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Retrieval of sea ice thickness using FY-3E/GNOS-II data

Satellite Navigation 2024年第3期5卷 269-281页

作者： Yunjian Xie Qingyun Yan School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Sea ice,a significant component in polar regions,plays a crucial role in climate change through its varying *** Global Navigation Satellite System-Reflectometry(GNSS-R)studies,the observed surface reflectivityΓserves as a tool to examine the physical characteristics of sea ice *** facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium ***,it is important to note that when Sea Ice Thickness(SIT)becomes thicker,the accuracy of SIT retrieval via this two-layer model begins to *** this paper,we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E(FY-3E).Soil Moisture Ocean Salinity(SMOS)data are treated as the *** performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT *** analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 *** comparing the retrieved SITs against reference values,the three-layer model achieved a Root Mean Square Error(RMSE)of 0.149 m and Correlation Coefficient(r)of 0.830,while the two-layer model reported the RMSE of 0.162 m and r value of 0.789.A scheme incorporating both models yielded superior results than either individual model,with the RMSE of 0.137 m and r reaching up to *** study is the first application of FY-3E for GNSS-R SIT retrieval,combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 *** provides a good reference for the future studies on GNSS-R SIT retrieval.

关键词： Global navigation satellite system-reflectometry Sea ice thickness,Fengyun-3E GNSS occultation sounder II(FY-3E/GNOS-ll) Soil moisture ocean salinity

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Assessing Heat Vulnerability in Ho Chi Minh City: Insights from Local Climate Zones and the Heat Vulnerability Index Model 5

Assessing Heat Vulnerability in Ho Chi Minh City: Insights f...

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Land Surface and Cryosphere remote sensing V 2024

作者： Pham, Chi Cuong Lin, Tang-Huang International Master of Science Program in Remote Sensing Science and Technology Center for Space and Remote Sensing Research National Central University Taoyuan32001 Taiwan Center for Space and Remote Sensing Research National Central University Taoyuan32001 Taiwan

ISBN: (数字)9781510682696

ISBN: (纸本)9781510682689

In the context of increasing climate change and rapid urban expansion in Ho Chi Minh City, rising temperatures pose significant heat stress and health risks to residents. Assessing heat vulnerability is crucial for improving thermal comfort and achieving sustainable urban development. However, there is limited research on heat vulnerability within Local Climate Zones (LCZs), which are essential for understanding the relationship between urban form and heat exposure. This study integrates the Heat Vulnerability Index (HVI) with LCZ analysis to evaluate heat vulnerability across the city. Data were sourced from Landsat 8 to calculate Land Surface Temperature (LST), the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI), the Modified Normalized Difference Water Index (MNDWI), and for LCZ classification using a random forest algorithm;LandScan for population density;OpenStreetMap for road and medical facility density;and VIIRS for nighttime lighting. The HVI model combines exposure (represented by Land Surface Temperature), sensitivity (calculated as an average of population density, road density, and ENDISI), and adaptability (calculated as an average of nighttime lighting, medical facilities, and MNDWI). The results show that built LCZs have significantly higher thermal exposure, sensitivity, and adaptability compared to natural LCZs. Greater vulnerability was observed in urbanized areas, with LCZ1 and LCZ2 having the highest HVI scores, and over 90% of their areas classified as highly vulnerable. Additionally, 158 out of 315 wards had HVI values exceeding 60%, with most located in urban areas. Notably, rural wards such as Pham Van Coi, Cu Chi, and Hoa Phu were identified as particularly vulnerable and in need of immediate attention. In conclusion, integrating LCZ and HVI provides key insights into heat vulnerability patterns, helping future studies optimize the spatial layouts of local climate zones to mitigate heat impacts and improve the urban therma

关键词： Health risks

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Extrapolation of upwelling irradiance just beneath the ocean surface from the in-water radiometric profile measurements

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Journal of Oceanology and Limnology 2023年第5期41卷 1694-1705页

作者： Shaoqi GONG School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044China

