CN115494086A - GNSS-IR soil humidity monitoring method considering abnormal interference phase - Google Patents

GNSS-IR soil humidity monitoring method considering abnormal interference phase Download PDF

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CN115494086A
CN115494086A CN202211064040.3A CN202211064040A CN115494086A CN 115494086 A CN115494086 A CN 115494086A CN 202211064040 A CN202211064040 A CN 202211064040A CN 115494086 A CN115494086 A CN 115494086A
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梁月吉
赖建民
任超
卢献健
刘银涛
晏红波
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Abstract

The invention relates to the technical field of soil humidity monitoring, in particular to a GNSS-IR soil humidity monitoring method considering abnormal interference phases, which effectively decomposes different frequency characteristic components implied in SNR through wavelet transformation, and the separated SNR trend item is more consistent with the integral trend of the SNR; meanwhile, for low-quality SNR data, the original interference phase of the satellite obtained by NLLS fitting is easy to generate abnormal jumping, the quality of the interference phase can be further improved by detecting and repairing the abnormal interference phase existing in each satellite by using IQR and MAF, the continuity of the interference phase on a time sequence is ensured, and more available effective satellites are provided for multi-satellite combination; interference phases of different satellites represent earth surface soil humidity information of different directions near a measuring point, and the constructed MLSR soil humidity monitoring model can fully combine soil humidity information reflected by the satellites of different directions, so that higher precision is realized, and the MLSR soil humidity monitoring model is suitable for soil humidity monitoring of different time lengths.

