A dual-antenna soil moisture monitoring method based on GNSS-IR

Abstract

The present invention provides a dual-antenna soil moisture monitoring method based on GNSS-IR, which belongs to the technical field of GNSS-IR soil moisture monitoring. The invention includes: assembling a dual-antenna GNSS-IR monitoring device; extracting direct and interference signal observation values ​​respectively; performing direct and reflected signal separation on the signal-to-noise ratio data at low elevation angles and high elevation angles respectively; calculating the amplitude and phase characteristic values ​​of the reflection components at low elevation angles and high elevation angles respectively; modeling the low elevation angle characteristic quantities and testing the high elevation angle characteristic quantities. The effectiveness of dual-antenna GNSS-IR soil moisture monitoring is proved by carrying out dual-antenna GNSS-IR soil moisture monitoring experiments. The dual-antenna GNSS-IR soil moisture monitoring method of the present invention makes up for the limitation of the traditional GNSS-IR soil moisture monitoring method on the elevation angle, and has the characteristics of wide observation area and low cost.

Description

Technical Field

[0001] The present invention relates to the technical field of soil moisture detection, and in particular to a dual-antenna soil moisture monitoring method based on GNSS-IR. Background Art

[0002] Soil moisture is an important parameter in agriculture, meteorology, hydrology and other fields. It is a basic component of the global water cycle and energy exchange. It is an important physical quantity that connects surface water, groundwater and atmospheric water. It plays an important role in the formation, transformation and consumption of land surface water. In agriculture, soil moisture is a basic physical parameter that characterizes the degree of soil drought. As the main source of water absorption for crops, the amount of water directly affects the growth and yield of crops. The more accurate measurement of soil moisture changes can provide strong technical support for agricultural environmental monitoring, drought and flood forecasting, and vegetation growth assessment. With the continuous development and maturity of GNSS technology, GNSS-R technology has made significant progress. GNSS-R technology extracts physical parameters of the earth's surface by receiving and processing GNSS signals reflected from the earth's surface. As a free and long-term stable signal source, GNSS can provide highly stable and continuous free L-band microwave signal resources. The monitoring of soil moisture by the global navigation satellite system has become a new remote sensing monitoring method.

[0003] Li Jie et al. conducted experiments on bare soil and vegetation using two antenna configurations: a dual-antenna mode, using antennas with RHCP and LHCP characteristics to receive direct and reflected signals; and a single-antenna mode, using a single antenna with RHCP characteristics to simultaneously receive direct and reflected GNSS signals. The results showed that the root mean square error of bare soil in both single and dual antenna modes was within 0.05 cm. 3 / cm 3 Liang Yueji and others improved the accuracy of the GNSS-IR multi-satellite inversion model by combining the two systems. The test accuracy after the combination was 0.013~0.025 cm 3 / cm 3 .

[0004] Although GNSS-R soil moisture monitoring technology has developed rapidly in recent years, achieved remarkable research results, and diversified observation modes, it also has limitations. GNSS-IR is an interference principle, usually a single antenna mode, where a single antenna receives interference signals. As the altitude angle increases, the signal oscillation weakens, and it is only suitable for low elevation angles. GNSS-R is a reflection principle, usually a multi-antenna mode where two or even three different antennas are combined to receive direct and reflected signals, but its signal storage space requirements are extremely large, making long-term monitoring more difficult. In view of the above, the present invention focuses on the effectiveness and feasibility of a soil moisture monitoring device based on a GNSS-IR dual antenna. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a dual-antenna soil moisture monitoring method based on GNSS-IR. The monitoring method of the present invention makes up for the limitation of the traditional GNSS-IR soil moisture monitoring method on the altitude angle, and has the characteristics of wide observation area and low cost.

[0006] The present invention adopts the following scheme:

[0007] A dual-antenna soil moisture monitoring method based on GNSS-IR, comprising:

[0008] S1. Build a dual-antenna GNSS-IR soil moisture monitoring device;

[0009] The device includes: an upward-looking antenna, a downward-looking antenna and two receivers of the same model, wherein the upward-looking antenna is a right-hand circularly polarized antenna, tilted upward by 30 degrees; the downward-looking antenna is a left-hand circularly polarized antenna, tilted downward by 30 degrees;

[0010] The signal received by the right-hand circularly polarized antenna is divided into two paths by a power divider, one path is connected to receiver 1, and the other path is connected to the left-hand circularly polarized antenna through a combiner and then connected to receiver 2;

[0011] The TDR soil moisture meters were placed within the measurement area at depths of 5 cm, 10 cm, and 15 cm respectively;

[0012] The method for detecting using the dual-antenna soil moisture monitoring device comprises the following steps:

[0013] S2, extracting the direct and interference signal observation values ​​respectively;

[0014] S3: Separation of direct and reflected signals at low and high elevation angles;

[0015] S4: Calculate the amplitude and phase eigenvalues ​​of the low-elevation and high-elevation reflection components;

[0016] S5: Model low elevation angle eigenvalues ​​and verify model accuracy for high elevation angle eigenvalues.