Accurate measurements of upwelling irradiance just beneath the ocean surface,E_(u)(λ,0^(-)),can be used to calculate ocean optical parameters,and further develop retrieval algorithms for remotely sensing water component *** to the effects of sea surface waves,perturbation from instrument platform(ship),and instrument self-shading,E_(u)(λ,0^(-))is often difficult to be accurately *** study presents a procedure for extrapolating the E_(u)(λ,0^(-))from the in-water radiometric profile *** the optical profile data from 13 bands(ranging from 381 to 779 nm)measured by 45 casts in the Ligurian Sea during 2003–2009,the E_(u)(λ,0^(-))was extrapolated from in-water upwelling irradiance measurements between the initial shallow depth,Z_(0),and an optimal bottom depth,Z_(1),by three linear models(linear,2-degree polynomial,and exponential)and two nonlinear models(LOESS and spline).The accumulated errors of extrapolated E_(u)(λ,0^(-))at each wavelength for the five models were *** was found that the optimal Z_(1) depth for the linear and exponential models was at the depth of80%of E_(u)(λ,Z_(0)),50%of E_(u)(λ,Z_(0))for the 2-degree polynomial model,40%of E_(u)(λ,Z_(0))for the LOESS model,and 15%of E_(u)(λ,Z_(0))for the spline *** extrapolated E_(u)(λ,0^(-))derived from the five models was in good agreement with the calculated true E_(u)(λ,0^(-)).In all bands,the 2-degree polynomial model achieved the highest accuracy,followed by the LOESS *** the short band of 381–559 nm,the linear and exponential models had the third-best performance,and the spline model performed worst within this *** the red band of 619–779 nm,the accuracies of the exponential and spline models had the third highest performance,and the linear model produced lowest ***,the 2-degree polynomial model was an optimal procedure for extrapolation of E_(u)(λ,0^(-))from the in-water radiometric profile measurements.

关键词： extrapolation upwelling irradiance ocean in-water accuracy

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China's poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

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地球空间信息科学学报（英文版） 2024年第1期27卷 111-131页

作者： Mengjie Wang Yanjun Wang Fei Teng Shaochun Li Yunhao Lin Hengfan Cai Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanChina Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina

Poverty has always been a global concern that has restricted human *** first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the *** establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG *** paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development *** temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been *** on the SDGs global indicator framework,this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model ***,we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation ***,we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation *** results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and *** estimated model R2 is 0.65,which can identify 72.77%of China's national poverty *** 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern *** addition,the average SDG 1 evaluation value of China's districts and counties increased by 23%from 2012 to *** paper is oriented to the United Nations SDGs frame-work,explores the SDG 1 localized evalu

关键词： Multisource remote sensing data Sustainable Development Goals(SDGs) poverty indicator system partial least squares machine learning

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Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

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Digital Communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su Nawaf Alharbe Shuxin Feng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied College Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things Crowdsensing

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On-orbit attitude planning for Earth observation imaging with star-based geometric calibration

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science China(Technological sciences) 2025年第2期68卷 243-256页

作者： Di HUANG Zhifu WANG Jianya GONG Haoran JI Institute of Aerospace Science and Technology School of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingSchool of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China National Key Laboratory of Air-based Information Perception and Fusion Luoyang 471009China Intelligent Game and Decision Laboratory Beijing 100071China

To improve the quality of remote sensing images,a novel on-orbit attitude planning method for Earth observation imaging with star-based geometric calibration is presented in this *** traditional imaging processes,the proposed method includes both pre-calibration and post-calibration stages to enhance the accuracy of satellite geometric positioning.A multiple-constraint attitude planning algorithm was developed to ensure star-based calibration,wherein the satellite camera and star sensors simultaneously capture the images of *** integration of cameras and multiple star sensors enables effective attitude *** the Earth imaging stage,a fast on-orbit attitude planning algorithm was developed to determine the satellite attitude and the optimal imaging times for ground *** with existing methods,the fast on-orbit attitude planning algorithm can significantly reduce computational time and resource consumption via initial value selection and *** demonstrate the effectiveness of the proposed attitude planning method,which was successfully applied to Wuhan 1 satellite.

关键词： attitude planning Earth observation imaging geometric calibration attitude constraints

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Advances in Deep-Learning-based Precipitation Nowcasting Techniques

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Journal of Tropical Meteorology 2024年第3期30卷 337-350页

作者： ZHENG Qun LIU Qi LAO Ping LU Zhen-ci School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing University of Science and Technology of ChinaHefei 230026 China

Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six *** approach relies heavily on real-time observations rather than numerical weather *** core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision ***,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation *** progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational *** paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field *** models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the *** particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical *** integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.

