CN115078408B - Soil water content monitoring method based on multi-satellite dual-frequency combination multi-path error - Google Patents

Soil water content monitoring method based on multi-satellite dual-frequency combination multi-path error Download PDF

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CN115078408B
CN115078408B CN202210726784.0A CN202210726784A CN115078408B CN 115078408 B CN115078408 B CN 115078408B CN 202210726784 A CN202210726784 A CN 202210726784A CN 115078408 B CN115078408 B CN 115078408B
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聂士海
王延霞
涂晋升
李鹏
王梦柯
黄丹妮
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Chuzhou University
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Abstract

The invention discloses a soil water content monitoring method based on multi-satellite dual-frequency combination multipath errors, which comprises the following steps of; step 1, enabling a ground satellite receiver to receive signals sent by a satellite to obtain observation data; the signals received by the ground satellite receiver comprise direct signals and reflected signals; step 2, extracting code measurement pseudo-range observation data and carrier phase observation data from the observation data, and constructing to obtain a double-frequency pseudo-range multi-path error and a double-frequency carrier phase multi-path error under an L1 carrier and an L2 carrier; step 3, constructing a double-frequency combined multi-path error equation based on the double-frequency pseudo-range multi-path error and the double-frequency carrier phase multi-path error; solving a double-frequency combined multipath error equation to obtain a delay phase; and 4, carrying out correlation analysis on the delay phase and the soil water content of the ground, thereby representing the soil water content by using the delay phase. The invention can obtain higher monitoring precision of soil water content.

Description

Soil water content monitoring method based on multi-satellite dual-frequency combination multi-path error
Technical Field
The invention relates to the field of soil water content measuring methods, in particular to a soil water content monitoring method based on multi-satellite dual-frequency combination multi-path errors.
Background
Soil Moisture Content (SMC) is a physical quantity that characterizes the dryness of Soil. SMC plays a positive role in climate research, prediction of slope stability, accurate prediction of flood disasters, and implementation of precision agriculture, as an indispensable environmental factor of the earth's surface. Therefore, the method has important significance in carrying out high-precision and quasi-real-time dynamic monitoring on the soil humidity.
The traditional soil water content measuring means is generally timing and fixed-point measurement, and has the defects of low spatial and temporal resolution, uneven distribution of soil humidity samples, difficulty in realizing large-range soil humidity monitoring and the like. The Global Navigation Satellite System (GNSS) can be widely applied to positioning, navigation and time service, and can also provide an all-weather, globally-covered and quasi-real-time L-band (1-2 GHz) microwave signal for remote sensing of surface environment parameters for a free charge, so as to generate a brand-new remote sensing means, namely a GNSS-IR (GNSS interference reflectance) technology.
Compared with the traditional soil water content detection technology, the GNSS-IR technology has the characteristics of high space-time resolution, all-weather, low cost and all-weather, and provides a fast and dynamic method for detecting the SMC with high time resolution. Since the application of GNSS-IR technology, signal-to-Noise Ratio (SNR) has been used as the main data source of GNSS-IR, however, the GNSS-IR soil water content inversion based on SNR observation has the following problems:
(1) SNR is not useful for most GNSS users because SNR is not always present in raw GNSS files and SNR is a single source of data problem as the only source of data for GNSS-IR;
(2) the performance of a GNSS-IR system with signal-to-noise ratio as the system input depends to a large extent on the quality of the observation of the signal-to-noise ratio and whether the direct component of the signal-to-noise ratio (the trend term) is successfully removed. However, the actual snr is often contaminated by abnormal noise, and therefore, the snr obtained by using the low-order polynomial to remove the trend term to characterize the multipath information is often inaccurate. As a result, GNSS-IR performance based on signal-to-noise ratio time series can be severely inaccurate.
Meanwhile, some researchers use the combination of the three-frequency signals for the soil moisture content inversion of the GNSS-IR, but the number of the three-frequency signal observation satellites is small, so that the time resolution of the GNSS-IR for inverting the surface physical parameters is low, and the requirement cannot be met. In addition, the duration time of the effective satellite elevation signal-to-noise ratio is short, and is basically maintained at about 0.5-2 h, so that the method is not beneficial to realizing the highly dynamic monitoring of soil moisture.
