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Journal of remote sensing 2024年第1期4卷 324-343页

作者： Jian Xu Zhuo Zhang Lanlan Rao Yapeng Wang Husi Letu Chong Shi Gegen Tana Wenyu Wang Songyan Zhu Shuanghui Liu Entao Shi Yongmei Wang Liangfu Chen Xiaolong Dong Jiancheng Shi National Space Science Center Chinese Academy of SciencesBeijingChina Key Laboratory of Microwave Remote Sensing Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Space Environment Exploration BeijingChina Key Laboratory of Science and Technology on Environmental Space Situation Awareness Chinese Academy of SciencesBeijingChina National Satellite Meteorological Center China Meteorological AdministrationBeijingChina Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina School of GeoSciences University of EdinburghEdinburghUK School of Astronomy and Space Science University of Chinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina

Ozone stands out as a crucial trace gas within the Earth’s atmosphere,exerting a substantial influence on climate change and air *** ozone plays an important role in the formation of photochemical smog,and its variations are associated with human *** utilization of satellite remote sensing technology for tropospheric ozone monitoring enables a quantitative analysis of its global and regional spatiotemporal *** also facilitates the investigation of the mechanisms involved in ozone formation within the *** significant progress in product accuracy and spatiotemporal resolution of ozone remote sensing products,including total ozone and vertical profiles,can be attributed to the extensive development of satellite remote sensing ***,the precision of tropospheric ozone products remains inadequate for contemporary scientific purposes,primarily because of faint signals in the lower atmosphere,the intricate nature of the underlying surface,and the existence of clouds and *** study places emphasis on the satellite remote sensing of tropospheric ozone,encompassing a comprehensive review of the advancements in satellite sensors and the characteristics and suitability of various retrieval ***,this research delves into the possible utilization of satellite remote sensing for the provision of reliable tropospheric ozone observation data on a global and regional level.

关键词： satellite remote sensing technology tropospheric ozone satellite remote sensing air pollutiontropospheric ozone investigation mechanisms photochemical smogand trace gas climate change

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Non-Destructive Radio Wave Scanning Method for the Inspection of Composite Materials

Non-Destructive Radio Wave Scanning Method for the Inspectio...

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2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information technology, USBEREIT 2023

作者： Vakhitov, Maksim Klygach, Denis Razevig, Vladimir Olivenko, Nikita Kudryavtsev, Oleg School of Electronic Engineering and Computer Science South Ural State University Chelyabinsk Russia Bauman Moscow State Technical University Remote Sensing Laboratory Moscow Russia Institute of Engineering and Technology South Ural State University Chelyabinsk Russia

ISBN: (纸本)9798350336054

Polymer composites, for example, carbon or aramid fibre-reinforced plastics, are widely used in aviation technology, aircraft engines for the manufacture of load-bearing elements. The main advantage of composite materials based on synthetic reinforcing fibers is a high specific strength, the ability to control the strength and rigidity of a structure over a wide range, achieving its high weight perfection. A significant disadvantage of traditional layered composites is the low resistance to transversal impact. Low-velocity impacts are commonly occurred in the operation of aircraft structures: tool drops during maintenance, hail, gravel from the runway, bird strikes, etc. Intra- and interlaminar damages (delamination, fibre fracture) that occur during impacts significantly affect the residual strength of the composite element and can lead to its sudden failure during normal operation. In this regard, an important part of health monitoring of composite structures is the identification of impact defects for their further repair or the decision to replace the entire element. In this regard, it is of considerable interest to determine the location of a possible defect in a semi-automatic mode using remote control methods, in particular, radar and radio waves. The paper presents the results of state control for a sample of an aramid plate with defects, obtained microwave holograms in the frequency range of 15-24 GHz, and reconstructed microwave images in three-dimensional planes. The proposed method of non-destructive testing can be successfully used to determine damage in composite structures reinforced with glass and organic fibers, etc. © 2023 IEEE.

关键词： Defects

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Urban rail transit line and station planning optimization based on GIS : - Take Haikou city as an example

Urban rail transit line and station planning optimization ba...