关键词： precipitation nowcasting deep learning neural network classic model large model

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Assessing the Applicability of Multi-Source Precipitation Products over the Chinese Mainland and Its Seven Regions

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Journal of Tropical Meteorology 2024年第3期30卷 275-288页

作者： TIAN Wei WU Yun-long LIN Chen ZHANG Jing-guo LIM KAM SIAN Kenny Thiam Choy School of Computer and Software Nanjing University of Information Science&TechnologyNanjing 210044 China School of Atmospheric Science and Remote Sensing Wuxi UniversityWuxiJiangsu 214105 China

Satellite-based and reanalysis precipitation products provide valuable information for various ***,their performance varies widely across regions due to different data sources and production *** paper evaluated the daily performance of four precipitation products(MSWEP,ERA5,PERSIANN,and TRMM)for seven regions of the Chinese mainland,using observations from 2462 ground stations across the country as a *** used four statistical and four classification indicators to describe their spatial and temporal accuracy,and capability to detect precipitation events while analyzing their *** results show that according to the precipitation char-acteristics and accuracy of different types of precipitation products over the Chinese mainland,MSWEP was the most suitable product over the Chinese mainland,having the lowest root mean square error and mean absolute error,along with the highest coefficient of *** was followed by TRMM and ERA5,whereas PERSIANN lagged behind in terms of *** terms of different regions,MSWEP still performed well,especially in North China and East *** accuracy of the four precipitation products was relatively low in the summer months,and they all overestimated in the northwest *** other months,MSWEP and TRMM were better than PERSIANN and *** four precipitation products had good detection performance over the Chinese mainland,with probability of detection above ***,with the increase of precipitation threshold,the detection capability of the four products decreased,and MSWEP and ERA5 had good detection capability for moderate ***’s detection capability for heavy rain and rainstorms was better than that of the other three products,and PERSIANN’s detection capability for moderate rain,heavy rain and rainstorms was relatively poor,with a large deviation.

关键词： precipitation product MSWEP TRMM ERA5 PERSIANN

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Prompt fission neutron uranium logging(Ⅱ):dead-time effect of the neutron time spectrum

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Nuclear science and Techniques 2025年第2期36卷 82-94页

作者： Yan Zhang Chi Liu Shi-Liang Liu Hao-Ran Zhang Hai-Tao Wang Jin-Hui Qu Wen-Xing Hu Ren-Bo Wang Bin Tang National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing East China University of TechnologyNanchang 330013China Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China University of TechnologyNanchang 330013China

The acquisition of neutron time spectrum data plays a pivotal role in the precise quantification of uranium via prompt fission neutron uranium logging(PFNUL).However,the impact of the detector dead-time effect remains paramount in the accurate acquisition of the neutron time ***,it is imperative for neutron logging instruments to establish a dead-time correction method that is not only uncomplicated but also practical and caters to various logging *** study has formulated an innovative equation for determining dead time and introduced a dead-time correction method for the neutron time spectrum,called the“dual flux method.”Using this approach,a logging instrument captures two neutron time spectra under disparate neutron *** carefully selecting specific“windows”on the neutron time spectrum,the dead time can be accurately *** substantiate its efficacy and discern the influencing factors,experiments were conducted utilizing a deuterium-tritium(D-T)neutron source,a Helium-3(3He)detector,and polyethylene shielding to collate and analyze the neutron time spectrum under varying neutron fluxes(at high voltages).The findings underscore that the“height”and“spacing”of the two windows are the most pivotal influencing ***,the“height”(fd)should surpass 2,and the“spacing”twd should exceed 200μ*** dead time of the 3 He detector determined in the experiment was 7.35μ*** the dead-time correction,the deviation of the decay coefficients from the theoretical values for the neutron time spectrum under varying neutron fluxes decreased from 12.4%to within 5%.Similarly,for the PFNUL instrument,the deviation in the decay coefficients decreased from 22.94 to 0.49%after correcting for the dead-time *** results demonstrate the exceptional efficacy of the proposed method in ensuring precise uranium *** dual flux method was experimentally validated as a universal approach applicable to pulsed neutron logging instrumen

关键词： PFNUL Neutron time spectrum Dead time Pulsed source Correction method

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Classification of architectural styles in Chinese traditional settlements using remote sensing images and building facade pictures

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Journal of Geographical sciences 2024年第12期34卷 2457-2476页

作者： ZHANG Xiaoxia LI Shaodan CHEN Changyao School of Geographic Science Hebei Normal UniversityShijiazhuang050024China Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change Shijiazhuang050024China

The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and *** innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify ***,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP ***,a feature fusion module was designed to fuse the features of the roofs and *** precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or *** same three indexes of the proposed model were improved by more than 2%compared with other deep learning *** results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.

关键词： Chinese traditional settlements architectural style classification convolutional neural network remote sensing images building facade pictures

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