[0017] As a further improvement of the present invention, in step S2, the direct and interference signal observation values ​​are extracted respectively; the specific implementation process is as follows:

[0018] (1) Receiver 1 stores the direct signal observation values ​​in the Rinex format in the receiver. The signal-to-noise ratio (SNR) of the direct signal at different frequencies at each epoch is extracted from the file. d , according to the recorded direct antenna position, combined with the precise ephemeris file, calculate the corresponding epoch elevation angle , azimuth information ;

[0019] (2) Receiver 2 stores the interference signal observation value, which is consistent with process (1) to extract the signal-to-noise ratio at each epoch. , Altitude angle , azimuth information .

[0020] As a further improvement of the present invention, in step S3, the separation of the direct and reflected signals at low elevation angles and high elevation angles is specifically implemented as follows:

[0021] (1) Analyze the direct and interference signals according to the actual situation and divide them into high elevation angle and low elevation angle areas;

[0022] (2) Calculate the reflection area and determine the effective range;

[0023] (3) High and low elevation angles separate the time series into ascending and descending segments;

[0024] (4) For high elevation angles, the interference signal-to-noise ratio of the reflected component Direct SNR Directly make the difference, denoted as ;

[0025] ;

[0026] (5) Process the direct component at low elevation angles to Perform a second-order polynomial fit to obtain the direct signal component , and then from the interference signal Separate the reflection component ;

[0027] ;

[0028] in, are the coefficients of the second-order polynomial.

[0029] As a further improvement of the present invention, in step S4, the calculation of the amplitude and phase characteristic values ​​of the low elevation angle and high elevation angle reflection components is specifically implemented as follows:

[0030] (1) The Lomb-Scargle transform method is used to calculate the vibration frequency of the reflected component SNR, that is: ;

[0031] (2) The high and low elevation angle reflection components are expressed as: ;

[0032] in, f is the frequency solved in (1); is the satellite altitude angle, are the phase and amplitude of the reflected signals at low elevation angle and high elevation angle respectively;

[0033] (3) Use the least squares method to fit the reflection component in (2) and solve the optimal amplitude and phase.

[0034] As a further improvement of the present invention, in step S5, the low elevation angle eigenvalue is modeled and the high elevation angle eigenvalue is verified for model accuracy, and the specific implementation process is as follows:

[0035] (1) The amplitude and phase eigenvalues ​​obtained at low elevation angles are used in combination with the measured soil moisture to construct the model: ;

[0036] Where a is the model slope, the superscript indicates low elevation angle, and the subscript indicates the eigenvalue; b is the soil moisture base, the superscript indicates low elevation angle, and the subscript indicates the eigenvalue; A is the amplitude observation, the superscript indicates low elevation angle, and the subscript indicates the SNR reflection component; For phase measurement, the superscript indicates low elevation angle, and the subscript indicates the SNR reflection component; V sm is the soil moisture value;

[0037] (2) Extract the amplitude and phase eigenvalues ​​of the reflected component at high elevation angles: ;

[0038] in, All are obtained by process (1); A is the amplitude observation, the superscript represents the high elevation angle, and the subscript represents the SNR reflection component; For phase measurement, the superscript indicates the high elevation angle, and the subscript indicates the SNR reflection component; is the estimated value of soil moisture;

[0039] (3) The soil moisture meter takes the average value per hour;

[0040] (4) Compare and analyze the high-elevation amplitude and phase inversion results of (2) with the hourly average results of the soil moisture meter in (3), and analyze the statistical data and verify the accuracy, including the correlation coefficient (R) and root mean square error (RMSE) obtained by comparing the inversion results of the data contained in different satellites with the actual data of the soil moisture meter, so as to verify the effectiveness and accuracy of the detection.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) After receiving the direct and reflected signals respectively through the upward-looking and downward-looking antennas, the interference through the combiner significantly improves the oscillation of the signal-to-noise ratio, avoiding the weakening of the signal-to-noise ratio oscillation with the increase of the altitude angle, which in turn causes the data inversion to be limited by the altitude angle. The monitoring effect is more obvious and the observation range is more comprehensive.