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2021 International Conference on Information Control, Electrical Engineering and Rail Transit, ICEERT 2021

作者： Zhao, Yang Xu, Jialu Yin, Jingqiu College of Rail Transit Binjiang College Nanjing University of Information Science and Technology Wuxi China College of Atmospheric and Remote Sensing Binjiang College Nanjing University of Information Science and Technology Wuxi China College of Atmospheric and Remote Sensing Nanjing University of Information Science and Technology Wuxi China

ISBN: (纸本)9781665438179

Haikou is the fulcrum city of "One Belt and One Road"strategy and the core city of Hainan Free Trade Port. Rail transit lines and theirs station planning and layout are an important part of urban development pattern in the future. In this paper, using GIS technology, considering urban planning, geological data, existing bus lines, traffic network conditions, large passenger flow distribution points, according to the October 2017 Didi order data, rail transit planning principles of Haikou urban rail transit lines and stations are planned. © 2021 IEEE.

关键词： K-means clustering

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Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

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Advances in Atmospheric sciences 2024年第8期41卷 1465-1482页

作者： Jiazhen ZHAO Shengping HE Ke FAN Huijun WANG Fei LI Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Geophysical Institute University of Bergen and Bjerknes Centre for Climate ResearchBergen 5007Norway School of Atmospheric Science Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research Bergen 5007Norway Nansen-Zhu International Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082China

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions ***, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

关键词： wintertime newly formed Arctic sea ice model democracy model weighting scheme model performance model independence

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Spatiotemporal Variations of Microwave Land Surface Emissivity(MLSE)over China Derived from Four-Year Recalibrated Fengyun 3B MWRI Data

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Advances in Atmospheric sciences 2022年第9期39卷 1536-1560页

作者： Rui LI Jiheng HU Shengli WU Peng ZHANG Husi LETU Yu WANG Xuewen WANG Yuyun FU Renjun ZHOU Ling SUN School of Earth and Space Sciences Comparative Planetary Excellence Innovation CenterChinese Academy of SciencesUniversity of Science and Technology of ChinaHefei 230026China State Key Laboratory of Fire Science University of Science and Technology of ChinaHefei 230026China National Satellite Meteorological Center of China Meteorological Administration Beijing 100044China State Key Laboratory of Remote Sensing Science The Aerospace Information Research InstituteChinese Academy of Sciences(CAS)Beijing 100101China Green Earth Research Inc. SlingerlandsNew York 12259USA

Microwave Land Surface Emissivity(MLSE)over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures(Tbs)from Fengyun-3B Microwave Radiation Imager(FY-3B MWRI),combined with cloud properties derived from Himawari-8 Advanced Himawari Imager(AHI)*** contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative *** MLSEs at horizontal polarizations at 10.65,18.7,and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren,or sparsely vegetated,*** maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau,which is highly consistent with MLSEs derived from *** demonstrates that the measurements of FY-3B MWRI Tbs,including its calibration and validation,are reliable,and the retrieval algorithm developed in this study works *** variations of MLSE in China are mainly driven by the combined effects of vegetation,rainfall,and snow *** tropical and southern forest regions,the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from *** the boreal area,snow causes a significant decrease of MLSE at 36.5 GHz in ***,the MLSE at lower frequencies experiences less *** the desert region in Xinjiang,increases of MLSEs at all frequencies are observed with increasing snow cover.

关键词： satellite remote sensing microwave surface emissivity Fengyun-3B MWRI Himawari-8 AHI

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Random Forest-Based Snow Cover Mapping in China Using Fengyun-3B VIRR Data

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Journal of Meteorological Research 2023年第5期37卷 666-689页

作者： Yuchen XIE Yonghong LIU Yeping ZHANG Fuzhong WENG Shanyou ZHU Zhaojun ZHENG Shihao TANG School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044 CMA Earth System Modeling and Prediction Centre China Meteorological Administration(CMA)Beijing 100081 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 National Satellite Meteorological Centre China Meteorological AdministrationBeijing 100081

