Soil moisture inversion method based on dual-polarization decomposition and earth surface scattering model

Abstract

The invention relates to a soil moisture inversion method based on dual-polarization decomposition and a surface scattering model, and the method comprises the steps: carrying out the preprocessing of a dual-polarization SAR image of a target research region; establishing an earth surface-vegetation scattering model for describing the scattering process between the earth surface and vegetation according to the dual-polarization generalized volume scattering model and the Oh earth surface scattering model; reconstructing the observation covariance matrix and the dual-polarization generalized volume scattering model into a projection on a Stokes vector, and calibrating earth surface root mean square height and vegetation structure parameters by using a cost function between back scattering observation values and back scattering simulation values of different polarization channels and ground actual measurement data; based on the calibrated earth surface-vegetation scattering model, a corresponding dielectric constant is obtained through nonlinear least square fitting calculation, the dielectric constant is converted into soil moisture through a Topp model, and a soil moisture inversion result is obtained. According to the embodiment of the invention, modeling is carried out on a scattering scene by using limited information of dual-polarized SAR data, and accurate inversion of soil moisture is realized on the basis of effectively decoupling earth surface and vegetation scattering signals.

Description

Technical Field

[0001] The present application relates to the technical field of polarization SAR information interpretation, and in particular to a soil moisture inversion method based on dual-polarization decomposition and a surface scattering model. Background Art

[0002] Accurate estimation of soil moisture at the field scale is crucial for agricultural applications such as irrigation management optimization and crop yield prediction. Compared with optical remote sensing, polarimetric synthetic aperture radar has stronger penetration and high-resolution imaging capabilities, and is sensitive to the canopy structure of vegetation and the dielectric properties of the surface, providing an effective means for estimating soil moisture in vegetation-covered areas. However, due to the complex interaction between electromagnetic waves on the surface and the vegetation canopy, accurately estimating the surface soil moisture from microwave backscattered signals remains a difficult challenge.

[0003] At present, scholars have conducted extensive research, and the soil moisture inversion strategies for vegetation coverage scenes can be mainly divided into three categories. The data-driven statistical regression method is not restricted by physical conditions, and the soil moisture is predicted by establishing connections within the feature space. However, the prediction process is opaque, lacks physical explanation, and has poor transferability. Iterative fitting methods based on empirical and semi-empirical models, such as water cloud models, use empirical parameters to modulate the relationship between backscatter observations and soil moisture, surface roughness, and vegetation parameters. This method is usually only applicable to specific surface and vegetation conditions, and requires NDVI constructed from optical data to describe the vegetation growth state, resulting in deviations in soil moisture estimation. In contrast, the analytical method based on the physical scattering model theoretically models different scattering mechanisms, so it has a clear process explanation and strong applicability. The typical representative is the polarimetric SAR decomposition technology.

[0004] Polarization decomposition technology can use signals from different polarization channels to describe the complex interaction process between soil and canopy, and realize the decoupling of scattering signals from the surface and vegetation and the inversion of soil moisture. In recent years, studies have made certain improvements to the limitations of polarization decomposition to improve the accuracy and robustness of soil moisture estimation. For example, by introducing a more generalized volume scattering model to make it more suitable for vegetation scenes with complex structures. Coupling a surface scattering model applicable to a wider range of soil roughness can also improve the inversion performance of soil moisture. In addition, by introducing multi-angle, multi-phase and multi-frequency SAR data to expand the observation space, it can assist in achieving fine modeling and positive parameter solution. However, due to the large number of unknown parameters in the model used to simulate the surface and vegetation scattering process, polarization decomposition relies on fully polarized SAR data, which usually leads to high data costs and limited coverage. Therefore, it is of great value to explore the soil moisture inversion method in vegetation-covered areas based on dual-polarization SAR data and physical scattering models. Summary of the invention

[0005] The present application provides a soil moisture inversion method based on dual polarization decomposition and a surface scattering model to solve the problem that related technologies are difficult to accurately estimate the soil moisture on the surface from microwave backscattering signals.

[0006] The first aspect of the present application provides a soil moisture inversion method based on dual-polarization decomposition and a surface scattering model, comprising the following steps: preprocessing the dual-polarization SAR image of the target study area to convert the dual-polarization SAR image into an observation covariance matrix; constructing a dual-polarization generalized volume scattering model considering vegetation structure parameters and an Oh surface scattering model considering the root mean square height of the surface, and establishing a surface-vegetation scattering model describing the scattering process between the surface and vegetation based on the dual-polarization generalized volume scattering model and the Oh surface scattering model; reconstructing the observation covariance matrix and the dual-polarization generalized volume scattering model into a Stokes vector A projection on a volume is performed to obtain a corresponding volume scattering power by using the projection, and backscattering simulation values ​​of different polarization channels are obtained based on the volume scattering power. The surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model are calibrated by using a cost function between the backscattering observation values ​​of different polarization channels and the backscattering simulation values ​​and ground measured data to obtain a calibrated surface-vegetation scattering model. Based on the calibrated surface-vegetation scattering model, the corresponding dielectric constant is obtained by nonlinear least squares fitting solution, and the dielectric constant is converted into soil moisture by using a Topp model to obtain a soil moisture inversion result.

