encryption algorithm(RS-IEA)suggested in this research.The proposed map is formed from the improved Logistic map and the memristor,which has wide phase space and hyperchaotic range and is exceptionally excellent to be utilized in specific applications.The proposed image algorithm uses the permutation-assignment-diffusion structure.Permutation generates two position matrices in a progressive manner to achieve an efficient random exchange of pixel positions,assignment is carried through on the image pixels of the permutated image to entirely remove the original image information,strengthening the relationship between permutation and diffusion,and loop diffusion in two different directions can use subtle changes of pixels to affect the whole plane.The random key and plain-image SHA-512 hash values are used to produce an additional key,which is then utilized to figure out the permutation parameters and the initial value of a chaotic map.The experimental results with the average NPCR=99.6094%(NPCR:number of pixels change rate),average UACI=33.4638%(UACI:unified average changing intensity),100%pass rate of the targets in the test set,the average correlation coefficient is 0.00075,and the local information entropy is 7.9025,which shows that the algorithm is able to defend against a variety of illegal attacks and provide more trustworthy protection than some of the existing state-of-the-art algorithms.

关键词： chaos memristive hyperchaotic map remote sensing SHA-512 image encryption RS-IEA

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remote sensing of quality traits in cereal and arable production systems:A review

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The Crop Journal 2024年第1期12卷 45-57页

作者： Zhenhai Li Chengzhi Fan Yu Zhao Xiuliang Jin Raffaele Casa Wenjiang Huang Xiaoyu Song Gerald Blasch Guijun Yang James Taylor Zhenhong 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 Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of Agriculture and Rural AffairsBeijing 100081China DAFNE Universitàdella TusciaVia San Camillo de Lellis01100 ViterboItaly Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Maize and Wheat Improvement Center(CIMMYT) PO Box 5689Addis AbabaEthiopia ITAP Univ.MontpellierINRAEInstitut AgroMontpellier 34000France College of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054ShaanxiChina

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

关键词： remote sensing Quality traits Grain protein Cereal

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Untethered Micro/Nanorobots for remote sensing:Toward Intelligent Platform

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Nano-Micro Letters 2024年第2期16卷 450-483页

作者： Qianqian Wang Shihao Yang Li Zhang Jiangsu Key Laboratory for Design and Manufacture of Micro‑Nano Biomedical Instruments School of Mechanical EngineeringSoutheast UniversityNanjing 211189People’s Republic of China Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinHong Kong 999077People’s Republic of China Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong KongShatinHong Kong 999077People’s Republic of China T Stone Robotics Institute The Chinese University of Hong KongShatinHong Kong 999077People’s Republic of China Department of Surgery The Chinese University of Hong KongShatinHong Kong 999077People’s Republic of China

Untethered micro/nanorobots that can wirelessly control their motion and deformation state have gained enormous interest in remote sensing applications due to their unique motion characteristics in various media and diverse functionalities.Researchers are developing micro/nanorobots as innovative tools to improve sensing performance and miniaturize sensing systems,enabling in situ detection of substances that traditional sensing methods struggle to achieve.Over the past decade of development,significant research progress has been made in designing sensing strategies based on micro/nanorobots,employing various coordinated control and sensing approaches.This review summarizes the latest developments on micro/nanorobots for remote sensing applications by utilizing the self-generated signals of the robots,robot behavior,microrobotic manipulation,and robot-environment interactions.Providing recent studies and relevant applications in remote sensing,we also discuss the challenges and future perspectives facing micro/nanorobots-based intelligent sensing platforms to achieve sensing in complex environments,translating lab research achievements into widespread real applications.

关键词： Micro/nanorobot remote sensing Wireless control Self-propulsion Actuation at small scales

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Semi-supervised remote sensing image scene classification with prototype-based consistency

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Chinese Journal of Aeronautics 2024年第2期37卷 459-470页

作者： Yang LI Zhang LI Zi WANG Kun WANG Qifeng YU College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China

Deep learning significantly improves the accuracy of remote sensing image scene classification,benefiting from the large-scale datasets.However,annotating the remote sensing images is time-consuming and even tough for experts.Deep neural networks trained using a few labeled samples usually generalize less to new unseen images.In this paper,we propose a semi-supervised approach for remote sensing image scene classification based on the prototype-based consistency,by exploring massive unlabeled images.To this end,we,first,propose a feature enhancement module to extract discriminative features.This is achieved by focusing the model on the foreground areas.Then,the prototype-based classifier is introduced to the framework,which is used to acquire consistent feature representations.We conduct a series of experiments on NWPU-RESISC45 and Aerial Image Dataset(AID).Our method improves the State-Of-The-Art(SOTA)method on NWPU-RESISC45 from 92.03%to 93.08%and on AID from 94.25%to 95.24%in terms of accuracy.

关键词： Semi-supervised learning remote sensing Scene classification Prototype network Deep learning

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CrossFormer Embedding DeepLabv3+ for remote sensing Images Semantic Segmentation

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Computers, Materials & Continua 2024年第4期79卷 1353-1375页

作者： Qixiang Tong Zhipeng Zhu Min Zhang Kerui Cao Haihua Xing School of Information Science and Technology Hainan Normal UniversityHaikou571158China

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

关键词： Semantic segmentation remote sensing multiscale self-attention

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An examination of thematic research,development,and trends in remote sensing applied to conservation agriculture

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International Soil and Water Conservation Research 2024年第1期12卷 77-95页

