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学者姓名：孙玉
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Regional relative sea level changes are most relevant for coastal communities and remain challenging to understand. China's adjacent seas are among the world's most vulnerable regions to sea level rise. This paper investigates the sea level budgets in China's adjacent seas over the past 20 years. We use multiple time-varying gravity field data and steric data to assess the uncertainties of some components in the sea level budget and the contributions of mass loss from ice sheets, glaciers, and terrestrial water storage changes to regional relative sea level changes were estimated using sea level fingerprints. The sea level budget results based on ensemble mean data show that the root mean square errors of the budget residuals in the Bohai Sea, Yellow Sea, East China Sea, South China Sea, and Northwest Pacific are 40 +/- 3 mm, 52 +/- 4 mm, 36 +/- 2 mm, 23 +/- 2 mm, and 11 +/- 1 mm, respectively. A single dataset fails to close the long-term sea level trends for all regions within a 65% confidence interval. We discussed the impacts of each component on the budget residuals and identified steric data and the ocean dynamics model as the main reasons for the excessive residuals. The de-aliasing product of the GRACE satellite, AOD1B model, is primarily responsible for the strong interannual signals in the residuals of the sea level budget in the Bohai Sea, Yellow Sea, and East China Sea.
Keyword ：
GRACE GRACE Regional sea level budget Regional sea level budget Sea level fingerprints Sea level fingerprints Steric Steric
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| GB/T 7714 | Li, Yang , Guo, Jinyun , Sun, Yu et al. Investigating the closures of sea level budgets in China's adjacent seas [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Li, Yang et al. "Investigating the closures of sea level budgets in China's adjacent seas" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Li, Yang , Guo, Jinyun , Sun, Yu , Zhou, Jiangcun , Sun, Heping . Investigating the closures of sea level budgets in China's adjacent seas . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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Accurate traffic forecasting is one of the key applications within Internet of Things (IoT)-based intelligent transportation systems (ITS), playing a vital role in enhancing traffic quality, optimizing public transportation, and planning infrastructure. However, existing spatial-temporal methods encounter two primary limitations: 1) they have difficulty differentiating samples over time and often ignore dependencies among road network nodes at different time scales and 2) they are limited in capturing dynamic spatial correlations with predefined and adaptive graphs. To overcome these limitations, we introduce a novel temporal identity interaction dynamic graph convolutional network (TIIDGCN) for traffic forecasting. The central concept involves assigning temporal identity features to raw data and extracting distinctive, representative spatial-temporal features through multiscale interactive learning. Specifically, we design a multiscale interactive model incorporating both spatial and temporal components. This network aims to explore spatial-temporal patterns at various scales from macro to micro, facilitating their mutual enhancement through positive feedback mechanisms. For the spatial component, we design a new dynamic graph learning method to depict the changing dependencies among nodes. We conduct comprehensive experiments using four real-world traffic datasets (PeMS04/07/08 and NYCTaxi Drop-off/Pick-up). Specifically, TIIDGCN achieves a 16.46% reduction in mean absolute error compared to the Spatial-Temporal Graph Attention Gated Recurrent Transformer Network model on the PeMS08 dataset.
