Acta Geodaetica et Cartographica Sinica

A method to establish the tomography model considering the data of GNSS stations outside the research area

ZHAO Qingzhi, YAO Yibin, YAO Wanqiang

2021, 50(3): 285-294. doi:10.11947/j.AGCS.2021.20200111

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To overcome the phenomenon of the traditional tomography technique that cannot use the data of GNSS stations outside the research area, a novel three-dimensional water vapor tomography modeling considering the data of GNSS stations outside the research area is proposed. The initial water vapor field of tomographic area in each voxel was calculated based on the GPT2w model. The proportional coefficient is introduced and combined with the initial value of water vapor density to determine the proportional coefficient expression. The water vapor content of the signals in the tomography area from the stations outside this area is estimated. Finally, the water vapor observation equation considering the data from the GNSS stations outside the tomographic region is established. The proposed method can be used to utilize the GNSS observation efficiently and improve the accuracy of tomographic result, but it only validated to the GNSS stations located in a certain range outside the tomographic area. Twenty-four GNSS stations and one radiosonde station in Zhejiang CORS network were used to verify the proposed tomography model in this paper. The experimental results show that compared with the traditional chromatography method, the utilization rate of GNSS signals and the voxel number with signals passing through are increased by 26.8% and 14.9%, respectively. Taking the radiosonde data as reference, it is found that the accuracy of integrated water vapor and water vapor density profiles calculated by the proposed method are better than those calculated by the traditional method.

Low latency cycle slip determination for ultra-high sampling rate kinematic GNSS measurement

FENG Wei, REN Xiaojie, ZHANG Xi, HUANG Dingfa

2021, 50(3): 295-303. doi:10.11947/j.AGCS.2021.20200143

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For high-speed and high-sampling-rate continuous real-time dynamic (RTK) positioning, fast cycle slip detection plays an important role. In this paper, based on the noise characteristics of ultra-high sampling rate data and the modified geometry-free (MGF) combination, a low-latency cycle slip determination method is proposed for kinematic GNSS data, named OMGF. Based on the noise characteristics of high sampling rate observation, cycle slips are preliminarily repaired by the pseudo-phase combination, and then MGF is used to perform secondary repair on the initial repair results. This paper deduces the success rate of OMGF cycle slip repair for pseudorange and carrier phase observations with different noise levels for different satellite systems, and the observation noise boundary conditions for OMGF method. OMGF method only needs the observation of a single satellite to detect and repair cycle slip, and extend the accuracy requirement of pseudorange observation for cycle slip determination. The experiment results with 20 Hz GPS/BDS/GLONASS measurements show that, the success rate of OMGF method is about 99.998%. Compared with the combination of GF and HMW, OMGF method is about 60 times faster. OMGF can perform cycle slip determination at low complexity and high calculation efficiency, which is beneficial for the mobile terminal with limited computing power resources to quickly process ultra-high sampling rate GNSS data online.

Optimization of GIVE algorithm for grid-based single shell ionospheric model over Chinese region based on residual statistics

MA Yuexin, TANG Chengpan, HU Xiaogong, CHANG Zhiqiao, PU Junyu, XING Nan, CAO Yueling, WANG Ningbo

2021, 50(3): 304-314. doi:10.11947/j.AGCS.2021.20200108

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To improve the service accuracy of the GNSS system and monitor the integrity of regional ionospheric activities, The BDSBAS system broadcasts the grid ionospheric correction parameters and GIVE to meet the precision approach needs. We use a planar fit algorithm to create an ionosphere map of the BDSBAS, in order to get a more accurate confidence bounds, we put forward a factor which is related to the Skewness and kurtosis of the residual distribution to control the decorrelation parameters to calculate the integrity parameter GIVE. The statistical results show that the ionospheric correction RMS of the BDSBAS grid-based single shell model is about 2~3 TECU, the correction percentage reaches 75%~79%; the GIVE envelope rate is better than 99.9%. Compared with the GPS basic navigation system, only adding the BDSBAS grid ionospheric correction can improve positioning accuracy by 20%~40%.

