Soil moisture retrieval method based on neighborhood change detection and passive microwave constraints

Abstract

The present application relates to a soil moisture inversion method based on neighborhood change detection and passive microwave constraints, wherein the method includes: obtaining a synthetic aperture radar (SAR) image covering the target study area and preprocessing the SAR image; obtaining neighborhood homogeneous pixels of the central pixel using pseudo-scattering type parameters and pseudo-scattering entropy parameters; establishing a neighborhood change detection model based on scattering characteristic similarity weighting based on the co-polarization backscattering observations of the central pixel and the neighborhood homogeneous pixels; using the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as the constraint range, performing a linear least squares fitting solution on the soil moisture within the constraint range to obtain a soil moisture inversion result. The embodiment of the present application reduces the uncertainty and data acquisition cost brought by the temporal variability of vegetation to soil moisture inversion, and solves the problem in the related art that the soil moisture temporal change detection method is affected by the temporal variability of vegetation and has low performance in actual application.

Description

Technical Field

[0001] The present application relates to the technical field of microwave remote sensing information interpretation, and in particular to a soil moisture inversion method based on neighborhood change detection and passive microwave constraints. Background Art

[0002] Soil moisture directly affects vegetation growth, water resource management, and the accuracy of meteorological simulations, and is of great significance to agricultural production, the ecological environment, and climate change. Traditional methods of obtaining discrete soil moisture measurements using handheld instruments or station monitoring are costly and difficult to cover large areas. Advances in remote sensing technology have made high-precision, large-scale monitoring of soil moisture more feasible. Polarimetric synthetic aperture radar (PAR) can penetrate clouds and vegetation and provide high-resolution surface information in all-weather conditions, offering unique advantages for soil moisture inversion. However, in practical applications, due to the combined effects of the interaction between surface and vegetation elements, it remains difficult to accurately invert soil moisture from radar backscatter signals.

[0003] Existing soil moisture inversion methods, such as the water cloud model, the Michigan model, and the polarization decomposition model, model the scattering mechanism between the surface and vegetation through theoretical, empirical, and semi-empirical models to extract soil moisture information related to the surface scattering component. The inversion accuracy of these methods is limited by the sophistication and accuracy of the modeling. Furthermore, due to the large number of model parameters, multidimensional data is often required to increase the redundancy of observational information and transform the ill-posed parameter inversion into a robust estimate with reliable constraints.

[0004] On the other hand, change detection techniques avoid the a priori assumption of the spatial distribution of surface and vegetation parameters, assuming them to be constant over a certain timeframe. This eliminates the influence of soil roughness and vegetation growth variations and links differences in time-series SAR observations to changes in soil moisture. The Alpha approximation model, a short-term change detection method, estimates soil moisture by establishing a relationship between the ratio of backscatter coefficients and Fresnel scattering coefficients between adjacent time phases. Its computational simplicity has attracted widespread attention. Time-series inversion methods are inevitably subject to some temporally varying perturbations, and their reliability relies on the validity of the assumption of temporal parameter invariance. However, soil roughness may change during the observation period due to human and natural factors such as erosion, rainfall, and tillage. Vegetation, especially agricultural crops, experience rapid growth during specific phenological periods, resulting in significant variations in morphological structure and moisture content. Eliminating the influence of temporal changes in surface and vegetation requires sufficiently high temporal resolution of the data. Therefore, reducing the uncertainty caused by the temporal variability of surface and vegetation parameters and the stringent requirement for high temporal resolution data will help improve the accuracy and application scope of soil moisture inversion using change detection methods. Summary of the Invention

[0005] This application provides a soil moisture inversion method based on neighborhood change detection and passive microwave constraints to solve the problems in related technologies, such as the soil moisture time series change detection method is affected by the temporal variability of vegetation, the performance of inverting soil moisture is low, and the data acquisition cost is high.

[0006] The first aspect of the present application provides a soil moisture inversion method based on neighborhood change detection and passive microwave constraints, which is applied to the model construction stage and includes the following steps: obtaining a synthetic aperture radar (SAR) image covering the target study area, and preprocessing the SAR image to obtain a preprocessed SAR image that meets preset conditions; based on the preprocessed SAR image, obtaining a neighborhood homogeneous pixel of the central pixel using a pseudo-scattering type parameter and a pseudo-scattering entropy parameter; and establishing a neighborhood change detection model based on scattering characteristic similarity weighting based on the co-polarization backscattering observations of the central pixel and the neighborhood homogeneous pixels.

