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

Abstract

The present application relates to a soil moisture inversion method based on dual-polarization decomposition and a surface scattering model, wherein the method comprises: preprocessing dual-polarization SAR images of a target study area; establishing a surface-vegetation scattering model that describes the scattering process between the surface and vegetation based on a dual-polarization generalized volume scattering model and an Oh surface scattering model; reconstructing the observation covariance matrix and the dual-polarization generalized volume scattering model as projections on a Stokes vector, and calibrating the surface root mean square height and vegetation structure parameters using a cost function between backscatter observations and backscatter simulation values ​​of different polarization channels and ground-measured data; based on the calibrated surface-vegetation scattering model, obtaining the corresponding dielectric constant through nonlinear least squares fitting, and converting the dielectric constant into soil moisture using a Topp model to obtain a soil moisture inversion result. The embodiment of the present application uses limited information from dual-polarization SAR data to model the scattering scene, achieving accurate inversion of soil moisture on the basis of effectively decoupling the surface and vegetation scattering signals.

Description

Technical Field

[0001] The present application relates to the technical field of polarimetric 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] Accurately estimating soil moisture at the field scale is crucial for agricultural applications such as optimizing irrigation management and predicting crop yields. Compared to optical remote sensing, polarimetric synthetic aperture radar (SAR) offers superior penetration and high-resolution imaging capabilities, as well as sensitivity to vegetation canopy structure and the dielectric properties of the ground surface, providing an effective means for estimating soil moisture in vegetated areas. However, accurately estimating surface soil moisture from microwave backscattered signals remains a difficult challenge due to the complex interaction between electromagnetic waves at the ground surface and the vegetation canopy.

[0003] Extensive research has been conducted by scholars, and soil moisture inversion strategies for vegetation cover scenarios can be mainly divided into three categories. Data-driven statistical regression methods are not constrained by physical conditions and predict soil moisture 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, analytical methods based on physical scattering models theoretically model different scattering mechanisms, and therefore have clear process explanations and strong applicability. A typical representative is polarimetric SAR decomposition technology.

[0004] Polarimetric decomposition techniques can utilize signals from different polarization channels to describe the complex interactions between soil and canopy, enabling decoupling of scattering signals from the ground surface and vegetation and soil moisture retrieval. Recent research has addressed the limitations of polarimetric decomposition to improve the accuracy and robustness of soil moisture estimation. For example, by introducing a more generalized volume scattering model, it is more applicable to vegetation scenes with complex structures. Coupling a surface scattering model that is applicable to a wider range of soil roughness can also improve soil moisture retrieval performance. Furthermore, expanding the observation space by incorporating multi-angle, multi-temporal, and multi-frequency SAR data can facilitate refined modeling and positive parameter calculation. However, due to the large number of unknown parameters in the models used to simulate surface and vegetation scattering processes, polarimetric decomposition relies on fully polarimetric SAR data, which often results in high data costs and limited coverage. Therefore, exploring soil moisture retrieval methods for vegetated areas based on dual-polarization SAR data and physical scattering models is of great value. 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 surface soil moisture from microwave backscattered signals.

[0006] The first embodiment of the present application provides a soil moisture inversion method based on dual-polarization decomposition and 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 that describes 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 the surface-vegetation scattering model is performed to obtain the 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, and the dielectric constant is converted into soil moisture by using the Topp model to obtain a soil moisture inversion result.

[0007] Optionally, in one embodiment of the present application, the dual-polarized SAR image of the target study area is preprocessed to convert the dual-polarized 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-polarized SAR image of the target study area to obtain the observation covariance matrix.

[0008] Optionally, in one embodiment of the present application, for the 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 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 represent the fully polarized wave and the corresponding scattered power, S v and m v are the Stokes vector of the dual-polarization generalized body scattering model and the corresponding scattering power, respectively, which are expressed as partially polarized waves;

[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, 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 the body scattering power mv The only solution is:

[0022]

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

[0024] Optionally, in one embodiment of the present application, the cost function between the backscatter observation values ​​of different polarization channels and the backscatter simulation values ​​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 the HH-HV and VV-VH imaging modes, the cost function between the backscatter observation value and the backscatter simulation value is calculated as follows:

[0026]

[0027]