Currently,there is variability in the spectral band thresholds for snow cover recognition using remote sensing in different regions and for complex *** Fengyun-3B Visible and Infra-Red Radiometer(FY-3B VIRR)satellite data,we applied random forest(RF)methodology and selected 13 feature variables to obtain snow cover.A training set was generated,containing approximately 1 million snow and nonsnow samples obtained in China from the snow monitoring reports issued by the National Satellite Meteorological Centre and four snow cover products from the Interactive Multi-sensor Snow and Ice Mapping System(IMS),the FY-3B Multi-Sensor Synergy(MULSS),the Moderate Resolution Imaging Spectroradiometer(MODIS)snow cover product(MYD10A1),and the National Cryosphere Desert Data Center(NCDC).This training set contained many different samples of cloud types and snow under forest cover to help effectively distinguish snow and clouds and improve the recognition rate of snow under forest ***,two RF snow cover recognition models were constructed for the snow and nonsnow seasons and they were used to conduct daily snow cover recognition in China from 2011 to *** results show that the RF models constructed based on FY-3B VIRR data have good recognition performance for shallow snow,understory snow,and snow on the Qinghai–Tibetan *** recognition accuracy against weather stations and the spatial consistency with the IMS product are better than the MULSS,MYD10A1,and NCDC *** overall accuracy of the RF product is 90.6%,and the recall rate is 93.8%.The omission and commission errors are 6.2%and11.1%,*** other existing snow cover algorithms,the established RF model skips the complicated atmospheric correction and cloud identification processes and does not involve external auxiliary data;thus,it is more easily popularized and operationally applicable to generating long-time series snow cover products.

关键词： snow identification random forest Fengyun-3B Visible and Infra-Red Radiometer feature selection

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Shallow Sea Bathymetry Mapping from Satellite SAR Observations Using Deep Learning 2

Shallow Sea Bathymetry Mapping from Satellite SAR Observatio...

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

作者： Cui, Yide Wang, Sheng Du, Yanlei Yu, Yang Liu, Guihong Ma, Wentao Yin, Junjun Yang, Xiaofeng Chinese Academy of Sciences Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute Beijing China Chinese Academy of Sciences National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Beijing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Nanjing University Institute of Space Earth Science Suzhou China

ISBN: (纸本)9798331515669

This study proposes a deep-learning model to map underwater topography from spaceborne synthetic aperture radar (SAR) images. For the model development, 18 high-resolution Sentinel-1 dual-polarization SAR images covering the southern Cape Cod area from 2014 to 2022 are collected, among which twelve images were used for model training, while the rest were used for testing. In order to facilitate the model learning complex and nonlinear relations among various parameters, the model input is designed to consist of 8 variables, which involve radar backscatter coefficient, radar observation geometry, geography information and marine dynamic environmental parameters. The deep learning network consists of a convolutional layer, two residual blocks, and a fully connected layer, facilitating feature extraction and regression prediction. The final model performances in retrieving shallow water depth are shown as follows: For the training set, the model achieved a root mean square error (RMSE) of 1.02 m and a relative error of 4.32%, with the maximum detectable water depth reaching 48.17 m. For testing, the best performance of the model presented with an RMSE of 1.59 m and a relative error of 5.29%, while the worst performance corresponds to an RMSE of 4.53 m with a relative error of 15.61%. Thus, the proposed model has the capacity of detecting the shallow water depth up to 50 meters with high precision. © 2024 IEEE.

关键词： Mean square error

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INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS DATA FOR UNDERSTANDING HEAVY PRECIPITATION AND FLOODING IN AN GIANG PROVINCE, VIET NAM 44

INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS D...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Minh, Ton Binh Chiang, Shou-Hao International Ph.D Program in Environmental Science and Technology University System of Taiwan No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Understanding the mechanisms of heavy precipitation and flooding is essential for effective climate change adaptation and response. The cloud-penetrating ability of microwaves makes Synthetic Aperture Radar (SAR) an ideal tool for providing weather-independent images for flood mapping. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a land-only rainfall dataset that incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for various analysis applications. This study employs intensity images from Sentinel-1 satellites to generate flood maps and utilizes CHIRPS to derive heavy rainfall frequency. The segmentation technique is applied to distinguish between pre-existing standing water, recently flooded areas, and non-flooded regions within the study area. By combining SAR and CHIRPS data, this research yields valuable insights into the relationship between heavy rainfall and flood occurrences in An Giang province, Viet Nam. The escalating frequency of heavy precipitation correlates with an augmentation in local rainfall amounts, and a positive correlation emerges between heavy rainfall and the extension of flood-prone areas. While recognizing that flooding influences water flow from the upstream of the Mekong River, flooding discharges from neighboring areas, and tidal levels, the accelerated trend of urbanization has also played a significant role. The expansion of impervious surfaces contributes to the severity of deep floods and elevates flood potential in the region. Furthermore, this research affirms the effectiveness of SAR intensity image mapping for flood detection, and the integration of SAR and CHIRPS data furthers our understanding of heavy precipitation mechanisms. As the implications of climate change persist, the findings are crucial for developing sustainable flood management strategies, as well as enhancing resilience to climate-induced challenges in the Mekong River Del