[0007] Optionally, in one embodiment of the present application, the dual-polarization SAR image of the target study area is preprocessed to convert the dual-polarization SAR image into an observation covariance matrix, including: performing orbit correction, radiation calibration, covariance matrix extraction, Lee speckle filtering and terrain correction preprocessing operations on the dual-polarization SAR image of the target study area to obtain the observation covariance matrix.

[0008] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formulas of the surface-vegetation scattering model are respectively:

[0009]

[0010]

[0011] in, and are the backscattering simulation values ​​of the different polarization channels respectively, and are respectively the surface backscattering simulation values ​​of the different polarization channels obtained according to the Oh surface scattering model, s is the surface root mean square height, ε is the dielectric constant, f v is the volume scattering amplitude, and γ is the vegetation structure parameter.

[0012] Optionally, in one embodiment of the present application, the observation covariance matrix and the dual-polarization generalized volume scattering model are projected to obtain an observed Stokes vector, and the observed Stokes vector is decomposed into a linear sum of a fully polarized wave and a partially polarized wave, and the calculation formula is:

[0013] S=m v S v +m p S p +nS n ,

[0014] Among them, nS n is the noise term that can be ignored, S p and m p denote the fully polarized wave and the corresponding scattered power, S v and m v are respectively the Stokes vector of the dual-polarization generalized volume scattering model and the corresponding scattering power, which is expressed as a partially polarized wave;

[0015] The fully polarized wave satisfies the equation:

[0016] (Sm v S v ) T G(Sm v S v )=0,

[0017]

[0018] Among them, T represents the transposition operation, S v and m v are the Stokes vector of the dual-polarization generalized volume scattering model and the volume scattering power, respectively, and S is the observed Stokes vector obtained by projection;

[0019] The equation satisfied by the fully polarized wave is expanded to obtain a quadratic equation:

[0020]

[0021] Where a, b, c are the coefficients of the quadratic equation, S i and S vi are vectors S and S respectively v The i-th element ofv The only solution is:

[0022]

[0023] Wherein, a, b, and c are coefficients of the quadratic equation respectively.

[0024] Optionally, in one embodiment of the present application, the cost function between the backscatter observation values ​​and the backscatter simulation values ​​of different polarization channels and the ground measured data are used to calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model, including: inputting the ground site measured value of soil moisture to construct a lookup table for the backscatter observation values ​​and the backscatter simulation values; based on the lookup table, minimizing the cost function between the backscatter observation values ​​and the backscatter simulation values, and calibrating the surface root mean square height and the vegetation structure parameters according to the cost function.

[0025] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formula of the cost function between the backscattering observation value and the backscattering simulation value is:

[0026]

[0027]

[0028] in, and are the backscattering observation values ​​of the different polarization channels respectively, and are respectively the backscattering simulation values ​​of the different polarization channels.

[0029] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formulas of the calibrated surface-vegetation scattering model are respectively:

[0030]

[0031]

[0032] in, and are the backscattering observation values ​​of the different polarization channels, s c is the calibrated surface RMS height, ε is the dielectric constant, f v is the volume scattering amplitude, γ c is the vegetation structure parameter after calibration;

[0033] Optionally, in one embodiment of the present application, the corresponding dielectric constant is obtained by nonlinear least squares fitting based on the calibrated surface-vegetation scattering model, and the calculation formula for converting the dielectric constant into soil moisture using the Topp model is:

[0034] SM=-5.3+2.92ε-0.055ε 2 +0.0043ε 3 ,

[0035] Wherein, SM is the soil moisture, and ε is the dielectric constant.

[0036] The second aspect of the present application provides a soil moisture inversion device based on dual-polarization decomposition and a surface scattering model, comprising: a preprocessing module, used to preprocess the dual-polarization SAR image of the target study area to convert the dual-polarization SAR image into an observation covariance matrix; a construction module, used to construct a dual-polarization generalized volume scattering model considering vegetation structure parameters and an Oh surface scattering model considering the root mean square height of the surface, and to establish a surface-vegetation scattering model describing the scattering process between the surface and vegetation based on the dual-polarization generalized volume scattering model and the Oh surface scattering model; a reconstruction module, used to reconstruct the observation covariance matrix and the dual-polarization generalized volume scattering model into an observation covariance matrix in St The invention relates to a method for calibrating the surface-vegetation scattering model by using a cost function between the backscattering observation values ​​of different polarization channels and the backscattering simulation values ​​and ground measured data to calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model to obtain a calibrated surface-vegetation scattering model; an inversion module is used to obtain the corresponding dielectric constant based on the calibrated surface-vegetation scattering model through nonlinear least squares fitting solution, and convert the dielectric constant into soil moisture using a Topp model to obtain a soil moisture inversion result.