作者： Zobaer Ahmed Aaron Shew Lawton Nalley Michael Popp V.Steven Green Kristofor Brye Center for Advanced Spatial Technologies University of ArkansasFayettevilleARUSA Environmental Dynamics Program University of ArkansasFayettevilleARUSA Department of Agricultural Economics and Agribusiness University of ArkansasFayettevilleARUSA College of Agriculture Arkansas State UniversityJonesboroARUSA University of Arkansas System Division of Agriculture Little RockARUSA Department of Crop Soiland Environmental SciencesUniversity of ArkansasFayettevilleARUSA

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural land.Since the 1990s,agricultural research has been conducted using remote sensing technologies;however,few previous reviews have been conducted focused on different conservation management practices.Most of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices,with some only providing a narrative review,others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops,and few others focused on single agricultural management practices.This paper used the preferred reporting items for systematic review(PRISMA)methodology to examine the last 30 years of thematic research,development,and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales.A set of predefined key concepts and keywords were applied in three databases:Scopus,Web of Science,and Google Scholar.A total of 188 articles were compiled for initial examination,where 68 articles were selected for final analysis and grouped into cover crops,crop residue,crop rotation,mulching,and tillage practices.Publications on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in 2020.Among the 68 articles,94%used a pixel-based,while only 6%used an object-based classification method.Prior to 2005,tillage practices were abundantly studied,then crop residue was a focused theme between 2004 and 2012.From 2012 to 2020,the focus shifted again to cover crops.Ten spectral indices were used in 76%of the 68 studies.This examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operational services for various conservation agricult

关键词： remote sensing Conservation agriculture Classification algorithm Spatial resolution Satellite Spectral indices PRISMA

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Study of coastal water zone ecosystem health in Zhejiang Province based on remote sensing data and GIS

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Acta Oceanologica Sinica 2010年第5期29卷 27-34页

作者： CHEN Zhenghua PAN Delu BAI Yan School of the Environment Gangxi University Nanning 530004 China State Environmental Protection Key Laboratory of Satellite remote sensing Ministry of Environmental Protection Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography State Oceanic Administration Hangzhou 310012 China

The coastal ecosystem health assessment is a field of increasing importance.In this paper,a preliminary assessment of ecosystem health in Zhejiang coastal water zone was made,mainly based on remote sensing data and GIS technique.Its spatial and quantitative evaluation was facilitated by the progress of remote sensing and GIS technique development.Firstly,human activities,hydrology and ecosystem problems in the study area were discussed and analyzed.Secondly,from 4 aspects of human stress,physical,chemical and biological responses to anthropogenic activities and natural stress,several indicators such as water transparency（Secchi Disk Depth,SDD）,suspended substance concentration,dissolved inorganic nitrogen,active phosphate,chlorophyll,harmful algae bloom,as well as distribution of sewage,sea lanes and port were employed.Thirdly,the Analytic Hierarchical Process was used for indicator weight calculation,and the ecosystem health criteria were established according to the integrative analysis of national water quality criteria,similar coastal ecosystem health research in other places or data inherent properties.The results indicated that from 2005 to 2007 the coastal water ecosystem health value in Zhejiang Province was unhealthy and needs ecological restoration by human intervention.

关键词： coastal water zone ecosystem health indicator remote sensing GIS

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Some recent advances in remote sensing-based monitoring of changes in the Greenland Ice Sheet

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Advances in Polar Science 2024年第2期35卷 275-280页

作者： FENG Tiantian JIA Jinyu WANG Wei YU Zeran LIU Xingchen LI Guojun GU Yuanyuan LI Rongxing The Center for Spatial Information Science and Sustainable Development Applications Tongji UniversityShanghai 200092China College of Surveying and Geo-Informatics Tongji UniversityShanghai 200092China College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China

The mass balance of the Greenland Ice Sheet(GrIS)plays a crucial role in global sea level change.Since the 1960s,remote sensing missions have been providing extensive and continuous observation data for change monitoring of the GrIS.In this paper,we present our recent research results from remote sensing-based GrIS change monitoring.First,historical satellite data are processed and used to fill data gaps and are combined with existing partial maps,completing an ice velocity map of the GrIS from the 1960s to 1980s.This map provides valuable data for estimating the historical mass balance of Greenland.Second,the monthly gravimetry-based mass balance of the GrIS from 2002 to 2020 is estimated by combining Gravity Recovery and Climate Experiment(GRACE)and GRACE Follow On(GRACE-FO)data.It is found that the GrIS has lost a total mass of approximately 4443±75 Gt during this period.Third,based on Global Land Ice Measurements from Space(GLIMS),an updated Greenland glacier inventory is achieved utilizing data collected between 2006 and 2020.This inventory provides more detailed and up-to-data glacier boundaries of Greenland.Overall,these advances provide essential data support for estimating the mass balance of the GrIS,contributing to the advancement of research on global sea level change.

关键词： Greenland Ice Sheet remote sensing change monitoring ice velocity satellite gravimetry glacier inventory

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Study on fluctuations of plume front and turbidity maximum in the Hangzhou Bay by remote sensing data

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Acta Oceanologica Sinica 1993年第1期12卷 51-62页

作者： Li Yan Li Jing Su Jilan and Ren Fuhu Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012. China Institute of remote sensing, Peking University, Beijing 100871, China Second Institute of Oceanography State Oceanic Administration Hangzhou 310012 China China

On the basis of the time series of AVHRR data from NOAA satellites and the geographical information system PURSIS, fluctuations of the plume front and the turbidity maximum in the Hangzhou Bay, reflected by the distributions of temperature and suspended sediment concentration respectively, are studied in view of long-term behaviors or seasonal and tidal cycles. The data suggest that the effect of front plays an important role in the development of the turbidity maximum close to the Changjiang Estuary > while the effect of tide dominates over the development of another very turbid water situated in the shoal areas in the southern Hangzhou Bay.

关键词： esturary front suspended sediment remote sensing geographical information system Hangzhou Bay

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