Satellite-borne polarization target decomposition optimization algorithm considering earth surface dielectric attribute constraint and Faraday rotation compensation

Abstract

The invention discloses a satellite-borne polarization target decomposition optimization algorithm considering earth surface dielectric attribute constraint and Faraday rotation compensation, and relates to the technical field of remote sensing image processing, and the algorithm comprises the steps: calculating the minimum value and the maximum value of an earth surface dielectric constant through employing a passive microwave soil humidity data product, and enabling the minimum value and the maximum value to serve as constraint conditions to be embedded into a decomposition model for calculation; meanwhile, the influence of the Faraday electromagnetic effect on satellite-borne long-band SAR electromagnetic wave signal transmission is considered, a Faraday rotation angle is calculated according to the earth magnetic field and ionosphere total amount data, unitary transformation is conducted on polarization coherence and a covariance matrix, and compensation of the Faraday rotation effect is achieved; the interaction between polarized electromagnetic waves and a vegetation structure is considered, and a co-polarized phase difference is calculated by using observation data and is used as an even-order scattering component of a priori knowledge improved decomposition model. Therefore, by adopting the method, the decomposition precision of polarimetric SAR earth surface, even number and vegetation volume scattering is effectively improved, and the method can be applied to agricultural growth monitoring and earth surface soil humidity inversion and mapping.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and in particular to a satellite-borne polarization target decomposition optimization algorithm that takes into account surface dielectric property constraints and Faraday rotation compensation. Background Art

[0002] Polarimetric target decomposition (PDD) is a core technology in polarimetric synthetic aperture radar (PolSAR) data processing. Its purpose is to decompose complex ground object scattering mechanisms into physically meaningful simple scattering components. Typical decomposition methods for distributed scatterers such as the ground surface include Freeman-Durden decomposition, Cloude-Posen decomposition, and Yamaguchi decomposition. Freeman-Durden decomposes ground object scattering into three components: surface scattering, dihedral scattering (double-bounce scattering), and volume scattering. Existing PDD algorithms employ scattering models corresponding to these three components: Bragg and X-Bragg scattering models, double-Fresnel scattering and the improved double-Fresnel scattering model (XD-Fresnel), volume scattering models with specific orientations (horizontal, vertical, and random), and the generalized volume scattering model (GVSM).

[0003] However, model-based polarization target decomposition still has the following problems: the decomposition model involves multiple unknown parameters, such as the dielectric constants of soil and tree trunks, which make the decomposition process an underdetermined problem; the co-polarization phase difference in the even-scattering model is always empirically set to zero, but in our actual experiments, it was found that this value is distributed between -90° and 90°; for spaceborne SAR data, its signal will be affected by the Faraday rotation effect during the process of penetrating the ionosphere, and the scattering matrix elements will rotate in the polarization plane. If this rotation angle is not compensated, it will lead to errors in the interpretation of radar scattering characteristics, reducing the quality of the scattering matrix and the accuracy of geophysical parameter inversion.

[0004] Based on this, the present invention proposes a spaceborne polarization target decomposition optimization algorithm that takes into account the constraints of the Earth's surface dielectric properties and Faraday rotation compensation, so as to overcome the defect of the existing model that does not consider the influence of the Faraday rotation effect of the spaceborne platform. The physical constraint of the Earth's dielectric constant is realized through passive microwave data. At the same time, the calculated co-polarization phase difference is used as prior knowledge to improve the even-order scattering model and realize the optimization of the polarization target model. Summary of the Invention

[0005] The purpose of the present invention is to provide a spaceborne polarization target decomposition optimization algorithm that takes into account the constraints of surface dielectric properties and Faraday rotation compensation. It can physically constrain the dielectric constant in the model and improve the accuracy of the model solution; at the same time, it overcomes the problem that spaceborne SAR signals are affected by the Faraday rotation effect.

[0006] To achieve the above objectives, the present invention provides a spaceborne polarization target decomposition optimization algorithm that takes into account surface dielectric property constraints and Faraday rotation compensation, comprising the following steps:

[0007] S1. Obtain satellite-borne long-waveband polarimetric SAR images of vegetation-covered areas and calculate the Faraday rotation angle.