Keyword ：
Adaptation models Adaptation models Correlation Correlation Data models Data models Dictionaries Dictionaries Feature extraction Feature extraction Forecasting Forecasting Graph convolutional network (GCN) Graph convolutional network (GCN) Internet of Things Internet of Things multiscale interaction multiscale interaction Roads Roads Time series analysis Time series analysis traffic forecasting traffic forecasting Training Training
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| GB/T 7714 | Yang, Shiyu , Wu, Qunyong , Li, Mengmeng et al. Temporal Identity Interaction Dynamic Graph Convolutional Network for Traffic Forecasting [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (11) : 15057-15072 . |
| MLA | Yang, Shiyu et al. "Temporal Identity Interaction Dynamic Graph Convolutional Network for Traffic Forecasting" . | IEEE INTERNET OF THINGS JOURNAL 12 . 11 (2025) : 15057-15072 . |
| APA | Yang, Shiyu , Wu, Qunyong , Li, Mengmeng , Sun, Yu . Temporal Identity Interaction Dynamic Graph Convolutional Network for Traffic Forecasting . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (11) , 15057-15072 . |
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This study aims to provide valuable scientific insights into various estimation techniques of geocentre motion (GCM) from the perspective of signal analysis, thereby enhancing Gravity Recovery and Climate Experiment (GRACE) users' understanding and application of GCM. Initially, it utilizes the satellite laser ranging (SLR) technique with the network shift approach to estimate over 30 yr of weekly GCM time-series from 1994 to 2024. Subsequently, we employ two approaches to estimate three types of monthly GCM time-series spanning more than 20 yr from 2002 to 2023: combining GRACE data with an ocean bottom pressure model (GRACE-OBP approach), the fingerprint approach (FPA), and the fingerprint approach with satellite altimetry data (FPA-SA, up to 2022). The former is referred to as SLR-based GCM estimates, while the latter, which uses GRACE Earth's gravity field models, is termed GRACE-based GCM estimates. Furthermore, this study pioneers the use of multichannel singular spectrum analysis (MSSA) for GCM analysis, especially focusing on the latest GRACE-based GCM estimates from the GRACE-OBP and FPA/FPA-SA approaches, marking the first comprehensive analysis of GCM estimated by various techniques. The results show that MSSA can effectively extract common signals from the three components of the GCM time-series. The seasonal components extracted from GRACE-based GCM estimates using MSSA are consistent with those from SLR-based GCM estimates, although the former exhibit slightly larger amplitudes of the annual and semi-annual signals. After correcting the atmosphere-ocean dealiasing, the amplitudes of the SLR-based estimates correspondingly decrease, remaining slightly larger but becoming closer to those of the GRACE-based estimates. However, a periodic signal with an approximate 160-d period is detectable in all GRACE-based GCM estimates, but it is absent in SLR-based GCM estimates. Further investigation using MSSA into higher degree spherical harmonic (SH) coefficients of the Earth's gravity field models reveals that these SH coefficients contain a 160-d periodic signal. This finding suggests that the signal detected in GRACE-based GCM estimates originates from systematic errors in these SH coefficients, offering new insights for improving the accuracy of GRACE Earth's gravity field solutions. Additionally, GRACE-based GCM estimates show significant secular non-zero trends, notably larger than those in SLR-based GCM estimates, which are not expected to exhibit any trends. However, the reliance of GRACE-based GCM estimates on geophysical models (e.g. glacier melting, glacial isostatic adjustment and hydrological models) limits the accuracy of their trends, underscoring the need for further validation. Overall, this study highlights new challenges regarding the accuracy of GRACE-based GCM estimates and emphasizes the necessity for further validation in mass change research.
Keyword ：
Reference systems Reference systems Satellite geodesy Satellite geodesy Satellite gravity Satellite gravity Time-series analysis Time-series analysis Time variable gravity Time variable gravity
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| GB/T 7714 | Yu, Hongjuan , Zhang, Yong , Sun, Yu et al. Unveiling GRACE-based estimation techniques: insights from multichannel singular spectrum analysis of geocentre motion [J]. | GEOPHYSICAL JOURNAL INTERNATIONAL , 2025 , 242 (1) . |
| MLA | Yu, Hongjuan et al. "Unveiling GRACE-based estimation techniques: insights from multichannel singular spectrum analysis of geocentre motion" . | GEOPHYSICAL JOURNAL INTERNATIONAL 242 . 1 (2025) . |
| APA | Yu, Hongjuan , Zhang, Yong , Sun, Yu , Sosnica, Krzysztof . Unveiling GRACE-based estimation techniques: insights from multichannel singular spectrum analysis of geocentre motion . | GEOPHYSICAL JOURNAL INTERNATIONAL , 2025 , 242 (1) . |
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Understanding the geophysical drivers of seasonal geocentre motion (GCM) variations remains challenging due to the complexity of Earth system interactions, limited data on individual mass redistribution components and model uncertainties. This study presents a comprehensive investigation of seasonal GCM signals from April 2002 to January 2024 using the Fingerprint Approach (FPA), which enables direct quantification of contributions from distinct Earth system components. Additionally, Multichannel Singular Spectrum Analysis (MSSA) is applied to quantify the influence of terrestrial water storage (TWS), atmosphere (ATM) and ocean (OCN) variability on seasonal GCM fluctuations. Correlation and lag analyses are employed to explore their temporal relationships and underlying geophysical linkages. The results reveal that TWS, ATM and OCN jointly explain 97.9 per cent, 98.1 per cent and 90.8 per cent of the seasonal variance in the X, Y and Z components of GCM, respectively. TWS exerts as the dominant contributor in the Y (66.4 per cent) and Z (67.9 per cent) components, while ATM and OCN each contribute less than 49 per cent to all components. Further analysis indicates that ATM, OCN and TWS exhibit varying lag relationships with GCM in the X and Z components, while TWS demonstrates a notably stronger correlation with GCM in the Y component. Importantly, an approximately 120-d periodic signal identified in GCM is, for the first time, linked to global precipitation variability, providing a novel geophysical interpretation. These findings enhance our understanding of climate-driven geophysical mass redistribution and offer new insights into the processes governing seasonal GCM variations.
