Method for retrieving sea surface wind vector from optimal fitting of shore-based gnss-r sea wave spectrum

Abstract

This invention discloses a method for sea surface wind vector inversion based on optimal fitting of the wave spectrum of a shore-based GNSS-R system. Relating to the field of microwave remote sensing technology, it addresses the technical problems of existing nearshore GNSS-R technologies, such as ineffective wind direction inversion, difficulty in signal separation, and lack of a synchronous inversion mechanism. The method includes: acquiring direct and reflected signals from a shore-based receiver; separating specular reflection and multi-order Bragg scattering spectral components using the MUSIC algorithm combined with iterative Gaussian fitting; extracting spectral width features from the specular reflection spectral components and extracting the positive-to-negative peak ratio and normalized Doppler frequency ratio features from the Bragg scattering spectral components; constructing a wind speed cost function based on the spectral width features and minimizing this function to invert wind speed; and constructing a wind direction cost function based on the two features and minimizing this function to invert wind direction. This invention enables synchronous wind speed and direction inversion on a shore-based platform, requires no external auxiliary data, effectively extracts weak Bragg scattering components, and exhibits high nearshore inversion accuracy and stability.

Description

Technical Field

[0001] This invention relates to the field of microwave remote sensing technology, and in particular to a method for sea surface wind vector inversion based on optimal fitting of shore-based GNSS-R wave spectrum. Background Technology

[0002] Sea surface wind fields are key environmental parameters in physical oceanography, marine meteorological forecasting, and marine disaster early warning. Traditional methods for monitoring sea surface wind fields mainly include buoys, scatterometers, and synthetic aperture radar, but these methods suffer from limited coverage, long revisit cycles, or high costs. Global Navigation Satellite System (GNSS) reflectance measurement technology, as an emerging opportunistic microwave remote sensing technology, utilizes the reflectance signals from navigation satellites such as GPS and BeiDou to retrieve physical parameters of the Earth's surface. It offers significant advantages such as all-weather operation, low cost, no need for dedicated transmitters, and high spatiotemporal resolution.

[0003] In nearshore GNSS-R observations, receivers are typically deployed on the coast or offshore platforms. Traditional nearshore GNSS-R wind field inversion methods are mainly based on the specular reflection assumption, which assumes that the received signal energy propagates primarily through the specular reflection mechanism of the sea surface. These methods usually utilize Kirchhoff's approximation or geometrical optics approximation for modeling, focusing on the signal-to-noise ratio or power waveform characteristics of the reflected signal. However, physical mechanisms show that the specular reflection component is primarily sensitive to the root-mean-square slope of the sea surface, which depends mainly on wind speed and is extremely insensitive to wave direction distribution. Therefore, while nearshore GNSS-R technology based on the traditional specular reflection mechanism can invert wind speed relatively well, it has inherent physical limitations in wind direction inversion, making it difficult to meet the requirements of high-precision vector wind field monitoring.

[0004] To address the challenge of wind direction retrieval, some research has begun to focus on non-reflective scattering mechanisms (such as Bragg scattering), as non-reflective scattering signals are more sensitive to changes in wind direction. However, existing related techniques still suffer from the following significant drawbacks: First, signal separation is difficult and the signal-to-noise ratio is low: Under near-shore observation geometry, strong specular reflection signals and weak non-spectral scattering signals often overlap significantly in both the time and spatial domains. Some existing non-spectral observation methods have to employ specific off-axis observation geometry to avoid specular reflection in order to obtain wind direction information. This directly leads to a significant reduction in the signal-to-noise ratio of the received signal, severely affecting the stability and accuracy of the inversion.

[0005] Second, there is a lack of a synchronous inversion mechanism: most existing studies independently verify the sensitivity of non-mirror signals to wind direction, and there is a lack of a synchronous inversion method that can utilize both specular reflection signals and non-mirror scattering signals under the same receiving architecture.

[0006] Third, there is a lack of experimental verification and physical model support: many studies on near-shore non-mirror scattering are still at the stage of numerical simulation, lacking spectral feature analysis and verification based on measured data, and insufficient exploration of the fine spectral features under the mixed scattering mechanism, such as asymmetry and Doppler shift.