Description

GNSS-IR soil humidity monitoring method considering abnormal interference phase
Technical Field
The invention relates to the technical field of soil humidity monitoring, in particular to a GNSS-IR soil humidity monitoring method considering abnormal interference phases.
Background
The soil humidity is a key geophysical index for predicting flood, drought and climate change and optimizing agricultural management, and scientific and accurate acquisition of the soil water content has important significance in the fields of hydrology, meteorology, agriculture and the like. In the traditional soil humidity measuring method, the defects of small application range, high cost, difficult realization of full-remote automatic monitoring and the like exist. In recent years, the monitoring of ground object parameters by using reflected GNSS signals as environmental data sources has become a new type of microwave remote sensing means, and this technology is called global navigation satellite system interferometric reflectometry (GNSS-IR). The device has the advantages of all weather, all-time, multiple signal sources, wide coverage and the like, and realizes effective monitoring of parameters such as vegetation coverage, soil humidity, sea surface height, snow thickness and the like.
However, the existing algorithms or models are difficult to realize the visualization of model parameters, and signal to noise ratio (SNR) data recorded by a measurement-type GNSS signal receiver often contains complex noise information, such as receiver noise, earth surface vegetation noise and other information, which can make the interference phase of each satellite easily generate abnormal jump in the resolving process.
Disclosure of Invention
The invention aims to provide a GNSS-IR soil humidity monitoring method considering abnormal interference phases, which aims to detect and repair the abnormal interference phases existing in each satellite, effectively improve the quality of the satellite interference phases and realize higher-precision soil humidity monitoring.
In order to achieve the above object, the present invention provides a GNSS-IR soil humidity monitoring method considering abnormal interference phase, comprising the following steps:
GNSS data and soil humidity are obtained;
GNSS data preprocessing;
separating satellite reflection signals;
detecting and repairing abnormal interference phases;
and constructing a multi-satellite linear regression soil humidity monitoring model to realize soil humidity monitoring.
In the process of acquiring GNSS data and soil humidity, soil humidity monitoring points are randomly selected, GNSS original observation data are received through a geodetic GNSS receiver, soil humidity acquisition probes are distributed around a station, soil humidity of each day is monitored in real time, and the depth is 0-5 cm.
The GNSS data preprocessing process specifically comprises the steps of extracting signal-to-noise ratio, altitude angle and azimuth angle information of all satellites from the GNSS data, and determining an effective soil humidity monitoring area according to the Huygens-Fresnel principle.
The process of separating the satellite reflection signals is specifically to decompose the signal-to-noise ratio of each satellite in a multi-scale manner through wavelet transformation, perform denoising processing on high-frequency characteristic components, and remove low-frequency characteristic components to obtain satellite reflection signals with better quality.
The process of detecting and repairing the abnormal interference phase comprises the following steps:
through reasonably setting the satellite altitude angle, the satellite reflected signal is resampled and converted into a sine relation with the satellite low altitude angle;
fitting the reflection signals by adopting a nonlinear least square method to obtain interference phases and amplitudes corresponding to the satellites, wherein the fitting formula is as follows:
Figure BDA0003827080970000021
wherein the SNR m Is the reflected signal of the satellite, lambda is the carrier wavelength, H is the vertical distance between the center of the interference phase of the GNSS antenna and the horizontal reflecting surface, theta is the satellite altitude, A m And
Figure BDA0003827080970000022
amplitude and interference phase of the reflected component, respectively;
and detecting and repairing the abnormal interference phase existing in each satellite by adopting a four-quadrant distance and moving average filtering algorithm according to the fitted original interference phase to obtain more accurate interference phases of each satellite.
Specifically, according to the correlation coefficient among the interference phases of each satellite, setting a correlation coefficient threshold value to select the satellite, and constructing the multi-satellite linear regression soil humidity monitoring model.
In the GNSS data preprocessing process, mirror reflection points and effective reflection areas of all GNSS satellites in different observation time periods in different directions are calculated according to a Huygens-Fresnel principle, fresnel reflection area ellipses of rising or falling arc sections of all the GNSS satellites are drawn, the effective monitoring range of soil humidity is determined, and the consistency of media in the monitoring area is guaranteed.
Wherein, the multipath effect of the signal-to-noise ratio of the satellite is expressed as:
Figure BDA0003827080970000023
wherein, A d And A m The amplitudes of the direct component and the reflected component are respectively, and psi is the interference phase difference between the 2 components.
In the process of constructing the multi-satellite linear regression soil humidity monitoring model to realize soil humidity monitoring, different methods are set to construct the multi-satellite linear regression soil humidity monitoring model, 2/3 of data is used as a training set of the model, and 1/3 of data is used as a test set of the model.