Keyword ：
Global change from geodesy Global change from geodesy Reference systems Reference systems Satellite geodesy Satellite geodesy Satellite gravity Satellite gravity Time-series analysis Time-series analysis Time variable gravity Time variable gravity
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| GB/T 7714 | Yu, Hongjuan , Zhang, Yong , Sun, Yu et al. Analysis of fingerprint-derived geocentre motion time-series using multichannel singular spectrum analysis [J]. | GEOPHYSICAL JOURNAL INTERNATIONAL , 2025 , 243 (1) . |
| MLA | Yu, Hongjuan et al. "Analysis of fingerprint-derived geocentre motion time-series using multichannel singular spectrum analysis" . | GEOPHYSICAL JOURNAL INTERNATIONAL 243 . 1 (2025) . |
| APA | Yu, Hongjuan , Zhang, Yong , Sun, Yu , Sosnica, Krzysztof , Shen, Yi . Analysis of fingerprint-derived geocentre motion time-series using multichannel singular spectrum analysis . | GEOPHYSICAL JOURNAL INTERNATIONAL , 2025 , 243 (1) . |
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The Gravity Recovery and Climate Experiment(GRACE) gravity satellites can only detect low-resolution marinegravity change. This study proposes to use satellite altimetrydata to construct high-resolution marine gravity change rate(MGCR) model. The marine gravity field change is mainlycaused by the seawater mass migration. Based on the sphericalharmonic function (SHF) method and mass loading theory,a spherical harmonic synthesis formula is constructed to cal-culate MGCR. This idea is utilized to establish MGCR modelin Arabian Sea (AS). First, the multisatellite altimeter datafrom 1993 to 2019 are grouped, preprocessed, and utilized toestablish mean sea-level models; then, the long-term altime-try sea-level change rate (SLCR) is estimated. Second, thealtimetry SLCR subtracts the effects of Steric and GlacialIsostatic Adjustment (GIA) to obtain the SLCR model caused bymass migration (AS_MM_SLCR). Finally, we perform sphericalharmonic analysis on AS_MM_SLCR and apply the sphericalharmonic synthesis formula to estimate MGCR model on 5 ' x5 ' grids (AS_SHF_MGCR). AS_SHF_MGCR has higher resolutionthan GRACE_MGCR, compensating for inability of GRACE todetect small-scale marine gravity changes; the MGCR mean ofAS_SHF_MGCR is 0.13 mu Gal/year, which indicates the long-termrising trend of marine gravity in AS. This letter proposes amethod for calculating the MGCR using satellite altimetry, whichoffers a novel approach for marine time-varying gravity research
Keyword ：
Altimetry Altimetry Data models Data models Earth Earth Gravity Gravity Harmonic analysis Harmonic analysis Microwave radiometry Microwave radiometry oceans and water oceans and water radar data radar data Satellites Satellites Sea level Sea level
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| GB/T 7714 | Zhu, Fengshun , Guo, Jinyun , Sun, Yu et al. A Calculation Method of Marine Gravity Change Rate Based on Satellite Altimetry [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
| MLA | Zhu, Fengshun et al. "A Calculation Method of Marine Gravity Change Rate Based on Satellite Altimetry" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21 (2024) . |
| APA | Zhu, Fengshun , Guo, Jinyun , Sun, Yu , Li, Zhen , Yuan, Jiajia , Sun, Heping . A Calculation Method of Marine Gravity Change Rate Based on Satellite Altimetry . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
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The time-varying gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission suffer from pronounced longitudinal stripe errors in the spatial domain. A potential way to mitigate such errors is to combine GRACE data with observations from other sources. In this study, we investigate the impacts on GRACE monthly gravity field solutions of incorporating the GPS data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission. To that end, we produce GRACE/GOCE combined monthly gravity field solutions through combination on the normal equation level and compare them with the GRACE-only solutions, for which we have considered the state-of-the-art ITSG-Grace2018 solutions. Analysis in the spectral domain reveals that the combined solutions have a notably lower noise level beyond degree 30, with cumulative errors up to degree 96 being reduced by 31%. A comparison of the formal errors reveals that the addition of GOCE GPS data mainly improves (near-) sectorial coefficients and resonant orders, which cannot be well determined by GRACE alone. In the spatial domain, we also observe a significant reduction by at least 30% in the noise of recovered mass changes after incorporating the GOCE GPS data. Furthermore, the signal-to-noise ratios of mass changes over 180 large river basins were improved by 8-20% (dependent on the applied Gaussian filter radius). These results demonstrate that the GOCE GPS data can augment the GRACE monthly gravity field solutions and support a future GOCE-type mission for tracking more accurate time-varying gravity fields.