[0008] S2. Calculate the unitary transformation matrix using the Faraday rotation angle to compensate the polarization coherence matrix and the polarization covariance matrix;

[0009] S3, using the polarization covariance matrix corrected in step S2, calculating the co-polarization phase difference of the observed data;

[0010] S4. Using the obtained co-polarization phase difference as prior knowledge, the even-order scattering model of the polarization target decomposition algorithm is improved;

[0011] S5. Collect passive microwave data products of the time and area corresponding to step S1, construct a spatial and temporal soil dielectric constant constraint mechanism, calculate the minimum and maximum values ​​of the surface dielectric constant, and embed them as constraints into the surface scattering model and the even-order scattering model.

[0012] S6. Solve the optimized polarization target decomposition model.

[0013] Preferably, step S1 includes:

[0014] S11. Obtain the angle between the wave propagation direction and the ground normal, magnetic induction intensity, magnetic inclination, magnetic declination, satellite nadir declination, and ionospheric total electron content when generating spaceborne long-wave polarimetric SAR images of vegetation-covered areas.

[0015] S12. Calculate the Faraday rotation angle using the formula:

[0016]

[0017] Where Ω is the Faraday rotation angle, B is the magnetic induction intensity, f is the signal center frequency, ψ is the satellite's nadir declination, TEC is the total electron content in the propagation path between the SAR satellite and the ground reflection point, and K is a constant of 2.36×10 4 ,θ B is the angle between the magnetic field and the propagation direction of the electromagnetic wave, which can be approximately calculated by the following formula:

[0018] cos(θB )=cosθ i sinΘ+sinθ i cosΘsinΦ;

[0019] Among them, θ i is the angle between the wave propagation direction and the normal to the ground plane, Θ is the magnetic inclination, and Φ is the magnetic declination.

[0020] Preferably, step S2 includes:

[0021] S21, using the Faraday rotation angle obtained in step S1, calculate the unitary transformation matrix [R p (Ω)], as follows:

[0022]

[0023] S22. Obtain the polarization covariance matrix and polarization coherence matrix affected by the Faraday rotation effect, and use the unitary transformation matrix to correct them. The expressions are as follows:

[0024] [C3]=[R p (Ω)] -1 [C3(Ω)][R p (Ω)] -1 ;

[0025] [T3]=[R p (Ω)] -1 [T3(Ω)][R p Ω)] -1 ;

[0026] After generating the polarization covariance matrix C3(Ω) and polarization coherence matrix T3(Ω) using spaceborne polarimetric SAR images, the corresponding matrices are inversely calculated according to the Faraday rotation angle Ω, that is, the left and right sides of [C3(Ω)] or [T3(Ω)] are multiplied by [R p (Ω)] -1 , the polarization covariance matrix C3 and polarization coherence matrix T3 after Faraday rotation effect correction are obtained to compensate for the deflection caused by the Faraday rotation effect.

[0027] Preferably, in step S3, the expression of the co-polarization phase difference is:

[0028]

[0029] Where C 13 is the first row and third column element of the polarization covariance matrix after Faraday rotation effect correction, is the polarization covariance matrix element C 13 The imaginary part of is the polarization covariance matrix element C 13The real part of .

[0030] Preferably, step S4 includes:

[0031] S41. The even-order scattering model adopts the improved double Fresnel scattering model, which is expressed as follows:

[0032]

[0033]

[0034] Among them, T XD_Fresnel For the improved even-order scattering double Fresnel scattering model, is the Pauli basis scattering vector of the target pixel, S XD is the even-order scattering matrix considering the surface depolarization angle, f d is the even-order scattered power, T XD11 、T XD22 、T XD12 、T XD21 、T XD33 is the even-scattering polarization coherence matrix element, δ is the surface roughness depolarization angle, ε t / s is the dielectric constant of vegetation stems, ε soil is the dielectric constant of soil, is the co-polarization phase difference parameter, θ is the incident angle;

[0035] S42, using the calculated co-polarization phase difference as prior knowledge to solve S XD .

[0036] Preferably, step S5 includes:

[0037] S51. The surface scattering model adopts the improved X-Bragg model, which is expressed as follows:

[0038]

[0039] Among them, T S_X-Bragg is the improved surface scattering Bragg scattering model, f s is the surface scattering power coefficient, β is the Bragg scattering coefficient, δ is the surface roughness depolarization angle, ε soil is the surface dielectric constant related to soil moisture, S HH and S VV are the horizontal and vertical polarization backscatter coefficients, respectively;

[0040] S52. Collect passive microwave data for the time and area corresponding to S1, calculate the minimum and maximum values ​​of the soil moisture data product for the selected time and area, and inversely calculate the minimum and maximum values ​​of the corresponding surface dielectric constant;

[0041] S53, the dielectric constant ε soil The maximum and minimum values ​​of are used as constraints, and the surface scattering model and even-order scattering model are embedded for solution.

