Integrated inversion method of sea surface wind and wave field parameters based on HFR and SAR

Abstract

The present invention relates to an integrated inversion method for sea surface wind and wave field parameters using HFR and SAR. The method mainly comprises: extracting the peak values ​​of the positive and negative first-order echo spectra of HFR to invert sea surface wind direction, and interpolating the HFR wind direction result to the SAR data grid through bilinear interpolation; inverting wind speed based on the registered HFR wind direction result using the CMOD5.N model and the SAR backscatter coefficient; constructing a wind wave spectrum based on the inverted wind speed and wind direction results; calculating the swell spectrum peak wave number, swell spectrum peak amplitude, and swell propagation direction using SAR image information, and constructing a swell spectrum based on this; superimposing the obtained wind wave spectrum with the swell spectrum to obtain a sea wave spectrum result, and then inverting the significant wave height. The method of the present invention will overcome the shortcomings of single-means observation of sea state parameters, realize integrated high-precision inversion of wind and wave parameters, and has important significance for marine environmental monitoring, disaster forecasting, shipping safety, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing monitoring of ocean dynamic environment, and in particular to an integrated inversion method for sea surface wind and wave field parameters (including wind direction, wind speed, significant wave height, and wave spectrum) by combining high-frequency ground wave radar (HFR) and spaceborne synthetic aperture radar (SAR). Background Art

[0002] Accurate monitoring of oceanographic parameters is crucial for maritime navigation safety, disaster warning, and marine engineering operations and maintenance. Shore-based high-frequency ground-wave radar (HFR) and spaceborne synthetic aperture radar (SAR) are two primary remote sensing methods capable of simultaneously monitoring the ocean environment over large areas and long distances.

[0003] HFR utilizes the diffraction and propagation properties of high-frequency electromagnetic waves on the sea surface to achieve large-scale, beyond-horizon, continuous observation of the marine environment and maritime vessels, with kilometer-level spatial resolution. HFR's first-order echo spectrum has a high signal-to-noise ratio, and parameters such as wind direction and currents derived from this first-order echo spectrum are highly accurate. However, HFR's second-order echo spectrum has a low signal-to-noise ratio at long distances, making it more difficult to infer wind speed and wave height from this echo spectrum, resulting in lower accuracy.

[0004] Spaceborne SAR operates in the microwave band and can acquire high-resolution, two-dimensional ocean surface images with a spatial resolution of meters. It can detect sea surface wind speed and wave parameters with high accuracy. However, SAR wind direction inversion relies on wind streaks in SAR images, leading to large errors in wind direction observations in complex ocean conditions and the problem of 180° ambiguity. Furthermore, SAR wind speed inversion depends on wind direction, and because high-frequency wind and wave energy is truncated in SAR images, SAR wave spectrum inversion also relies on wind field information. The wind field information required for SAR observations is typically provided by scatterometers, but synchronized scatterometer wind field information is difficult to obtain.

[0005] In summary, HFR and SAR each have advantages and disadvantages in remote sensing monitoring of ocean dynamic environmental information. HFR and SAR alone cannot meet the requirements for high-precision inversion of ocean state parameters. HFR and spaceborne SAR have a relatively good match in nearshore observation range. By combining HFR and SAR to conduct collaborative observations of the same ocean surface at different scales and perspectives, more comprehensive observations of wind and wave parameters can be obtained. Currently, no research has combined HFR and SAR for collaborative inversion of wind and wave parameters. Summary of the Invention

[0006] (1) Purpose of the invention

[0007] The present invention aims to provide an integrated inversion method for sea surface wind and wave field parameters by combining HFR and SAR. The method aims to comprehensively utilize the observation advantages of HFR for wind direction and the observation capabilities of SAR for wind speed and waves, compensate for the observation disadvantages of HFR for sea surface wind speed and wave height and SAR for sea surface wind direction, and realize accurate observation of sea surface wind direction, wind speed, significant wave height, wave spectrum and other parameters.

[0008] (2) Technical solution

[0009] The present invention comprises the following steps:

[0010] Step 1: Wind direction inversion and spatial registration: Implement HFR wind direction inversion and perform spatial registration on the synchronized HFR wind direction results and SAR data.