关键词： CHIRPS climate change Flood Mekong River Delta Sentinel-1

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Extreme Wind Variability and Wind Map Development in Western Java,Indonesia

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International Journal of Disaster Risk science 2022年第3期13卷 465-480页

作者： Muhammad Rais Abdillah Prasanti Widyasih Sarli Hafidz Rizky Firmansyah Anjar Dimara Sakti Faiz Rohman Fajary Robi Muharsyah Gian Gardian Sudarman Atmospheric Science Research Group Faculty of Earth Science and TechnologyInstitut Teknologi BandungBandung40132Indonesia Structural Engineering Research Group Faculty of Civil and Environmental EngineeringInstitut Teknologi BandungBandung40132Indonesia Center for Infrastructures and Built Environment Institut Teknologi BandungBandung40132Indonesia Civil Engineering Department Faculty of Civil and Environmental EngineeringInstitut Teknologi BandungBandung40132Indonesia Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungBandung40132Indonesia Center for Remote Sensing Institut Teknologi BandungBandung40132Indonesia Center for Climate Change and Information The Agency for MeteorologyClimatology and Geophysics of the Republic of Indonesia(BMKG)Jakarta10720Indonesia Subdivision for Executive Secretary Administration The Agency for MeteorologyClimatology and Geophysics of the Republic of Indonesia(BMKG)Jakarta10720Indonesia Earth Science Department Faculty of Earth Science and TechnologyInstitut Teknologi BandungBandung40132Indonesia

Wind-related disasters are one of the most frequent disasters in *** can cause severe damages of residential construction,especially in the world's most populated island of *** the characteristics of extreme winds is crucial for mitigating the disasters and for defining structural design *** study investigated the spatiotemporal variations of extreme winds and pioneered a design wind map in Indonesia by focusing on western *** on gust data observed in recent years from 24 stations,the extreme winds exhibit a clear annual cycle where northwestern and southeastern sides of western Java show out-of-phase relationship due to reversal ***,extreme wind occurrences are mostly affected by small-scale weather systems,regardless of seasons and *** build the wind map,we used bias-corrected gust from ERA5 and applied the Gumbel method to predict extreme winds with different return *** wind map highlights some drawbacks of the current national design standards,which use single wind speed values regardless of location and return *** a fundamental improvement for wind design,this study will benefit disaster risk mapping and other applications that require extreme wind speed distribution.

关键词： ERA5 reanalysis Extreme wind Mixed weather system Tropical climate

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POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNING LAND COVER CLASSIFICATION USING VERY-HIGH RESOLUTION SATELLITE IMAGERY 44

POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNI...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Hakim, Yofri Furqani Tsai, Fuan International Ph.D. Program in Environmental Science and Technology National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Land cover is one of the fundamental data utilized in spatial analysis for a range of applications, including climate, environment, natural resources, agriculture, forestry, planning, health, and even social issues. The demand for land cover data is diverse, ranging from global scale, regional scale, and detailed scale. Furthermore, data updates also become a necessity for users. The current trend is the requirement for more accurate and up-to-date land cover data. The growing demand for accurate and up-to-date land cover data is in sync with improvements in satellite imagery data acquisition technology, which now offers improved spatial resolution and more effective satellite imagery data processing. Satellite imagery data processing technology is also growing rapidly with the advance of artificial intelligence for semantic classification or segmentation. This research uses a deep learning approach to classify land cover on Pleiades very high-resolution satellite imagery. A post-classification enhancement is also carried out to improve the consistency and accuracy of the deep learning classification results. The preliminary research results show that post-classification enhancement with the algorithms proposed in this study can increase the accuracy of classification results using deep learning-based approaches by approximately 2%. The original Overall Accuracy and Kappa of the deep learning classification results were 0.84 and 0.79. After the post-classification enhancement process, the Overall Accuracy and Kappa values increased to 0.86 and 0.81. © 2023 ACRS. All Rights Reserved.

关键词： Deep Learning Image Morphology Land Cover Classification Post-Classification Enhancement Semantic Segmentation U-Net

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