Query：
学者姓名：孙玉
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为提升在不同复杂场景下的车辆检测性能，提出一种基于改进Mask R-CNN的车辆检测算法.在算法的主干网络ResNet50中引入PSA极自注意力机制提升主干网络特征提取能力；在特征金字塔顶层网络中添加一个带有ECA注意力机制的分支与原分支进行特征融合，缓解顶层特征由于通道降维造成的信息损失.重新设计卷积检测头使得边框回归更为准确，并使用余弦退火算法和Soft-NMS算法来优化训练过程和后处理结果.实验结果表明，改进的Mask R-CNN车辆检测算法相比原Mask R-CNN算法在复杂场景下具有更高的检测精度，在CNRPark-EXT测试集中平均精确度提高3.8%,在更具挑战性的MiniPark测试集中平均精确度提高7.9%.
Keyword ：
ECA注意力机制 ECA注意力机制 Mask R-CNN算法 Mask R-CNN算法 PSA极自注意力机制 PSA极自注意力机制 Soft-NMS算法 Soft-NMS算法 车辆检测 车辆检测
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 汪菊 , 孙玉 , 吴宜良 . 改进Mask R-CNN的车辆检测算法 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (04) : 421-429 . |
| MLA | 汪菊 et al. "改进Mask R-CNN的车辆检测算法" . | 福州大学学报(自然科学版) 52 . 04 (2024) : 421-429 . |
| APA | 汪菊 , 孙玉 , 吴宜良 . 改进Mask R-CNN的车辆检测算法 . | 福州大学学报(自然科学版) , 2024 , 52 (04) , 421-429 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
High-precision and high-resolution satellite altimetry data from CryoSat-2 are widely utilized for marine gravity inversion. The vertical gravity gradient is a crucial parameter of the Earth's gravity field. To evaluate the performance of vertical gravity gradient determined from CryoSat-2 altimeter data, the pre-processed along-track sea surface heights (SSHs) are obtained through error correction. The study area focused on the Arabian Sea and its surrounding region, where the along-track geoid was derived by subtracting the mean dynamic topography of the ocean from the along-track SSH of CryoSat-2. The residual along-track geoidal gradients were obtained by adjusting the along-track geoid gradients calculated from CryoSat-2 altimeter data using the remove-restore method. This was done by subtracting the geoid gradients calculated by the gravity field model XGM2019e_2159. After obtaining the residual along-track geoidal gradients, the residual gridded deflections of the vertical (DOV) are calculated using the least-squares collocation (LSC) method. The residual gridded DOV are then used to compute the residual gridded gravity anomaly gradients in the study area using the finite-difference method. After restoring the gravity anomaly gradients computed by the XGM2019e_2159 model, a high-resolution gravity anomaly gradient model with a resolution of 1 ' x1 ' is obtained for the Arabian Sea and its surrounding area. To evaluate the accuracy of the gravity anomaly gradient model derived from CryoSat-2, it was compared with the SIO V32.1 gravity anomaly gradient model released by the Scripps Institution of Oceanography. The comparison showed that the root mean square (RMS) of the differences between the two models is 7.69E, demonstrating the high accuracy and precision of the vertical gravity gradient determined from CryoSat-2 altimeter data.
Keyword ：
Dynamics: gravity and tectonics Dynamics: gravity and tectonics Gravity anomalies and Earth structure Gravity anomalies and Earth structure Indian Ocean Indian Ocean Satellite geodesy Satellite geodesy Satellite gravity Satellite gravity
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhou, Ruichen , Liu, Xin , Li, Zhen et al. On performance of vertical gravity gradient determined from CryoSat-2 altimeter data over Arabian Sea [J]. | GEOPHYSICAL JOURNAL INTERNATIONAL , 2023 , 234 (2) : 1519-1529 . |
| MLA | Zhou, Ruichen et al. "On performance of vertical gravity gradient determined from CryoSat-2 altimeter data over Arabian Sea" . | GEOPHYSICAL JOURNAL INTERNATIONAL 234 . 2 (2023) : 1519-1529 . |
| APA | Zhou, Ruichen , Liu, Xin , Li, Zhen , Sun, Yu , Yuan, Jiajia , Guo, Jinyun et al. On performance of vertical gravity gradient determined from CryoSat-2 altimeter data over Arabian Sea . | GEOPHYSICAL JOURNAL INTERNATIONAL , 2023 , 234 (2) , 1519-1529 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Recently, C-30 coefficients of time-variable gravity field models from GRACE and GRACE-Follow On (GRACE/GRACEFO) are reported to contain larger uncertainties when only one of the two onboard accelerometers is fully functional, which mainly concerns the GRACE-FO period and the final stage of the GRACE period. Using these problematic coefficients leads to incorrect mass change (rate) estimates, especially over Antarctic Ice-Sheet (AIS), and a replacement with those from satellite laser ranging (SLR) is currently recommended. In this study, we aim to discuss the possibility of improving the C-30 coefficients by extending the GRACE-OBP approach that has previously been applied to the estimation of geocenter motion and variations in the Earth's dynamic oblateness. Such an approach mainly relies on GRACE/GRACE-FO level 2 products and an ocean bottom pressure model, and it produces compatible coefficients with the GRACE/GRACE-FO product labeled as GSM. With a numerical experiment, we demonstrate the effectiveness of the proposed approach and identify the optimal implementation parameter setup. The resulting C-30 coefficient time series is generally consistent with those based on SLR and the original solutions from the GRACE dual-accelerometer period, but with differences in the annual amplitude estimates. Then, we obtain C-30 coefficients based on real data and check the AIS mass change time series with and without replacing the original ones with our solution. Our C-30 solution ensures consistent linear trend estimates for the dual-and single-accelerometer periods.
