Yanxiong Liu

First Institute of Oceanography

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Yang Liu

Ministry of Natural Resources

Wenxue Xu

Harbin Engineering University

Yikai Feng

First Institute of Oceanography

Qiuhua Tang

First Institute of Oceanography

Zhipeng Dong

First Institute of Oceanography

Zhilu Wu

Northwest University

Tiegang Li

Chinese Academy of Geological Sciences

Guanxu Chen

First Institute of Oceanography

Ziwen Tian

First Institute of Oceanography

Cuixian Lu

Wuhan University

Cooperative Institutions

Ministry of Natural Resources

First Institute of Oceanography

Wuhan University

Shandong University of Science and Technology

Ocean University of China

Chinese Academy of Sciences

Ministry of Water Resources of the People's Republic of China

Hong Kong Polytechnic University

Nanjing Surveying and Mapping Research Institute (China)

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

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Simulative Evaluation of the Underwater Geodetic Network Configuration on Kinematic Positioning Performance

Remote Sensing (2022)

Menghao Li Yang Liu Yanxiong Liu Guanxu Chen Qiuhua Tang

The construction of underwater geodetic networks (UGN) is crucial in marine geodesy. To provide high-precision kinematic positioning for underwater submersibles, an underwater acoustic geodetic network configuration of three seafloor base stations, one subsurface buoy, and one sea surface buoy is proposed. The simulation results show that, for a 3 km-deep sea, based on the proposed UGN, the submersible positioning range and positioning accuracy are primarily affected by the size of the seafloor base station array, while the height of the subsurface buoy has a greater impact on the submersible positioning accuracy than the positioning range. Considering current acoustic ranging technology, the kinematic positioning performance of the UGN is optimal when the seafloor base stations are 9~13 km apart and the subsurface buoy is less than 2.5 km above the seafloor, which can achieve a submersible positioning accuracy of less than 30 m within an underwater space of 25 km × 25 km × 3 km. The proposed cost-effective UGN configuration can provide high-precision submersible kinematic positioning performance for seafloor surveying and ocean precision engineering. The impact of the underwater environment on the acoustic transmission characteristics should be further investigated.

Buoy

Seafloor Spreading

10.3390/rs14081939

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Equivalence of network-solution and PPP-solution

Geo-spatial Information Science (2006)

Peng Lin Yanxiong Liu Xinghua Zhou Yongting Wu

The technique of precise point positioning (PPP) is gradually becoming a popular method in GPS data-processing. In GPS observation equation, the unknown parameters can be separated into two parts: global parameters and local parameters. The global parameters include orbit, satellite clock and geodynamic parameters. The local parameters are site-occupation-spectific, such as position, tropospheric delay, etc. The formulas of local parameters are firstly derived under the network-solution and the PPP-solution conditions respectively. If the weight matrix of global parameters in PPP-solution is small enough, the cofactor matrices of local parameters are the same as that in network-solution. Then, 16 daily solutions are obtained in both PPP mode and network mode. Three sites are selected to compare the solutions. The experimental results demonstrated that the difference between two solutions in coordinates and tropospheric delays are only few millimeters. This level of difference can be neglected so that the solutions from both PPP mode and network mode can be taken as the same in the actual application.

Precise Point Positioning

Mode (computer interface)

Position (finance)

10.1007/bf02826933

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Remote sensing of atmospheric water vapor using GPS data in the Hong Kong region

Yanxiong Liu

Source

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Citations (9)

Effects of errors-in-variables on the internal and external reliability measures

Geodesy and Geodynamics (2024)

Yanxiong Liu Yun Shi Peiliang Xu Wenxian Zeng Jingnan Liu

The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables (EIV) models have been intensively investigated, little has been done about reliability theory for EIV models. This paper first investigates the effect of a random coefficient matrix A on the conventional geodetic reliability measures as if the coefficient matrix were deterministic. The effects of such geodetic internal and external reliability measures due to the randomness of the coefficient matrix are worked out, which are shown to depend not only on the noise level of the random elements of A but also on the values of parameters. An alternative, linear approximate reliability theory is accordingly developed for use in EIV models. Both the EIV-affected reliability measures and the corresponding linear approximate measures fully account for the random errors of both the coefficient matrix and the observations, though formulated in a slightly different way. Numerical experiments have been carried to demonstrate the effects of errors-in-variables on reliability measures and compared with the conventional Baarda's reliability measures. The simulations have confirmed our theoretical results that the EIV-reliability measures depend on both the noise level of A and the parameter values. The larger the noise level of A, the larger the EIV-affected internal and external reliability measures; the larger the parameters, the larger the EIV-affected internal and external reliability measures.