[0042] Preferably, step S6 includes:

[0043] S61. Using the adaptive volume scattering model GVSM as the volume scattering model, an optimized polarization target decomposition model is constructed as follows:

[0044]

[0045] Where T3 is the polarization coherence matrix after Faraday rotation effect correction, T S_X-Bragg is the improved surface scattering Bragg scattering model, T XD_Fresnel For the improved even-order scattering double Fresnel scattering model, [T V ] GVSM is an adaptive volume scattering model;

[0046] S62 , integrating steps S1 - S5 , optimizing the surface scattering model, the even-order scattering model and the volume scattering model, and iteratively solving each scattering power using a multivariate nonlinear equation.

[0047] Therefore, the present invention adopts the above-mentioned satellite-borne polarization target decomposition optimization algorithm that takes into account the surface dielectric property constraints and Faraday rotation compensation, which has the following technical effects:

[0048] (1) The present invention uses passive microwave soil moisture data products to obtain the minimum and maximum values ​​of the surface dielectric constant, and embeds them as constraints into the decomposition model solution, thereby improving the solution accuracy.

[0049] (2) The present invention takes into account the influence of Faraday electromagnetic effect on the transmission of long-wave SAR electromagnetic wave signals, calculates the Faraday rotation angle based on the Earth's magnetic field and the total ionosphere data, and then performs unitary transformation on the polarization coherence and covariance matrix to achieve compensation for the Faraday rotation effect.

[0050] (3) The present invention considers the interaction between polarized electromagnetic waves and vegetation structure, uses observation data to solve the co-polarization phase difference, and uses it as prior knowledge to improve the even-order scattering model, which can effectively improve the accuracy of the polarization target decomposition model and is suitable for agricultural growth monitoring, surface soil moisture inversion and mapping, etc.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1This is a flow chart of a spaceborne polarization target decomposition optimization algorithm that takes into account the constraints of the Earth's surface dielectric properties and Faraday rotation compensation;

[0053] Figure 2 The effect of the Faraday rotation angle on the coherence matrix elements in an embodiment of a spaceborne polarization target decomposition optimization algorithm that takes into account the surface dielectric property constraints and Faraday rotation compensation, where a is the element T of the coherence matrix 22 , b is the element of the coherence matrix T 33 ;

[0054] Figure 3 The invention relates to a time series variation of a co-polarization phase difference value in an embodiment of a spaceborne polarization target decomposition optimization algorithm that takes into account surface dielectric property constraints and Faraday rotation compensation. DETAILED DESCRIPTION

[0055] The present invention can be explained in more detail by the following examples. The purpose of disclosing the present invention is to protect all changes and improvements within the scope of the present invention. The present invention is not limited to the following examples.

[0056] like Figure 1 As shown, the present invention provides a satellite-borne polarization target decomposition optimization algorithm that takes into account the surface dielectric property constraints and Faraday rotation compensation, including a surface scattering model, an even-order scattering model and a volume scattering model.

[0057] In this embodiment, the surface scattering model involves the X-Bragg model, which is expressed as follows:

[0058]

[0059] Among them, T S_X-Bragg is the improved surface scattering Bragg scattering model, f s is the surface scattering power coefficient, β is the Bragg scattering coefficient; δ is the surface roughness depolarization angle, and its correlation coefficient with circular polarization γ RRLL The specific form of the relevant parameters is as follows:

[0060]

[0061] Where θ is the angle of incidence; ε soil is the surface dielectric constant related to soil moisture; S HH and S VV are the horizontal and vertical polarization backscatter coefficients respectively; S RR and S LL are the right-hand and left-hand circularly polarized scattering coefficients, respectively.

[0062] The even-order scattering model adopts the improved double Fresnel scattering model, which is expressed as follows:

[0063]

[0064] Among them, T XD_Fresnel It is an improved even-scattering double Fresnel scattering model, that is, an improved double Fresnel scattering coherence matrix; is the Pauli basis scattering vector of the target pixel; S XD is the even-order scattering matrix considering the surface depolarization angle, that is, the 2×2 Sinclair scattering matrix of the target pixel; f d is the even-order scattered power; T XD11 、T XD22 、T XD12 、T XD21 、T XD33 is the even-scattering polarization coherence matrix element, each element is related to the surface dielectric constant (ε soil ), vegetation dielectric constant (ε trunk ), a function related to the angle of incidence (θ).