Determining the deflection of the vertical of VLBI stations using radio telescope axis information

MA Xiaohui, SUN Zhongmiao, ZHANG Zhibin, ZHANG Ali, YUAN Ye, SUN Zhengxiong, WANG Hong

2021, 50(3): 315-323. doi:10.11947/j.AGCS.2021.20200256

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The complete expression of pointing calibration model (PCM) for the axis related errors (ARE) of the radio telescope is derived directly. The definition of the common axis related parameters in both pointing correction model of ARE and Lösler's indirect model (IM) is unified, then the relationship between PCM and IM of the radio telescope is established. Thus, the deflection of the vertical (DOV) determination of VLBI stations is realized by using radio telescope azimuth axis information. As a direct surveying approach, this method does not rely on special DOV surveying equipment. It could only depend on pointing calibration surveying data for telescope aperiodic maintenance and the local surveying data including leveling. Verified by a variety of DOV surveying methods, the DOV obtained by our method shows a good agreement with real surveyed DOV in west-east component. Owing to the non-uniform distribution of the radio sources sky coverage of the station during its pointing calibration, the north-south component of DOV has the same direction as the surveyed DOV, but has a difference in value. In the future, further providing an optimized telescope pointing calibration, the high precision surveying of DOV will be obtained by using VLBI telescope azimuth axis.

The impact analysis of corrections to GOCE satellite gravity gradient observations by accounting for temporal gravity field variations

CHEN Jianhua, ZHANG Xingfu, SHEN Yunzhong, CHEN Qiujie, LI Weichao

2021, 50(3): 324-332. doi:10.11947/j.AGCS.2021.20200231

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As an important data source for high-degree static gravity field recovery, the gravity gradient observations from GOCE satellite should be processed prior to gravity field estimation by removing the temporal gravity field variations. In this study, the data processing method to account for the temporal gravity field variations in GOCE gradiometric data is discussed, to better remove the influence of temporal gravity field variations, the standard and background models from ESA are updated. Consequently, high-degree static gravity field models can be directly determined from the self-processed GOCE Level 1b data. To discuss the impacts of temporal gravity field variations on high-degree static gravity field modelling, we design three different data processing schemes to remove the temporal gravity field variations. Our results show that the temporal gravity field variations have an effect on deriving high-degree gravity field models, with a maximum geoid height difference of above 1 cm over some particular regions. Therefore, during deriving high-degree static gravity field solution from GOCE data, we suggest that updating standard and background models are better to mitigate the effect of temporal gravity field variations.

Data quality assessment of time-varying terrestrial gravity observation in South China

YANG Jinling, CHEN Shi, WANG Linhai, LU Hongyan, LI Honglei, ZHANG Bei

2021, 50(3): 333-342. doi:10.11947/j.AGCS.2021.20200386

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The terrestrial time-varying microgravity observation can be used to study the crustal mass transfer. The uncertainty of the gravity instrument is an important factor that can easily affect the data quality of gravity observations. In this paper, we focus on the time-varying gravity dataset from 2015 to 2018 in South China. Based on Bayesian analysis method, we quantify the related uncertainty sources of the gravity instruments, including the nonlinear drift of relative gravimeter and the change of scale factor of gravimeter. Furthermore, the cross-validation of the absolute gravity in-situ observation in network is applied to assess the gravity data results using this new approach. The results show that the uncertainty caused by the scale factor is larger than that caused by nonlinear drift for the relative gravimeters used in the South China survey network. The magnitude can reach up to 20×10^-8 m/s² for gravity change. Nevertheless, this uncertainty can be effectively reduced by means of the Bayesian method and absolute gravity datum control. This study can be used to assess the data quality of terrestrial gravity observation, to understand the causes of gravity change, and analysis the great earthquake preparation and mass transfer, etc.

A method of establishing a time reference without a free paper time scale and its performance

WU Yiwei, WANG Shichao

2021, 50(3): 343-355. doi:10.11947/j.AGCS.2021.20190505

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This paper proposes a method of establishing a time reference without a free paper time scale. The core idea is predicting and steering each atomic clock versus external reference time scale, respectively.Each clock can obtain a time scale steered by the external reference time scale. Then, the time reference is a weighted average of the steered time scales. The new method avoids the problems resulting from the correlation of the paper time scale and the time difference of each contributing clocks of traditional methods. Moreover, prediction algorithms and steering algorithms can be optimized according to different clocks with different characteristics, respectively. At the beginning, this paper briefly describes the basic principles of traditional methods and their weaknesses. Then, the basic principle, the theoretical advantages and the design principles of the prediction algorithm, steering algorithm and weighting algorithm of the new method are described in detail. An experiment shows the weakness of traditional methods. A simulation shows the traditional method and new method performances, and also preliminarily validates the excellent performances of this method.