In general, the current GNSS-IR is not beneficial to realizing high dynamic monitoring of soil water content and has single data source and the like for monitoring the soil water content. Therefore, a soil water content monitoring method which can be beneficial to realizing high dynamic monitoring of soil water content and enriching GNSS-IR data sources is greatly necessary.
Disclosure of Invention
The invention aims to provide a soil water content monitoring method based on multi-satellite dual-frequency combined multipath errors, and aims to solve the problems of poor accuracy and single data source of GNSS-IR in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the soil water content monitoring method based on the multi-satellite dual-frequency combination multipath error comprises the following steps;
step 1, enabling a ground satellite receiver to receive signals sent by a satellite so that the ground satellite receiver can obtain observation data; the signals received by the ground satellite receiver are synthetic signals, and the synthetic signals comprise direct signals directly received by the ground satellite receiver after the signals are sent out by the satellite and reflected signals received by the ground satellite receiver after the signals are sent out by the satellite and reflected by ground soil;
step 2, extracting code measurement pseudo range observation data and carrier phase observation data from observation data obtained by a ground satellite receiver, respectively constructing and obtaining a dual-frequency pseudo range multi-path error and a dual-frequency carrier phase multi-path error under an L1 carrier and an L2 carrier based on the code measurement pseudo range observation data and the carrier phase observation data, and performing high-order fitting on the dual-frequency carrier phase multi-path error to remove ionospheric delays of the L1 carrier and the L2 carrier, thereby obtaining the dual-frequency carrier phase multi-path error influenced by a de-ionosphere;
step 3, constructing a double-frequency combined multi-path error equation based on the double-frequency pseudo-range multi-path error obtained in the step 2 and the double-frequency carrier phase multi-path error influenced by the deionization layer, and solving the double-frequency combined multi-path error equation to obtain the delay phase of the reflected signal relative to the direct signal
Figure GDA0003957897850000021
Constructing a linearized error equation and aligning the delays based on the constructed linearized error equationPhase position->
Figure GDA0003957897850000022
Linearizing to obtain a delayed phase of the corrected reflected signal compared to the direct signal>
Figure GDA0003957897850000023
The linearized error equation is shown in the following equation:
Figure GDA0003957897850000024
wherein, beta (t) is a dual-frequency combined multipath error value;
V β(t) the residual error is corresponding to the double-frequency pseudo-range multi-path error and the double-frequency carrier phase multi-path error;
κ 0 an initial value representing an amplitude decay factor;
Figure GDA0003957897850000031
is the initial value of the delay phase;
adopting 5 epochs before and after the time point and adding error equations of 11 epochs at the time point to obtain 11 linearized error equations, and expressing the 11 linearized error equations by using the following matrix:
V β(t) ＝AX-l，
wherein, V β(t) A residual error of β (t), which is a matrix of 11 rows and 1 columns;
l is a constant term matrix;
x is a correction parameter matrix;
a is a coefficient matrix of the correction parameters;
based on the adjustment criterion of least square adjustment, X can be obtained by the following formula:
Figure GDA0003957897850000032
wherein I is a unit weight;
thereby obtaining the delay phase of the corrected reflected signal compared with the direct signal
Figure GDA0003957897850000033
And &>
Figure GDA0003957897850000034
The amplitude attenuation factor is shown as follows: />
Figure GDA0003957897850000035
Step 4, comparing the delay phase of the corrected reflection signal obtained in the step 3 with the delay phase of the direct signal
Figure GDA0003957897850000036
And carrying out correlation analysis with the soil water content of the ground, thereby representing the soil water content by a delay phase, and monitoring the soil water content change of the ground corresponding to the reflection signal through the delay phase change.
In a further step 2, the dual-frequency pseudorange multipath error is obtained by linear combination of pseudorange observed values and carrier phase observed values of the L1 carrier and the L2 carrier.
In a further step 3, the dual-frequency carrier phase multipath error is obtained by a difference between phase observations of the L1 carrier and the L2 carrier.
In a further step 4, a correlation analysis of the delay phase and the soil water content is carried out by respectively utilizing a linear regression, a back propagation neural network and a radial basis function neural network to obtain a soil water content prediction model, and then the soil water content change is monitored through the soil water content prediction model according to the delay phase change.