[0037] Optionally, in one embodiment of the present application, the preprocessing module includes: a preprocessing unit, used to perform orbit correction, radiation calibration, covariance matrix extraction, Lee speckle filtering and terrain correction preprocessing operations on the dual-polarization SAR image of the target study area to obtain the observation covariance matrix.

[0038] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formulas of the surface-vegetation scattering model are respectively:

[0039]

[0040]

[0041] in, and are the backscattering simulation values ​​of the different polarization channels respectively, and are respectively the surface backscattering simulation values ​​of the different polarization channels obtained according to the Oh surface scattering model, s is the surface root mean square height, ε is the dielectric constant, f v is the volume scattering amplitude, and γ is the vegetation structure parameter.

[0042] Optionally, in one embodiment of the present application, the observation covariance matrix and the dual-polarization generalized volume scattering model are projected to obtain an observed Stokes vector, and the observed Stokes vector is decomposed into a linear sum of a fully polarized wave and a partially polarized wave, and the calculation formula is:

[0043] S=m v S v +m p S p +nS n ,

[0044] Among them, nS n is the noise term that can be ignored, S p and m p denote the fully polarized wave and the corresponding scattered power, S v and m v are respectively the Stokes vector of the dual-polarization generalized volume scattering model and the corresponding scattering power, which is expressed as a partially polarized wave;

[0045] The fully polarized wave satisfies the equation:

[0046] (Sm v S v ) T G(Sm v S v )=0,

[0047]

[0048] Among them, T represents the transposition operation, S v and m v are the Stokes vector of the dual-polarization generalized volume scattering model and the volume scattering power, respectively, and S is the observed Stokes vector obtained by projection;

[0049] The equation satisfied by the fully polarized wave is expanded to obtain a quadratic equation:

[0050]

[0051] Where a, b, c are the coefficients of the quadratic equation, S i and S vi are vectors S and S respectively v The i-th element of v The only solution is:

[0052]

[0053] Wherein, a, b, and c are coefficients of the quadratic equation respectively.

[0054] Optionally, in one embodiment of the present application, the reconstruction module includes: a construction unit for inputting ground site measured values ​​of soil moisture to construct a lookup table for the backscatter observation values ​​and the backscatter simulation values; a calibration unit for minimizing the cost function between the backscatter observation values ​​and the backscatter simulation values ​​based on the lookup table, and calibrating the surface root mean square height and the vegetation structure parameters according to the cost function.

[0055] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formula of the cost function between the backscattering observation value and the backscattering simulation value is:

[0056]

[0057]

[0058] in, and are the backscattering observation values ​​of the different polarization channels respectively, and are respectively the backscattering simulation values ​​of the different polarization channels.

[0059] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calibrated surface-vegetation scattering model calculation formulas are respectively:

[0060]

[0061]

[0062] in, and are the backscattering observation values ​​of the different polarization channels, s c is the calibrated surface RMS height, ε is the dielectric constant, f v is the volume scattering amplitude, γ c is the vegetation structure parameter after calibration;

[0063] Optionally, in one embodiment of the present application, the corresponding dielectric constant is obtained by nonlinear least squares fitting based on the calibrated surface-vegetation scattering model, and the calculation formula for converting the dielectric constant into soil moisture using the Topp model is:

[0064] SM=-5.3+2.92ε-0.055ε 2 +0.0043ε 3 ,

[0065] Wherein, SM is the soil moisture, and ε is the dielectric constant.

[0066] The embodiment of the present application can utilize the limited observation information of dual-polarization SAR in different imaging modes, consider the influence of surface roughness, and adaptively model the heterogeneity of vegetation canopy based on the dual-polarization generalized volume scattering model, and utilize the properties of the Stokes vector to invert soil moisture on the basis of reasonable separation of surface and vegetation scattering signals, thereby improving the applicability and accuracy of the soil moisture inversion method. Thus, the problem that it is difficult for related technologies to accurately estimate surface soil moisture from microwave backscattering signals is solved.

