CN115048952A - GNSS-IR soil humidity inversion method integrating robust estimation and machine learning - Google Patents
GNSS-IR soil humidity inversion method integrating robust estimation and machine learning Download PDFInfo
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Abstract
The invention discloses a GNSS-IR soil humidity inversion method fusing robust estimation and machine learning, relates to the technical field, and particularly relates to a GNSS-IR soil humidity inversion method fusing robust estimation and machine learning, which comprises the following steps: s1, extracting the signal-to-noise ratio and the altitude angle data of the GNSS satellite data; s2, intercepting the signal-to-noise ratio data of a specific altitude angle, fitting the direct signal by adopting low-order polynomial fitting, removing the direct signal, and extracting the signal-to-noise ratio component of the reflected signal; s3, processing the SNR observed value of the reflected signal by a Lomb-Scargle spectrum analysis method, wherein the frequency corresponding to the maximum value of the power spectrum amplitude of the spectrogram is the oscillation frequency of the reflected signal component, and calculating the effective antenna height; and S4, performing cosine fitting through a nonlinear least square algorithm to obtain the amplitude and the phase of the characteristic parameters of the reflected signal. The method can effectively reduce the influence of the quality of signal-to-noise ratio observation data and complex surface environment factors, fully utilize the information of the reflected signals and improve the soil humidity inversion accuracy of the GNSS-IR.
Description
Technical Field
The invention relates to an inversion method, in particular to a GNSS-IR soil humidity inversion method integrating robust estimation and machine learning, and belongs to the technical field of satellite remote sensing.
Background
Soil is an important homeland resource. The soil humidity plays an important role in the aspects of researching ecological water circulation, vegetation water supply, land bearing capacity and the like, so that the timely and accurate monitoring of soil humidity information is very necessary. The traditional soil humidity monitoring means such as a soil hygrometer method has accurate results but consumes a large amount of manpower and material resources, and the measurement period is long; although the satellite remote sensing observation mode can overcome the defect of low spatial resolution of the traditional soil humidity monitoring means, the time resolution is not ideal. Therefore, the GNSS-IR technology based on the satellite interference signal gradually becomes a new technology for monitoring soil humidity by virtue of its high time resolution and high spatial resolution and draws much attention.
GNSS-IR techniques use Right Hand Circular Polarization (RHCP) antennas to receive SNR data to invert surface physical parameters. Bilich and the like research a correlation algorithm and a flow for carrying out soil humidity inversion by utilizing a signal-to-noise ratio observation value by utilizing signal-to-noise ratio observation data and meteorological observation data recorded by a GPS receiver, indicate that the frequency of the signal-to-noise ratio is related to the height change of a relative antenna, and the amplitude and the phase are related to the soil humidity change, and can monitor the soil humidity in a large range through a GPS observation station network. Bilich et al verified that strong correlation exists between multipath interference phase, amplitude, and effective height obtained by frequency calculation and soil humidity by using signal-to-noise ratio observation data of a continental US plate boundary observation network and actually measured soil humidity data. Larson indicates that the amplitude of the multipath signal is related to vegetation moisture content or height. Chew states that GPS multipath signal indicators show good agreement with field observations of vegetation height and vegetation moisture content. The vegetation water content and the soil humidity act on GNSS signals together, and the independent inversion of the vegetation water content or the soil humidity is not strict and inaccurate. However, the existing soil humidity inversion research about GNSS-IR mostly focuses on quantifying the correlation between a soil humidity value and a certain reflection signal characteristic parameter, linear modeling is carried out by combining a reference value of soil humidity, and the influence of observation errors caused by factors such as reflection surface information carried by the other two characteristic parameters and the surrounding environment of a survey station is ignored. In addition, the environment of the surface reflection surface is often very complex, and factors such as the roughness of the soil surface, the vegetation coverage condition, the surface temperature and the like influence each other and are reflected on the change of the soil humidity value. The surface environmental factors are difficult to comprehensively consider, a complex nonlinear problem is formed, and the establishment of a refined soil humidity inversion model through the formula derivation and quantification of the correlation is particularly difficult. In summary, in the aspect of the research of the GNSS-IR soil humidity inversion algorithm, the conventional GNSS-IR soil humidity inversion model is established based on the characteristic parameters of the single reflection signal, and is influenced by the quality of the signal-to-noise ratio observation data and the factors of the complex earth surface environment, the characteristic parameters of the modeling variable reflection signal inevitably have abnormal values, and the linear model is difficult to comprehensively and quantitatively analyze the relationship among the factors, so that the model has poor fitting effect and weak generalization capability
Therefore, in order to more effectively, accurately and timely invert soil humidity information by utilizing a GNSS-IR technology, fully consider the influence of signal-to-noise ratio observation data quality and complex earth surface environment factors, and fully utilize reflecting surface information carried by multi-reflection signal characteristic parameters, a GNSS-IR soil humidity inversion method for controlling data quality and comprehensively utilizing the multi-reflection signal characteristic parameters is urgently needed.