The method is based on GNSS-IR technical means, and is characterized in that a Double Frequency pseudo range (DFP) and a Double Frequency carrier phase (L4 Ionosphere Free, L4_ IF) multi-path error influenced by a deion layer are constructed by using a code measurement pseudo range and a carrier phase observed value respectively, and the soil water content is monitored by using the multi-path error with less epochs. The method solves the defects of low space-time resolution and the like in the traditional soil moisture content measuring means, and overcomes the defects that the low time resolution is not beneficial to realizing high dynamic monitoring of the soil moisture content and the like in the GNSS-IR technical means.
The method combines the current GNSS multi-mode multi-frequency development mode, can obtain higher monitoring precision of the soil water content with less multipath errors of epochs, and can more easily realize the high time resolution of the GNSS-IR soil water content inversion. Meanwhile, data sources of the GNSS-IR are enriched, reliability of the GNSS-IR is enhanced, and the capability of the GNSS for serving for environment monitoring is improved.
Compared with the prior art, the invention has the advantages that:
(1) According to the method, the multi-satellite dual-frequency combined multi-path error is utilized, and the high monitoring precision of the soil water content can be obtained through the multi-path error of less epochs (by adopting multi-path error data of 11 epochs including 5 epochs before and after a time point), so that the high time resolution of the GNSS-IR soil water content inversion is greatly improved, and the high dynamic monitoring of the soil water content is more easily realized.
(2) Based on the GNSS-IR technical means, the method and the device construct different multi-path error calculation models respectively according to the code measurement pseudo-range and the carrier phase observation value of the GNSS observation data, utilize the multi-path errors to monitor and study the soil water content, make up for the defects of SNR (signal to noise ratio), tri-frequency carrier phase combination and other data sources, enrich the data sources of the GNSS-IR, enhance the reliability of the GNSS-IR, and promote the development and application of the GNSS-IR technology.
(3) According to the method, a soil moisture prediction model is constructed by respectively utilizing a linear regression (ULR), a Back Propagation Neural Network (BPNN) and a Radial Basis Function Neural Network (RBFNN), the precision of each model is evaluated, and the soil moisture content inversion precision of the GNSS-IR is greatly improved.
(4) According to the method, through verifying and analyzing the correlation between the GNSS reflected signal and the SMC, a quick and dynamic new method is provided for obtaining the SMC with high time resolution, the SMC is inverted by fully utilizing multi-satellite dual-frequency combined information, the accuracy and the continuity of SMC observation data in a local area are further improved, the soil environment monitoring and the crop growth comprehensive grasping are facilitated, and the environment monitoring capability of the GNSS is greatly improved.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a diagram illustrating a dual-frequency pseudorange multi-path error (MP 2) and a dual-frequency carrier-phase combined multi-path error (L4 _ IF) as a function of satellite altitude according to an embodiment of the invention.
Fig. 3 is a diagram illustrating dual-frequency pseudorange multipath error (MP 2) and dual-frequency carrier-phase combined multipath error (L4 _ IF) at low elevation angles (5-25 °) according to a sinusoidal variation of satellite elevation angle according to an embodiment of the present invention.
FIG. 4 is a diagram of a first Fresnel reflection zone of a station according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of the Lomb-Scargle spectrum analysis according to the embodiment of the present invention.
Fig. 6 is a diagram illustrating a result of multipath error inversion using dual-band carrier phase combining according to an embodiment of the present invention.
Fig. 7 is a diagram of a result of multi-path error inversion using dual-frequency pseudoranges according to an embodiment of the present invention.
FIG. 8 is a graph comparing results of a soil moisture prediction model constructed Using Linear Regression (ULR), back Propagation Neural Network (BPNN), and Radial Basis Function Neural Network (RBFNN) according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, the present embodiment is described by taking a GNSS as an example, and includes the following steps:
step 1, enabling the GNSS receiver to receive signals sent by the GNSS satellite so that the GNSS receiver can obtain observation data. According to the observation data of the GNSS receiver, the observation data are converted into an observation value O file and a navigation message N file by means of a RINEX format conversion tool, and data such as a carrier phase observation value, a code measurement pseudo-range observation value, a satellite azimuth angle, a satellite altitude angle and an observation epoch are extracted by utilizing an RTKLIB open source code. And selecting a low satellite altitude angle (5-25 degrees) and selecting an effective satellite by utilizing a first Fresnel reflection area. The low satellite altitude is selected and obtained from the extracted data by programming, and the first Fresnel reflection area is used for calculating and drawing a first Fresnel reflection area graph. The purpose of selecting a low satellite height angle and utilizing a first Fresnel reflection area is to better select an effective satellite, so that the inversion precision and accuracy of the water content of the soil are improved.