[0067] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0069] Figure 1 A flow chart of a soil moisture inversion method based on dual polarization decomposition and a surface scattering model provided according to an embodiment of the present application;

[0070] Figure 2 This is a diagram showing the response of the Oh model to the surface parameters of the soil moisture inversion method based on dual-polarization decomposition and surface scattering model according to one embodiment of the present application;

[0071] Figure 3 The present invention is an overall technical roadmap of a soil moisture inversion method based on dual polarization decomposition and a surface scattering model according to an embodiment of the present application;

[0072] Figure 4 A schematic diagram of the structure of a soil moisture inversion device based on dual-polarization decomposition and surface scattering model provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0073] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0074] The following describes the soil moisture inversion method based on dual polarization decomposition and surface scattering model of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the related art mentioned in the above background technology is difficult to accurately estimate the soil moisture of the surface from the microwave backscattering signal, the present application provides a soil moisture inversion method based on dual polarization decomposition and surface scattering model. In this method, the limited observation information of dual polarization SAR in different imaging modes can be used, and the influence of surface roughness is considered. Based on the dual polarization generalized volume scattering model, the heterogeneity of the vegetation canopy is adaptively modeled, and the properties of the Stokes vector are used to invert the soil moisture on the basis of reasonably separating the surface and vegetation scattering signals, thereby improving the applicability and accuracy of the soil moisture inversion method. As a result, the problem that the related art is difficult to accurately estimate the soil moisture of the surface from the microwave backscattering signal is solved.

[0075] Specifically, Figure 1 A schematic flow chart of a soil moisture inversion method based on dual-polarization decomposition and a surface scattering model provided in an embodiment of the present application.

[0076] like Figure 1 As shown, the soil moisture inversion method based on dual polarization decomposition and surface scattering model includes the following steps:

[0077] In step S101, the dual-polarization SAR image of the target study area is preprocessed to convert the dual-polarization SAR image into an observation covariance matrix.

[0078] During the actual implementation process, the embodiment of the present application can obtain the dual-polarization SAR image of the target study area and perform preprocessing operations to convert the dual-polarization SAR image into an observation covariance matrix, thereby improving the quality of the data and making the subsequent soil moisture inversion analysis more efficient and accurate.

[0079] Optionally, in one embodiment of the present application, the dual-polarization SAR image of the target study area is preprocessed to convert the dual-polarization SAR image into an observation covariance matrix, including: performing orbit correction, radiation calibration, covariance matrix extraction, Lee speckle filtering and terrain correction preprocessing operations on the dual-polarization SAR image of the target study area to obtain the observation covariance matrix.

[0080] As a possible implementation method, the embodiment of the present application can perform preprocessing operations such as orbit correction, radiation calibration, covariance matrix extraction, refined Lee speckle filtering and terrain correction on the dual-polarized SAR image of the target study area, and convert the original dual-polarized SAR image into an observation covariance matrix, thereby improving the data quality used in subsequent analysis.

[0081] In step S102, a dual-polarization generalized volume scattering model considering vegetation structure parameters and an Oh surface scattering model considering the surface root mean square height are constructed, and a surface-vegetation scattering model describing the scattering process between the surface and vegetation is established based on the dual-polarization generalized volume scattering model and the Oh surface scattering model.

[0082] In the actual implementation process, the embodiment of the present application can use a dual-polarization generalized volume scattering model that takes into account the heterogeneity of vegetation structure and an Oh surface scattering model that takes into account the root mean square height of the surface to model the surface-vegetation scattering process, thereby establishing a surface-vegetation scattering model that describes the scattering process between the surface and vegetation, and then obtaining backscattering simulation values ​​of different polarization channels.

[0083] Among them, the generalized volume scattering model takes into account the asymmetric particle reflection based on the full polarization SAR decomposition theory, assumes that the scattering particle shape is a dipole and models the canopy scattering process based on vegetation structure parameters. The covariance matrix can be expressed as:

[0084]

[0085] Among them, γ is the vegetation structure parameter, f v is the volume scattering amplitude. According to the mapping relationship between the full polarization and dual polarization covariance matrices, the 2×2 dual polarization generalized volume scattering model covariance matrices corresponding to the HH-HV and VV-VH imaging modes can be written as:

[0086]

[0087]

[0088] The Oh surface scattering model is a semi-empirical model based on a large amount of measured data and observation data from different radar systems. The formula is written as:

[0089]

[0090] g=0.7(1-exp(-0.65(ks) 1.8 )),

[0091]

[0092]

[0093] in, and are the simulated values ​​of surface backscattering of different polarization channels obtained by Oh model, k is the wave number, θ is the local incident angle, s is the root mean square height of the surface, and ε is the dielectric constant. Figure 2 Shows the Oh model obtained and Response as the dielectric constant and ground RMS height parameters vary.