Keyword ：
GOCE GOCE GRACE GRACE time-varying gravity field modeling time-varying gravity field modeling
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| GB/T 7714 | Guo, Xiang , Lian, Yidu , Sun, Yu et al. Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions [J]. | REMOTE SENSING , 2024 , 16 (9) . |
| MLA | Guo, Xiang et al. "Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions" . | REMOTE SENSING 16 . 9 (2024) . |
| APA | Guo, Xiang , Lian, Yidu , Sun, Yu , Zhou, Hao , Luo, Zhicai . Assessment of the Added Value of the GOCE GPS Data on the GRACE Monthly Gravity Field Solutions . | REMOTE SENSING , 2024 , 16 (9) . |
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We propose a method for enhancing the accuracy of bathymetry models based on a multilayer perceptron (MLP) neural network that integrates the differences in multisource marine geodetic data (MMGD) (longitude, latitude, reference bathymetry, slope, the meridional and prime components of vertical deflection, gravity anomaly, vertical gravity gradient, and mean dynamic topography). First, we use the MMGD differences between the shipborne sounding control points within 8 ' x 8 ' grid points and shipborne sounding control points as input data, as well as the differences between the topo_24.1 model and the measured bathymetric values at the control points as output data to train the MLP model. Second, we feed the input data from the central point of a 1 ' x 1 ' grid into the MLP model to obtain predictions, and then use the topo_24.1 model to recover the predicted bathymetry at the prediction point. We focus on the Caribbean Sea, and construct a Caribbean Bathymetric Chart of the Oceans (CBCO1) model using MLP neural network. The reliability of MMGD, a CBCO2 model using MMGD, and the reliability and effectiveness of the overall method are demonstrated through comparisons with the CBCO2, GEBCO_2022, topo_24.1, DTU18 models at the checkpoints.
Keyword ：
Caribbean Sea Caribbean Sea Multilayer perceptron neural network Multilayer perceptron neural network multisource marine geodetic data multisource marine geodetic data seafloor topography seafloor topography
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| GB/T 7714 | Zhou, Shuai , Liu, Xin , Sun, Yu et al. Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
| MLA | Zhou, Shuai et al. "Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 17 . 1 (2024) . |
| APA | Zhou, Shuai , Liu, Xin , Sun, Yu , Chang, Xiaotao , Jia, Yongjun , Guo, Jinyun et al. Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
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Maize yield in China accounts for more than one-fourth of the global maize yield, but it is challenged by frequent extreme weather and increasing food demand. Accurate and timely estimation of maize yield is of great significance to crop management and food security. Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate and water content factors. Only a few studies have attempted to systematically evaluate the sensitivity of these indexes. The sensitivity of the spectral indexes, combined indexes and climate factors and the effect of temporal aggregation data need to be evaluated. Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model. Results showed that the normalized moisture difference index (NMDI) is the index most sensitive to yield estimation. Furthermore, the potential of adopting the combined indexes, especially NMDI_NDNI, was verified. Compared with the whole-growth period data and the eight-day time series, the vegetative growth period and the reproductive growth period data were more sensitive to yield estimation. The maize yield in China can be estimated by integrating multiple spectral indexes into the indexes for the vegetative and reproductive growth periods. The obtained R-2 of maize yield estimation reached 0.8. This study can provide feature knowledge and references for index assessments for yield estimation research.