In actual measurement, the signals received by the antenna of the GNSS receiver not only are signals from the satellite and directly received by the GNSS receiver (i.e., direct signals), but also are often accompanied by indirect signals from the satellite after being reflected by the ground soil (i.e., reflected signals). Therefore, the distance Δ S (multi-path delay) that the reflected signal travels more than the direct signal can be expressed as:
ΔS＝2h sinθ (1)，
delayed phase due to multipath delay
Figure GDA0003957897850000061
Comprises the following steps:
Figure GDA0003957897850000062
in the formulas (1) and (2), h is the vertical height from the phase center of the GNSS receiver antenna to the ground; theta is the satellite elevation; λ is the carrier wavelength; and t is an observation epoch.
For the GNSS receiver to receive the composite signal formed by the superposition of the direct signal and the reflected signal, the composite signal S C Can be expressed as:
Figure GDA0003957897850000063
synthesizing the signal S C Amplitude A of C Phase difference (phase delay) of the reflected signal compared to the direct signal
Figure GDA0003957897850000064
Can be expressed as: />
Figure GDA0003957897850000065
In the formulae (3) and (4), S L And S M Direct signals and reflected signals, respectively; a. The d And A m Amplitude of the direct signal and reflected signal, respectively, where A m ＝κ·A d κ represents an amplitude attenuation factor as a function of the reflection coefficient and antenna gain;
Figure GDA0003957897850000066
delay phase due to multipath delay; omega 0 Is the signal angular frequency; s C Is a composite signal; a. The C Is S C Of the amplitude of (c).
Phase difference (phase delay) of reflected signal compared to direct signal
Figure GDA0003957897850000067
Can be approximately expressed as:
Figure GDA0003957897850000068
and 2, extracting code measurement pseudo range observation data and carrier phase observation data from observation data obtained by the ground satellite receiver, and respectively constructing and obtaining a Double-Frequency pseudo range (DFP) multi-path error and a Double-Frequency carrier phase (L4 Ionosphere Free, L4_ IF) multi-path error under an L1 carrier and an L2 carrier based on the code measurement pseudo range observation data and the carrier phase observation data.
Specifically, the dual-frequency pseudo-path multipath error may be calculated by a linear combination of the pseudo-range observations and the carrier-phase observations, and may be represented as:
Figure GDA0003957897850000071
in the formula (6), MP 1 、MP 2 Dual-frequency dummy of L1 and L2 carriers respectivelyDistance multipath error; p 1 、P 2 Pseudo-range observed values of L1 and L2 carriers respectively; f. of 1 、f 2 The frequencies of the L1 and L2 carriers respectively; lambda [ alpha ] 1 、λ 2 L1 and L2 carrier wavelengths, respectively;
Figure GDA0003957897850000072
respectively as L1 and L2 carrier phase observed values; n is a radical of 1 、N 2 Integer ambiguity of L1 and L2 carrier waves respectively; epsilon P1 、ε P2 Other unmodeled errors.
Δ(N 1 ,N 2 ) The integer ambiguity combination is usually a constant, and when no cycle slip occurs, it does not affect the overall variation trend of the multipath error. Therefore, the factors affecting the quality of the dual-band pseudorange multipath error are mainly the noise of the pseudorange observation (epsilon in equation (6)) P1 、ε P2 ) And the accuracy of the carrier phase observations and the code-finding pseudorange observations. If cycle slip occurs in the carrier phase observed value, the magnitude of the pseudo-range multi-path error is influenced, so that the cycle slip needs to be detected and repaired respectively for the pseudo-range multi-path error of each adopted time period.