Disclosure of Invention
Aiming at the problems in the prior art, on the basis of removing abnormal values of characteristic parameters of a reflection signal by using a steady estimation MCD, three machine learning algorithms of a back propagation neural network, Gaussian process regression and random forest are introduced, a soil humidity inversion nonlinear model based on GNSS-IR is respectively established by comprehensively considering the frequency, the amplitude and the phase of the reflection signal, and the soil humidity is more effectively and accurately inverted.
In order to achieve the purpose, the invention provides a GNSS-IR soil moisture inversion method integrating robust estimation and machine learning, which comprises the following steps:
s1, extracting the signal-to-noise ratio and the altitude angle data of the GNSS satellite data;
s2, intercepting the signal-to-noise ratio data of a specific altitude angle, fitting the direct signal by adopting low-order polynomial fitting, removing the direct signal, and extracting the signal-to-noise ratio component of the reflected signal;
s3, processing the SNR observed value of the reflected signal by a Lomb-Scargle spectrum analysis method, wherein the frequency corresponding to the maximum value of the power spectrum amplitude of the spectrogram is the oscillation frequency of the reflected signal component, and calculating the effective antenna height;
s4, performing cosine fitting through a nonlinear least square algorithm to obtain the amplitude and the phase of the characteristic parameters of the reflected signal;
s5, respectively adopting MCD robust estimation to the frequency, amplitude and phase time sequence of the generated reflection signal characteristic parameters to carry out robust processing to form a new reflection signal characteristic parameter time sequence, and forming a data set with the soil humidity value;
s6, dividing the data set into a training set and a prediction set, respectively substituting the training set into a BP neural network, a Gaussian process regression and a random forest to model, and comparing the accuracy of the three machine learning algorithms through the prediction set;
and S7, using the accurate model for predicting the soil humidity value.
The invention provides a product quality detection device for radar part production, which has the following beneficial effects:
the method introduces an abnormal value detection method based on MCD steady estimation to preprocess the time sequence of the characteristic parameters of the reflection signals, establishes a nonlinear mapping relation between the characteristic parameters of the GNSS reflection signals and the soil humidity value on the basis, introduces various machine learning methods to fully consider the influence of each characteristic parameter to carry out soil humidity inversion modeling, and effectively improves the accuracy and generalization capability of a soil humidity inversion model.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention;
FIG. 3 is a comparison of the inversion results of the present invention and the inversion results of the conventional multiple linear regression model.
Detailed description of the preferred embodiment
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1
As shown in fig. 1, the GNSS-IR soil humidity inversion method integrating robust estimation and machine learning of the present invention specifically includes the following steps:
s1, extracting the signal-to-noise ratio and the altitude angle data of the GNSS satellite data;
s2, intercepting the signal-to-noise ratio data of a specific altitude angle, fitting the direct signal by adopting low-order polynomial fitting, removing the direct signal, and extracting the signal-to-noise ratio component of the reflected signal;
s3, processing the SNR observed value of the reflected signal by a Lomb-Scargle spectrum analysis method, wherein the frequency corresponding to the maximum value of the power spectrum amplitude of the spectrogram is the oscillation frequency of the reflected signal component, and calculating the effective antenna height;
s4, performing cosine fitting through a nonlinear least square algorithm to obtain the amplitude and the phase of the characteristic parameters of the reflected signal;
s5, respectively adopting MCD robust estimation to the frequency, amplitude and phase time sequence of the generated reflection signal characteristic parameters to carry out robust processing to form a new reflection signal characteristic parameter time sequence, and forming a data set with the soil humidity value;
s6, dividing the data set into a training set and a prediction set, respectively substituting the training set into a BP neural network, a Gaussian process regression and a random forest to model, and comparing the accuracy of the three machine learning algorithms through the prediction set;
and S7, using the accurate model for predicting the soil humidity value.