[0007] Therefore, there is an urgent need for a new method that can effectively separate specular reflection and non-spectral Bragg scattering components based on a shore-based platform, and utilize their complementary characteristics to achieve high-precision synchronous inversion of sea surface wind speed and direction. Summary of the Invention

[0008] The purpose of this invention is to provide a sea surface wind vector inversion method with optimal fitting of the wave spectrum of shore-based GNSS-R, so as to solve the problem that existing nearshore GNSS-R cannot effectively invert wind direction, and to achieve high-precision synchronous inversion of wind speed and wind direction.

[0009] To achieve the above objectives, this invention provides a method for inverting sea surface wind vectors by optimal fitting of shore-based GNSS-R wave spectra, the steps of which are as follows: S1. Obtain the direct signal and sea surface reflected signal received by the shore-based receiver, and construct a composite signal model including specular reflection component and non-spectral Bragg scattering component; S2. Based on the composite spectrum assumption of specular reflection and Bragg scattering, the MUSIC algorithm for multiple signal classification combined with the iterative Gaussian fitting method is used to perform spectrum separation on the sea surface reflection signal to obtain independent specular reflection spectrum components and Bragg scattering spectrum components of multiple orders. S3. Extract spectral width features from the separated specular reflection spectrum components; S4. Extract the positive and negative peak ratio features and the normalized Doppler frequency ratio features from the separated Bragg scattering spectral components; S5. Construct a wind speed cost function based on the spectral width feature, and obtain the sea surface wind speed by traversing and searching within a preset wind speed range and minimizing the wind speed cost function. S6. Construct a wind direction cost function based on the positive and negative peak ratio characteristics and the normalized Doppler frequency ratio characteristics, and invert the sea surface wind direction by minimizing the wind direction cost function.

[0010] Preferably, step S1 further includes constructing a composite signal model, which includes a specular reflection component and a non-spectral Bragg scattering component. The specular reflection component is constructed based on the Kirchhoff approximation, and the non-spectral Bragg scattering component is constructed based on the small slope approximation.

[0011] Preferably, the specific process of performing spectral separation of the sea surface reflection signal using the MUSIC algorithm combined with the iterative Gaussian fitting method in step S2 is as follows: S2.1. Use the MUSIC algorithm to estimate the power spectral density of the reflected signal and obtain the frequency. Spectral power value at ; S2.2 Construct the initial Gaussian function for specular reflection, and set the peak value of this function to be... The center frequency shift is The spectral width is an empirical preset value based on the observation characteristics of shore-based GNSS-R. Gaussian fitting is performed starting from the initial Gaussian function, and the fitted specular reflection spectrum is output. S2.3 Subtract the specular reflection spectrum from the original power spectral density to obtain the residual Bragg spectrum; S2.4 Set the loop variable i=1, search for the maximum peak value in the residual Bragg spectrum, and record its power and corresponding frequency; S2.5. Using the frequency corresponding to the maximum peak value as the center, extract the spectrum data within a preset frequency range, construct the i-th order Bragg initial Gaussian function and perform Gaussian fitting to obtain the i-th order Bragg scattering spectrum. S2.6 Determine if i < 4 is true. If yes, subtract the i-th order Bragg scattering spectrum from the residual Bragg spectrum, set i = i + 1, and return to step S2.4. Otherwise, end the iteration and output all separated spectral components.

[0012] Preferably, the specific method for extracting the spectral width feature in step S3 is as follows: Gaussian fitting is performed on the separated specular reflection spectral components to obtain the standard deviation parameter or half-power point width of the fitted Gaussian function, and the standard deviation parameter or half-power point width is used as the specular reflection spectral width feature.

[0013] Preferably, the specific process for extracting the positive and negative peak ratio features and the normalized Doppler frequency ratio features in step S4 is as follows: S4.1 Select the separated first-order Bragg scattering spectral components and extract the center frequency and peak power of the positive frequency part, as well as the center frequency and peak power of the negative frequency part. S4.2 Calculate the Bragg frequency and the wave motion-induced Doppler frequency based on the center frequencies of the positive and negative frequency components. S4.3. Based on the peak power of the positive and negative frequency components, calculate the positive and negative peak power ratio characteristics; S4.4. Based on the Bragg frequency and Doppler frequency, calculate the normalized Doppler frequency ratio characteristic.