The dual-band carrier phase multipath error may be differentiated by the phase observations of the L1 carrier and the L2 carrier, and may be expressed as:
L4＝L1-L2＝I 1 -I 2 +λ 1 β 1 (t)-λ 2 β 2 (t) (7)，
in the formula (7), I 1 、I 2 Respectively L1 and L2 carrier ionospheric delays; beta is a 1 (t)、β 2 And (t) phase errors of the L1 carrier phase and the L2 carrier phase caused by multipath effect respectively.
Therefore, in the double-frequency carrier phase multi-path error shown in the formula (7), not only the satellite clock error, the receiver clock error and the troposphere delay are eliminated, but also the geometric distance between the satellite and the receiver is eliminated, namely the L4 observed value is influenced by the ionosphere delay and the phase error. In the analysis of the multipath error, an appropriate method is adopted to weaken the influence of the ionospheric delay on the multipath error, and the L4_ IF (L4 ionosphere free) observation value without the influence of the ionospheric delay, namely the dual-frequency carrier phase multipath error without the influence of the ionospheric delay, is obtained by adopting a high-order polynomial to fit the L4 multipath error.
And 3, constructing a double-frequency combined multi-path error equation based on the double-frequency pseudo-range multi-path error and the double-frequency carrier phase multi-path error obtained in the step 2. Solving the double-frequency combined multipath error equation to obtain the delay phase of the reflected signal relative to the direct signal
Figure GDA0003957897850000081
Specifically, according to equations (1) and (2), the phase delay and the path delay are functions of the satellite elevation angle and the reflecting surface height. The height of the reflecting surface varied with the soil moisture content SMC, which contributed to the variation in phase and path delays, indicating that h was not as consistent as SMC
Figure GDA0003957897850000082
The consistency with SMC is strong. Thus, the delay phase caused by multipath delay can be used
Figure GDA0003957897850000083
To characterize changes in the SMC. To do this, equation (5) must first be linearized. Thus, the error equation can be expressed as:
Figure GDA0003957897850000084
in the formula (8), β (t) is a dual-frequency combined multipath error, and can be calculated by the formulas (6) and (7); when the double-frequency pseudo range multipath error is considered, the beta (t) is the double-frequency pseudo range multipath error, and when the double-frequency carrier phase multipath error is considered, the beta (t) is the double-frequency carrier phase multipath error.
V β(t) Is the residual corresponding to the DFP error and the L4_ IF error.
κ 0 Presentation breadthAn initial value of the degree attenuation factor. Considering the surface reflectivity of the soil to be between 0.3 and 0.8, the invention focuses on the amplitude and phase of the multipath error, so that the true value of kappa is not needed, and the initial value kappa of the verified value kappa is taken 0 ＝0.3。
Figure GDA0003957897850000085
The initial value of the delay phase is obtained by respectively substituting the carrier wave wavelength, the GNSS receiver antenna height and the satellite elevation into the formulas (1) and (2); v k And &>
Figure GDA0003957897850000086
Is corresponding to κ 0 And &>
Figure GDA0003957897850000087
Two correction parameters need to be solved.
In order to ensure the reliability of the solution and avoid the influence of excessive difference of the multipath errors on the solution of the correction parameters, the correction parameters are solved for the multipath errors by the adjustment of the least square method under the assumption that the SMC is kept unchanged for a short time (for example, within 5 minutes).
Due to the fact that
Figure GDA0003957897850000088
The equation of (2) is not a linear equation, when the least square method is used for solving, linearization needs to be carried out firstly, and errors exist in the linearization process, so that the correction number (correction parameter) needs to be solved, and the correction parameter value is that the final solved result is more accurate and accords with the least square criterion; the initial value of the delayed phase is->
Figure GDA0003957897850000089
Can be calculated by the formulas (1) and (2).