[0007] Optionally, in one embodiment of the present application, the preprocessing of the SAR image to obtain a preprocessed SAR image that meets preset conditions includes: performing orbit correction, thermal noise removal, radiation calibration, coherent speckle filtering and geocoding preprocessing operations on the SAR image to obtain the preprocessed SAR image that meets the preset conditions.

[0008] Optionally, in one embodiment of the present application, the calculation formula of the pseudo scattering type parameter is:

[0009]

[0010] Where q is the ratio of the backscatter coefficient of cross-polarization to that of co-polarization, and ζ1 and ζ2 are auxiliary parameters;

[0011] The auxiliary parameters are defined as:

[0012]

[0013] in, and are the backscatter observation values ​​of co-polarization and cross-polarization, X and Y represent horizontal H or vertical V polarization, respectively, m c is the copolarization purity parameter ranging from 0 to 1;

[0014] The calculation formula of the pseudo-scattering entropy parameter is:

[0015]

[0016] Among them, p i represents a pseudo-probability measure.

[0017] Optionally, in one embodiment of the present application, the surface co-polarization backscatter values ​​of the central pixel and the neighboring homogeneous pixels are expressed using a first-order small perturbation model as follows:

[0018]

[0019] Where k is the wave number, θ is the incident angle, h is the root mean square height, W is the two-dimensional normalized surface roughness spectrum function, α XX is the Fresnel scattering coefficient for horizontal or vertical polarization;

[0020] The expression for the ratio of the co-polarization backscatter observation values ​​of the central pixel and the homogeneous neighboring pixels is:

[0021]

[0022] Among them, hi represents the homogeneous pixel in the i-th neighborhood, r represents the central pixel, α XX is the Fresnel scattering coefficient of the horizontal or vertical polarization;

[0023] The neighborhood change detection model expression of the scattering characteristic similarity weighted is:

[0024]

[0025] in, is an N-1 dimensional zero vector, w hi is the weight of the homogeneous pixel in the i-th neighborhood, S hi,r is the co-polarization backscatter ratio of the i-th neighborhood homogeneous pixel to the central pixel, α XX,hi and α XX,r are the horizontal or vertical polarization Fresnel scattering coefficients of the i-th neighborhood homogeneous pixel and the central pixel, respectively, and are the pseudo scattering entropy parameters of the i-th neighborhood homogeneous pixel and the central pixel respectively, and are the pseudo scattering type parameters of the i-th neighborhood homogeneous pixel and the central pixel, respectively, and i=1, 2…N.

[0026] The first aspect of the present application provides a soil moisture inversion method based on neighborhood change detection and passive microwave constraints, which is applied to the model application stage and includes the following steps: obtaining co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels; inputting the co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels and the soil moisture into a pre-constructed neighborhood change detection model, and based on the pre-constructed neighborhood change detection model, using the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as the constraint range, performing a linear least squares fitting solution on the soil moisture within the constraint range to obtain a soil moisture inversion result, wherein the neighborhood change detection model is constructed by the co-polarization backscatter observations and the soil moisture.

[0027] Optionally, in one embodiment of the present application, the constraint range is:

[0028] SM min <SM r ,SM hi <SM max , i∈(1,2,…,N),

[0029] Among them, SM min is the minimum soil moisture, SM max is the maximum soil moisture, SM r is the soil moisture value of the central pixel, SM hi is the soil moisture value of the homogeneous pixel in the i-th neighborhood.

[0030] In a third aspect, an embodiment of the present application provides a soil moisture inversion device based on neighborhood change detection and passive microwave constraints, which is applied to the model construction stage and includes: a preprocessing module for acquiring a synthetic aperture radar (SAR) image covering the target study area, and preprocessing the SAR image to obtain a preprocessed SAR image that meets preset conditions; a generation module for obtaining a neighborhood homogeneous pixel of a central pixel based on the preprocessed SAR image using a pseudo-scattering type parameter and a pseudo-scattering entropy parameter; and an establishment module for establishing a neighborhood change detection model based on weighted scattering characteristic similarity based on the co-polarization backscattering observations of the central pixel and the neighborhood homogeneous pixels.

[0031] Optionally, in one embodiment of the present application, the preprocessing module includes: a preprocessing unit, used to perform orbit correction, thermal noise removal, radiation calibration, coherent speckle filtering and geocoding preprocessing operations on the SAR image to obtain the preprocessed SAR image that meets the preset conditions.