The invention provides a GNSS-IR soil humidity monitoring method considering abnormal interference phases, which effectively decomposes different frequency characteristic components implied in SNR through wavelet transformation, and the separated SNR trend item is more consistent with the integral trend of the SNR; meanwhile, for low-quality SNR data, the phenomenon that the original interference phase of the satellite obtained by fitting through a nonlinear least square method is easy to generate abnormal jump is avoided, the quality of the interference phase can be further improved by detecting and repairing the abnormal interference phase existing in each satellite through the four-quadrant distance and the moving average filtering, the continuity of the interference phase on a time sequence is ensured, and more available effective satellites are provided for multi-satellite combination; interference phases of different satellites represent earth surface soil humidity information of different directions near a measuring point, a multi-satellite linear regression soil humidity monitoring model is constructed, the soil humidity information reflected by the satellites in different directions can be fully combined, higher precision is achieved, and the method is suitable for soil humidity monitoring of different time lengths.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a GNSS-IR soil moisture monitoring method with consideration of abnormal interference phases according to the present invention.
FIG. 2 is a flowchart illustrating an embodiment of the present invention.
FIG. 3 is a schematic representation of the Fresnel reflection region (5-20) of a GNSS satellite according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a geometric model of multipath effect in an embodiment of the present invention.
Fig. 5 is a diagram illustrating SNR versus satellite altitude in an embodiment of the present invention.
Fig. 6 is a diagram illustrating the results of the wavelet decomposition 6 layers of low and high frequency components in a specific embodiment of the present invention.
Fig. 7 is a comparison diagram of SNR trend terms of wavelet transform and low-order polynomial reconstruction in an embodiment of the present invention.
FIG. 8 is a graph illustrating a linear regression analysis of the original interference phase versus soil moisture in an embodiment of the present invention.
Fig. 9 is a diagram illustrating the monitoring results of the MLSR model in an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The terms used in the present invention and the corresponding explanations are as follows:
global navigation satellite system interferometric reflectometry (GNSS-IR);
an interquartile range (IQR);
moving Average Filter (MAF);
non-linear least squares (NLLS);
least Squares Estimation (LSE);
multi-satellite linear regression (MSLR).
Referring to fig. 1, the present invention provides a GNSS-IR soil moisture monitoring method considering abnormal interference phase, including the following steps:
s1: GNSS data and soil humidity are obtained;
s2: GNSS data preprocessing;
s3: separating satellite reflection signals;
s4: detecting and repairing abnormal interference phases;
s5: and constructing a multi-satellite linear regression soil humidity monitoring model to realize soil humidity monitoring.
Further, the present invention will be described in detail with reference to specific embodiments and implementation process steps:
referring to fig. 2, fig. 2 shows specific steps of an implementation process.
S1: and acquiring GNSS data and soil humidity. And randomly selecting soil humidity monitoring points, receiving GNSS original observation data through a geodetic GNSS receiver, distributing soil humidity acquisition probes around the stations, and monitoring the soil humidity at the depth of 0-5cm every day in real time.
S2: GNSS data preprocessing;
converting original observation data acquired by a GNSS receiver into an observation file (O file) and a hybrid navigation file (P file) through ToRinex4 format conversion software; and extracting SNR, elevation angle, azimuth angle and other information of all satellites by using RTKLIB or TEQC software. Meanwhile, according to the Huygens-Fresnel principle, calculating mirror reflection points and effective reflection areas of all GNSS satellites in different observation periods in different directions, and drawing Fresnel reflection area ellipses of ascending or descending arc sections of each GNSS satellite. The size and direction of a First Fresnel Zone (FFZ) are related to the height of a receiver antenna, the carrier wavelength, the satellite altitude angle and the azimuth angle, and the calculation method comprises the following steps:
Figure BDA0003827080970000051
wherein R is F And L F Respectively, a short half shaft and a long half shaft of the FFZ, lambda is the carrier wavelength, h is the antenna height, and theta is the satellite height angle. It can be seen that the lower the satellite altitude angle, the greater the FFZ. According to the major and minor semi-axes of the FFZ ellipse, the area of the reflection region can be calculated:
Figure BDA0003827080970000052
when h is 1.8m and theta is 5 degrees, the FFZ elliptical area of the GPS L2 carrier satellite orbit is 181.8m 2 (different systems and frequency bands correspond to different wavelengths, and the obtained FFZ elliptical areas are slightly different). When all GNSS satellite orbits are combined together, the total sensing area of the receiver is about 6000m 2 As shown in fig. 3. The effective monitoring range of the soil humidity can be determined through calculation of the FFZ, the consistency of media in a monitoring area is guaranteed, and certain basis is provided for area planning, arrangement of soil humidity acquisition probes and implementation of better selection of a multi-satellite combination in the S1 step.
S3: separating satellite reflection signals;
FIG. 3 is a geometric model diagram of multipath effects resulting from the receiver receiving direct and reflected signals from GNSS satellites. Specifically, the multipath effect in SNR can be expressed as:
Figure BDA0003827080970000053