[0014] Preferably, the specific process of retrieving sea surface wind speed based on spectral width characteristics in step S5 is as follows: S5.1 Using the Elfouhaily wave spectrum model, combined with satellite elevation angle, preset effective wind zone length and water depth, calculate the theoretical specular reflection spectrum width corresponding to different wind speeds to be inverted; S5.2. Integrate the measured spectral width characteristics of multiple visible satellites to construct a wind speed cost function that includes the deviation between measured and theoretical values; S5.3. Traverse and search within the preset wind speed range, and take the wind speed to be inverted that minimizes the wind speed cost function as the inverted sea surface wind speed.

[0015] Preferably, the specific process of retrieving sea surface wind direction based on two types of features in step S6 is as follows: S6.1 Based on the physical mechanism of Bragg scattering at sea surface, construct a theoretical peak ratio model and a theoretical frequency ratio model related to the wind direction to be inverted; S6.2. By integrating the measured positive and negative peak ratio characteristics and the measured normalized Doppler frequency ratio characteristics of multiple visible satellites, a wind direction cost function containing the deviation between the measured characteristics and the theoretical model is constructed. S6.3. Traverse the search within the wind direction range of 0° to 180°, and take the wind direction to be inverted that minimizes the wind direction cost function as the inverted sea surface wind direction.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sea surface wind vector inversion method for optimal fitting of the shore-based GNSS-R wave spectrum as described above.

[0017] An electronic device includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the sea surface wind vector inversion method for optimal fitting of shore-based GNSS-R wave spectrum as described above.

[0018] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: 1. Breaking through the limitation of traditional GNSS-R technology that can only retrieve wind speed, it enables synchronous retrieval of sea surface wind speed and direction without external auxiliary data under the same shore-based architecture, thus meeting the needs of nearshore vector wind field monitoring.

[0019] 2. The MUSIC algorithm combined with iterative Gaussian fitting is used to efficiently separate specular reflection and Bragg scattering signals, accurately extract weak Bragg scattering components, eliminate the need for off-axis observation, ensure the signal-to-noise ratio, and improve inversion stability.

[0020] 3. Based on a shore-based platform, relying on existing GNSS systems, the equipment is low-cost and flexible in deployment. The inversion steps are simple and the algorithm is easy to engineer, which is suitable for the actual business needs of high spatiotemporal resolution wind field monitoring in nearshore waters.

[0021] 4. It improves the physical basis of GNSS-R hybrid scattering inversion, expands the application boundary of this technology in nearshore vector wind field monitoring, can complement traditional monitoring methods, and enriches the nearshore marine wind field monitoring system.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is an overall flowchart of the sea surface wind speed and direction synchronous inversion method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the specific process of using the MUSIC algorithm combined with iterative Gaussian fitting for spectral separation in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Example like Figure 1 As shown, the method for inverting sea surface wind vectors by optimal fitting of the shore-based GNSS-R wave spectrum involves the following steps: S1: Acquire direct signals and sea surface reflected signals to construct a composite signal model.

[0028] A GNSS-R receiver is deployed on a shore-based platform. The receiver simultaneously receives direct signals from navigation satellites and reflected signals from the sea surface. After preprocessing the reflected signals, a composite signal model is constructed, including specular reflection and non-spectral Bragg scattering components. The model expression is as follows:

[0029] in, The composite sea surface reflection signal received by the receiver. Indicates the specular reflection component. This represents the non-mirror Bragg scattering component.

[0030] Specular reflection component Based on Kirchhoff's approximation, its expression is:

[0031] in, The receiver height; Non-mirror Bragg scattering component Based on the small slope approximation, its expression is:

[0032] S2: Spectral separation is performed using the MUSIC algorithm combined with iterative Gaussian fitting.