[0032] Optionally, in one embodiment of the present application, the calculation formula of the pseudo scattering type parameter is:

[0033]

[0034] Where q is the ratio of the backscatter coefficient of cross-polarization to that of co-polarization, and ζ1 and ζ2 are auxiliary parameters;

[0035] The auxiliary parameters are defined as:

[0036]

[0037] in, and are the backscatter observation values ​​of co-polarization and cross-polarization, X and Y represent horizontal H or vertical V polarization, respectively, m c is the copolarization purity parameter ranging from 0 to 1;

[0038] The calculation formula of the pseudo-scattering entropy parameter is:

[0039]

[0040] Among them, p i represents a pseudo-probability measure.

[0041] Optionally, in one embodiment of the present application, the surface co-polarization backscatter values ​​of the central pixel and the neighboring homogeneous pixels are expressed using a first-order small perturbation model as follows:

[0042]

[0043] Where k is the wave number, θ is the incident angle, h is the root mean square height, W is the two-dimensional normalized surface roughness spectrum function, α XX is the Fresnel scattering coefficient for horizontal or vertical polarization;

[0044] The expression for the ratio of the co-polarization backscatter observation values ​​of the central pixel and the homogeneous neighboring pixels is:

[0045]

[0046] Among them, hi represents the homogeneous pixel in the i-th neighborhood, r represents the central pixel, α XX is the Fresnel scattering coefficient of the horizontal or vertical polarization;

[0047] The neighborhood change detection model expression of the scattering characteristic similarity weighted is:

[0048]

[0049] in, is an N-1 dimensional zero vector, w hi is the weight of the homogeneous pixel in the i-th neighborhood, Shi,r is the co-polarization backscatter ratio of the i-th neighborhood homogeneous pixel to the central pixel, α XX,hi and α XX,r are the horizontal or vertical polarization Fresnel scattering coefficients of the i-th neighborhood homogeneous pixel and the central pixel, respectively, and are the pseudo scattering entropy parameters of the i-th neighborhood homogeneous pixel and the central pixel respectively, and are the pseudo scattering type parameters of the i-th neighborhood homogeneous pixel and the central pixel, respectively, and i=1, 2…N.

[0050] The fourth aspect of the present application provides a soil moisture inversion device based on neighborhood change detection and passive microwave constraints, which is applied to the model application stage, including: an acquisition module for acquiring co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels; an input module for inputting the co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels and the soil moisture into a pre-constructed neighborhood change detection model, and based on the pre-constructed neighborhood change detection model, using the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as the constraint range, performing a linear least squares fitting solution on the soil moisture within the constraint range to obtain a soil moisture inversion result, wherein the neighborhood change detection model is constructed by the co-polarization backscatter observations and the soil moisture.

[0051] Optionally, in one embodiment of the present application, the constraint range is:

[0052] SM min <SM r ,SM hi <SM max , i∈(1,2,…,N),

[0053] Among them, SM min is the minimum soil moisture, SM max is the maximum soil moisture, SM r is the soil moisture value of the central pixel, SM hi is the soil moisture value of the homogeneous pixel in the i-th neighborhood.

[0054] The fifth aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the soil moisture inversion method based on neighborhood change detection and passive microwave constraints as described in the above embodiment.

[0055] The sixth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned soil moisture inversion method based on neighborhood change detection and passive microwave constraints.

[0056] This embodiment of the present application assumes that changes in backscatter observations within a certain spatial range are caused by soil moisture. It uses neighborhood information to expand the observation space and rationally constrains the parameter solution process based on passive microwave products. This reduces the uncertainty and data acquisition cost associated with vegetation temporal variability in soil moisture inversion, thereby improving the performance of change detection methods in inverting soil moisture. This solves the problem in related technologies where soil moisture temporal change detection methods are affected by vegetation temporal variability, resulting in low soil moisture inversion performance and high data acquisition costs.

[0057] 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

[0058] 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:

[0059] Figure 1 A flowchart of a soil moisture inversion method based on neighborhood change detection and passive microwave constraints provided in an embodiment of the present application applied to the model building stage;

[0060] Figure 2 A flowchart of a soil moisture inversion method based on neighborhood change detection and passive microwave constraints provided in an embodiment of the present application applied to a model application stage;

[0061] Figure 3 This is a technical flow chart of a soil moisture inversion method based on neighborhood change detection and passive microwave constraints according to one embodiment of the present application;

[0062] Figure 4 H is a soil moisture inversion method based on neighborhood change detection and passive microwave constraints according to an embodiment of the present application. c -θ c Floor plan;

[0063] Figure 5 Schematic diagram of a sliding average strategy of a soil moisture inversion method based on neighborhood change detection and passive microwave constraints according to an embodiment of the present application;

[0064] Figure 6A schematic structural diagram of a soil moisture inversion device based on neighborhood change detection and passive microwave constraint provided in an embodiment of the present application, applied to a model building stage;

[0065] Figure 7 A schematic structural diagram of a soil moisture inversion device based on neighborhood change detection and passive microwave constraints provided in an embodiment of the present application, applied to a model application stage;

[0066] Figure 8 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0067] 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.