[0011] Step 1.1: Invert the HFR wind direction. After obtaining the HFR range-Doppler echo spectrum data, digital beamforming is used to extract the positive and negative first-order echo spectrum peaks to invert the sea surface wind direction. The formula is: , where θ is the wind direction, θ0 is the radar beam direction, R is the energy ratio of the left and right first-order peaks, and s is the angular dispersion coefficient.

[0012] Step 1.2, spatially align the synchronized HFR wind direction results with the SAR data. Since the latitude and longitude grids of HFR and SAR data observed in the same sea area are different, after obtaining the HFR wind direction results, they need to be matched to the SAR coordinate grid. Divide the SAR image into several small images of 1024*1024 pixels. The longitude and latitude of the center of each small image are the longitude and latitude of the small image, forming the SAR longitude and latitude matrix. Then perform bilinear interpolation with the HFR longitude and latitude matrix and the wind direction results, and interpolate the wind direction results into the uniform longitude and latitude grid of the SAR, so that the two methods maintain the same detection range and longitude and latitude grid. The formula for bilinear interpolation is: , where (x, y) is the SAR latitude and longitude point to be matched, (x1, y1), (x1, y 2) , (x2, y1), (x2, y2) are the four HFR longitude and latitude points around the SAR longitude and latitude point, and the wind direction values ​​of the four points are f(Q 11 ), f(Q 12 ), f(Q 21 ), f(Q 22 ).

[0013] Step 2, wind speed inversion: Use the CMOD5.N model to realize SAR wind speed inversion based on HFR wind direction results.

[0014] The CMOD5.N model is a C-band geophysical model function that relates radar backscatter coefficient, wind speed, radar incidence angle, and relative wind direction. The CMOD5.N model function is: , where σ 0 is the SAR backscatter coefficient, U 10 is the wind speed, is the SAR incident angle, θ ω = is the angle between the HFR wind direction obtained in step 1 and the SAR line of sight. Since the backscatter coefficient in the CMOD5.N model is a dependent variable, wind speed cannot be directly calculated using the formula. Therefore, the backscatter coefficient is calculated over wind speeds of 0 to 30 m / s in steps of 0.1 m / s. This is then compared with the actual backscatter coefficient. The wind speed with the smallest difference is the final wind speed result.

[0015] Step 3: Construct wind and wave spectrum: Use the wind direction inverted in step 1 and the wind speed inverted in step 2 to construct the sea surface wind and wave spectrum.

[0016] The sea surface wind wave spectrum can be represented by the product of the wind wave spectrum and the wind wave direction function, and is controlled by the wind speed and wind direction obtained by inversion. The wind wave spectrum model used in the present invention is the JONSWAP spectrum, and the formula is: , where g is the acceleration of gravity, k is the wave number; α w is the peak amplitude coefficient, k m is the peak wave number. Under wind and wave conditions, the formula is: , , where is the length of the wind zone; U 10 is the wind speed at 10 m above the sea surface, which is provided by the wind speed inverted in step 2; γ is the wave peak factor, which has an average value of 3.3; , σ is the peak shape parameter, and its value is .

[0017] The wind and wave direction function is expressed as: , where A c is the normalization factor; s is the angular dispersion coefficient, and the empirical value is s=2; θ ω It is the angle between the HFR wind direction result obtained in step 1 and the SAR viewing direction.

[0018] The sea surface wind wave spectrum is the product of the wind wave spectrum and the wind wave direction function, and the formula is: .

[0019] Step 4: Swell spectrum construction: Use SAR image information to construct the sea surface surge spectrum.

[0020] The sea surface surge spectrum can be represented by the product of the surge spectrum and the surge direction function. The surge spectrum model used in the present invention is the JONSWAP spectrum, and the formula is: , where g is the acceleration due to gravity, α s is the peak amplitude of the surge, which is calculated from the SAR image spectrum information; k is the wave number, k m is the surge peak wave number, which is determined by the wave number of the SAR image spectrum peak point; γ is the wave peak factor, which has an average value of 3.3; , σ is the peak shape parameter, and its value is .

[0021] The surge direction function is expressed as: , where A c is the normalization factor, s is the angular dispersion coefficient, and the empirical value is s=2; θ s is the surge propagation direction, which is determined by the negative value of the imaginary part of the cross spectrum of the SAR image.

[0022] Specifically, the cross spectrum is calculated from the three sub-view images obtained after the SAR image is processed. The cross spectrum calculation formula is: , where , , I i , I j is the spectrum of the SAR image, * represents the conjugate calculation, i > j > is the average intensity value of the spectroscopic image.