In the embodiment, in order to better improve the time resolution, multipath error data of 5 epochs before and after the time point and 11 epochs at the time point are added, and the delay phase is solved based on the adjustment of the least square method. The solution of the delay phase is as follows:
for 11 multipath errors of a single GPS satellite in one observation period, 11 double-frequency combined multipath error equations can be obtained, which are respectively shown in the formula (8). These error equations can then be represented by the following matrix:
V β(t) ＝AX-l (9)，
in the formula (9), V β(t) The residual error is β (t), and is a matrix of 11 rows and 1 column, and l is a matrix of constant terms. X is a correction parameter matrix, and A is a coefficient matrix of the correction parameters. Therefore, the adjustment criterion (V) based on least square adjustment T PV = min, P is weight. For this embodiment P is a unit weight, i.e. P = I). X can be obtained by the following formula:
Figure GDA0003957897850000091
/>
so that the delayed phase of the corrected reflected signal compared to the direct signal can be obtained
Figure GDA0003957897850000092
And &>
Figure GDA0003957897850000093
Amplitude attenuation factor:
Figure GDA0003957897850000094
in this embodiment, when solving the delay phase of the reflected signal compared with the direct signal by using the adjustment of the least square method according to the equations (9) - (11) for the constructed dual-frequency combined multipath error equation, the data processing follows the following principle:
1) Detecting and repairing cycle slip of the carrier phase observed value;
2) Assuming that the amplitude attenuation factor and the phase delay are unchanged for a short time;
3) The influence of excessive difference of multipath errors on correction parameter solution is avoided, the soil humidity is assumed to be kept unchanged in a short period, necessary observation number is considered, time points in corresponding time periods and 5 epochs before and after the time points are taken, 11 multipath errors are adopted as observation values in total, namely the change of phase delay along with the satellite altitude angle in the short period is ignored, and the multipath error selected in each time period is regarded as repeated observation of the same parameter.
Finding the corrected delay phase
Figure GDA0003957897850000095
And the method is used for carrying out correlation analysis on the soil water content in the subsequent steps.
And 4, performing correlation analysis with the soil water content SMC according to the delay phase obtained in the step 3, performing correlation analysis of the delay phase and the soil water content by respectively utilizing a linear regression (ULR), a Back Propagation Neural Network (BPNN) and a Radial Basis Function Neural Network (RBFNN) to obtain a soil water content prediction model in order to improve the inversion accuracy of the GNSS-IR technology on the soil water content SMC, and then monitoring the soil water content change according to the delay phase change through the soil water content prediction model.
The feasibility verification process of the soil water content monitoring method in the embodiment is as follows:
in the verification data in the embodiment, a GPS Observation value of a P041 survey station of a Plant Border Observation (PBO) is adopted to record soil humidity data of 9 days including 14 days of 2 months to 11 days of 5 months (DOY: 2014-45 to 2014-131, 57, 67-69, 71, 82, 94, 104 and 105 in 2014, and is deleted or removed), so that the data time selected in the test is 78 days, and the soil humidity data is derived from P041 actual measurement data. The P041 kiosk is located in colorado, usa. The observation environment is good, the site is flat and open, the surrounding vegetation is rare and is not blocked by large-scale barriers, the vegetation type is lawn, the test data is selected to be late winter and early spring, and the ground surface near the field can be regarded as bare soil. Therefore, the surface reflection signals are less affected by vegetation attenuation.
Fig. 2 (a) and (b) are graphs of the variation trend of the dual-frequency pseudorange multipath error and the dual-frequency carrier phase multipath error in one complete epoch, respectively. As can be seen from the figure, MP 2 Variation of multipath error and L4_ IF multipath errorAre all closely related to the altitude of the satellite, and when the altitude of the satellite is lower, the MP 2 The value oscillation amplitude can reach several meters, and the L4_ IF value oscillation amplitude is relatively large; when the satellite altitude is higher, MP 2 Both the value and the amplitude of the L4_ IF value oscillation should be reduced. When the satellite elevation angle is low, the multipath part of the GNSS signal fluctuates greatly, as shown in (a), (b) of fig. 3. As can be seen from (a) and (b) in fig. 3, as the altitude of the satellite increases, the multipath error is in an obvious downward trend, which indirectly indicates that the carrier pseudorange and the carrier phase observed value of the low-altitude satellite are more susceptible to the multipath error, thereby providing a theoretical basis for selecting the altitude of the GNSS-IR satellite, and limiting the altitude of the satellite in the test to 5 ° to 25 °. Meanwhile, the fact that signals received by the GNSS antenna mainly affect the ground surface reflection surface is explained, and the signals carry relevant information such as soil through a ground medium, so that inversion of land remote sensing parameters can be achieved through analysis of the relevant signals.