[0033] The specific process for this step is as follows: Figure 2 As shown, the core is to separate the mixed reflected signal spectrum into independent specular reflection spectrum components and multi-order Bragg scattering spectrum components. The specific operation is as follows: S2.1. The power spectral density of the preprocessed reflected signal is estimated using the Multiple Signal Classification (MUSIC) algorithm to obtain the frequency. Spectral power value at ; S2.2 Construct the initial Gaussian function for specular reflection, and set the peak value of this function to be... The center frequency shift is Spectral width is a preset value Starting with the initial Gaussian function, perform Gaussian fitting and output the fitted specular reflection spectrum. ; S2.3 Subtract the specular reflection spectrum from the original power spectral density. This yields a residual Bragg spectrum containing only the Bragg scattering component.

[0034] S2.4, Set the loop variable Search for the maximum peak in the residual Bragg spectrum and record its power. and frequency ; S2.5, with Centered on, the frequency range is intercepted. The spectrum data within; where the frequency truncation step size Preset experience value This operation can effectively eliminate noise interference other than peak values; Construct the first The initial Gaussian function of order Bragg is set to have a peak value of 1. The center frequency shift is Spectral width is a preset value Perform Gaussian fitting on the extracted spectral data to obtain the first... Bragg scattering spectrum ; S2.6, Determine the loop variable Does this hold true? If so, subtract the first from the residual Bragg spectrum. Bragg scattering spectrum ,make If not, return to step S24 to continue searching for the next peak; otherwise, end the iteration process and output the separated specular reflection spectrum components and 1st-4th order Bragg scattering spectrum components.

[0035] S3: Extract spectral width features from the separated specular reflection spectral components.

[0036] Gaussian fitting is performed on the specular components of the mirror reflection obtained in S2 to obtain the standard deviation parameter or half-power point width of the fitted Gaussian function. The standard deviation parameter or half-power point width is denoted as the specular reflection spectral width feature. This feature is the core input feature for subsequent sea surface wind speed inversion.

[0037] S4: Extract the positive and negative peak ratio features and the normalized Doppler frequency ratio features from the separated Bragg scattering spectral components. The steps are as follows: S4.1, Select the first segment separated from S2 (n=1) order Bragg scattering spectral components, extract the center frequency of the positive frequency portion respectively. and peak power and the center frequency of the negative frequency portion and peak power ; S4.2 Calculate the Bragg frequency based on the center frequencies of the positive and negative frequency components. Doppler frequency induced by wave motion The formula is as follows:

[0038]

[0039] in, This represents the center frequency of the first-order Bragg spectral component. It represents the center frequency of the first-order Bragg negative spectrum component.

[0040] S4.3 Calculate the positive and negative peak ratio characteristics The formula is as follows:

[0041] in, This represents the peak power of the first-order Bragg spectral component. This represents the peak power of the first-order Bragg negative spectral component.

[0042] S4.4 Calculate the normalized Doppler frequency ratio characteristic The formula is as follows:

[0043] S5: Construct a wind speed cost function based on spectral width characteristics to invert sea surface wind speed. Using the extracted specular reflection spectral width features as the core, a wind speed cost function is constructed in conjunction with the Elfouhaily wave spectral model. Sea surface wind speed inversion is achieved by minimizing the cost function. The specific operation is as follows: S5.1, Based on the current satellite elevation angle Preset effective wind zone length and water depth Using the Elfouhaily wave spectrum model, different wind speeds to be inverted were calculated. Corresponding root mean square height of sea level and related time The theoretical specular reflection spectral width under different wind speeds was obtained. .

[0044] S5.2. Integrate observation data from multiple visible satellites to construct a wind speed cost function. The formula is as follows:

[0045] in, Indicates the number of visible satellites. Indicates the first The measured specular reflectance of the satellite is wide. Indicates the first The elevation angle of a satellite.

[0046] S5.3. Based on the actual wind speed distribution range in nearshore waters, a wind speed traversal interval is set to be retrieved. Traversing and searching within this interval will improve the wind speed cost function. Minimum corresponding The sea surface wind speed obtained from the inversion .