[0023] The sea surface surge spectrum is the product of the surge spectrum and the surge direction function, and the formula is: .

[0024] Specifically, the peak amplitude parameters in the surge spectrum are calculated from the SAR image spectrum. The peak amplitude parameters are constructed by traversing the surge peak amplitude range from 0 to 1 in steps of 0.01. The spectrum is then forward-mapped to the simulated SAR image spectrum and compared to the actual SAR image spectrum. The peak amplitude parameter corresponding to the minimum difference is the final peak amplitude parameter result. The resulting surge spectrum is the final surge spectrum result.

[0025] Specifically, the mapping relationship between the surge spectrum and the SAR image spectrum is a linear mapping. The linear mapping relationship equation is: , where P l R is the simulated SAR image spectrum, F k For the wave spectrum, T k R is the SAR linear modulation function. k R By the tilt modulation function T k ​​tilt , hydrodynamic modulation function T k hydr , range-direction beamforming modulation function T k rb Composition, formula is: .

[0026] Step 5: Wave spectrum synthesis and wave height inversion: Combine the wind wave spectrum and swell spectrum into the wave spectrum result, and invert the effective wave height.

[0027] The ocean wave spectrum can be composed of the superposition of the wind wave spectrum and the swell wave spectrum, and the formula is: The effective wave height is inverted using the obtained wave spectrum results. The effective wave height is the first-order moment of the wave spectrum and is expressed as: .

[0028] (3) Beneficial effects

[0029] The advantages of the present invention are:

[0030] This invention innovatively combines the advantages of both HFR and SAR remote sensing methods for sea surface observation. By temporally and spatially registering HFR wind direction results with SAR data, the HFR wind direction results are used as input for SAR wind speed inversion, yielding accurate wind field results. Furthermore, the proposed method for inverting ocean wave spectra and ocean wave parameters is divided into wind and wave components and swell components. This effectively combines the inverted wind field results to achieve an integrated inversion of wind and wave field parameters. This method also avoids the tedious computational process associated with the nonlinear mapping between the wind and wave spectra and the SAR image spectrum, improving computational efficiency and enabling accurate inversion of ocean wave spectra and wave parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Flowchart of the integrated inversion method for wind and wave field parameters combining HFR and SAR provided by the present invention;

[0032] Figure 2 Schematic diagram of HFR and SAR data grid matching provided by the present invention;

[0033] Figure 3 The wind direction results provided by the present invention and their comparison with the ERA5 wind direction;

[0034] Figure 4 The wind speed inversion result diagram provided by the present invention;

[0035] Figure 5 The wind and wave spectrum result diagram provided by the present invention;

[0036] Figure 6 The SAR image cross spectrum and surge propagation direction result diagram provided by the present invention;

[0037] Figure 7The SAR image spectrum result diagram provided by the present invention;

[0038] Figure 8 The optimal simulated SAR image spectrum result diagram provided by the present invention;

[0039] Figure 9 The surge spectrum result diagram provided by the present invention; DETAILED DESCRIPTION

[0040] In order to make the purpose, content and advantages of the present invention more clear, the specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0041] Take a set of synchronized HFR and SAR observation data as an example. The selected area is the east coast of Australia, and the time is 19:30 (UTC) on March 30, 2019. Among them, the HFR data comes from the WERA HFR radar wind direction product deployed in the sea area, with an operating frequency of 13.5MHz; the SAR data comes from the Sentinel-1A satellite, the data type is SLC data, the imaging mode is interferometric wide swath mode, the range resolution is 2m, and the azimuth resolution is 14m. The time difference between SAR imaging time and HFR data is less than 5 minutes. The detection range and matching area of ​​HFR and SAR are as follows: Figure 2 shown.

[0042] Step 1: Wind direction inversion and spatial registration: Implement HFR wind direction inversion and perform spatial registration on the synchronized HFR wind direction results and SAR data.

[0043] Step 1.1, realize HFR wind direction inversion. The HFR wind direction result is as follows Figure 3 Comparing the wind direction data with the ECMWF ERA5 model, the root mean square error between the HFR wind direction results and the ECMWF ERA5 model wind direction results is 38.90°, which is less than the wind direction observation index of 40°.