A regional weighted mean temperature model that takes into account climate differences: taking Shaanxi, China as an example

ZHU Hai, HUANG Guanwen, ZHANG Juqing

2021, 50(3): 356-367. doi:10.11947/j.AGCS.2021.20200163

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The weighted mean temperature T_m is a key parameter of the global navigation satellite system (GNSS) inversion of precipitation. Taking the Shaanxi area in China as an example, this paper combines the reanalysis data of the European Weather Forecast Center (ECMWF) with the data of three sounding stations, and establishes a T_m regionalized regression model considering periodicity based on the principle of least squares. The data from three radiosonde stations in Shaanxi Province were used for verification. The results show that the T_m regional model established in this paper taking into account the cycle has an average improvement rate of 16.1% compared with the traditional Bevis model. In addition, in view of the differences in regions with different climate types, this paper establishes a sub-climatic zone T_m model with a piecewise linear form that changes with latitude, and solves the problem of adaptability of the regression model in different climate zones. Compared with sounding data, the T_m model that takes into account the climate difference has an external accuracy (RMS) range of 1.47~2.06 K. Compared with the Bevis model, the average accuracy improvement rate is 44.9%, and the improvement effect is significant; using ECMWF data to select 19 each grid point evaluates the accuracy of the model. The results show that the average RMS is 3.26 K and the maximum RMS is 3.67 K; the average STD is 2.69 K and the maximum STD is 3.19 K.

Extracting an ionospheric phase scintillation index based on 1 Hz GNSS observations and its verification in the Arctic region

ZHAO Dongsheng, LI Wang, LI Chendong, TANG Xu, ZHANG Kefei

2021, 50(3): 368-383. doi:10.11947/j.AGCS.2021.20200454

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The ionospheric scintillation, as one of the astronomical disasters occurring frequently in Arctic regions, poses great challenges to GNSS positioning navigation and timing (PNT) services. This calls for an urgent need in studying and effectively monitoring the scintillation to overcome its adverse impact. With the capability of high frequency sampling, ionospheric scintillation monitoring receivers (ISMR) are usually required to monitor the ionospheric scintillation, but the distribution of ISMR restricts the comprehensive monitoring in larger areas (such as the Arctic region). Therefore, based on GNSS observations with 1 Hz sampling, this paper studies the relevant empirical parameters and methods of extracting the ionospheric scintillation signal from the carrier phase observations by using geodetic detrending, precise point positioning and wavelet transform techniques, to construct a new phase scintillation index, which can be used to monitor the ionospheric scintillation. Its effectiveness and accuracy are verified by 188-day observations from 11 stations provided by the Canadian High Arctic Ionospheric Network (CHAIN). The results show that, compared with the commonly used ROTI index, both the scintillation index proposed in this paper and ROTI can effectively detect the occurrence of ionospheric scintillation, but the scintillation index proposed in this paper has a better correlation with the phase scintillation index given by ISMR, especially during periods with strong ionospheric scintillation, indicating that the proposed scintillation index has better ionospheric scintillation monitoring capability.

GF-7 dual-beam laser altimeter on-orbit geometric calibration and test verification

TANG Xinming, XIE Junfeng, MO Fan, DOU Xianhui, LI Xin, LI Shaoning, LI Song, HUANG Genghua, FU Xingke, LIU Ren, ZHU Guangbin, OUYANG Sida, TANG Hongzhao, CHEN Hui

2021, 50(3): 384-395. doi:10.11947/j.AGCS.2021.20200397

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The GF-7 satellite is carrying the Chinese first dual-beam laser altimeter system for earth observation, aimed at assisting stereo optical cameras to realize 1∶10 000 mapping. Due to the influence of the vibration of the satellite during launch and the difference of environment between space and ground, the calibration values of the altimetry parameters on ground are deviated from the actual values in space. In order to improve the altimetry, a two-step on-orbit geometric calibration scheme from coarse to fine is proposed for the GF-7 dual-beam laser altimeter. Firstly, a single beam laser on-orbit geometric calibration model is constructed to estimate the location of the laser footprints based on the typical waveform analysis, so as to realize the single beam laser rough calibration. Secondly, based on the geometric calibration model of single beam laser, the geometric calibration model of dual-beam laser is constructed. Considering the factors such as atmospheric delay and tidal correction, the spot is captured by ground detector array as the ground control point, and realize the joint precision calibration of dual-beam laser. Finally, the relative and absolute elevation measurement accuracy after calibration was verified by using the laser altimeters data on the calm lake surface and the ground control data. The experimental results show that the relative accuracy of laser elevation measurement of GF-7 satellite is better than 0.06 m (1σ), and the absolute accuracy of laser elevation measurement in flat areas is up to 0.10 m (1σ).