[0047] S6: Construct a wind direction cost function based on the positive and negative peak value ratio characteristics and the normalized Doppler frequency ratio characteristics, and invert the sea surface wind direction by minimizing the wind direction cost function. The steps are as follows: S6.1. Based on the physical mechanism of sea surface Bragg scattering, a theoretical peak ratio model is constructed. and theoretical frequency ratio model And, the formula is as follows:

[0048]

[0049] in, Indicates the wind direction to be inverted. This indicates the directional diffusion parameter, which is preset based on the wave characteristics of nearshore waters; S6.2. Integrate wind direction characteristic observation data from multiple visible satellites to construct a wind direction cost function. The formula is as follows:

[0050] in, Indicates the first The measured positive and negative peak ratio characteristics of each satellite Indicates the first Measured normalized Doppler frequency ratio characteristics of the satellites Number of visible satellites; S6.3, in to Traversing the search within the wind direction range will make the wind direction cost function... Minimum corresponding The sea surface wind direction obtained from the inversion .

[0051] This embodiment describes a method for synchronous inversion of sea surface wind speed and direction based on a shore-based GNSS-R composite spectrum. It fully realizes the spectral separation, feature extraction, and synchronous inversion of wind field parameters from GNSS-R reflected signals in nearshore scenarios. Through reasonable algorithm design and physical model combination, it effectively solves the inherent defects of traditional nearshore GNSS-R technology in wind direction inversion, while ensuring both the effectiveness of signal extraction and the stability of the inversion process. The method's operation steps are logically clear and the process is standardized. The parameter settings and algorithm selections in each step are closely aligned with the actual observation characteristics of nearshore sea areas, possessing good operability and engineering implementation conditions. The implementation of this method effectively enhances the application value of shore-based GNSS-R technology in nearshore vector wind field monitoring, provides a feasible technical solution for high-precision, high spatiotemporal resolution monitoring of nearshore marine wind fields, and lays a practical foundation for the further application and optimization of GNSS-R microwave remote sensing technology in the field of marine environmental monitoring.

[0052] The remaining technical features in the above embodiments can be flexibly selected by those skilled in the art to meet different specific practical needs according to actual circumstances. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims. In the above description, numerous specific details have been set forth to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to implement the present invention. In other instances, to avoid obscuring the present invention, well-known techniques, such as specific construction details, operating conditions, and other technical conditions, have not been specifically described.

[0053] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for inverting sea surface wind vectors by optimal fitting of shore-based GNSS-R wave spectra, characterized in that, The steps are as follows: S1. Obtain the direct signal and sea surface reflected signal received by the shore-based receiver, and construct a composite signal model including specular reflection component and non-spectral Bragg scattering component; S2. Based on the composite spectrum assumption of specular reflection and Bragg scattering, the MUSIC algorithm for multiple signal classification combined with the iterative Gaussian fitting method is used to perform spectrum separation on the sea surface reflection signal to obtain independent specular reflection spectrum components and Bragg scattering spectrum components of multiple orders. S3. Extract spectral width features from the separated specular reflection spectrum components; S4. Extract the positive and negative peak ratio features and the normalized Doppler frequency ratio features from the separated Bragg scattering spectral components; S5. Construct a wind speed cost function based on the spectral width feature, and obtain the sea surface wind speed by traversing and searching within a preset wind speed range and minimizing the wind speed cost function. S6. Construct a wind direction cost function based on the positive and negative peak ratio characteristics and the normalized Doppler frequency ratio characteristics, and invert the sea surface wind direction by minimizing the wind direction cost function.

2. The method for sea surface wind vector inversion based on optimal fitting of shore-based GNSS-R wave spectrum according to claim 1, characterized in that: Step S1 also includes constructing a composite signal model, which includes a specular reflection component and a non-spectral Bragg scattering component. The specular reflection component is constructed based on the Kirchhoff approximation, and the non-spectral Bragg scattering component is constructed based on the small slope approximation.

3. The method for optimal fitting of the shore-based GNSS-R wave spectrum to retrieve the sea surface wind vector according to claim 2, characterized in that: The specific process of performing spectral separation of the sea surface reflection signal using the MUSIC algorithm combined with the iterative Gaussian fitting method in step S2 is as follows: S2.