In view of the fact that certain spatial differences exist among factors such as soil humidity and surface environment in the same research area, soil humidity at different positions is not completely consistent, and therefore differences tend to exist among results obtained through inversion based on observed values at different reflection positions. The fresnel region of GNSS signals is a set of ellipses related to satellite altitude, azimuth, and antenna height. The "first fresnel reflection zone" contributes most to the reflected signal and variations in the reflection medium within the reflection zone will greatly affect the relevant physical characteristics of the reflected signal. The influence of other multipath sources such as high buildings, vegetation, water areas and the like can be avoided by utilizing the 'first Fresnel reflection area', so that the unicity of a reflection medium in the reflection area is ensured to the maximum extent, meanwhile, a certain basis can be provided for the design of an experimental scheme, the determination of the direction of a soil humidity sample collection point and the selection of an effective satellite, and the figure 4 is a 'first Fresnel reflection area' diagram of a P041 measurement station (DOY: 2014-065).
In FIG. 5, (a) and (b) are the dual-band pseudorange multi-path errors (MP), respectively 2 ) And the spectrum analysis diagram Lomb-Scargele spectrum analysis diagram of the dual-frequency carrier phase combined multipath error (L4 _ IF). Research shows that the maximum power spectral density canTo some extent characterizing the quality of the multipath error signal. Spectral analysis using the Lomb-Scargle method can be used to determine satellite signal quality. In general, the dominant frequency should have at least twice the power of the secondary dominant peak frequency, and the selection of active satellites can be performed by Lomb-Scargle spectral analysis.
And combining the results of satellite selection every day, and calculating the multipath error for the selected single satellite according to the double-frequency signals of 11 epochs before and after the corresponding time. And (4) combining the principle and the method, and solving the delay phase corresponding to the soil humidity acquisition moment through least square adjustment. Thus, one phase delay may be calculated per observation period, and 12 phase delays may be calculated for 12 observation periods within a day. By averaging the 12 results, the mean phase delay is obtained, i.e. the daily phase delay. Respectively calculating the L4_ IF method and the DFP method according to the time scale of each day to obtain corresponding day phase delay, respectively drawing time variation trend graphs of soil humidity and delay phase, and respectively showing double-frequency pseudo-range multi-path error (MP) in the graphs of FIG. 6 and FIG. 7 2 ) And dual-frequency carrier-phase combined multipath error (L4 _ IF). As can be seen from fig. 6 and 7, there are a plurality of distinct peaks in both phase delay and soil moisture, and the overall variation trend has strong consistency. Correlation coefficients between the soil moisture and the phase delay obtained based on the DFP multipath error and the L4_ IF multipath error were 0.91 and 0.97, respectively, and statistically significant correlation was observed.
To further verify the results based on DFP multipath error and L4_ IF multipath error, in addition, the neural network model is less sensitive to the effects of soil flatness, surface vegetation and soil surface roughness. Therefore, in order to improve the inversion accuracy of the soil humidity of the GNSS-R, the delay phase and the soil water content based on the P041 station are taken as data sources, and the data sources are as follows: 3, dividing the training set and the test set. I.e., 50d data as training set data and 28d data as test set data. The model is predicted by constructing a Back Propagation Neural Network (BPNN) and a Radial Basis Function Neural Network (RBFNN) and is compared with a conventional linear regression (ULR) model for analysis. Fig. 8 (a) and (b) are graphs comparing predicted values of three models, namely a linear regression (ULR), a Back Propagation Neural Network (BPNN) and a Radial Basis Function Neural Network (RBFNN), with measured values of soil water content for a dual-frequency pseudorange multi-path error and a dual-frequency carrier phase multi-path error, respectively, according to the present invention.
The described embodiments of the present invention are only for describing the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and the technical solutions of the present invention should be modified and improved by those skilled in the art without departing from the design concept of the present invention, and the technical contents of the present invention which are claimed are all described in the claims.