[0043] (2) The signal is stored in Rinex format through the receiver, which improves the disadvantage of the GNSS-R dual-antenna equipment that requires a large amount of storage space to collect intermediate frequency signals, and can achieve long-term observation.

[0044] (3) The feasibility and effectiveness of the present invention have been verified through experiments. By conducting specific experiments to collect observations of the signal-to-noise ratio of direct and interference signals, the direct reflection signals at high and low elevation angles are separated and the eigenvalues ​​are calculated. Combined with the comparative data obtained in the experiment, the eigenvalues ​​of high and low elevation angles are analyzed and soil moisture is inverted. The results show that the method of the present invention has smaller errors and higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0046] Figure 2 It is a schematic diagram of the device composition of the present invention;

[0047] Figure 3 is a schematic diagram of the GNSS-IR principle in the method of the present invention;

[0048] Figure 4 is a vector relationship diagram of interference signals in the method of the present invention;

[0049] Figure 5 This is a comparison of the signal-to-noise ratios of the direct and interference signals extracted in step 2 of the method of the present invention.

[0050] Figure 6 is the signal-to-noise ratio time series in the method of the present invention;

[0051] Figure 7 It is a specific flow chart of steps 3 to 5 in the method of the present invention;

[0052] Figure 8 It is the result of Lomb-Scargle spectrum analysis at low elevation angle;

[0053] Fig. 9 is the least square fitting result of the reflection component at low elevation angle;

[0054] Fig.10 It is the result of Lomb-Scargle spectrum analysis at high elevation angle;

[0055] Fig.11 is the least square fitting result of the reflection component at high elevation angle;

[0056] Fig.12 It is the result of estimating the amplitude characteristic value of S1P of BDS C22 satellite;

[0057] Fig.13is the estimated result of the phase eigenvalue of S1P of BDS C22 satellite;

[0058] Fig.14 It is the result of estimating the amplitude characteristic value of S5P of BDS C22 satellite;

[0059] Fig.15 It is the estimation result of the phase characteristic value of S5P of BDS C22 satellite. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0062] A dual-antenna soil moisture monitoring method based on GNSS-IR Figure 2 As shown in the figure, a dual-antenna soil moisture monitoring device based on GNSS-IR was built, and a monitoring experiment was carried out at the Weihai Academy of Agricultural Sciences. The experimental time was April 12, 2024 - May 20, 2024. BDS multi-frequency data was collected and recorded in Rinex format files. The specific steps are as follows (refer to Figure 1 shown):

[0063] S1. Build a dual-antenna GNSS-IR soil moisture monitoring device;

[0064] (1) The monitoring equipment consists of an upward-looking antenna, a downward-looking antenna, and two receivers of the same model. The upward-looking antenna is a right-hand circularly polarized antenna, tilted upward by 30° and at a height of 2.17 m; the downward-looking antenna is a left-hand circularly polarized antenna, tilted downward by 30° and at a height of 1.88 m.

[0065] (2) The signal received by the right-hand circularly polarized antenna is divided into two paths by a power divider. One path is connected to receiver 1, and the other path is connected to the left-hand circularly polarized antenna through a combiner and then connected to receiver 2.

[0066] (3) Place the TDR soil moisture meter within the measurement area at depths of 5 cm, 10 cm, and 15 cm respectively.

[0067] S2. Extract the direct and interference signal observation values ​​respectively;

[0068] (1) Receiver 1 stores the direct signal observation values, which are recorded in the receiver in Rinex format. Extract the signal-to-noise ratio of the direct signal at different frequencies at each epoch from the file , according to the recorded direct antenna position, combined with the precise ephemeris file, calculate the corresponding epoch elevation angle , azimuth information ;

[0069] (2) Receiver 2 stores the interference signal observation value, which is consistent with process (1) to extract the signal-to-noise ratio at each epoch. , Altitude angle , azimuth information ;

[0070] S3. Separation of direct and reflected signals at low and high elevation angles;

[0071] (1) Based on Figure 5 Comparison of direct and interference signals. The signals are classified into high and low elevation angles with 30° as the boundary. Signals with elevation angles greater than 30° are classified as high elevation angles, and signals with elevation angles less than 30° are classified as low elevation angles.