[0068] The following describes the soil moisture inversion method based on neighborhood change detection and passive microwave constraints according to an embodiment of the present application with reference to the accompanying drawings. In view of the problem that the soil moisture time series change detection method mentioned in the above background technology is affected by the temporal variability of vegetation, the performance of inverting soil moisture is low, and the cost of data acquisition is high, the present application provides a soil moisture inversion method based on neighborhood change detection and passive microwave constraints. In this method, it can be assumed that the change of backscattered observations within a certain spatial range is caused by soil moisture, the observation space is expanded by using neighborhood information, and the parameter solution process is reasonably constrained based on passive microwave products, thereby reducing the uncertainty and data acquisition cost brought by the temporal variability of vegetation to soil moisture inversion, and improving the performance of the change detection method in inverting soil moisture. Thus, the problem that the soil moisture time series change detection method is affected by the temporal variability of vegetation, the performance of inverting soil moisture is low, and the cost of data acquisition is high in the related technology is solved.

[0069] Specifically, Figure 1 A flowchart of a soil moisture inversion method based on neighborhood change detection and passive microwave constraints provided in an embodiment of the present application.

[0070] like Figure 1 As shown in Figure 2, the soil moisture retrieval method based on neighborhood change detection and passive microwave constraints includes the following steps:

[0071] In step S101, a synthetic aperture radar (SAR) image covering the target study area is acquired, and the SAR image is preprocessed to obtain a preprocessed SAR image that meets preset conditions.

[0072] It can be understood that the pre-processed SAR image that meets the preset conditions in the embodiment of the present application can be a SAR image in the GRD format.

[0073] During the actual implementation process, the embodiment of the present application can obtain a single-scene SAR image and a long-time passive microwave soil moisture product covering the selected study area, and perform preprocessing operations on the SAR image in turn to convert the original SAR image into a SAR image in GRD format.

[0074] It should be noted that the preset conditions can be set by those skilled in the art according to actual conditions and are not specifically limited here.

[0075] Optionally, in one embodiment of the present application, the SAR image is preprocessed to obtain a preprocessed SAR image that meets preset conditions, including: performing orbit correction, thermal noise removal, radiation calibration, coherent speckle filtering and geocoding preprocessing operations on the SAR image to obtain a preprocessed SAR image that meets preset conditions.

[0076] As a possible implementation method, the embodiment of the present application can sequentially perform preprocessing operations such as orbit correction, thermal noise removal, radiation calibration, speckle filtering, and geocoding on the SAR image to convert the original SAR image into a SAR image in GRD format and extract backscatter observation values.

[0077] In step S102, based on the pre-processed SAR image, pseudo scattering type parameters and pseudo scattering entropy parameters are used to obtain homogeneous pixels in the neighborhood of the central pixel.

[0078] During the actual implementation process, the embodiment of the present application can select homogeneous neighborhood pixels of the central pixel based on the preprocessed SAR image using pseudo scattering type parameters and pseudo scattering entropy parameters, thereby providing support for the subsequent establishment of a neighborhood change detection model based on weighted scattering characteristic similarity.

[0079] In one embodiment of the present application, the pseudo-scattering type parameter θ is used to identify and characterize different scattering mechanisms. c The calculation formula is:

[0080]

[0081] Where q is the ratio of the backscatter coefficient of cross-polarization to that of co-polarization. Two auxiliary parameters can be defined;

[0082] The auxiliary parameters are defined as:

[0083]

[0084] in, and are the backscatter observation values ​​of co-polarization and cross-polarization, X and Y represent horizontal H or vertical V polarization, respectively, m c is the copolarization purity parameter ranging from 0 to 1, m c =(1-q) / (1+q);

[0085] When m c =0,θ c = 0°, indicating random scattering of complex targets; when m c =1,θ c =45°, indicating pure dihedral scattering of deterministic targets.

[0086] Pseudo-scattering entropy parameter H c It can characterize the degree of disorder in the scattering process, and the calculation formula is:

[0087]

[0088] Among them, p1=1 / (1+q), p2=q / (1+q), p i represents a pseudo-probability measure.

[0089] Using the pseudo scattering type and pseudo scattering entropy parameters, we can construct an H c -θ c The plane is used to describe the scattering characteristics of the target. In the M×M window, based on the neighborhood pixels and the center pixel in H c -θ c The distance between the planes determines the similarity of the scattering characteristics between them and selects the neighborhood homogeneous pixels. When the distance is less than 0.1, the neighborhood pixels are considered to be homogeneous pixels of the central pixel. The distance formula is written as:

[0090]

[0091] Among them, n represents the neighborhood pixel and r represents the center pixel.

