Estimation of snow depth using pseudorange and carrier phase observations of GNSS single-frequency signal

Li, Yunwei; Chang, Xiaoping; Yu, Kegen; Wang, Shuyao; Li, Jiancheng

doi:10.1007/s10291-019-0912-5

Cited by 36 publications

(23 citation statements)

References 22 publications

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“…Recent satellite-based remote sensing technology shows a SLR rate of 3.2 mm/year since 1992 [38]. Moreover, since the snow/ice has different characteristics in a variety of areas (such as Alaska and Texas in the U.S), it leads to a tremendously different impact weight in GNSS time series [39]. Notably, we expect the hybrid method to be fully tested and have the characteristics of generality and efficiency.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Liu

Yang

2021

Journal of Sensors

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A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

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Section: Journal Of Sensorsmentioning

confidence: 99%

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Liu

Yang

2021

Journal of Sensors

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“…where θ(t) is the time-varying satellite elevation angle, H 0 is the antenna height relative to the reflection surface, and λ is the wavelength of GNSS signal. Due to the geometrical relationship, the squared amplitude (A) of received signal can be expressed as [38][39][40]…”

Section: Normalization Of Signal-noise Ratio Multipath Signalmentioning

confidence: 99%

“…Here, A d and A r are the amplitude of direct and reflected signal, and α is the amplitude attenuation factor (AAF) as studied in our previous work [40]. The AAF mainly depends on the characteristics of scattering surface and antenna gain patterns.…”

Section: Normalization Of Signal-noise Ratio Multipath Signalmentioning

confidence: 99%

Soil Moisture Estimation by GNSS Multipath Signal

Chang

Jin

et al. 2019

Remote Sensing

Self Cite

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Global navigation satellite system (GNSS) multipath signals received by a geodetic-quality GNSS receiver can be used to estimate the water content of soil around the antenna. The direct signals from satellite to GNSS antenna are the most valuable signals in geodetic measurement, such as positioning, navigation, GNSS control network, deformation monitoring, and so on. However, the GNSS antenna also captures the reflected signals from the ground, which contain information of surrounding environment, so that useful information about the reflection surface can be inferred by analyzing the reflected signal. This technique is termed as GNSS-interferometric reflectometry. The signal-to-noise ratio (SNR) data recorded by a receiver contains SNR component of reflected signals, which is related to the soil moisture of the ground. The changes of soil moisture content will cause the change of soil permittivity and reflectivity which are the key factors that make further change of the SNR of reflected signals. We used the measured data to evaluate the correlation between amplitude of multipath induced SNR time series and real soil moisture. An improved soil moisture estimation algorithm based on multipath induced SNR amplitude data is proposed in this paper. The performance of the proposed soil moisture estimation method is evaluated using the 15-month data recorded by PBO H2O GNSS station and a 14-day experiment in Wuhan, China. The experimental results show that the estimated soil moisture has a strong correlation with the real soil moisture and the estimation accuracy in terms of root-mean-square error (RMSE) is 0.0345 cm3cm−3 and 0.0339 cm3cm−3, respectively. Meanwhile, the application scope of the method is given.

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“…Combined GPS and GLONASS observations have no significant improvement on the precision, but improve the spatial resolution because of more satellites. Li et al 11 proposed a new method to estimate snow depth by using the single frequency GNSS signal combined by pseudo-range and carrier phase observations and a weighted method for combining multiple snow depth values generated by observations of multiple individual GNSS satellites. Yu et al 22 proposed two methods for inverting snow depth using dual GNSS receiving systems, one using dual-frequency carrier phase observation, and the other using a single-frequency carrier phase and pseudorange observation.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for GNSS-IR Snow Depth Monitoring With Slope Correction and Error Prediction

Chen

Liang

Zheng

2021

Preprint

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With the continuous development of GNSS (Global Navigation Satellite System) technology, GNSS-IR (GNSS Interferometric Reflectometry) has become a research hotspot in the field of snow surface monitoring, and the accuracy and reliability have been initially verified. We focus on the reasons for the low accuracy of the existing GNSS-IR snow depth inversion. Therefore, we use the P351 station data in the PBO (Plate Boundary Observatory) network in the United States to monitor the snow depth during the four years from 2011 to 2015. The actual measured snow depth at station 490 in the SONTEL network is used as the true value for accuracy verification. We studied the relationship between the inversion error caused by the slope and the slope angle and the satellite elevation angle, and proposed a slope correction method. The results show that the RMSE (Root Mean Square Error) of snow depth inversion after slope correction is reduced from 12.1 cm to 10.7 cm, the accuracy is improved by 11.6 %. In addition, it is found that there is an apparent correlation between the retrieved snow depth and the inversion error. With the increase of snow depth, the error gradually changes from positive to negative, and the absolute value of error still increases with the increase of snow depth after the error changes to negative. To this end, we introduce BPNN (Back Propagation Neural Network) to train the inversion snow depth and inversion error of the three snowfall periods from 2011 to 2014, then predicts and corrects the snow depth inversion error during the snowfall period from 2014 to 2015. The results show that the RMSE of the corrected GNSS-IR snow depth inversion is reduced from 10.7 cm to 5.7 cm, and the accuracy is increased by 46.7 %. The overall accuracy of the GNSS-IR snow depth inversion is improved by 52.9 % after the slope correction and the BPNN error prediction is performed, which further verifies the accuracy and effective of the approach that proposed by us.

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Estimation of snow depth using pseudorange and carrier phase observations of GNSS single-frequency signal

Cited by 36 publications

References 22 publications

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Soil Moisture Estimation by GNSS Multipath Signal

A New Approach for GNSS-IR Snow Depth Monitoring With Slope Correction and Error Prediction

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Estimation of snow depth using pseudorange and carrier phase observations of GNSS single-frequency signal

Cited by 36 publications

References 22 publications

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Analyzing the Impact of Climate Factors on GNSS‐Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Soil Moisture Estimation by GNSS Multipath Signal

A New Approach for GNSS-IR&nbsp;Snow Depth Monitoring With Slope Correction and Error Prediction

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A New Approach for GNSS-IR Snow Depth Monitoring With Slope Correction and Error Prediction