[0065] The volume scattering model uses the adaptive volume scattering model GVSM, which is expressed as follows:

[0066]

[0067] γ=<|S HH | 2 > / <|S VV | 2 >,

[0068] Among them, γ, ρ, and e are vegetation scattering model parameters, e=2<|S HV | 2 > / <|S VV | 2 >, in HH S VV * >Under the assumption of rotational symmetry, we can obtain f v is the power of the scattered component of vegetation;

[0069] The polarization covariance matrix C3 and polarization coherence matrix T3 are as follows:

[0070]

[0071] In this embodiment, on the one hand, considering that the spaceborne SAR is affected by the Faraday rotation effect of the ionosphere, the scattering matrix elements will rotate in the polarization plane (see Figure 2 ), the polarization covariance matrix and the coherence matrix are compensated; on the other hand, the model solution accuracy is improved by physically constraining the dielectric constant, which specifically includes the following steps:

[0072] ​S1. Obtain a satellite-borne long-waveband polarimetric SAR image of the vegetation-covered area and calculate the Faraday rotation angle as follows:

[0073] S11. Obtain the angle between the wave propagation direction and the normal to the ground plane, magnetic induction intensity, magnetic inclination, magnetic declination, satellite nadir declination, and total electron content of the ionosphere when generating satellite-borne long-wave polarimetric SAR images of vegetation-covered areas.

[0074] S12. Calculate the Faraday rotation angle using the formula:

[0075]

[0076] Where Ω is the Faraday rotation angle; B is the magnetic induction intensity; f is the signal center frequency; ψ is the satellite's nadir declination; TEC is the total electron content in the propagation path between the SAR satellite and the ground reflection point, which can be obtained from the International GNSS Service Center IGS; K is a constant of 2.36×10 4 θ B is the angle between the magnetic field and the propagation direction of the electromagnetic wave, which can be approximately calculated by the following formula:

[0077] cos(θ B )=cosθ i sinΘ+sinθ i cosΘsinΦ;

[0078] Among them, θ i is the angle between the wave propagation direction and the normal to the ground plane, Θ is the magnetic inclination, and Φ is the magnetic declination.

[0079] S2. Calculate the unitary transformation matrix using the Faraday rotation angle obtained in step S1 to compensate the polarization coherence matrix and the polarization covariance matrix as follows:

[0080] S21. Calculate the unitary transformation matrix using the Faraday rotation angle obtained in step S1:

[0081]

[0082] S22. Perform unitary transformation on the polarization covariance matrix C3 and the polarization coherence matrix T3 to obtain the polarization covariance matrix and polarization coherence matrix affected by the Faraday rotation effect, which are expressed as follows:

[0083] [C3]=[R p (Ω)] -1 [C3(Ω)][R p (Ω)] -1 ;

[0084] [T3]=[R p (Ω)] -1 [T3(Ω)][Rp (Ω)] -1 ;

[0085] After generating the polarization covariance matrix C3(Ω) and polarization coherence matrix T3(Ω) using spaceborne polarimetric SAR images, the corresponding matrices are inversely calculated according to the Faraday rotation angle Ω, that is, the left and right sides of [C3(Ω)] or [T3(Ω)] are multiplied by [R p (Ω)] -1 , we can obtain the polarization covariance matrix C3 and polarization coherence matrix T3 after Faraday rotation effect correction, compensating for the deflection caused by Faraday rotation effect.

[0086] S3. Calculate the co-polarization phase difference of the observed data using the polarization covariance matrix corrected in step S2.

[0087] The formula for calculating the co-polarization phase difference is usually as follows:

[0088]

[0089] Using the corrected polarization covariance matrix, calculate the co-polarization phase difference Δψ c(hh-νν) Then:

[0090]

[0091] Where C 13 is the first row and third column element of the polarization covariance matrix after Faraday rotation effect correction, is the polarization covariance matrix element C 13 The imaginary part of is the polarization covariance matrix element C 13 The real part of .

[0092] S4. Using the co-polarization phase difference obtained in step S3 as prior knowledge, improve the even-scattering model of the polarization target decomposition algorithm as follows:

[0093] S41. The parameters in the coherence matrix of the improved double Fresnel scattering model are as follows:

[0094]

[0095] in, is the Pauli basis scattering vector of the target pixel, δ is the surface roughness depolarization angle, ε t / s is the dielectric constant of vegetation stems, ε soil is the dielectric constant of soil, is the co-polarization phase difference parameter.