[0094] Specifically, the embodiment of the present application can construct a surface-vegetation scattering model by combining the dual-polarization generalized volume scattering model and the Oh surface scattering model to describe the scattering process between the surface and vegetation. When the imaging mode is HH-HV, the surface-vegetation scattering model formula is:

[0095]

[0096] When the imaging mode is VV-VH, the surface-vegetation scattering model formula is:

[0097]

[0098] in, and They are the backscattering simulation values ​​of different polarization channels.

[0099] In step S103, the observation covariance matrix and the dual-polarization generalized volume scattering model are reconstructed as projections on the Stokes vector, so as to obtain the corresponding volume scattering power by using the projection, and the backscattering simulation values ​​of different polarization channels are obtained based on the volume scattering power. The surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model are calibrated by using the cost function between the backscattering observation values ​​and the backscattering simulation values ​​of different polarization channels and the ground measured data to obtain the calibrated surface-vegetation scattering model.

[0100] It can be understood that the embodiment of the present application takes into account the imbalance between the limited observation values ​​of the dual-polarization SAR and the number of unknown parameters in the scattering model. In order to achieve a stable solution of the model parameters, the volume scattering power is simply obtained by projecting the observation covariance matrix and the dual-polarization generalized volume scattering model into the form of a Stokes vector, and the backscattering simulation values ​​of different polarization channels are obtained based on the volume scattering power. The cost function between the backscattering observation values ​​and the backscattering simulation values ​​of different polarization channels and the ground measured data are used to calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model to obtain a calibrated surface-vegetation scattering model, thereby utilizing the properties of the Stokes vector to invert soil moisture on the basis of reasonably separating the surface and vegetation scattering signals, thereby improving the applicability and accuracy of the soil moisture inversion method.

[0101] In one embodiment of the present application, the observation covariance matrix and the dual-polarization generalized volume scattering model are reconstructed as projections on the Stokes vector, that is, the observed Stokes vector S obtained by projection can be decomposed into a linear sum of a fully polarized wave and a partially polarized wave, and the calculation formula is:

[0102] S=m v S v +m p S p +nS n ,

[0103] Among them, nS n is the noise term that can be ignored, S p and m p denote the fully polarized wave and the corresponding scattered power, S v and m v They are the Stokes vector and the corresponding scattered power of the dual-polarization generalized volume scattering model, which appear as partially polarized waves.

[0104] According to the mathematical properties of the fully polarized wave, the following equation can be established in the embodiment of the present application:

[0105] (Sm v S v ) T G(Sm v S v )=0,

[0106]

[0107] Where T represents the transposition operation. Expanding the above formula can obtain a quadratic equation:

[0108]

[0109] Among them, S i and vi are vectors S and S respectively v The ith element of . Further, we can directly get m v The two roots of , select the root less than the total power S1 as m v The unique solution to obtain the volume scattered power is:

[0110]

[0111] Among them, a, b, and c are the coefficients of the quadratic equation.

[0112] Optionally, in one embodiment of the present application, the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model are calibrated using the cost function between the backscattering observation values ​​and the backscattering simulation values ​​of different polarization channels and the ground measured data, including: inputting the ground station measured value of soil moisture to construct a lookup table for backscattering observation values ​​and backscattering simulation values; based on the lookup table, minimizing the cost function between the backscattering observation values ​​and the backscattering simulation values, and calibrating the surface root mean square height and vegetation structure parameters according to the cost function.

[0113] In the actual implementation process, the embodiment of the present application can set the range of the surface root mean square height to [0, 10], the step size to 0.05, and the unit is centimeter, and the range of vegetation structure parameters to [-5, 5], the step size to 0.05, and the unit is decibel. The ground site measured value of soil moisture is input to construct a lookup table for backscatter observations and simulated values. The surface root mean square height and vegetation structure parameters are calibrated by minimizing the cost function between the backscatter observations and the simulated values.

[0114] In one embodiment of the present application, the cost function formulas of the HH-HV and VV-VH imaging modes are respectively:

[0115]

[0116]

[0117] in, and are the backscatter observation values ​​of different polarization channels, and are the simulated backscatter values ​​of different polarization channels, which come directly from the observation covariance matrix.

[0118] In one embodiment of the present application, the calibrated surface root mean square height and vegetation structure parameters are respectively substituted into the Oh surface scattering model and the dual-polarization generalized volume scattering model to obtain a calibrated scattering model. At this time, the backscattering observation values ​​of different polarization channels of the HH-HV and VV-VH imaging modes can be expressed as:

[0119]

[0120]

[0121] in, and are the backscattering observation values ​​of different polarization channels, s c is the calibrated surface RMS height, ε is the dielectric constant, f v is the volume scattering amplitude, γc is the vegetation structure parameter after calibration;

[0122] In step S104, based on the calibrated surface-vegetation scattering model, the corresponding dielectric constant is obtained by nonlinear least squares fitting, and the dielectric constant is converted into soil moisture using the Topp model to obtain the soil moisture inversion result.