[0044] Step 1.2, spatially align the synchronized HFR wind direction results with the SAR data. Since the latitude and longitude grids of HFR and SAR data observed in the same sea area are different, after obtaining the HFR wind direction results, they need to be matched to the SAR coordinate grid. Divide the SAR image into several small images of 1024*1024 pixels. The longitude and latitude of the center of each small image are the longitude and latitude of the small image, forming a SAR longitude and latitude matrix. Then perform bilinear interpolation with the HFR longitude and latitude matrix and the wind direction results, and interpolate the wind direction results into the uniform longitude and latitude grid of the SAR, so that the two methods maintain the same detection range and longitude and latitude grid. The matching diagram of HFR and SAR grids is shown as follows: Figure 2 As shown. The formula for bilinear interpolation is: , where (x, y) is the SAR latitude and longitude point to be matched, (x1, y1), (x1, y 2) , (x2, y1), (x2, y2) are the four HFR longitude and latitude points around the SAR longitude and latitude point, and the wind direction values ​​of the four points are f (Q 11 ), f (Q 12 ), f (Q 21 ), f (Q 22 ).

[0045] Step 2, wind speed inversion: Use the CMOD5.N model to realize SAR wind speed inversion based on HFR wind direction results.

[0046] The CMOD5.N model is a C-band geophysical model function that relates the radar backscatter coefficient to wind speed, radar incidence angle, and relative wind direction. The CMOD5.N model function is: , where σ 0 is the SAR backscatter coefficient, U 10 is the wind speed, is the SAR incident angle, θ ω = is the angle between the HFR wind direction result obtained in step 1 and the SAR line of sight. Since the backscatter coefficient in the CMOD5.N model is a dependent variable, the wind speed cannot be directly calculated using the formula. Therefore, it is necessary to calculate the backscatter coefficient from 0 to 30 m / s with a step size of 0.1 m / s and compare it with the actual backscatter coefficient. The wind speed with the smallest difference is the final wind speed result. The wind speed inversion result is as follows Figure 4 Comparison with the ECMWF ERA5 model wind speed data shows that the root mean square error of the wind speed at this moment is 1.58 m / s, which is less than the wind speed observation index of 2 m / s.

[0047] Step 3: Construct wind and wave spectrum: Use the wind direction inverted in step 1 and the wind speed inverted in step 2 to construct the sea surface wind and wave spectrum.

[0048] The sea surface wind wave spectrum can be represented by the product of the wind wave spectrum and the wind wave direction function, and is controlled by the wind speed and wind direction obtained by inversion. The wind wave spectrum model used in the present invention is the JONSWAP spectrum, and the formula is: , where g is the acceleration of gravity, k is the wave number; α w is the peak amplitude coefficient, k m is the peak wave number. Under wind and wave conditions, the formula is: , , where is the length of the wind zone; U 10is the wind speed at 10 m above the sea surface, which is provided by the wind speed inverted in step 2; γ is the wave peak factor, which has an average value of 3.3; , σ is the peak shape parameter, and its value is .

[0049] The wind and wave direction function is expressed as: , where A c is the normalization factor; s is the angular dispersion coefficient, and the empirical value is s=2; θ ω It is the angle between the HFR wind direction result obtained in step 1 and the SAR viewing direction.

[0050] The sea surface wind wave spectrum is the product of the wind wave spectrum and the wind wave direction function, and the formula is: The wind and wave spectrum results are as follows: Figure 5 shown.

[0051] Step 4: Swell spectrum construction: Use SAR image information to construct the sea surface surge spectrum.

[0052] The sea surface surge spectrum can be represented by the product of the surge spectrum and the surge direction function. The surge spectrum model used in the present invention is the JONSWAP spectrum, and the formula is: , where g is the acceleration due to gravity, α s is the peak amplitude of the surge, which is calculated from the SAR image spectrum information; k is the wave number, k m is the surge peak wave number, which is determined by the wave number of the SAR image spectrum peak point; γ is the wave peak factor, which takes an average value of 3.3. , σ is the peak shape parameter, and its value is .

[0053] The surge direction function is expressed as: , where A c is the normalization factor, s is the angular dispersion coefficient, and the empirical value is s=2; θ s is the surge propagation direction, which is determined by the negative value of the imaginary part of the cross spectrum of the SAR image.