Example 2
As shown in fig. 2, the method for GNSS-IR soil humidity inversion integrating robust estimation and machine learning of the present invention specifically includes the following steps in soil humidity inversion of a BDS navigation system:
and S1, acquiring BDS satellite data. Importing observation data received by a receiver into TEQC software for data preprocessing, acquiring an SNR (signal to noise ratio) observation value, a satellite altitude angle and the like, and providing a data source for the next characteristic parameter extraction;
s2, intercepting signal-to-noise ratio data of a specific altitude angle (5 degrees to 25 degrees), fitting the direct signal by adopting low-order polynomial fitting, removing the direct signal, and extracting a signal-to-noise ratio component of the reflected signal;
s3, the SNR observed value after the direct signal is removed and sin theta can be expressed by a cosine function with fixed oscillation frequency, resampling is needed, and the relation between the SNR observed value after the direct signal is removed and sin theta is obtained, as shown in formula (1). Processing the SNR observed value of the reflection signal by a Lomb-Scargle spectrum analysis method, wherein the frequency corresponding to the maximum value of the power spectrum amplitude of the spectrogram is the oscillation frequency of the reflection signal component, and calculating the effective antenna height;
where is SNR m For the signal-to-noise ratio of the reflected signal, A m As an amplitude parameter of the reflected signal,
is the phase parameter of the reflected signal.
S4, performing cosine fitting on the formula (1) through a nonlinear least square algorithm to obtain the amplitude and the phase of the characteristic parameters of the reflected signal;
s5, respectively adopting MCD robust estimation to carry out robust estimation on the frequency, amplitude and phase time sequence of the generated reflection signal characteristic parameters to form a new reflection signal characteristic parameter time sequence, and forming a data set with the soil humidity value;
s6, dividing the data set into a training set and a prediction set, respectively substituting the training set into three machine learning algorithms of BP neural network, Gaussian process regression and random forest for modeling, and comparing the precision of the three machine learning algorithms through the prediction set;
and S7, using the accurate model for predicting the soil humidity value.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (3)
1. A GNSS-IR soil moisture inversion method integrating robust estimation and machine learning is characterized by comprising the following steps:
s1, extracting the signal-to-noise ratio and the altitude angle data of the GNSS satellite data;
s2, intercepting the signal-to-noise ratio data of a specific altitude angle, fitting the direct signal by adopting low-order polynomial fitting, removing the direct signal, and extracting the signal-to-noise ratio component of the reflected signal;
s3, processing the SNR observed value of the reflected signal by a Lomb-Scargle spectrum analysis method, wherein the frequency corresponding to the maximum value of the power spectrum amplitude of the spectrogram is the oscillation frequency of the reflected signal component, and calculating the effective antenna height;
s4, performing cosine fitting through a nonlinear least square algorithm to obtain the amplitude and the phase of the characteristic parameters of the reflected signal;
s5, respectively adopting MCD robust estimation to carry out robust estimation on the frequency, amplitude and phase time sequence of the generated reflection signal characteristic parameters to form a new reflection signal characteristic parameter time sequence, and forming a data set with the soil humidity value;
s6, dividing the data set into a training set and a prediction set, respectively substituting the training set into a BP neural network, a Gaussian process regression and a random forest to model, and comparing the accuracy of the three machine learning algorithms through the prediction set;
and S7, using the accurate model for predicting the soil humidity value.
2. The GNSS-IR soil moisture inversion method with robust estimation and machine learning combined as claimed in claim 1, wherein the step S5 employs an anti-aliasing method to denoise the time series of 3 feature parameters.
3. The GNSS-IR soil moisture inversion method with robust estimation and machine learning combined as claimed in claim 1 or 2, wherein the step S6 is performed by performing multivariate nonlinear regression modeling by using 3 characteristic parameters as variables and substituting them into machine learning algorithm.
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Cited By (3)
| 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 |
| CN116304524A (en) * | 2022-12-20 | 2023-06-23 | 宁夏回族自治区气象科学研究所 | Soil water content monitoring method, equipment, storage medium and device |
| CN117571968A (en) * | 2024-01-12 | 2024-02-20 | 山东大学 | GNSS-IR-based soil humidity calculation method |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116304524A (en) * | 2022-12-20 | 2023-06-23 | 宁夏回族自治区气象科学研究所 | Soil water content monitoring method, equipment, storage medium and device |
| CN116304524B (en) * | 2022-12-20 | 2024-04-09 | 宁夏回族自治区气象科学研究所 | Soil water content monitoring method, equipment, storage medium and device |
| 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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