[0096] S42, using the calculated co-polarization phase difference as prior knowledge to solve S XD .

[0097] S5. Collect passive microwave data products corresponding to the time and area of ​​step S1, establish a spatiotemporal soil dielectric constant constraint mechanism, calculate the minimum and maximum values ​​of the surface dielectric constant, and then embed them as constraints into the solution of the surface scattering model and the even-order scattering model. Specifically, the following steps are performed:

[0098] S51. The improved X-Bragg model is used as the surface scattering model, and its expression is as follows:

[0099]

[0100] Among them, T S_X-Bragg is the polarization coherence matrix of the improved X-Bragg model; f s is the surface scattering power coefficient; β is the Bragg scattering coefficient; δ is the surface roughness depolarization angle, which is related to the circular polarization correlation coefficient (γ RRLL ) related; soil is the surface dielectric constant related to soil moisture; S HH and S VV are the horizontal and vertical polarization backscatter coefficients, respectively.

[0101] S52. Collect passive microwave data for the time and area corresponding to S1, calculate the minimum and maximum values ​​of the soil moisture data product within the selected time and area, and inversely calculate the minimum and maximum values ​​of the corresponding surface dielectric constant.

[0102] S53, the dielectric constant ε soil The maximum and minimum values ​​of are used as constraints and embedded in the solution of surface scattering model and even-order scattering model.

[0103] S6. Solving the optimized polarization target decomposition model, specifically including the following steps:

[0104] S61. In this embodiment, an adaptive volume scattering model GVSM is used as the volume scattering model, and a polarization target decomposition model is constructed as follows:

[0105]

[0106] Where T3 is the polarization coherence matrix after Faraday rotation effect correction, T S_X-Bragg is the improved surface scattering Bragg scattering model, T XD_Fresnel For the improved even-order scattering double Fresnel scattering model, [T V ] GVSM Adaptive volume scattering model

[0107] S62, combining steps S1-S5, optimizing the surface scattering model, the even-order scattering model, and the volume scattering model, and iteratively solving each scattering power using a multivariate nonlinear equation.

[0108] Therefore, the present invention adopts the above-mentioned satellite-borne polarization target decomposition optimization algorithm that takes into account the surface dielectric property constraints and Faraday rotation compensation, which can effectively improve the decomposition accuracy of polarimetric SAR surface, even-order and vegetation body scattering, and has great application potential in agricultural growth monitoring, surface soil moisture inversion and mapping.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A spaceborne polarization target decomposition optimization algorithm that takes into account the constraints of surface dielectric properties and Faraday rotation compensation, characterized by: The following steps are involved: S1. Obtain satellite-borne long-waveband polarimetric SAR images of vegetation-covered areas and calculate the Faraday rotation angle. S2. Calculate the unitary transformation matrix using the Faraday rotation angle to compensate the polarization coherence matrix and the polarization covariance matrix; S3, using the polarization covariance matrix corrected in step S2, calculating the co-polarization phase difference of the observed data; S4. Using the obtained co-polarization phase difference as prior knowledge, the even-order scattering model of the polarization target decomposition algorithm is improved; S5. Collect passive microwave data products of the time and area corresponding to step S1, construct a spatial and temporal soil dielectric constant constraint mechanism, calculate the minimum and maximum values ​​of the surface dielectric constant, and embed them as constraints into the surface scattering model and the even-order scattering model. S6. Solve the optimized polarization target decomposition model.

2. The spaceborne polarization target decomposition optimization algorithm taking into account the surface dielectric property constraints and Faraday rotation compensation according to claim 1, characterized in that: Step S1 includes: S11. Obtain the angle between the wave propagation direction and the ground normal, magnetic induction intensity, magnetic inclination, magnetic declination, satellite nadir declination, and ionospheric total electron content when generating spaceborne long-wave polarimetric SAR images of vegetation-covered areas. S12. Calculate the Faraday rotation angle using the formula: Where Ω is the Faraday rotation angle, B is the magnetic induction intensity, f is the signal center frequency, ψ is the satellite's nadir declination, TEC is the total electron content in the propagation path between the SAR satellite and the ground reflection point, and K is a constant of 2.36×10 4 ,θ B is the angle between the magnetic field and the propagation direction of the electromagnetic wave, which can be approximately calculated by the following formula: cos(θ B )=cosθ i sinθ+sinθ i cosΘsinΦ; Among them, θ i is the angle between the wave propagation direction and the normal to the ground plane, Θ is the magnetic inclination, and Φ is the magnetic declination.