Errors-in-Variables Models

10.1016/j.geog.2024.03.002

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A New Processing Chain for Real-Time Ground-Based SAR (RT-GBSAR) Deformation Monitoring

Remote Sensing (2019)

Wang Zheng Zhenhong Li Yanxiong Liu Junhuan Peng Sichun Long

Due to the high temporal resolution (e.g., 10 s) required, and large data volumes (e.g., 360 images per hour) that result, there remain significant issues in processing continuous ground-based synthetic aperture radar (GBSAR) data. This includes the delay in creating displacement maps, the cost of computational memory, and the loss of temporal evolution in the simultaneous processing of all data together. In this paper, a new processing chain for real-time GBSAR (RT-GBSAR) is proposed on the basis of the interferometric SAR small baseline subset concept, whereby GBSAR images are processed unit by unit. The outstanding issues have been resolved by the proposed RT-GBSAR chain with three notable features: (i) low requirement of computational memory; (ii) insights into the temporal evolution of surface movements through temporarily-coherent pixels; and (iii) real-time capability of processing a theoretically infinite number of images. The feasibility of the proposed RT-GBSAR chain is demonstrated through its application to both a fast-changing sand dune and a coastal cliff with submillimeter precision.

Data Processing

10.3390/rs11202437

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Global statistical assessment of Haiyang-2B scanning microwave radiometer precipitable water vapor

Frontiers in Earth Science (2023)

Zhilu Wu Cuixian Lu Yang Liu H. Lin Yuxin Zheng

Introduction: As a successor to Haiyang-2A (HY-2A), HY-2B is China’s second marine dynamic satellite. Equipped with a scanning microwave radiometer (SMR), it can measure the precipitable water vapor (PWV) over the oceans, providing valuable climate and weather insights. This study aims to evaluate the accuracy of HY-2B SMR PWV data from January 2019 to December 2021 using various validation methods. Methods: to validate HY-2B SMR PWV, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data, radiosonde data, and global navigation satellite system (GNSS) observations are used. Results: the validation shows that the HY-2B SMR PWV data agree well with the ERA5 PWV data, with a root mean square error (RMSE) of 1.61 mm and a mean value of 0.71 mm. However, RMSEs of approximately 3.5 mm are observed when comparing the HY-2B SMR PWV data to GNSS and radiosonde data, likely due to temporal and spatial gaps. Geographically, there are greater differences between the HY-2B SMR and ERA5 in lower-latitude areas. The reason could be attributed to a higher water vapor content and more frequent cloudy weather in the areas. Discussion: The results demonstrate that the HY-2B SMR PWV meets requirement (RMSE≤3.5 mm). The HY-2B SMR PWV has a high quality, with a slight observational drift of only 0.1 mm/year, but regular validation and calibration are still necessary.

Precipitable water

Microwave radiometer

10.3389/feart.2023.1084285

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Seafloor geodetic network establishment and key technologies

Science China Earth Sciences (2020)

Yuanxi Yang Yanxiong Liu Dajun Sun Tianhe Xu Shuqiang Xue

Seafloor Spreading

10.1007/s11430-019-9602-3

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Deep submerged speleothems in the Sansha Yongle Blue Hole (South China Sea) as determination of low sea levels during the Last Glacial Maximum

Frontiers in Marine Science (2022)

