Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Zheng, Naiquan; Chai, Hongzhou; Chen, Lingqiu; Ma, Yongchao; Tian, Xiangyu

doi:10.1016/j.asr.2022.10.014

Cited by 12 publications

(7 citation statements)

References 45 publications

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“…After obtaining ZWD, it is converted into PWV using a conversion factor. The conversion method is shown in equation (5).…”

Section: ) Gnss-pwv Spatial Interpolation Based On the Idea Of Remova...mentioning

confidence: 99%

“…The atmosphere water vapor has complex temporal and spatial changes, and accurately measuring, modeling, and predicting the content and changes of water vapor is of great importance for studying the greenhouse effect and climate change. With the continuous expansion of Global Navigation Satellite System (GNSS) and its related fields, GNSS technology used to study the atmosphere water vapor spatial distribution (AWVSD) has made significant progress [4,5]. To achieve a more accurate solution of water vapor distribution, research is conducted on the estimation of atmospheric precipitable water volume (PWV) based on GNSS technology, and the idea of removal interpolation restoration is introduced to optimize the accuracy of the calculation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

Wei,

Liu,

Guo

et al. 2023

IJACSA

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The inversion of water vapor spatial distribution using ground-based global navigation satellite systems is a technique that utilizes the propagation delay of satellite signals in the atmosphere to retrieve atmospheric water vapor information. To further promote the accuracy of the information obtained by this method, a satellite system is designed to solve the spatial distribution of atmospheric water vapor based on chromatography technology and genetic algorithm. Firstly, the accuracy of the empirical air temperature and pressure model to calculate the zenith statics delay is analyzed. To optimize the global weighted average temperature model, a model that considers the decreasing rate of atmospheric weighted average temperature and a model based on the linear relationship between surface heat and weighted average temperature are proposed. The idea of removal interpolation restoration is introduced to achieve regional interpolation of atmospheric precipitable water. Finally, in response to the problem of multiple solutions in the current water vapor chromatography equation, a genetic algorithm based chromatography method is put forward to achieve the solution of atmospheric water vapor spatial distribution. The experimental analysis shows that the average root mean square error and average absolute error of the design method of the research institute are 1.78g/m3 and 1.41g/m3, respectively, which can realize the calculation of atmospheric water vapor density distribution with high accuracy.

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“…After obtaining ZWD, it is converted into PWV using a conversion factor. The conversion method is shown in equation (5).…”

Section: ) Gnss-pwv Spatial Interpolation Based On the Idea Of Remova...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

Wei,

Liu,

Guo

et al. 2023

IJACSA

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show abstract

“…), the rotation direction is reversed, and the electromagnetic waves with different rotation directions have a large value of polarization isolation. SNR is mainly composed of the following two parts [28]:…”

Section: Gnss-ir Sm Inversion Principlementioning

confidence: 99%

GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects

Wei,

Yang,

Pan

et al. 2023

Remote Sensing

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The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique provides a new remote sensing method that shows great potential for soil moisture detection and vegetation growth, as well as for climate research, water cycle management, and ecological environment monitoring. Considering that the land surface is always covered by vegetation, it is essential to take into account the impacts of vegetation growth when detecting soil moisture (SM). In this paper, based on the GNSS-IR technique, the SM was retrieved from multi-GNSS and multi-frequency data using a machine learning model, accounting for the impact of the vegetation moisture content (VMC). Both the signal-to-noise ratio (SNR) data that was used to retrieve SM and the multipath data that was used to eliminate the vegetation influence were collected from a standard geodetic GNSS station located in Nanjing, China. The normalized microwave reflectance index (NMRI) calculated by multipath data was mapped to a normalized difference vegetation index (NDVI), which was derived from Sentinel-2 data on the Google Earth Engine platform to estimate and eliminate the influence of VMC. Based on the characteristic parameters of amplitude and phase extracted from detrended SNR signals and NDVI derived from multipath data, three machine learning methods, including random forest (RF), multiple linear regression (MLR), and multivariate adaptive regression spline (MARS), were employed for data fusion. The results show that the vegetation effect can be well eliminated using the NMRI method. Comparing MLR and MARS, RF is more suitable for GNSS-IR SM inversion. Furthermore, the SM reversed from amplitude and phase fusion is better than only those from either amplitude fusion or phase fusion. The results prove the feasibility of the proposed method based on a multipath approach to characterize the vegetation effect, as well as the RF model to fuse multi-GNSS and multi-frequency data to retrieve SM with vegetation error-correcting.

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“…In the field of remote sensing, GNSS-R technology has received increasing attention, and many scientists are continuously researching it. GNSS-R technology has been widely used to monitor sea level height [4][5][6], snow depth [7][8][9], soil moisture [10][11][12], and sea ice detection [13,14], etc. In the field of snow depth retrieval technology, Larson et al [15] used global positioning system (GPS) receivers and traditional methods to monitor heavy snowfall, respectively, and the experimental results showed that the snow depth could be effectively monitored using GPS receivers, and the monitoring results were very similar to the classical methods.…”

Section: Introductionmentioning

confidence: 99%

GNSS-R snow depth retrieval algorithm based on PSO-LSTM

Hu,

Qu,

Liu

et al. 2024

Meas. Sci. Technol.

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The GNSS-R technique has been applied to retrieve snow depth, which has a high potential for application. However, in the GNSS-R classical algorithm to retrieve snow depths, the retrieval errors of high and low snow depths are large due to the influence of factors such as surface vegetation and terrain environment. In this paper, we propose a snow depth retrieval algorithm based on a particle swarm optimized long short-term memory (PSO-LSTM) neural network. The algorithm extracted three characteristic parameters (frequency, amplitude, and phase) from the SNR data as inputs, and optimized the LSTM hyperparameters by the PSO algorithm to improve the retrieval accuracy for low snow depths and snow depths close to the antenna. The snow depth retrieval results of GPS data collected from the P351 station in 2022 were evaluated in this paper. The snow depth retrieval results of the PSO-LSTM algorithm were in high agreement with the snow depth data provided by the SNOTEL network; the R-square reached 0.986, and the RMSE and MAE were 7.3 cm and 4.94 cm, respectively.It is noteworthy that the PSO-LSTM algorithm could effectively retrieve the change in snow depth below 15cm; the value of the retrieval errors was kept within a small range. Compared with the classical algorithm, the RMSE and MAE decreased by 83.4% and 88.6%, respectively, during this period. The PSO-LSTM algorithm marginally improved the retrieval accuracy for snow depths exceeding 117 cm during this period. Compared with the classical algorithm, the RMSE and MAE decreased by 26.2% and 40.4% in this period, respectively.In addition, the snow depth retrieval algorithm was proposed in this paper does not require antenna height and empirical formulas to realize snow depth retrieval, and at the same time, the algorithm effectively improved the retrieval accuracy for both high and low snow depths.

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Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Cited by 12 publications

References 45 publications

The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects

GNSS-R snow depth retrieval algorithm based on PSO-LSTM

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