Keyword ：
combined index combined index maize yield maize yield multiple spectral indexes multiple spectral indexes temporal aggregation temporal aggregation yield estimation yield estimation
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| GB/T 7714 | He, Yuhua , Qiu, Bingwen , Cheng, Feifei et al. National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation [J]. | REMOTE SENSING , 2023 , 15 (2) . |
| MLA | He, Yuhua et al. "National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation" . | REMOTE SENSING 15 . 2 (2023) . |
| APA | He, Yuhua , Qiu, Bingwen , Cheng, Feifei , Chen, Chongcheng , Sun, Yu , Zhang, Dongshui et al. National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation . | REMOTE SENSING , 2023 , 15 (2) . |
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为充分提取 3D点云的深层特征以提高复杂室内点云场景的语义分割精度,提出一种结合局部特征和全局特征的室内点云语义分割网络GSFNet.在局部特征部分,加入几何特征信息,并设计几何与语义特征信息编码模块,以更好地捕获室内点云局部信息.对全局特征部分,在编码解码器结构中间层加入全局关系依赖模块,构建不同邻域对象之间的关系提取有效分割信息.使用斯坦福大规模室内数据集(S3DIS)进行实验验证,在测试数据集上测试的总体精度(OA)和平均交并比(mIoU)分别为 87.2％和 61.1％,实验结果表明,GSFNet对复杂室内环境有较好的语义分割效果.
Keyword ：
几何特征 几何特征 深度学习 深度学习 点云 点云 语义分割 语义分割
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| GB/T 7714 | 黄逸群 , 孙玉 , 吴宜良 . 融合几何特征与全局关系的室内点云语义分割 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (3) : 371-378 . |
| MLA | 黄逸群 et al. "融合几何特征与全局关系的室内点云语义分割" . | 福州大学学报（自然科学版） 51 . 3 (2023) : 371-378 . |
| APA | 黄逸群 , 孙玉 , 吴宜良 . 融合几何特征与全局关系的室内点云语义分割 . | 福州大学学报（自然科学版） , 2023 , 51 (3) , 371-378 . |
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Based on the nonlinear relationship between multisource marine geodetic data and seafloor topography, the multilayer perceptron (MLP) neural network is introduced into bathymetry prediction to improve the accuracy of bathymetry model. This method not only integrates multisource marine geodetic data, but also takes into consideration the nonlinear relationships between these data and seafloor topography. Firstly, we utilize terrain information and the multisource marine geodetic data [vertical deflection, gravity anomaly, vertical gravity gradient (VGG), mean dynamic topography (MDT)] around the shipborne sounding control points within a 6 ' x 6 ' grid as input data, while using the actual bathymetry at control points as output data to train the MLP neural network model. Subsequently, inputting the input data from the central point of a 1 ' x 1 ' grid within the study area into the MLP model to predict the bathymetry at the grid's center. Then, based on the predicted bathymetry, a bathymetry model is established of this research area. Utilizing this methodology, this article establishes the Gulf of Mexico Bathymetric Chart of the Oceans (MBCO1) model. Due to the influence of complex seafloor topography and the distribution of shipborne bathymetry points, there are differences in training and prediction among different regions. To address this, this study divides the research area into five subregions (A, B, C, D, and E) and establishes bathymetry model (MBCO2 models) through each sub-region. Finally, we evaluated the accuracy and effectiveness of this method by comparing it with existing bathymetry models, as well as shipboard depths.
Keyword ：
Bathymetry Bathymetry Computational modeling Computational modeling Data models Data models Gravity Gravity Gravity anomalies Gravity anomalies Gulf of Mexico Gulf of Mexico mean dynamic topography (MDT) mean dynamic topography (MDT) multilayer perceptron (MLP) multilayer perceptron (MLP) Oceans Oceans Predictive models Predictive models seafloor topography seafloor topography Surfaces Surfaces vertical deflection vertical deflection vertical gravity gradients (VGGs) vertical gravity gradients (VGGs)
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| GB/T 7714 | Zhou, Shuai , Liu, Xin , Guo, Jinyun et al. Bathymetry of the Gulf of Mexico Predicted With Multilayer Perceptron From Multisource Marine Geodetic Data [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| MLA | Zhou, Shuai et al. "Bathymetry of the Gulf of Mexico Predicted With Multilayer Perceptron From Multisource Marine Geodetic Data" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61 (2023) . |
| APA | Zhou, Shuai , Liu, Xin , Guo, Jinyun , Jin, Xin , Yang, Lei , Sun, Yu et al. Bathymetry of the Gulf of Mexico Predicted With Multilayer Perceptron From Multisource Marine Geodetic Data . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
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