[0072] (2) Based on Figure 3 GNSS-IR principle diagram. The reflection area is related to the satellite altitude and azimuth. The reflection area is calculated to determine the effective range. In the low elevation area, the altitude range is 15°~30°, and the azimuth is between 60°~155°; in the high elevation area, the altitude range is 30°~60°, and the azimuth is between 35°~160°.

[0073] (3) If Figure 6 As shown, the time series of the original signal-to-noise ratio has an upward and downward trend. According to its time series changes, it is separated into upward and downward segments;

[0074] (4) In accordance with Figure 7 The process shown in the figure, for high elevation angles, according to Figure 4 The direct reflection signal is proportional to the reflection component, and the interference signal-to-noise ratio Direct SNR Directly make the difference, denoted as ;

[0075] ;

[0076] (5) In accordance with Figure 7 The process shown, for low elevation angles, the signal-to-noise ratio The oscillation is obvious. Considering that there is a reflected signal in the direct signal, the direct component at low elevation angles is processed according to the traditional method, that is, the interference signal A second-order polynomial fitting is performed to obtain the interference signal Separate the reflection component , specifically expressed as: ;

[0077] in, are the coefficients of the second-order polynomial;

[0078] S4. Calculate the amplitude and phase characteristic values ​​of the low-elevation and high-elevation reflection components;

[0079] (1) In accordance with Figure 7 The process shown, the vibration frequency of the reflected component SNR f It is linearly related to the height h of the reflecting surface. The frequency is calculated using the Lomb-Scargle transformation method. Figure 8 and Fig.10 They are the spectrum analysis results in the low elevation angle and high elevation angle regions, and then the height of the reflecting surface is calculated, namely: ;

[0080] (2) The high and low elevation angle reflection components are expressed as: ;

[0081] in, f The frequency of solving (1); is the satellite altitude angle, are the phase and amplitude of the reflected signals at low elevation angle and high elevation angle respectively;

[0082] (3) Use the least squares method to fit the reflection component in (2) and solve the optimal amplitude and phase using the following formula: ;

[0083] Fig. 9 , Fig.11 The comparison between the signal-to-noise ratio reflection component and the least square fitting result under low elevation angle and high elevation angle, respectively. The solid line is the signal-to-noise ratio reflection component, and the dotted line is the least square fitting result.

[0084] S5. Model low elevation angle eigenvalues ​​and verify model accuracy for high elevation angle eigenvalues;

[0085] (1) In accordance with Figure 7 The process shown uses the amplitude and phase eigenvalues ​​obtained at low elevation angles and combines them with the measured soil moisture to build the model: ;

[0086] Where a is the model slope, the superscript indicates low elevation angle, and the subscript indicates the eigenvalue; b is the soil moisture base, the superscript indicates low elevation angle, and the subscript indicates the eigenvalue; A is the amplitude observation, the superscript indicates low elevation angle, and the subscript indicates the SNR reflection component; For phase measurement, the superscript indicates low elevation angle, and the subscript indicates the SNR reflection component;V sm is the soil moisture value;

[0087] (2) Traditional GNSS-IR uses a single antenna to receive interference signals. As the elevation angle increases, the interference effect becomes weaker and weaker. Therefore, traditional GNSS-IR is only suitable for processing low elevation angles. The device constructed by the present invention uses an upward-looking antenna and a downward-looking antenna to receive the direct reflection signal respectively, and then collects the interference signal through a combiner. This avoids the situation where the interference effect weakens as the elevation angle increases. Therefore, the amplitude and phase characteristic values ​​of the reflection component can also be extracted at high elevation angles: ;

[0088] in, All are obtained by process (1); A is the amplitude observation, the superscript represents the high elevation angle, and the subscript represents the SNR reflection component; For phase measurement, the superscript indicates the high elevation angle, and the subscript indicates the SNR reflection component; is the estimated value of soil moisture;

[0089] (3) In order to improve the temporal resolution of the data and ensure that its signal-to-noise ratio sequence does not affect the data volume requirements of the spectrum analysis and eigenvalue solution process, the data is grouped by hour. The temporal resolution of the soil moisture meter is 30 minutes. To match the temporal resolution of GNSS, the soil moisture meter takes the hourly average value.