Keyword ：
Antarctic Ice-Sheet Antarctic Ice-Sheet C-30 C-30 GRACE GRACE GRACE-FO GRACE-FO Mass change Mass change Satellite Laser Ranging Satellite Laser Ranging
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Sun, Yu , Li, Yang , Guo, Xiang et al. Estimating C-30 coefficients for GRACE/GRACE-FO time-variable gravity field models using the GRACE-OBP approach [J]. | JOURNAL OF GEODESY , 2023 , 97 (3) . |
| MLA | Sun, Yu et al. "Estimating C-30 coefficients for GRACE/GRACE-FO time-variable gravity field models using the GRACE-OBP approach" . | JOURNAL OF GEODESY 97 . 3 (2023) . |
| APA | Sun, Yu , Li, Yang , Guo, Xiang , Guo, Jinyun . Estimating C-30 coefficients for GRACE/GRACE-FO time-variable gravity field models using the GRACE-OBP approach . | JOURNAL OF GEODESY , 2023 , 97 (3) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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)
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Chlorophyll a concentration (CHL) is an important proxy of the marine ecological environment and phytoplankton production. Long-term trends in CHL of the South China Sea (SCS) reflect the changes in the ecosystem's productivity and functionality in the regional carbon cycle. In this study, we applied a previously reconstructed 15-a (2005-2019) CHL product, which has a complete coverage at 4 km and daily resolutions, to analyze the long-term trends of CHL in the SCS. Quantile regression was used to elaborate on the long-term trends of high, median, and low CHL values, as an extended method of conventional linear regression. The results showed downward trends of the SCS CHL for the 75th, 50th, and 25th quantile in the past 15 a, which were -0.004 0 mg/(m(3)center dot a) (-1.62% per year), -0.002 3 mg/(m(3)center dot a) (-1.10% per year), and -0.001 9 mg/(m(3)center dot a) (-1.01% per year). The negative trends in winter (November to March) were more prominent than those in summer (May to September). In terms of spatial distribution, the downward trend was more significant in regions with higher CHL. These led to a reduced standard deviation of CHL over time and space. We further explored the influence of various dynamic factors on CHL trends for the entire SCS and two typical systems (winter Luzon Strait (LZ) and summer Vietnam Upwelling System (SV)) with single-variate linear regression and multivariate Random Forest analysis. The multivariate analysis suggested the CHL trend pattern can be best explained by the trends of wind speed and mixed-layer depth. The divergent importance of controlling factors for LZ and SV can explain the different CHL trends for the two systems. This study expanded our understanding of the long-term changes of CHL in the SCS and provided a reference for investigating changes in the marine ecosystem.
Keyword ：
chlorophyll a concentration chlorophyll a concentration quantile trends quantile trends remote sensing reconstruction remote sensing reconstruction South China Sea South China Sea
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Tianhao , Sun, Yu , Su, Hua et al. Declined trends of chlorophyll a in the South China Sea over 2005-2019 from remote sensing reconstruction [J]. | ACTA OCEANOLOGICA SINICA , 2023 , 42 (1) : 12-24 . |
| MLA | Wang, Tianhao et al. "Declined trends of chlorophyll a in the South China Sea over 2005-2019 from remote sensing reconstruction" . | ACTA OCEANOLOGICA SINICA 42 . 1 (2023) : 12-24 . |
| APA | Wang, Tianhao , Sun, Yu , Su, Hua , Lu, Wenfang . Declined trends of chlorophyll a in the South China Sea over 2005-2019 from remote sensing reconstruction . | ACTA OCEANOLOGICA SINICA , 2023 , 42 (1) , 12-24 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