[0123] During the actual implementation process, the embodiment of the present application can obtain the corresponding dielectric constant based on the calibrated surface-vegetation scattering model through nonlinear least squares fitting, and use the Topp model to convert the corresponding dielectric constant into the corresponding soil moisture inversion result, thereby achieving accurate inversion of soil moisture on the basis of effectively decoupling the surface and vegetation scattering signals.

[0124] In one embodiment of the present application, based on the calibrated surface-vegetation scattering model, the corresponding dielectric constant is obtained by nonlinear least squares fitting, and the dielectric constant is converted into soil moisture using the Topp model as follows:

[0125] SM=-5.3+2.92ε-0.055ε 2 +0.0043ε 3 ,

[0126] Where SM is the soil moisture and ε is the dielectric constant.

[0127] like Figure 3 As shown, the embodiment of the present application may include step 1: dual-polarization SAR image preprocessing.

[0128] Step 2: Construction of surface-vegetation scattering model.

[0129] Step 3: Calibration of surface-vegetation scattering model parameters.

[0130] Step 4: Soil moisture inversion.

[0131] According to the soil moisture inversion method based on dual-polarization decomposition and surface scattering model proposed in the embodiment of the present application, the limited observation information of dual-polarization SAR in different imaging modes can be used, the influence of surface roughness is considered, and the heterogeneity of vegetation canopy is adaptively modeled based on the dual-polarization generalized volume scattering model, and the properties of the Stokes vector are used to invert soil moisture on the basis of reasonable separation of surface and vegetation scattering signals, thereby improving the applicability and accuracy of the soil moisture inversion method. Thus, the problem that the related technology is difficult to accurately estimate the surface soil moisture from the microwave backscattering signal is solved.

[0132] Next, a soil moisture inversion device based on dual-polarization decomposition and a surface scattering model proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0133] Figure 4 It is a structural schematic diagram of a soil moisture inversion device based on dual-polarization decomposition and surface scattering model in an embodiment of the present application.

[0134] like Figure 4 As shown, the soil moisture inversion device 10 based on dual-polarization decomposition and surface scattering model includes: a preprocessing module 100, a construction module 200, a reconstruction module 300 and an inversion module 400.

[0135] Specifically, the preprocessing module 100 is used to preprocess the dual-polarization SAR image of the target study area to convert the dual-polarization SAR image into an observation covariance matrix.

[0136] The construction module 200 is used to construct a dual-polarization generalized volume scattering model considering vegetation structure parameters and an Oh surface scattering model considering the root mean square height of the surface, and to establish a surface-vegetation scattering model describing the scattering process between the surface and vegetation based on the dual-polarization generalized volume scattering model and the Oh surface scattering model.

[0137] The reconstruction module 300 is used to reconstruct the observation covariance matrix and the dual-polarization generalized volume scattering model into projections on the Stokes vector, so as to obtain the corresponding volume scattering power by using the projection, and obtain the backscattering simulation values ​​of different polarization channels based on the volume scattering power, and calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model by using the cost function between the backscattering observation values ​​and the backscattering simulation values ​​of different polarization channels and the ground measured data to obtain the calibrated surface-vegetation scattering model.

[0138] The inversion module 400 is used to obtain the corresponding dielectric constant through nonlinear least square fitting based on the calibrated surface-vegetation scattering model, and convert the dielectric constant into soil moisture using the Topp model to obtain the soil moisture inversion result.

[0139] Optionally, in one embodiment of the present application, the preprocessing module 100 includes: a preprocessing unit.

[0140] Among them, the preprocessing unit is used to perform orbit correction, radiation calibration, covariance matrix extraction, Lee speckle filtering and terrain correction preprocessing operations on the dual-polarization SAR images of the target study area to obtain the observation covariance matrix.

[0141] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formulas of the surface-vegetation scattering model are respectively:

[0142]

[0143]

[0144] in, and are the backscattering simulation values ​​of different polarization channels, and are the simulated values ​​of the surface backscattering of different polarization channels obtained according to the Oh surface scattering model, s is the root mean square height of the surface, ε is the dielectric constant, and f v is the volume scattering amplitude, and γ is the vegetation structure parameter.