[0090] (4) Compare and analyze the high-elevation amplitude and phase inversion results in (2) with the hourly average results of the soil moisture meter in (3), and verify the accuracy including the correlation coefficient (R) and root mean square error (RMSE) obtained by comparing the inversion results of the data contained in different satellites with the measured data of the soil moisture meter;

[0091] The effectiveness of this method is verified through the GNSS-IR dual-antenna soil moisture monitoring experiment. Fig.12 , Fig.13 and Fig.14 , Fig.15 These are the amplitude and phase eigenvalue estimation results of the B1 and B2a frequency points of BeiDou III. Taking C22 as an example, the original data is realized and the scattered points are estimated data. The amplitude eigenvalue has a high degree of consistency with the true value, with the highest correlation coefficient reaching 0.98 and the optimal root mean square error being 0.007m 3 / m 3. Comparing the amplitude and phase estimation results, the amplitude estimation result has a smaller root mean square error and higher accuracy. The overall situation of the amplitude and phase eigenvalue estimation results of the BDS-3 B1 and B2a frequency points is shown in Tables 1 and 2, where R_A and RMSE_A represent the correlation coefficient and root mean square error between the amplitude eigenvalue estimation result and the original data, respectively; R_p and RMSE_p represent the correlation coefficient and root mean square error between the phase eigenvalue estimation result and the original data, respectively.

[0092] Table 1 BDS S1P soil moisture estimation results

[0093] PRN R_A <![CDATA[RMSE_A(m 3 / m 3 )]]> R_p <![CDATA[RMSE_p(m 3 / m 3 )]]> 19 0.41 0.0129 0.63 0.0232 20 0.19 0.0297 0.57 0.0387 21 0.21 0.0129 0.20 0.0117 22 0.67 0.0078 0.62 0.0467 23 0.51 0.0092 0.61 0.0097 24 0.59 0.0086 0.45 0.0214 25 0.96 0.0035 0.53 0.0229 29 0.81 0.0104 0.77 0.0961 30 0.19 0.0139 0.87 0.0630 32 0.36 0.0169 0.70 0.0445 33 0.56 0.0095 0.87 0.0390 34 0.22 0.0133 0.64 0.0443 36 0.89 0.0056 0.21 0.0256 37 0.71 0.0224 0.58 0.0128 42 0.62 0.0094 0.93 0.0097 43 0.64 0.0088 1.00 0.0044 44 0.24 0.0108 0.62 0.0691 46 0.55 0.0216 0.50 0.0400

[0094] Table 2 BDS S5P soil moisture estimation results

[0095] PRN R_A <![CDATA[RMSE_A(m 3 / m 3 )]]> R_p <![CDATA[RMSE_p(m 3 / m 3 )]]> 19 0.73 0.0069 0.32 0.0142 20 0.24 0.0231 0.90 0.0188 21 0.54 0.0079 0.99 0.0039 22 0.96 0.0074 0.87 0.0443 23 0.14 0.1313 0.81 0.0423 24 0.95 0.0039 0.99 0.0180 25 0.78 0.0101 0.14 0.0125 29 0.55 0.0527 0.08 0.0223 30 0.82 0.0483 0.58 0.0333 32 0.21 0.0282 0.64 0.0416 33 0.52 0.0163 0.39 0.1252 34 0.25 0.0190 0.84 0.0988 36 0.05 0.0174 0.19 0.0701 37 0.97 0.0190 0.30 0.0342 42 0.90 0.0076 0.91 0.0085 43 0.98 0.0053 1.00 0.0260 44 0.84 0.0141 0.66 0.0355 46 0.22 0.0190 0.70 0.0590 ;

[0096] Compared with the GNSS-IR single antenna and the GNSS-R dual antenna, the dual antenna soil moisture monitoring of the present invention is no longer limited to the low elevation angle range and the high elevation angle range, so that the observation area is more comprehensive and the observation area is wide; the GNSS-R dual antenna soil moisture monitoring collects digital intermediate frequency signals with a sampling rate of 62 MHz, A / D quantization of 2 bit real sampling, and a storage space of about 18G for 5 minutes, while the GNSS-IR dual antenna soil moisture monitoring device uses a geodetic receiver to record observations, which are stored in the Rinex format with a sampling rate of 1s. The storage space required for 24-hour collection of multi-system and multi-frequency signals is about 1.4G, which is much smaller than the storage space required for the GNSS-R dual antenna soil moisture monitoring device, and can realize long-term series monitoring.