1. Use the MUSIC algorithm to estimate the power spectral density of the reflected signal and obtain the frequency. Spectral power value at ; S2.2 Construct the initial Gaussian function for specular reflection, and set the peak value of this function to be... The center frequency shift is The spectral width is an empirical preset value based on the observation characteristics of shore-based GNSS-R. Gaussian fitting is performed starting from the initial Gaussian function, and the fitted specular reflection spectrum is output. S2.3 Subtract the specular reflection spectrum from the original power spectral density to obtain the residual Bragg spectrum; S2.4 Set the loop variable i=1, search for the maximum peak value in the residual Bragg spectrum, and record its power and corresponding frequency; S2.

5. Using the frequency corresponding to the maximum peak value as the center, extract the spectrum data within a preset frequency range, construct the i-th order Bragg initial Gaussian function and perform Gaussian fitting to obtain the i-th order Bragg scattering spectrum. S2.6 Determine if i < 4 is true. If yes, subtract the i-th order Bragg scattering spectrum from the residual Bragg spectrum, set i = i + 1, and return to step S2.

4. Otherwise, end the iteration and output all separated spectral components.

4. The method for sea surface wind vector inversion based on optimal fitting of shore-based GNSS-R wave spectrum according to claim 3, characterized in that: The specific method for extracting the spectral width feature in step S3 is as follows: Gaussian fitting is performed on the separated specular reflection spectral components to obtain the standard deviation parameter or half-power point width of the fitted Gaussian function, and the standard deviation parameter or half-power point width is used as the specular reflection spectral width feature.

5. The method for optimal fitting of the shore-based GNSS-R wave spectrum to retrieve the sea surface wind vector according to claim 4, characterized in that: The specific process for extracting the positive and negative peak ratio features and the normalized Doppler frequency ratio features in step S4 is as follows: S4.1 Select the separated first-order Bragg scattering spectral components and extract the center frequency and peak power of the positive frequency part, as well as the center frequency and peak power of the negative frequency part. S4.2 Calculate the Bragg frequency and the wave motion-induced Doppler frequency based on the center frequencies of the positive and negative frequency components. S4.

3. Based on the peak power of the positive and negative frequency components, calculate the positive and negative peak power ratio characteristics; S4.

4. Based on the Bragg frequency and Doppler frequency, calculate the normalized Doppler frequency ratio characteristic.

6. The method for optimal fitting of the shore-based GNSS-R wave spectrum to retrieve the sea surface wind vector according to claim 5, characterized in that: The specific process of retrieving sea surface wind speed based on spectral width characteristics in step S5 is as follows: S5.1 Using the Elfouhaily wave spectrum model, combined with satellite elevation angle, preset effective wind zone length and water depth, calculate the theoretical specular reflection spectrum width corresponding to different wind speeds to be inverted; S5.

2. Integrate the measured spectral width characteristics of multiple visible satellites to construct a wind speed cost function that includes the deviation between measured and theoretical values; S5.

3. Traverse and search within the preset wind speed range, and take the wind speed to be inverted that minimizes the wind speed cost function as the inverted sea surface wind speed.

7. The method for sea surface wind vector inversion based on optimal fitting of shore-based GNSS-R wave spectrum according to claim 6, characterized in that: The specific process of retrieving sea surface wind direction based on two types of features in step S6 is as follows: S6.1 Based on the physical mechanism of Bragg scattering at sea surface, construct a theoretical peak ratio model and a theoretical frequency ratio model related to the wind direction to be inverted; S6.

2. By integrating the measured positive and negative peak ratio characteristics and the measured normalized Doppler frequency ratio characteristics of multiple visible satellites, a wind direction cost function containing the deviation between the measured characteristics and the theoretical model is constructed. S6.

3. Traverse the search within the wind direction range of 0° to 180°, and take the wind direction to be inverted that minimizes the wind direction cost function as the inverted sea surface wind direction.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the sea surface wind vector inversion method for optimal fitting of the shore-based GNSS-R wave spectrum as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the sea surface wind vector inversion method for optimal fitting of the shore-based GNSS-R wave spectrum as described in any one of claims 1 to 7.

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