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

[0029] Optionally, in one embodiment of the present application, for the 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 backscatter 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 dielectric constant is converted into soil moisture using the Topp model. The calculation formula 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 embodiment of the present application provides a soil moisture inversion device based on dual-polarization decomposition and surface scattering model, comprising: a preprocessing module 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 for 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; a reconstruction module for reconstructing 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 performing a projection on the okes vector, using the projection to obtain the corresponding body scattering power, and obtaining backscattering simulation values ​​of different polarization channels based on the body 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 the ground measured data 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, and convert the dielectric constant into soil moisture using the 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 the 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 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 represent the fully polarized wave and the corresponding scattered power, S v and m v are the Stokes vector of the dual-polarization generalized body scattering model and the corresponding scattering power, respectively, which are expressed as partially polarized waves;

[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, 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 v The only solution is:

[0052]

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

[0054] Optionally, in one embodiment of the present application, the reconstruction module includes: a construction unit for inputting the ground site measured value of soil moisture to construct a lookup table for the backscatter observation value and the backscatter simulation value; a calibration unit for minimizing the cost function between the backscatter observation value and the backscatter simulation value 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 the HH-HV and VV-VH imaging modes, the cost function between the backscatter observation value and the backscatter simulation value is calculated as follows:

[0056]

[0057]

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

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

[0060]

[0061]

[0062] in, and are the backscatter 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 dielectric constant is converted into soil moisture using the Topp model. The calculation formula 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 embodiments of the present application utilize limited observational information from dual-polarization SAR (SAR) in different imaging modes. By considering the influence of surface roughness and adaptively modeling the heterogeneity of vegetation canopies based on a dual-polarization generalized volume scattering model, the Stokes vector properties are utilized to rationally separate surface and vegetation scattered signals to invert soil moisture, thereby improving the applicability and accuracy of the soil moisture inversion method. This solves the problem that related technologies have difficulty in accurately estimating surface soil moisture from microwave backscatter signals.

[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 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 flowchart 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 surface parameters in a soil moisture inversion method based on dual-polarization decomposition and a surface scattering model according to one embodiment of the present application;

[0071] Figure 3 This 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 This is a structural schematic diagram 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 following describes in detail embodiments of the present application. 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 an embodiment of the present application with reference to the accompanying drawings. In response to the problem that the related technologies mentioned in the above background technology are difficult to accurately estimate the soil moisture of the surface from the microwave backscattered 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 with different imaging modes can be utilized. By considering the influence of surface roughness and adaptively modeling the heterogeneity of the vegetation canopy based on the dual-polarization generalized volume scattering model, 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 technologies are difficult to accurately estimate the soil moisture of the surface from the microwave backscattered signal is solved.

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

[0076] like Figure 1 As shown in FIG, 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 dual-polarization SAR images of the target study area and perform preprocessing operations to convert the dual-polarization SAR images into observation covariance matrices, thereby improving the quality of the data and making 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 root mean square height of the surface are constructed. Then, 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 polarimetric SAR decomposition theory. It assumes that the scattering particles are dipole-shaped 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 backscatter values ​​of the ground surface for 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 ground surface, and ε is the dielectric constant. Figure 2 Shows the Oh model and Response as a function of dielectric constant and ground RMS height parameters.

[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 are the backscattering simulation values ​​of different polarization channels respectively.

[0099] In step S103, the observation covariance matrix and the dual-polarization generalized volume scattering model are reconstructed as projections on the Stokes vector to obtain the corresponding volume scattering power 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 using the cost function between the backscattering observations and the backscattering simulation values ​​of different polarization channels and the ground measured data to obtain a 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. 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 represent 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 body scattering model, respectively, 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 transpose operation. Expanding the above formula yields a quadratic equation:

[0108]

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

[0110]

[0111] Where 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 backscatter observation values ​​and the backscatter 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 backscatter observation values ​​and 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 vegetation structure parameters according to the cost function.

[0113] In actual implementation, the embodiment of the present application can set the range of the surface RMS height to [0, 10], with a step size of 0.05, and the unit is centimeter. The range of the vegetation structure parameter can be set to [-5, 5], with a step size of 0.05, and the unit is decibel. The ground station measured value of soil moisture is input to construct a lookup table for backscatter observations and simulated values. The surface RMS height and vegetation structure parameters are calibrated by minimizing the cost function between the backscatter observations and 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 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 backscatter observation values ​​of different polarization channels in the HH-HV and VV-VH imaging modes can be expressed as:

[0119]

[0120]

[0121] in, and are the backscatter 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] The soil moisture inversion method based on dual-polarization decomposition and a surface scattering model proposed in the embodiments of the present application can utilize the limited observation information of dual-polarization SAR in different imaging modes. By considering the influence of surface roughness and adaptively modeling the heterogeneity of vegetation canopies based on a dual-polarization generalized volume scattering model, the soil moisture is inverted based on the reasonable separation of surface and vegetation scattering signals using the properties of the Stokes vector, thereby improving the applicability and accuracy of the soil moisture inversion method. This solves the problem that related technologies have difficulty in accurately estimating surface soil moisture from microwave backscattered signals.