[0097] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dual-antenna soil moisture monitoring method based on GNSS-IR, characterized in that: include: S1. Build a dual-antenna GNSS-IR soil moisture monitoring device; The device includes: an upward-looking antenna, a downward-looking antenna and two receivers of the same model, wherein the upward-looking antenna is a right-hand circularly polarized antenna, tilted upward by 30 degrees; the downward-looking antenna is a left-hand circularly polarized antenna, tilted downward by 30 degrees; The signal received by the right-hand circularly polarized antenna is divided into two paths by a power divider, one path is connected to receiver 1, and the other path is connected to the left-hand circularly polarized antenna through a combiner and then connected to receiver 2; The TDR soil moisture meters were placed within the measurement area at depths of 5 cm, 10 cm, and 15 cm respectively; The method for detecting using the dual-antenna soil moisture monitoring device comprises the following steps: S2, extracting the direct and interference signal observation values ​​respectively; S3: Separation of direct and reflected signals at low and high elevation angles; the specific implementation process is as follows: (1) Analyze the direct and interference signals according to the actual situation and divide them into high elevation angle and low elevation angle areas; (2) Calculate the reflection area and determine the effective range; (3) High and low elevation angles separate the time series into ascending and descending segments; (4) For the high elevation angle reflection component, it is expressed as , by the interference signal-to-noise ratio Direct SNR Direct difference income; ； (5) Process the direct component at low elevation angles to Perform a second-order polynomial fit to obtain the direct signal component , and then from the interference signal Separate the reflection component ; ； in, are the coefficients of the second-order polynomial; is the satellite altitude angle; S4: Calculate the amplitude and phase eigenvalues ​​of the low-elevation and high-elevation reflection components; S5: Model the low elevation angle eigenvalues ​​and verify the model accuracy of the high elevation angle eigenvalues; the specific implementation process is as follows: (1) The amplitude and phase eigenvalues ​​obtained at low elevation angles are used in combination with the measured soil moisture to construct the model: ； Where a is the model slope, the superscript indicates low elevation angle, and the subscript indicates the eigenvalue; b is the soil moisture base, the superscript indicates low elevation angle, and the subscript indicates the eigenvalue; A is the amplitude observation, the superscript indicates low elevation angle, and the subscript indicates the SNR reflection component; For phase measurement, the superscript indicates low elevation angle, and the subscript indicates the SNR reflection component; V sm is the soil moisture value; (2) Extract the amplitude and phase eigenvalues ​​of the reflected component at high elevation angles: ; in, All are obtained by process (1); A is the amplitude observation, the superscript represents the high elevation angle, and the subscript represents the SNR reflection component; For phase measurement, the superscript indicates the high elevation angle, and the subscript indicates the SNR reflection component; is the estimated value of soil moisture; (3) The soil moisture meter takes the average value per hour; (4) Compare and analyze the high-elevation amplitude and phase inversion results of (2) with the hourly average results of the soil moisture meter in (3), and analyze the statistical data and verify the accuracy, including the correlation coefficient and root mean square error obtained by comparing the inversion results of the data contained in different satellites with the actual measured data of the soil moisture meter, so as to verify the effectiveness and accuracy of the detection.

2. The dual-antenna soil moisture monitoring method based on GNSS-IR according to claim 1 is characterized in that: In step S2, the direct and interference signal observation values ​​are extracted respectively; the specific implementation process is as follows: (1) Receiver 1 stores the direct signal observation values ​​in the Rinex format in the receiver. The signal-to-noise ratio (SNR) of the direct signal at different frequencies at each epoch is extracted from the file. d , according to the recorded direct antenna position, combined with the precise ephemeris file, calculate the corresponding epoch elevation angle , azimuth information; (2) Receiver 2 stores the interference signal observation value, which is consistent with process (1) to extract the signal-to-noise ratio at each epoch. , Altitude angle , azimuth information.

3. The dual-antenna soil moisture monitoring method based on GNSS-IR according to claim 1 is characterized in that: In step S4, the calculation of the amplitude and phase eigenvalues ​​of the low-elevation angle and high-elevation angle reflection components is specifically implemented as follows: (1) The vibration frequency of the reflection component SNR is calculated using the Lomb-Scargle transformation method, that is: ; in, h is the height of the reflecting surface; (2) The high and low elevation angle reflection components are expressed as: ; in, f is the frequency solved in (1); is the satellite altitude angle, are the phase and amplitude of the reflected signals at low elevation angles and high elevation angles, respectively; (3) Use the least squares method to fit the reflection component in (2) and solve the optimal amplitude and phase.

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