Xiaoxiao Yu Baichuan Duan Jingyao Zhao Dongqi Gu Aiping Feng

Although Last Glacial Maximum (LGM) sea levels have been the focus of much attention in climate and marine sciences, the timing, duration, and magnitude need further research. Here we present observations and analyses of the deepest speleothems (-116 m) collected from the deepest known blue hole on a global scale, the Sansha Yongle Blue Hole, Xisha Islands, South China Sea. The field investigations illustrate that submerged speleothems are irregular cauliflower-like coatings on the downward cave ceiling from water depths of ~90 to 120 m. The downward growth direction and negative stable carbon and oxygen stable isotopes of submerged speleothems suggest that they may deposit in an air-filled condition through gravity drip, indicating the maximum LGM sea level. The deep submerged speleothems were dated, indicating two U–Th ages of 29.16 ± 0.17 and 26.04 ± 0.18 ka BP and one radiocarbon age of 18.64 ± 0.12 ka BP, respectively. The investigated deep submerged speleothems therefore clearly determine the minimum onset and maximum termination times for LGM terms of sea level. The results show that LGM began at ~29 ka BP and ended at ~18.5 ka BP. This study therefore provides initial evidence for the use of deep speleothems to determine LGM sea levels and emphasizes the importance of deep submerged speleothems in the reconstruction of Pleistocene low sea levels.

Last Glacial Maximum

Speleothem

10.3389/fmars.2022.1079301

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Refraction error correction of Airborne LiDAR Bathymetry data considering sea surface waves

International Journal of Applied Earth Observation and Geoinformation (2021)

Wenxue Xu Kai Guo Yanxiong Liu Ziwen Tian Qiuhua Tang

Airborne Light Detection And Ranging (LiDAR) Bathymetry (ALB) has an unparalleled advantage in integrated sea-land measurements, especially in the acquisition of topographic data in shallow water areas. Sea surface waves are an important factor affecting the quality of ALB data. This paper presents a method that strictly corrects the sea surface wave-induced refraction error for each seabed laser pulse without any complex modeling of the sea surface. In this method, an adaptive neighborhood selection method is first used to calculate the normal sea surface at the moment when a laser pulse enters the water, and then a laser pulse refraction error correction model considering sea surface waves is constructed. The distance condition equation, the angle condition equation and the coplanar condition equation are established based on Snell's law and the geometric relationship among the incident laser pulse, the normal vector of the wavy sea surface and the actual refracted ray in order to calculate the coordinates of each laser point at the seabed after refraction error correction. The experimental results show that sea surface waves have a significant impact on the three-dimensional point cloud coordinates of the underwater topography. Even in calm sea conditions, the plane coordinate displacement error of the seabed point caused by the sea surface waves may reach the meter level, and the depth coordinate displacement error can also exceed 0.2 m. The corrected displacement errors of the planimetric coordinates are significantly greater than the corrected displacement errors of the depth coordinates, which can effectively improve the quality of the ALB data.

Seabed

10.1016/j.jag.2021.102402

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Citations (18)

Enteromorpha Prolifera Detection in High-Resolution Remote Sensing Imagery Based on Boundary-Assisted Dual-Path Convolutional Neural Networks

IEEE Transactions on Geoscience and Remote Sensing (2023)

Zhipeng Dong Yanxiong Liu Yanli Wang Yikai Feng Yilan Chen

Enteromorpha prolifera is as a frequent marine ecological environment disaster. How to quickly and accurately monitor Enteromorpha prolifera is of great significance to its management and protection of the marine ecological environment. The detection of Enteromorpha prolifera from high spatial resolution remote sensing images (HSRIs) is an important technical means for monitoring Enteromorpha prolifera disasters. With respect to the difficulty of accurate detection of Enteromorpha prolifera area boundary in HSRIs, this paper proposes an Enteromorpha prolifera detection method for HSRIs based on boundary-assisted dual-path convolutional neural networks (CNN). First, a large-scale HSRIs Enteromorpha prolifera detection dataset, FIO-EP, is created and made publication to facilitate the field of HSRIs Enteromorpha prolifera detection. Then, a boundary-assisted dual-path CNN framework is designed to detect Enteromorpha prolifera in HSRIs based on the shape distribution characteristics of Enteromorpha prolifera. In the CNN framework, accurate detection of Enteromorpha prolifera areas in HSRIs is achieved by fusing initial detection and boundary detection results of Enteromorpha prolifera. The proposed method is compared with some state-of-the-art Enteromorpha prolifera detection algorithms using the FIO-EP dataset. The experimental findings demonstrate that the proposed method can obtain 88.28% F1-score and 79.02% intersection-over-union (IOU), and is superior to other state-of-the-art Enteromorpha prolifera detection algorithms.

10.1109/tgrs.2023.3326500

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