Time series prediction method of large-scale surface subsidence based on deep learning

LIU Qinghao, ZHANG Yonghong, DENG Min, WU Hongan, KANG Yonghui, WEI Jujie

2021, 50(3): 396-404. doi:10.11947/j.AGCS.2021.20200038

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Surface subsidence not only affects the sustainable development of social economy, but also threatens the safety of human life. High precision prediction of surface subsidence is of great significance for the prevention of geological disasters. However, the existing prediction methods are difficult to obtain reliable prediction results because of the model parameters or the lack of relevant data. For this problem, a method of surface subsidence prediction based on deep learning is proposed. Firstly, the multiple master-image coherent target small-baseline InSAR (MCTSB-InSAR) is used to obtain the inversion results of large area and high precision ground deformation time series. Secondly, the cyclic neural network is used as the network framework, and the long short-term memory (LSTM) model is used to learn the characteristics of ground settlement. Finally, the grid search method is used to adjust the model parameters, then get the optimal combination scheme of model parameters. The actual observation results show that the average absolute error (0.3 mm) of the prediction model proposed in this paper is reduced by 27.3% at least, and the average prediction accuracy of differential settlement is improved by 8.9% at least. The results of spatial pattern analysis show that the LSTM model is effective for the short-term prediction of large-scale time series deformation.

Unsupervised band selection for hyperspectral image classification using the Wasserstein metric-based configuration entropy

ZHANG Hong, WU Zhiwei, WANG Jicheng, GAO Peichao

2021, 50(3): 405-415. doi:10.11947/j.AGCS.2021.20200006

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Band selection relies on the quantification of band information. Conventional measurements such as Shannon entropy only consider the composition information (e.g., types and ratios of pixels) but ignore the configuration information (e.g., the spatial distribution of pixels). The latter could be quantified by Boltzmann entropy. Among all the metrics of Boltzmann entropy, the Wasserstein metric-based configuration entropy (Wasserstein entropy for short) removes the redundant information of the continuous pixels. However, it is limited to 4-neighborhood. This article improves it to 8-neighborhood. Taking the hyperspectral images of Indian Pines and Italian Pavia University as examples, we used the difference of Wasserstein entropy to measure band correlation and then employed the unsupervised sub-optimal searching algorithm to determine the optimal band combination. We used the support vector machine classifier for image classification. Finally, we compared the accuracy of image classification based on the difference of Wasserstein entropy, mutual information, four types of normalized mutual information, and two variants of relative entropy. Results show that both the 4-neighborhood and 8-neighborhood Wasserstein entropy can be used for band selection of hyperspectral images, especially when few bands are considered. The 8-neighborhood Wasserstein entropy works better than 4-neighborhood.

Subspace analysis isolation forest for hyperspectral anomaly detection

HUANG Yuancheng, XUE Yuanyuan, LI Pengfei

2021, 50(3): 416-425. doi:10.11947/j.AGCS.2021.20200036

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Since the anomalies are usually “rare and different” in the hyperspectral image scene, they tend to be more easily isolated from the background pixels by appropriate splitting criterion. In view of this, we propose a hyperspectral anomaly detection method based isolation forest (iForest) with subspace analysis. Firstly, orthogonal subspace background suppression and dimension reduction techniques were used to improve the reliability of the isolation tree-splitting criterion. Secondly, the iForest-based detection may produce a number of false alarms since the forest is constructed using the randomly selected pixels in the whole scene. In order to solve this problem, the initial anomaly detection map was refined by local iForest processing. Compared with original iForest method, our approach can not only handle high dimensional problem, but also make full use of the local information. The experiments demonstrate the AUC score have been significantly improved in our approach.

A generalization of geographic conditions maps constrained by both spatial and semantic scales

YIN Hongmei

2021, 50(3): 426-426. doi:10.11947/j.AGCS.2021.20200058

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