3. The spaceborne polarization target decomposition optimization algorithm taking into account the surface dielectric property constraints and Faraday rotation compensation according to claim 1 is characterized in that: Step S2 includes: S21, using the Faraday rotation angle obtained in step S1, calculate the unitary transformation matrix [R p (Ω)], as follows: S22. Obtain the polarization covariance matrix and polarization coherence matrix affected by the Faraday rotation effect, and use the unitary transformation matrix to correct them. The expressions are as follows: [C3]=[R p (Ω)] -1 [C3(Ω)][R p (Ω)] -1 ; [T3]=[R p (Oh)] -1 [T3(Ω)][R] p [(Oh)] -1 ; After generating the polarization covariance matrix C3(Ω) and polarization coherence matrix T3(Ω) using spaceborne polarimetric SAR images, the corresponding matrices are inversely calculated according to the Faraday rotation angle Ω, that is, the left and right sides of [C3(Ω)] or [T3(Ω)] are multiplied by [R p (Ω)] -1 , the polarization covariance matrix C3 and polarization coherence matrix T3 after Faraday rotation effect correction are obtained to compensate for the deflection caused by the Faraday rotation effect.

4. The spaceborne polarization target decomposition optimization algorithm taking into account the surface dielectric property constraints and Faraday rotation compensation according to claim 1, characterized in that: In step S3, the expression of the co-polarization phase difference is: Where C 13 is the first row and third column element of the polarization covariance matrix after Faraday rotation effect correction, is the polarization covariance matrix element C 13 The imaginary part of is the polarization covariance matrix element C 13 The real part of .

5. The spaceborne polarization target decomposition optimization algorithm taking into account the surface dielectric property constraints and Faraday rotation compensation according to claim 1, characterized in that: Step S4 includes: S41. The even-order scattering model adopts the improved double Fresnel scattering model, which is expressed as follows: Among them, T XD_Fresnel For the improved even-order scattering double Fresnel scattering model, is the Pauli basis scattering vector of the target pixel, S XD is the even-order scattering matrix considering the surface depolarization angle, f d is the even-order scattered power, T XD11 、T XD22 、T XD12 、T XD21 、T XD33 is the even-scattering polarization coherence matrix element, δ is the surface roughness depolarization angle, ε t / s is the dielectric constant of vegetation stems, ε soil is the dielectric constant of soil, is the co-polarization phase difference parameter, θ is the incident angle; S42, using the calculated co-polarization phase difference as prior knowledge to solve S XD .

6. The spaceborne polarization target decomposition optimization algorithm taking into account the surface dielectric property constraints and Faraday rotation compensation according to claim 1, characterized in that: Step S5 includes: S51. The surface scattering model adopts the improved X-Bragg model, which is expressed as follows: Among them, T S_X-Bragg is the improved surface scattering Bragg scattering model, f s is the surface scattering power coefficient, β is the Bragg scattering coefficient, δ is the surface roughness depolarization angle, ε soil is the surface dielectric constant related to soil moisture, S HH and S VV are the horizontal and vertical polarization backscatter coefficients, respectively; S52. Collect passive microwave data for the time and area corresponding to S1, calculate the minimum and maximum values ​​of the soil moisture data product for the selected time and area, and inversely calculate the minimum and maximum values ​​of the corresponding surface dielectric constant; S53, the dielectric constant ε soil The maximum and minimum values ​​of are used as constraints, and the surface scattering model and even-order scattering model are embedded for solution.

7. The spaceborne polarization target decomposition optimization algorithm taking into account the surface dielectric property constraints and Faraday rotation compensation according to claim 1, characterized in that: Step S6 includes: S61. Using the adaptive volume scattering model GVSM as the volume scattering model, an optimized polarization target decomposition model is constructed as follows: Where T3 is the polarization coherence matrix of synthetic aperture radar observation, T S_X-Bragg is the improved surface scattering Bragg scattering model, T XD_Fresnel For the improved even-order scattering double Fresnel scattering model, [T V ] GVSM is an adaptive volume scattering model; S62 , integrating steps S1 - S5 , optimizing the surface scattering model, the even-order scattering model and the volume scattering model, and iteratively solving each scattering power using a multivariate nonlinear equation.

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