[0092] In step S103, a neighborhood change detection model based on scattering characteristic similarity weighting is established based on the co-polarization backscatter observation values ​​of the central pixel and the neighborhood homogeneous pixels.

[0093] In one embodiment of the present application, the co-polarization backscatter value of the ground surface can be represented by a first-order small perturbation model, and the calculation formula is:

[0094]

[0095] Where k is the wave number, θ is the incident angle, h is the root mean square height, W is the two-dimensional normalized surface roughness spectrum function, α XXis the Fresnel scattering coefficient for horizontal or vertical polarization, which can be expressed as:

[0096]

[0097] Where ε is the dielectric constant. The Mironov dielectric model is used to represent the nonlinear relationship between the dielectric constant and soil moisture.

[0098] Assuming that the surface roughness and vegetation parameters between the central pixel and the neighboring homogeneous pixels do not change, the change in the co-polarization backscatter observation value is only related to the change in soil moisture. The ratio of the co-polarization backscatter observation values ​​of the central pixel and the neighboring homogeneous pixels can be approximated as the Fresnel scattering coefficient ratio, which is expressed as:

[0099]

[0100] Among them, hi represents the homogeneous pixel in the i-th neighborhood, r represents the central pixel, α XX is the Fresnel scattering coefficient for horizontal or vertical polarization;

[0101] If the number of homogeneous pixels in the neighborhood is N, a linear equation system consisting of N-1 equations and N unknowns can be established, and the equations are weighted based on the similarity of the scattering characteristics between the central pixel and the homogeneous pixels in the neighborhood. The constructed neighborhood change detection model based on the weighted scattering characteristic similarity is expressed as follows:

[0102]

[0103] in, is an N-1 dimensional zero vector, w hi is the weight of the homogeneous pixel in the i-th neighborhood, S hi,r is the co-polarization backscatter ratio of the i-th neighborhood homogeneous pixel to the central pixel, α XX,hi and α XX,r are the horizontal or vertical polarization Fresnel scattering coefficients of the ith neighborhood homogeneous pixel and the central pixel, respectively. and are the pseudo scattering entropy parameters of the ith neighborhood homogeneous pixel and the central pixel, and are the pseudo scattering type parameters of the i-th neighborhood homogeneous pixel and the central pixel, respectively, i = 1, 2…N.

[0104] like Figure 2 As shown in Figure 1, the soil moisture inversion method based on neighborhood change detection and passive microwave constraints is applied in the model application stage and includes the following steps:

[0105] In step S201 , co-polarization backscatter observation values ​​of the central pixel and neighboring homogeneous pixels are obtained.

[0106] In step S202, the co-polarization backscatter observations of the central pixel and the neighboring homogeneous pixels are input into a pre-constructed neighborhood change detection model. Based on the pre-constructed neighborhood change detection model, the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product are used as constraints, and a linear least squares fitting solution is performed on the soil moisture within the constraints to obtain the soil moisture inversion result. The neighborhood change detection model is constructed by the central pixel and the co-polarization backscatter observations.

[0107] Among them, the embodiment of the present application can assume that the change in the co-polarization backscatter observation value of the neighborhood homogeneous pixels is only related to the soil moisture, and input the co-polarization backscatter observation value of the neighborhood homogeneous pixels into a pre-constructed neighborhood change detection model. Based on the pre-constructed neighborhood change detection model, the upper and lower bounds of the long-time series passive microwave soil moisture product are used as the constraint range. Within the constraint range, the soil moisture is solved by linear least squares fitting, and finally the soil moisture inversion result is obtained.

[0108] In one embodiment of the present application, considering that the solution of the underdetermined system of equations is easily affected by the local optimal solution, the minimum and maximum values ​​obtained by the long-time series passive microwave soil moisture product are used as the constraint boundaries, and a linear least squares fitting solution is performed on the soil moisture within the constraint boundaries. The constraint range is:

[0109] SM min <SM r ,SM hi <SM max , i∈(1,2,…,N),

[0110] Among them, SM min is the minimum soil moisture, SM max is the maximum soil moisture, SM r is the soil moisture value of the central pixel, SM hi is the soil moisture value of the homogeneous pixel in the i-th neighborhood.

[0111] In order to reduce the spatial variation error of soil moisture, a sliding solution is performed within a larger window of size 2M-1, and the average of the soil moisture inversion results of the central pixel obtained in each sliding is taken as the final soil moisture inversion result.