[0132] Next, a soil moisture inversion device based on dual-polarization decomposition and surface scattering model proposed in an embodiment of the present application will be 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] Construction module 200 is used to construct a dual-polarization generalized volume scattering model that takes into account vegetation structure parameters and an Oh surface scattering model that takes into account the root mean square height of the surface, and to establish a surface-vegetation scattering model that describes the scattering process between the surface and vegetation based on the dual-polarization generalized volume scattering model and the Oh surface scattering model.

[0137] 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 using the projection, and obtain backscattering simulation values ​​of different polarization channels based on the volume scattering power. 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 observations and the backscattering simulation values ​​of different polarization channels and ground measured data to obtain a calibrated surface-vegetation scattering model.

[0138] The inversion module 400 is used to obtain the corresponding dielectric constant through nonlinear least squares 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 the HH-HV and VV-VH imaging modes, the calculation formulas of the surface-vegetation scattering model are:

[0142]

[0143]

[0144] in, and are the backscatter simulation values ​​of different polarization channels, and are the surface backscatter simulation values ​​of different polarization channels obtained according to the Oh surface scattering model, s is the surface root mean square height, 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 the linear sum of the fully polarized wave and the 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 represent 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 body scattering model, which appears as a partially polarized wave;

[0148] The 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] Where 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 backscatter observation values ​​and backscatter 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 cost function between the backscatter observation value and the backscatter simulation value is calculated as follows:

[0162]

[0163]

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

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

[0166]

[0167]

[0168] in, and are the backscatter 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, and will not be repeated here.

[0173] The soil moisture inversion device based on dual-polarization decomposition and a surface scattering model proposed in the embodiments of the present application can utilize the limited observation information of dual-polarization SAR in different imaging modes. By considering the influence of surface roughness and adaptively modeling the heterogeneity of vegetation canopies based on a dual-polarization generalized volume scattering model, the soil moisture is inverted based on the reasonable separation of surface and vegetation scattering signals using the properties of the Stokes vector, thereby improving the applicability and accuracy of the soil moisture inversion method. This solves the problem that related technologies have difficulty in accurately estimating surface soil moisture from microwave backscattered signals.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0175] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise 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 a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

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 that takes into account vegetation structure parameters and an Oh surface scattering model that takes into account the root mean square height of the surface, and establishing a surface-vegetation scattering model that describes 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 a Stokes vector, obtaining corresponding volume scattering powers using the projections, and obtaining backscattering simulation values ​​for different polarization channels based on the volume scattering powers. Calibrating the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model using a cost function between the backscattering observations for 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, 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 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. 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 represent the fully polarized wave and the corresponding scattered power, S v and m v are the Stokes vector of the dual-polarization generalized body scattering model and the body scattering power, respectively, which appear 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, 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 ; The volume scattered power m v The only solution is: Wherein, a, b, and c are the coefficients of the quadratic equation respectively.

5. The method according to claim 1, wherein The method utilizes a cost function between 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, including: Inputting the measured value of soil moisture at a ground site to construct a lookup table related to the backscatter observation value and the backscatter simulation value; Based on the lookup table, a cost function between the backscatter observation value and the backscatter simulation value is minimized, and the surface root mean square height and the vegetation structure parameter are calibrated according to the cost function.

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

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 backscatter 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, wherein 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: 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, configured 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 is used to construct a dual-polarization generalized volume scattering model that takes into account vegetation structure parameters and an Oh surface scattering model that takes into account the root mean square height of the surface, and to establish a surface-vegetation scattering model that describes 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, configured to reconstruct the observation covariance matrix and the dual-polarization generalized volume scattering model into projections on a Stokes vector, obtain corresponding volume scattering powers using the projections, obtain backscattering simulation values ​​for different polarization channels based on the volume scattering powers, and calibrate the surface root mean square height and vegetation structure parameters in the surface-vegetation scattering model using a cost function between the backscattering observations for different polarization channels and the backscattering simulation values ​​and ground measured data, to obtain a 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 a soil moisture inversion result.

10. The device according to claim 9, characterized in that The pre-processing 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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