[0145] Optionally, in one embodiment of the present application, the observation covariance matrix and the dual-polarization generalized volume scattering model are projected to obtain the observed Stokes vector, and the observed Stokes vector is decomposed into a linear sum of a fully polarized wave and a partially polarized wave, and the calculation formula is:

[0146] S=m v S v +m p S p +nS n ,

[0147] Among them, nS n is the noise term that can be ignored, S p and m p denote the fully polarized wave and the corresponding scattered power, S v and m v are the Stokes vector and the corresponding scattered power of the dual-polarization generalized volume scattering model, which is manifested as a partially polarized wave;

[0148] A fully polarized wave satisfies the equation:

[0149] (Sm v S v ) T G(Sm v S v )=0,

[0150]

[0151] Among them, T represents the transposition operation, S v and m v are the Stokes vector and volume scattering power of the dual-polarization generalized volume scattering model, respectively, and S is the observed Stokes vector obtained by projection;

[0152] The equation satisfied by the fully polarized wave is expanded to obtain a quadratic equation:

[0153]

[0154] Among them, a, b, and c are the coefficients of the quadratic equation, S i and S vi are vectors S and S respectively v The i-th element of ;

[0155] Volume scattering power m v The only solution is:

[0156]

[0157] Among them, a, b, and c are the coefficients of the quadratic equation.

[0158] Optionally, in one embodiment of the present application, the reconstruction module 300 includes: a construction unit and a calibration unit.

[0159] The construction unit is used to input the measured value of soil moisture at the ground site to construct a lookup table for backscattering observation values ​​and backscattering simulation values.

[0160] The calibration unit is used to minimize the cost function between the backscatter observation value and the backscatter simulation value based on the lookup table, and calibrate the surface root mean square height and vegetation structure parameters according to the cost function.

[0161] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formula of the cost function between the backscattering observation value and the backscattering simulation value is:

[0162]

[0163]

[0164] in, and are the backscatter observation values ​​of different polarization channels, and They are the backscattering simulation values ​​of different polarization channels.

[0165] Optionally, in one embodiment of the present application, for HH-HV and VV-VH imaging modes, the calculation formulas of the calibrated surface-vegetation scattering model are respectively:

[0166]

[0167]

[0168] in, and are the backscattering observation values ​​of different polarization channels, s c is the calibrated surface RMS height, ε is the dielectric constant, fv is the volume scattering amplitude, γ c is the calibrated vegetation structure parameter.

[0169] Optionally, in one embodiment of the present application, based on the calibrated surface-vegetation scattering model, the corresponding dielectric constant is obtained by nonlinear least squares fitting, and the dielectric constant is converted into soil moisture using the Topp model as follows:

[0170] SM=-5.3+2.92ε-0.055ε 2 +0.0043ε 3 ,

[0171] Where SM is the soil moisture and ε is the dielectric constant.

[0172] It should be noted that the above explanation of the embodiment of the soil moisture inversion method based on dual polarization decomposition and surface scattering model is also applicable to the soil moisture inversion device based on dual polarization decomposition and surface scattering model in this embodiment, which will not be repeated here.

[0173] According to the soil moisture inversion device based on dual polarization decomposition and surface scattering model proposed in the embodiment of the present application, the limited observation information of dual polarization SAR in different imaging modes can be utilized, the influence of surface roughness is considered, and the heterogeneity of vegetation canopy is adaptively modeled based on the dual polarization generalized volume scattering model, and the properties of Stokes vector are used to invert soil moisture on the basis of reasonable separation of surface and vegetation scattering signals, thereby improving the applicability and accuracy of the soil moisture inversion method. Thus, the problem that the related technology is difficult to accurately estimate the surface soil moisture from microwave backscattering signals is solved.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0175] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0176] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

Claims

1. A soil moisture inversion method based on dual polarization decomposition and surface scattering model, characterized in that: The following steps are involved: Preprocessing the dual-polarization SAR image of the target study area to convert the dual-polarization SAR image into an observation covariance matrix; Constructing a dual-polarization generalized volume scattering model considering vegetation structure parameters and an Oh surface scattering model considering the root mean square height of the surface, and establishing a surface-vegetation scattering model describing the scattering process between the surface and vegetation based on the dual-polarization generalized volume scattering model and the Oh surface scattering model; Reconstructing the observation covariance matrix and the dual-polarization generalized volume scattering model as projections on the Stokes vector, so as to obtain corresponding volume scattering power by using the projection, and obtaining backscattering simulation values ​​of different polarization channels based on the volume scattering power, and calibrating the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model by using the cost function between the backscattering observation values ​​of different polarization channels and the backscattering simulation values ​​and ground measured data to obtain a calibrated surface-vegetation scattering model; Based on the calibrated surface-vegetation scattering model, the corresponding dielectric constant is obtained by nonlinear least square fitting, and the dielectric constant is converted into soil moisture using the Topp model to obtain a soil moisture inversion result.

2. The method according to claim 1, characterized in that The preprocessing of the dual-polarization SAR image of the target study area to convert the dual-polarization SAR image into an observation covariance matrix includes: The dual-polarization SAR image of the target study area is subjected to orbit correction, radiation calibration, covariance matrix extraction, Lee speckle filtering and terrain correction preprocessing operations to obtain the observation covariance matrix.