[0112] Specifically, it can be combined Figures 3 to 5 As shown, the working principle of the soil moisture inversion method based on neighborhood change detection and passive microwave constraint in the embodiment of the present application is described in detail with a specific embodiment.

[0113] like Figure 3 As shown, the embodiments of the present application may include:

[0114] Step 1: Data preprocessing of SAR images.

[0115] Step 2: Use the pseudo scattering type parameter and pseudo scattering entropy parameter to select homogeneous pixels in the neighborhood of the central pixel.

[0116] Step 3: Establish a neighborhood change detection model based on weighted scattering characteristic similarity.

[0117] Step 4: Using the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as the constraint range, perform a linear least squares fitting solution on the soil moisture within the constraint range to obtain the soil moisture inversion result.

[0118] like Figure 4 As shown, the embodiment of the present application can use the pseudo scattering type and pseudo scattering entropy parameters to construct an H c -θ c plane to describe the scattering characteristics of the target. Figure 5 A schematic diagram of the sliding average strategy is given.

[0119] The soil moisture inversion method based on neighborhood change detection and passive microwave constraints proposed in the embodiments of this application can assume that changes in backscattered observations within a certain spatial range are caused by soil moisture. This expands the observation space using neighborhood information and rationally constrains the parameter solution process based on passive microwave products. This reduces the uncertainty and data acquisition cost associated with vegetation temporal variability in soil moisture inversion, thereby improving the performance of the change detection method in inverting soil moisture. This solves the problem in related technologies where soil moisture temporal change detection methods are affected by vegetation temporal variability, resulting in low soil moisture inversion performance and high data acquisition costs.

[0120] Next, a soil moisture inversion device based on neighborhood change detection and passive microwave constraints proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0121] Figure 6 It is a structural diagram of the soil moisture inversion device based on neighborhood change detection and passive microwave constraints applied to the model construction stage in an embodiment of the present application.

[0122] like Figure 6 As shown, the soil moisture inversion device 10 based on neighborhood change detection and passive microwave constraint includes: a preprocessing module 100 , a generation module 200 and an establishment module 300 .

[0123] Specifically, the preprocessing module 100 is used to acquire a synthetic aperture radar (SAR) image covering the target study area, and preprocess the SAR image to obtain a preprocessed SAR image that meets preset conditions.

[0124] The generating module 200 is used to obtain homogeneous pixels in the neighborhood of the central pixel based on the pre-processed SAR image using the pseudo scattering type parameter and the pseudo scattering entropy parameter.

[0125] The module 300 is established to establish a neighborhood change detection model based on weighted scattering characteristic similarity based on the co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels.

[0126] Optionally, in one embodiment of the present application, the preprocessing module 100 includes: a preprocessing unit.

[0127] The preprocessing unit is used to perform orbit correction, thermal noise removal, radiation calibration, speckle filtering and geocoding preprocessing operations on the SAR image to obtain a preprocessed SAR image that meets preset conditions.

[0128] Optionally, in one embodiment of the present application, the calculation formula of the pseudo scattering type parameter is:

[0129]

[0130] Where q is the ratio of the backscatter coefficient of cross-polarization to that of co-polarization, and ζ1 and ζ2 are auxiliary parameters;

[0131] The auxiliary parameters are defined as:

[0132]

[0133] in, and are the backscatter observation values ​​of co-polarization and cross-polarization, X and Y represent horizontal H or vertical V polarization, respectively, m c is the copolarization purity parameter ranging from 0 to 1;

[0134] The calculation formula of pseudo-scattering entropy parameter is:

[0135]

[0136] Among them, p i represents a pseudo-probability measure.

[0137] Optionally, in one embodiment of the present application, the surface co-polarization backscatter values ​​of the central pixel and the neighboring homogeneous pixels are expressed using a first-order small perturbation model as follows:

[0138]

[0139] Where k is the wave number, θ is the incident angle, h is the root mean square height, W is the two-dimensional normalized surface roughness spectrum function, α XX is the Fresnel scattering coefficient for horizontal or vertical polarization;

[0140] The expression for the ratio of the co-polarization backscatter observations of the central pixel and the neighboring homogeneous pixels is:

[0141]

[0142] Among them, hi represents the homogeneous pixel in the i-th neighborhood, r represents the central pixel, α XX is the Fresnel scattering coefficient for horizontal or vertical polarization;

[0143] The neighborhood change detection model expression of scattering characteristic similarity weighted is:

[0144]

[0145] in, is an N-1 dimensional zero vector, w hi is the weight of the homogeneous pixel in the i-th neighborhood, S hi,r is the co-polarization backscatter ratio of the i-th neighborhood homogeneous pixel to the central pixel, α XX,hi and α XX,r are the horizontal or vertical polarization Fresnel scattering coefficients of the ith neighborhood homogeneous pixel and the central pixel, respectively. and are the pseudo scattering entropy parameters of the ith neighborhood homogeneous pixel and the central pixel, and are the pseudo scattering type parameters of the i-th neighborhood homogeneous pixel and the central pixel, respectively, i = 1, 2…N.