wherein, A d And A m The amplitudes of the direct component and the reflected component are respectively, and psi is the interference phase difference between the 2 components. According to fig. 4, fig. 5 and the above formula, in the present embodiment, SNR of each satellite is decomposed in a multi-scale manner by coif5 wavelet, denoising processing is performed on high-frequency characteristic components, and low-frequency characteristic components are removed, so as to obtain satellite reflection signals with better quality. The SNR data for each GNSS satellite is given by the following equation: s (t) = [ S = 1 ，S 2 ，…，S t ](t =1,2, \8230;, k), where t represents the observation epoch and k represents the number of observation epochs. At resolution 2 -(I+1) Then, wavelet decomposition is carried out on S (t), the decomposition level number is I, and a low-frequency characteristic component c is obtained I And a set of high-frequency characteristic components d at different scales r (r =1,2, \8230;, I). Then, S (t) can be expressed as:
Figure BDA0003827080970000054
wherein, c I And d r Representing the direct and reflected signals of the satellite, respectively. The results of decomposing the low and high frequency components of the 6 layers of the SNR sequence using the coif5 wavelet are shown in fig. 6. The original SNR data is subtracted from the reconstructed SNR trend term (fig. 7) to obtain the satellite reflection signal.
S4: detecting and repairing abnormal interference phases;
through reasonably setting the satellite altitude angle, the satellite reflected signal is resampled and converted into a sine relation with the satellite low altitude angle; fitting the reflection signals by a non-linear least square (NLLS) method to obtain characteristic parameters (interference phase and amplitude) corresponding to each satellite, wherein the fitting formula is as follows:
Figure BDA0003827080970000061
wherein the SNR m Is the reflected signal of the satellite, λ is the carrier wavelength, H is in the interference phase of the GNSS antennaThe vertical distance between the center and the horizontal reflecting surface, theta is the satellite altitude, A m And
Figure BDA0003827080970000062
respectively the amplitude and the interference phase of the reflected component. According to the linear regression analysis (fig. 8) performed on the original interference phase and the soil moisture reference value fitted by the above formula, it can be seen that the original interference phase of the satellite has an abnormal jump phenomenon, which is mainly due to the low quality SNR data causing NLLS fitting abnormality. Therefore, the IQR is used to detect the presence of an anomalous interference phase for each satellite. In descriptive statistics, IQR is the range between the first and third quartile, which is a measure of statistical dispersion, and Q is used for each 1 And Q 2 And (4) showing. Q 1 And Q 2 Representing the median values of the upper and lower halves of the data set, respectively, in descending order, the formula for the IQR range is expressed as: iqr = Q 2 -Q 1 . Under the IQR criterion, the detection interval of the abnormal interference phase is as follows:
[Q 1 -1.5·IQR，Q 2 +1.5·IQR]
and smoothing the abnormal interference phase detected by the IQR criterion by using the MAF, repairing the abnormal interference phase and obtaining more accurate interference phase of each satellite.
S5: and constructing a multi-satellite linear regression soil humidity monitoring model to realize soil humidity monitoring.
And setting a correlation coefficient threshold value according to the correlation coefficient between the interference phases of the satellites to select the satellites, constructing an MLSR soil humidity monitoring model and realizing high-precision soil humidity monitoring. The GNSS multi-satellite interferometric phase set (x) after detection and repair can be represented as:
Figure BDA0003827080970000063
wherein the content of the first and second substances,
Figure BDA0003827080970000064
and
Figure BDA0003827080970000065
the interference phase sets of the GPS, BDS and GLONASS systems are respectively represented, i, j and k respectively correspond to the numbers of the satellites of the systems, and m represents the length of the interference phase sequence of the GNSS satellite. Interference phase set combined by GNSS multiple satellites
Figure BDA0003827080970000066
Figure BDA0003827080970000067
As an input sample of MLSR model training, the output sample corresponding to soil humidity is y s ；
Figure BDA0003827080970000068
As input samples for MLSR model testing. The MLSR model is expressed as follows:
Figure BDA0003827080970000071
wherein, t 1 Representing the length of the model, o representing the number of GNSS satellites participating in the model, α 0 ，α 1 ，…，α o Is the regression coefficient of the model, ε s Is a random error. Converting the above equation into a matrix expression:
Figure BDA0003827080970000072
wherein Y is a soil humidity vector, X is an interference phase vector, A is a parameter vector to be solved, and epsilon is a random error vector. The MLSR model can be expressed as:
Y＝XA+ε
the Least Square Estimation (LSE) is carried out on the regression coefficient, and the regression coefficient is obtained:
A LSE ＝(X T X) -1 X T Y
finally, will
Figure BDA0003827080970000073
And inputting the result into a trained model to obtain an accurate result of the GNSS-IR soil humidity monitoring.
The monitoring results are schematically shown in fig. 9.
In conclusion, the invention provides the GNSS-IR soil humidity monitoring method considering abnormal interference phase detection and repair, the method can detect and repair the abnormal interference phases existing in each satellite through a four-quadrant distance and moving average filtering algorithm, and the quality of the satellite interference phases is effectively improved. Meanwhile, a multi-satellite linear regression soil humidity monitoring model is constructed according to the multiple linear regression principle, and ground soil humidity information reflected by satellites in different directions is effectively combined to realize higher-precision soil humidity monitoring.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