Claims (4)

1. The soil water content monitoring method based on the multi-satellite dual-frequency combination multipath error is characterized by comprising the following steps of;
step 1, enabling a ground satellite receiver to receive signals sent by a satellite so that the ground satellite receiver can obtain observation data; the signals received by the ground satellite receiver are synthetic signals, and the synthetic signals comprise direct signals directly received by the ground satellite receiver after the signals are sent out by the satellite and reflected signals received by the ground satellite receiver after the signals are sent out by the satellite and reflected by ground soil;
step 2, extracting code measurement pseudo range observation data and carrier phase observation data from observation data obtained by a ground satellite receiver, respectively constructing and obtaining a dual-frequency pseudo range multi-path error and a dual-frequency carrier phase multi-path error under an L1 carrier and an L2 carrier based on the code measurement pseudo range observation data and the carrier phase observation data, and performing high-order fitting on the dual-frequency carrier phase multi-path error to remove ionospheric delays of the L1 carrier and the L2 carrier, thereby obtaining the dual-frequency carrier phase multi-path error influenced by a de-ionosphere;
step 3, constructing a double-frequency combined multi-path error equation based on the double-frequency pseudo-range multi-path error obtained in the step 2 and the double-frequency carrier phase multi-path error influenced by the deionization layer, and solving the double-frequency combined multi-path error equation to obtain the delay phase of the reflected signal relative to the direct signal
Figure DEST_PATH_IMAGE002
Constructing a linearized error equation and basing the constructed linearized error equation on the delayed phase->
Figure DEST_PATH_IMAGE002A
Linearizing to obtain a delayed phase of the corrected reflected signal compared to the direct signal>
Figure DEST_PATH_IMAGE004
Wherein:
the linearized error equation is shown in the following equation:
Figure DEST_PATH_IMAGE006
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE008
combining the multipath error values for the dual frequency;
Figure DEST_PATH_IMAGE010
the residual error is corresponding to the double-frequency pseudo-range multi-path error and the double-frequency carrier phase multi-path error;
Figure DEST_PATH_IMAGE012
an initial value representing an amplitude decay factor;
Figure DEST_PATH_IMAGE014
is the initial value of the delay phase;
adopting 5 epochs before and after the time point and adding error equations of 11 epochs at the time point to obtain 11 linearized error equations, and expressing the 11 linearized error equations by using the following matrix:
Figure DEST_PATH_IMAGE016
，
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE018
is->
Figure DEST_PATH_IMAGE020
Is a matrix of 11 rows and 1 column;
lis a matrix of constant terms;
Xis a correction parameter matrix;
Aa coefficient matrix which is a correction parameter;
based on the adjustment criterion of least square adjustment,Xcan be obtained by the following formula:
Figure DEST_PATH_IMAGE022
，
wherein the content of the first and second substances,Iis a unit weight;
thereby obtaining the delay phase of the corrected reflected signal compared with the direct signal
Figure DEST_PATH_IMAGE004A
And &>
Figure DEST_PATH_IMAGE024
The amplitude attenuation factor is shown as follows:
Figure DEST_PATH_IMAGE026
；
step 4, comparing the delay phase of the corrected reflection signal obtained in the step 3 with the delay phase of the direct signal
Figure DEST_PATH_IMAGE004AA
Performing correlation analysis with the soil moisture content of the ground, therebyAnd characterizing the soil water content by a delay phase, and monitoring the change of the soil water content of the ground corresponding to the reflection signal through the delay phase change.
2. The soil water content monitoring method based on the multi-satellite dual-frequency combination multi-path error as claimed in claim 1, wherein in the step 2, the dual-frequency pseudo-range multi-path error is obtained by linear combination of pseudo-range observed values and carrier phase observed values of an L1 carrier and an L2 carrier.
3. The soil water content monitoring method based on the multi-satellite dual-frequency combination multi-path error as claimed in claim 1, wherein in the step 3, the dual-frequency carrier phase multi-path error is obtained by a difference value of phase observed values of an L1 carrier and an L2 carrier.
4. The soil water content monitoring method based on the multi-satellite dual-frequency combination multi-path error as claimed in claim 1, wherein in the step 4, a correlation analysis between the delay phase and the soil water content is performed by respectively using a linear regression, a back propagation neural network and a radial basis function neural network to obtain a soil water content prediction model, and then the soil water content change is monitored through the soil water content prediction model according to the delay phase change.
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