[0054] Specifically, the cross spectrum is calculated from the three sub-view images obtained after the SAR image is processed. The cross spectrum calculation formula is: , where , , I i , I j is the spectrum of the SAR image, * represents the conjugate calculation, i > j > is the average intensity value of the spectroscopic image. The results of the SAR image cross spectrum imaginary part and surge propagation direction are as follows Figure 6 As shown in Figure 2, the surge propagation direction is 89° at this time.

[0055] ​​The sea surface surge spectrum is the product of the surge spectrum and the surge direction function, and the formula is: .

[0056] Specifically, the peak amplitude parameters in the surge spectrum are calculated from the SAR image spectrum. The peak amplitude parameters are constructed by traversing the surge peak amplitude range from 0 to 1 in steps of 0.01. The spectrum is then forward-mapped to the simulated SAR image spectrum and compared to the actual SAR image spectrum. The peak amplitude parameter corresponding to the minimum difference is the final peak amplitude parameter result. The resulting surge spectrum is the final surge spectrum result. Figure 7 The SAR image spectrum is shown. Figure 8 The optimal simulated SAR image spectrum result with the smallest difference from the SAR image spectrum is shown. Figure 9 The swell spectrum results are shown.

[0057] Specifically, the mapping relationship between the surge spectrum and the SAR image spectrum is a linear mapping. The linear mapping relationship equation is: , where P l R is the simulated SAR image spectrum, F k For the wave spectrum, T k R is the SAR linear modulation function. k R By the tilt modulation function T k tilt , hydrodynamic modulation function T k hydr , range-direction beamforming modulation function T k rb Composition, formula is: .

[0058] Step 5: Wave spectrum synthesis and wave height inversion: Combine the wind wave spectrum and swell spectrum into the wave spectrum result, and invert the effective wave height.

[0059] The ocean wave spectrum can be composed of the superposition of the wind wave spectrum and the swell wave spectrum, and the formula is: ; Use the obtained wave spectrum results to invert the effective wave height. The effective wave height is the first-order moment of the wave spectrum and is expressed as: .

[0060] After comparing with the buoy data, the observed significant wave height at that moment was 2.93 m, while the buoy significant wave height result was 2.51 m, with an error of 0.42 m, which was 0.5 m less than the observed significant wave height indicator.

[0061] In summary, the wind direction, wind speed, wave height and other wind and wave field parameters obtained by the proposed method have high sea state observation accuracy.

Claims

1. An integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR, characterized in that: The following steps are involved: (1) Wind direction inversion and spatial registration: Extract the positive and negative first-order echo spectrum peaks of the high-frequency ground wave radar and invert the sea surface wind direction; spatially register the synchronized HFR wind direction results with the spaceborne synthetic aperture radar data, and use bilinear interpolation to interpolate the HFR wind direction results into the SAR latitude and longitude grid to obtain the HFR wind direction results under the SAR latitude and longitude grid; (2) Wind speed inversion: Using the CMOD5.N model, based on the HFR wind direction results obtained in step (1), SAR wind speed inversion based on the HFR wind direction results is achieved; (3) Wind wave spectrum construction: Use the wind speed result obtained in step (2) to construct the wind wave spectrum JONSWAP spectrum, and use the wind direction result obtained in step (1) to construct the wind wave direction function. The wind wave spectrum is the product of the wind wave spectrum and the wind wave direction function. (4) Construction of surge spectrum: The surge spectrum JONSWAP spectrum is constructed using the surge spectrum peak wave number and surge spectrum peak amplitude, and the surge direction function is constructed using the surge propagation direction obtained from the SAR image cross spectrum. The surge spectrum model is the product of the JONSWAP spectrum and the surge direction function. When calculating the surge peak amplitude, it is necessary to traverse the surge peak amplitude and forward map the SAR image spectrum through the linear mapping relationship between the surge spectrum and the SAR image spectrum. The surge peak amplitude with the smallest difference between the simulated SAR image spectrum and the actual SAR image spectrum is the final surge peak amplitude result. (5) Wave spectrum synthesis and effective wave height inversion: The wind wave spectrum results of step (3) and the surge wave spectrum results of step (4) are superimposed to synthesize the wave spectrum results, and the effective wave height is inverted based on the wave spectrum results.

2. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 1 is characterized in that: In the HFR wind direction inversion method described in step (1), the calculation formula for sea surface wind direction is: , where θ is the wind direction result, θ0 is the radar beam direction, R is the energy ratio of the left and right first-order peaks of the radar echo spectrum, and s is the angular dispersion coefficient; the spatial registration method includes dividing the SAR image into several small images of 1024*1024 pixels, and the longitude and latitude of the center of each small image is the longitude and latitude of the small image, forming a SAR longitude and latitude matrix; for the SAR longitude and latitude point (x, y) to be matched, the longitude and latitude points of the four HFR range-beam coordinate systems around it are (x1, y1), (x1, y 2) , (x2, y1), (x2, y2), the corresponding wind direction values ​​are f(Q 11 ), f (Q 12 ), f (Q 21 ), f (Q 22 ), the formula for calculating the wind direction value f (x, y) by bilinear interpolation is: , and finally the HFR wind direction results under the SAR latitude and longitude grid are obtained.

3. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 1 is characterized in that: The wind speed retrieval CMOD5.N model function described in step (2) is: , where σ 0 is the SAR backscatter coefficient, U 10 is the wind speed, is the SAR incident angle, θ ω is the angle between the HFR wind direction result obtained in step (1) and the SAR line of sight; when inverting the wind speed, the backscatter coefficient is calculated by traversing the wind speed of 0~30m / s with a step size of 0.1m / s, and compared with the actual SAR backscatter coefficient. The wind speed with the smallest difference is the final wind speed result.

4. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 1 is characterized in that: The wind wave spectrum JONSWAP spectrum formula in step (3) is: , where g is the acceleration due to gravity, α w is the peak amplitude of wind waves, k is the wave number, k m is the peak wave number, and the peak amplitude of wind and wave under wind and wave conditions , peak wave number of wind waves , is the length of the wind zone, U 10 is the wind speed at 10 m above the sea surface, which is provided by the wind speed inverted in step (2); γ is the wave peak factor, which takes an average value of 3.

3. , σ is the peak shape parameter, and its value is ; The wind and wave direction function is expressed as: , where A c is the normalization factor, s is the angular dispersion coefficient, and the empirical value is s=2, θ ω is the angle between the HFR wind direction result obtained in step (1) and the SAR line of sight; the wind wave spectrum is the product of the wind wave spectrum and the wind wave direction function, and the formula is: .

5. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 1 is characterized in that: The surge spectrum JONSWAP spectrum formula in step (4) is: , where g is the acceleration due to gravity, α s is the peak amplitude of the surge, which needs to be calculated with the help of SAR image spectrum information, k is the wave number, k m is the surge peak wave number, which is determined by the wave number of the SAR image spectrum peak point; γ is the wave peak factor, which takes an average value of 3.

3. , σ is the peak shape parameter, and its value is ; The surge direction function is expressed as: , where A c is the normalization factor, s is the angular dispersion coefficient, and the empirical value is s=2, θ s is the surge propagation direction, which is determined by the negative value of the imaginary part of the cross spectrum of the SAR image; the surge spectrum is expressed as the product of the surge spectrum and the surge direction function, that is, .

6. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 5 is characterized in that: The calculation method of the surge peak amplitude is: traverse the surge peak amplitude value from 0 to 1 with a step size of 0.01, and forward map the simulated SAR image spectrum through the linear mapping relationship between the surge spectrum and the SAR image spectrum. The linear mapping relationship is: , where P l R is the simulated SAR image spectrum, F k For the wave spectrum, T k R is the SAR linear modulation function; T k R By the tilt modulation function T k tilt , hydrodynamic modulation function T k hydr , range-direction beamforming modulation function T k rb Composition, formula is: ; Calculate the difference between the mapped simulated SAR image spectrum and the actual SAR image spectrum, and the surge peak amplitude corresponding to the minimum difference is determined as the final surge peak amplitude result.

7. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 5 is characterized in that: The cross spectrum is calculated as follows: after the SAR image is subjected to dichroic processing, it is divided into three sub-view images, and the cross spectrum is calculated. The cross spectrum formula is: , where , , I i , I j is the spectrum of the SAR image, * represents the conjugate calculation, i > j > is the average intensity value of the spectroscopic image.​​ 8. The integrated inversion method for sea surface wind and wave field parameters combining HFR and SAR according to claim 1 is characterized in that: The ocean wave spectrum result in step (5) is the superposition of the wind wave spectrum and the swell wave spectrum, and the formula is: ; The significant wave height inversion formula is: .

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