Claims (9)

1. A GNSS-IR soil moisture monitoring method considering abnormal interference phase is characterized by comprising the following steps:
GNSS data and soil humidity are obtained;
GNSS data preprocessing;
separating satellite reflection signals;
detecting and repairing abnormal interference phases;
and constructing a multi-satellite linear regression soil humidity monitoring model to realize soil humidity monitoring.
2. The GNSS-IR soil moisture monitoring method accounting for abnormal interferometric phases of claim 1,
in the process of acquiring the GNSS data and the soil humidity, a soil humidity monitoring point is randomly selected, the GNSS original observation data are received through a geodetic GNSS receiver, soil humidity acquisition probes are distributed around a measuring station, the soil humidity of each day is monitored in real time, and the depth is 0-5 cm.
3. The GNSS-IR soil moisture monitoring method considering abnormal interferometric phases according to claim 1,
the GNSS data preprocessing process specifically comprises the steps of extracting signal-to-noise ratio, altitude angle and azimuth angle information of all satellites from the GNSS data, and determining an effective monitoring area of soil humidity according to a Huygens-Fresnel principle.
4. The GNSS-IR soil moisture monitoring method accounting for abnormal interferometric phases of claim 1,
the process of satellite reflection signal separation specifically includes decomposing signal-to-noise ratio of each satellite in a multi-scale mode through wavelet transformation, denoising high-frequency characteristic components, and removing low-frequency characteristic components to obtain satellite reflection signals with good quality.
5. The GNSS-IR soil moisture monitoring method accounting for abnormal interferometric phases of claim 1,
a process for detecting and repairing an anomalous interference phase comprising the steps of:
the satellite height angle is reasonably set, and the satellite reflection signal is resampled and converted into a sine relation with the satellite low height angle;
fitting the reflection signals by adopting a nonlinear least square method to obtain interference phases and amplitudes corresponding to the satellites, wherein the fitting formula is as follows:
Figure FDA0003827080960000021
wherein the SNR m Is the reflected signal of the satellite, lambda is the carrier wavelength, H is the vertical distance between the center of the interference phase of the GNSS antenna and the horizontal reflecting surface, theta is the satellite altitude, A m And
Figure FDA0003827080960000022
amplitude and interference phase of the reflected component, respectivelyA bit;
and detecting and repairing the abnormal interference phase existing in each satellite by adopting a four-quadrant distance and moving average filtering algorithm according to the fitted original interference phase to obtain more accurate interference phases of each satellite.
6. The GNSS-IR soil moisture monitoring method accounting for abnormal interferometric phases of claim 1,
the method comprises the steps of establishing a multi-satellite linear regression soil humidity monitoring model, specifically, setting a correlation coefficient threshold value according to correlation coefficients among interference phases of all satellites, selecting satellites, and establishing the multi-satellite linear regression soil humidity monitoring model.
7. The GNSS-IR soil moisture monitoring method considering abnormal interferometric phases according to claim 1,
in the GNSS data preprocessing process, mirror reflection points and effective reflection areas of GNSS satellites in different directions in different observation periods are calculated according to a Huygens-Fresnel principle, fresnel reflection area ellipses of ascending or descending arc sections of each GNSS satellite are drawn, the effective monitoring range of soil humidity is determined, and the consistency of media in the monitoring area is guaranteed.
8. The GNSS-IR soil moisture monitoring method considering abnormal interferometric phases according to claim 4,
the multipath effect of the signal-to-noise ratio of a satellite is expressed as:
Figure FDA0003827080960000023
wherein, A d And A m The amplitudes of the direct component and the reflected component are respectively, and psi is the interference phase difference between the 2 components.
9. The GNSS-IR soil moisture monitoring method accounting for abnormal interferometric phases of claim 1,
in the process of constructing the multi-satellite linear regression soil humidity monitoring model to realize soil humidity monitoring, different methods are set to construct the multi-satellite linear regression soil humidity monitoring model, 2/3 of data is used as a training set of the model, and 1/3 of data is used as a test set of the model.
CN202211064040.3A 2022-09-01 2022-09-01 GNSS-IR soil humidity monitoring method considering abnormal interference phase Pending CN115494086A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115980317A (en) * 2023-03-20 2023-04-18 中国科学院地理科学与资源研究所 Foundation GNSS-R data soil moisture estimation method based on corrected phase
CN117571968A (en) * 2024-01-12 2024-02-20 山东大学 GNSS-IR-based soil humidity calculation method

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115980317A (en) * 2023-03-20 2023-04-18 中国科学院地理科学与资源研究所 Foundation GNSS-R data soil moisture estimation method based on corrected phase
CN117571968A (en) * 2024-01-12 2024-02-20 山东大学 GNSS-IR-based soil humidity calculation method
CN117571968B (en) * 2024-01-12 2024-04-05 山东大学 GNSS-IR-based soil humidity calculation method

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