[0146] Figure 7 It is a structural diagram of a soil moisture inversion device based on neighborhood change detection and passive microwave constraints in an embodiment of the present application applied to the model application stage.

[0147] like Figure 7 As shown, the soil moisture inversion device 20 based on neighborhood change detection and passive microwave constraint includes: an acquisition module 400 and an input module 500.

[0148] Specifically, the acquisition module 400 is used to obtain co-polarization backscatter observation values ​​of the central pixel and neighboring homogeneous pixels.

[0149] Input module 500 is used to input the co-polarization backscatter observations of the central pixel and the neighboring homogeneous pixels into a pre-constructed neighborhood change detection model, and based on the pre-constructed neighborhood change detection model, use the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as the constraint range, perform a linear least squares fitting solution on the soil moisture within the constraint range to obtain a soil moisture inversion result, wherein the neighborhood change detection model is constructed by the central pixel and the co-polarization backscatter observations.

[0150] Optionally, in one embodiment of the present application, the constraint range is:

[0151] SM min <SM r ,SM hi <SM max , i∈(1,2,…,N),

[0152] Among them, SM min is the minimum soil moisture, SM max is the maximum soil moisture, SM r is the soil moisture value of the central pixel, SM hi is the soil moisture value of the homogeneous pixel in the i-th neighborhood.

[0153] It should be noted that the above explanation of the embodiment of the soil moisture inversion method based on neighborhood change detection and passive microwave constraints is also applicable to the soil moisture inversion device based on neighborhood change detection and passive microwave constraints in this embodiment, and will not be repeated here.

[0154] The soil moisture inversion device based on neighborhood change detection and passive microwave constraints proposed in the embodiments of this application can assume that changes in backscattered observations within a certain spatial range are caused by soil moisture. This expands the observation space using neighborhood information and rationally constrains the parameter solution process based on passive microwave products. This reduces the uncertainty and data acquisition cost associated with vegetation temporal variability in soil moisture inversion, thereby improving the performance of change detection methods in inverting soil moisture. This solves the problem in related technologies where soil moisture temporal change detection methods are affected by vegetation temporal variability, resulting in low soil moisture inversion performance and high data acquisition costs.

[0155] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0156] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0157] When the processor 802 executes the program, the soil moisture inversion method based on neighborhood change detection and passive microwave constraints provided in the above embodiment is implemented.

[0158] Furthermore, the electronic device further includes:

[0159] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0160] The memory 801 is used to store computer programs that can be run on the processor 802.

[0161] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0162] If the memory 801, processor 802, and communication interface 803 are implemented independently, the communication interface 803, memory 801, and processor 802 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0163] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0164] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0165] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the soil moisture inversion method based on neighborhood change detection and passive microwave constraints is implemented as described above.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0170] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0171] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0172] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0173] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A soil moisture inversion method based on neighborhood change detection and passive microwave constraints, characterized in that: Applied to the model building phase, it includes the following steps: Acquire a synthetic aperture radar (SAR) image covering the target study area, and preprocess the SAR image to obtain a preprocessed SAR image that meets preset conditions; Based on the preprocessed SAR image, the pseudo scattering type parameter and the pseudo scattering entropy parameter are used to obtain the homogeneous pixels in the neighborhood of the central pixel, wherein the calculation formula of the pseudo scattering type parameter is: Where q is the ratio of the backscatter coefficient of cross-polarization to that of co-polarization, and ζ1 and ζ2 are auxiliary parameters; The auxiliary parameters are defined as: in, and are the backscatter observation values ​​of co-polarization and cross-polarization, X and Y represent horizontal H or vertical V polarization, respectively, m c is the copolarization purity parameter ranging from 0 to 1; The calculation formula of the pseudo-scattering entropy parameter is: Among them, p i represents a pseudo-probability measure; Based on the co-polarization backscatter observation values ​​of the central pixel and the neighborhood homogeneous pixels, a neighborhood change detection model based on scattering characteristic similarity weighting is established.