3. The method according to claim 1, characterized in that For the HH-HV and VV-VH imaging modes, the calculation formulas of the surface-vegetation scattering model are: in, and are the backscattering simulation values ​​of the different polarization channels respectively, and are respectively the surface backscattering simulation values ​​of the different polarization channels obtained according to the Oh surface scattering model, s is the surface root mean square height, ε is the dielectric constant, f v is the volume scattering amplitude, and γ is the vegetation structure parameter.

4. The method according to claim 3, characterized in that The observation covariance matrix and the dual-polarization generalized volume scattering model are projected to obtain the observation Stokes vector, and the observation Stokes vector is decomposed into the linear sum of the fully polarized wave and the partially polarized wave, and the calculation formula is: S=m v S v +m p S p +nS n , Among them, nS n is the noise term that can be ignored, S p and m p denote the fully polarized wave and the corresponding scattered power, S v and m v are the Stokes vector of the dual-polarization generalized volume scattering model and the volume scattering power, respectively, which are expressed as partially polarized waves; The fully polarized wave satisfies the equation: (S-m v S v ) T G(S-m v S v )=0, Among them, T represents the transposition operation, S v and m v are the Stokes vector of the dual-polarization generalized volume scattering model and the volume scattering power, respectively, and S is the observed Stokes vector obtained by projection; The equation satisfied by the fully polarized wave is expanded to obtain a quadratic equation: Where a, b, c are the coefficients of the quadratic equation, S i and S vi are vectors S and S respectively v The i-th element of ; The volume scattered power m v The only solution is: Wherein, a, b, and c are coefficients of the quadratic equation respectively.

5. The method according to claim 1, characterized in that The method of using the cost function between the backscatter observation values ​​of different polarization channels and the backscatter simulation values ​​and ground measured data to calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model includes: Inputting the measured value of soil moisture at a ground site to construct a lookup table for the backscattering observation value and the backscattering simulation value; Based on the lookup table, a cost function between the backscatter observation value and the backscatter simulation value is minimized, and the ground surface root mean square height and the vegetation structure parameter are calibrated according to the cost function.

6. The method according to claim 1, characterized in that For HH-HV and VV-VH imaging modes, the cost function between the backscatter observation value and the backscatter simulation value is calculated as: in, and are the backscattering observation values ​​of the different polarization channels respectively, and are respectively the backscattering simulation values ​​of the different polarization channels.

7. The method according to claim 6, characterized in that For the HH-HV and VV-VH imaging modes, the calculation formulas of the calibrated surface-vegetation scattering model are: in, and are the backscattering observation values ​​of the different polarization channels, s c is the calibrated surface RMS height, ε is the dielectric constant, f v is the volume scattering amplitude, γ c is the calibrated vegetation structure parameter.

8. The method according to claim 1, characterized in that Based on the calibrated surface-vegetation scattering model, the corresponding dielectric constant is obtained by nonlinear least squares fitting, and the calculation formula for converting the dielectric constant into soil moisture using the Topp model is: SM=-5.3+2.92ε-0.055ε 2 +0.0043e 3 , Wherein, SM is the soil moisture, and ε is the dielectric constant.

9. A soil moisture inversion device based on dual polarization decomposition and surface scattering model, characterized in that: include: A preprocessing module, used for preprocessing the dual-polarization SAR image of the target study area to convert the dual-polarization SAR image into an observation covariance matrix; A construction module is used to construct a dual-polarization generalized volume scattering model considering vegetation structure parameters and an Oh surface scattering model considering the root mean square height of the surface, and to establish a surface-vegetation scattering model describing the scattering process between the surface and vegetation based on the dual-polarization generalized volume scattering model and the Oh surface scattering model; A reconstruction module, used to reconstruct the observation covariance matrix and the dual-polarization generalized volume scattering model into projections on the Stokes vector, so as to obtain the corresponding volume scattering power by using the projection, and obtain backscattering simulation values ​​of different polarization channels based on the volume scattering power, and calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model by using the cost function between the backscattering observation values ​​of different polarization channels and the backscattering simulation values ​​and ground measured data to obtain the calibrated surface-vegetation scattering model; The inversion module is used to obtain the corresponding dielectric constant based on the calibrated surface-vegetation scattering model through nonlinear least squares fitting, and convert the dielectric constant into soil moisture using the Topp model to obtain the soil moisture inversion result.

10. The device according to claim 9, characterized in that The preprocessing module comprises: The preprocessing unit is used to perform orbit correction, radiation calibration, covariance matrix extraction, Lee speckle filtering and terrain correction preprocessing operations on the dual-polarization SAR image of the target study area to obtain the observation covariance matrix.

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