2. The method according to claim 1, characterized in that The preprocessing of the SAR image to obtain a preprocessed SAR image that meets a preset condition includes: The SAR image is subjected to orbit correction, thermal noise removal, radiation calibration, speckle filtering and geocoding preprocessing operations to obtain the preprocessed SAR image that meets the preset conditions.

3. The method according to claim 1, characterized in that The surface co-polarization backscatter values ​​of the central pixel and the neighboring homogeneous pixels are expressed using a first-order small perturbation model as follows: Where k is the wave number, θ is the incident angle, h is the root mean square height, W is the two-dimensional normalized surface roughness spectrum function, α XX is the Fresnel scattering coefficient for horizontal or vertical polarization; The expression for the ratio of the co-polarization backscatter observation values ​​of the central pixel and the homogeneous neighboring pixels is: Among them, hi represents the homogeneous pixel in the i-th neighborhood, r represents the central pixel, α XX is the Fresnel scattering coefficient of the horizontal or vertical polarization; The neighborhood change detection model expression of the scattering characteristic similarity weighted is: in, is an N-1 dimensional zero vector, w hi is the weight of the homogeneous pixel in the i-th neighborhood, S hi,r is the co-polarization backscatter ratio of the i-th neighborhood homogeneous pixel to the central pixel, α XX,hi and α XX,r are the horizontal or vertical polarization Fresnel scattering coefficients of the i-th neighborhood homogeneous pixel and the central pixel, respectively, and are the pseudo scattering entropy parameters of the i-th neighborhood homogeneous pixel and the central pixel respectively, and are the pseudo scattering type parameters of the i-th neighborhood homogeneous pixel and the central pixel, respectively, and i=1, 2…N.

4. A soil moisture inversion method based on neighborhood change detection and passive microwave constraints, characterized in that: Applied to the model application phase, it includes the following steps: Obtain the co-polarization backscatter observation values ​​of the central pixel and the neighboring homogeneous pixels; The co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels are input into a neighborhood change detection model in the method according to any one of claims 1 to 3, and based on the neighborhood change detection model, the soil moisture is subjected to a linear least squares fitting solution within the constraint range using the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as a constraint range to obtain a soil moisture inversion result, wherein the neighborhood change detection model is constructed from the co-polarization backscatter observations.

5. The method according to claim 4, characterized in that The constraints are: SM min <SM r ,SM hi <SM max ,i∈(1,2,…,N), Among them, SM min is the minimum soil moisture, SM max is the maximum soil moisture, SM r is the soil moisture value of the central pixel, SM hi is the soil moisture value of the homogeneous pixel in the i-th neighborhood.

6. A soil moisture inversion device based on neighborhood change detection and passive microwave constraints, characterized in that: Applied in the model building phase, including: A preprocessing module is used to obtain a synthetic aperture radar (SAR) image covering the target study area and preprocess the SAR image to obtain a preprocessed SAR image that meets preset conditions; A generation module is used to obtain homogeneous pixels in the neighborhood of the central pixel based on the preprocessed SAR image using pseudo scattering type parameters and pseudo scattering entropy parameters, wherein the calculation formula of the pseudo scattering type parameters is: Where q is the ratio of the backscatter coefficient of cross-polarization to that of co-polarization, and ζ1 and ζ2 are auxiliary parameters; The auxiliary parameters are defined as: in, and are the backscatter observation values ​​of co-polarization and cross-polarization, X and Y represent horizontal H or vertical V polarization, respectively, m c is the copolarization purity parameter ranging from 0 to 1; The calculation formula of the pseudo-scattering entropy parameter is: Among them, p i represents a pseudo-probability measure; A module is established for establishing a neighborhood change detection model based on weighted scattering characteristic similarity based on the co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels.

7. A soil moisture inversion device based on neighborhood change detection and passive microwave constraints, characterized in that: Applied in the model application phase, including: An acquisition module is used to obtain the co-polarization backscatter observation values ​​of the central pixel and the neighboring homogeneous pixels; An input module is used to input the co-polarization backscatter observations of the central pixel and the neighborhood homogeneous pixels into the neighborhood change detection model in the method according to any one of claims 1 to 3, and based on the neighborhood change detection model, with the upper and lower bounds corresponding to the long-time series passive microwave soil moisture product as the constraint range, perform a linear least squares fitting solution on the soil moisture within the constraint range to obtain a soil moisture inversion result, wherein the neighborhood change detection model is constructed by the central pixel and the co-polarization backscatter observations.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the soil moisture inversion method based on neighborhood change detection and passive microwave constraints as described in any one of claims 1-3 or 4-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the soil moisture inversion method based on neighborhood change detection and passive microwave constraints as described in any one of claims 1-3 or 4-5.

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