Soil moisture inversion method based on phase deviation correction of time-series polarimetric interferometry

Abstract

The present invention provides a soil moisture inversion method based on time-series polarization interference phase deviation correction, which relates to the field of environmental technology, including obtaining image space interference coherence information, obtaining out-of-phase polarization SAR data, establishing a spatial continuous complex interference model of all interference pairs within a given time span; using a spatial averaging method to filter out the influence of high-frequency scattering components and low-frequency scattering components in the interference model, and establishing a time-series change model of soil moisture sensitivity within a given time span; decomposing the full-resolution interference mean after spatial averaging, and correcting the phase change model caused by soil moisture changes; establishing the corrected interference phase model and the time series change law of soil moisture to achieve the best fit of soil moisture sensitivity and invert soil moisture changes. By establishing the best fitting function of soil moisture sensitivity to solve the time series change of soil moisture, no other auxiliary data is required, and reliable acquisition of soil moisture in arid areas is achieved based on a rigorous theoretical model.

Description

Technical Field

[0001] The invention relates to environmental technology, and in particular to a soil moisture inversion method based on time-series polarization interference phase deviation correction. Background Art

[0002] Soil moisture is an important state variable in the fields of hydrology, climate, ecology and agriculture. Traditional methods of monitoring surface soil moisture mainly include weighing, moisture meter measurement, nuclear magnetic resonance, infrared radiation, etc., or monitoring soil moisture in local surface areas by deploying ground monitoring base stations. These methods consume a lot of manpower and material resources, are costly, have sparse measurement points, and have long re-measurement intervals, and cannot achieve time-series monitoring of changes in surface soil moisture over a large area.

[0003] With the development of remote sensing technology, interferometric synthetic aperture radar (InSAR) observation technology can obtain surface scattering information and interference pattern data. It has gradually been promoted and applied in the fields of hydrological monitoring and surface deformation research. Among them, polarimetric SAR data can obtain scattering information under a variety of polarization states, and is sensitive to the vertical structure and shape and orientation of volume scattering. It can generate complex coherent images under arbitrary polarization scattering mechanisms and can provide more comprehensive ground object scattering information.

[0004] In polarimetric InSAR technology, for interference pairs spanning a long time interval, a higher interference coherence can be obtained when the scattering characteristics of the target object remain stable between two observations. The structure, density and height of the vegetation itself will affect the propagation and scattering of radar waves. The near-surface soil topography will also cause scattering errors of radar scattered waves, which will lead to deviations in the results of soil moisture inversion. Especially in farmland, forests and other areas with potential pixel aliasing, it is difficult to distinguish the influence of ground displacement from the influence of soil moisture using the obtained interference information.

[0005] In response to the problems and challenges of existing methods, the present invention proposes a soil moisture inversion method based on time-series polarization interferometry phase deviation correction to filter out the influence of other interference factors, correct the interference phase information caused by soil moisture changes, and improve the accuracy of soil moisture inversion results. Summary of the invention

[0006] The embodiment of the present invention provides a soil moisture inversion method based on time-series polarization interferometry phase deviation correction, which can solve the problems in the prior art.

[0007] A first aspect of an embodiment of the present invention provides a soil moisture inversion method based on time-series polarization interferometry phase deviation correction, comprising:

[0008] Acquire multi-view time-series SAR remote sensing images of the study area, perform radiation correction, registration and filtering on the multi-view time-series SAR remote sensing images, and obtain polarization scattering information, incident angle information and vertical beam information of the SAR remote sensing images; generate interference pairs according to the relationship between the time baseline and the spatial baseline between the multi-view time-series SAR remote sensing images, and obtain spatial interference information between images of different dates;

[0009] The phase gradient differences between different interference pairs across the same time interval were compared to establish spatial phase closure models of three groups of SAR image interference pairs on three dates; the spatial interference information was spatially averaged to filter out high-frequency scattering components and low-frequency scattering components, and a phase gradient change model caused by soil moisture changes in different time spans was established;

[0010] The complex interference value of the full-resolution interferogram is decomposed into interference components caused by atmospheric changes, interference components generated by ground displacement deformation, interference components caused by ground noise, and interference components caused by soil moisture changes, and the correlation components and interference phase components caused by soil moisture changes are corrected; based on the correction results of the correlation components and interference phase components, the best fit of the spatial average value is calculated to obtain the optimal expected value of soil moisture sensitivity, and then the soil moisture inversion result in arid areas is obtained.

[0011] In an optional embodiment,

[0012] The method also includes calculating the spatially continuous phase gradient differences of all interferometric pairs:

[0013] ;

[0014] in, The term is the complex conjugate of the spatially averaged complex coherence over the longest time span, * Indicates conjugate calculation, the right side The term refers to the product of all interference pairs with time span n, Indicates a moment in time, Indicates the time span interval; Indicates the time span and The spatially continuous phase gradient differences of all interference pairs.

[0015] In an optional embodiment,

[0016] The method also includes processing the image interference information by spatial averaging, filtering out the influence of low-pass scattering components and high-pass scattering components, and establishing an interference information change model caused by soil moisture changes in different time spans of SAR images:

[0017] It is assumed that there is no spatial correlation between the individual pixel values ​​in the SAR image, and each pixel value is associated with a value that represents the soil moisture sensitivity. Related, The asymmetric distribution of will lead to a non-zero phase closure model;

[0018] Define each date A dimensionless factor related to the soil moisture measurement When the soil is completely dry, the value is zero and increases with the increase of moisture. Soil moisture sensitivity and The linear correlation is calculated by the following formula:

[0019] ;

[0020] in, represents the complex interference value associated with soil moisture changes, and express and The soil moisture value at the time, Indicates soil moisture changes;

[0021] Establish soil moisture sensitivity distribution function, if is a random variable that follows an exponential distribution, and its probability density function Calculated by the following formula:

[0022] ;

[0023] in, represents the natural exponential function, is a random variable;

[0024] Calculate Date and In the time span N The spatial average of the complex interference value related to soil moisture changes within a pixel interval is calculated by the following formula:

[0025] ;

[0026] Given Date and Pixels in time span Related Interference Correlation Calculated by the following formula:

[0027] ;

[0028] The spatial average of its interference phase Calculated by the following formula:

[0029] .

[0030] In an optional embodiment,

[0031] The spatial complex interferometric mean of the uncorrected full-resolution time-series SAR image interferometric pair The interference component is mainly divided into the interference component caused by soil moisture change, the interference component caused by atmospheric change, the interference component caused by all other types of ground noise, and the interference component caused by ground deformation. , , , express.

[0032] In an optional embodiment,

[0033] use represents the spatial average of the atmospheric variation term and the ground displacement variation term of the spatial scale, and the noise term The spatial average value of N increases and approaches zero;

[0034] Derived components of soil moisture variation in full-resolution interferograms Calculated by the following formula:

[0035] .

[0036] In an optional embodiment,

[0037] Correction of the correlation component caused by soil moisture changes without interference from other noise sources and phase change component , soil moisture sensitivity The best expectation is calculated by the following formula:

[0038] ;

[0039] in, is the spatial phase mean caused by soil moisture variation, is the soil moisture change value.

[0040] A second aspect of an embodiment of the present invention provides a soil moisture inversion system based on time-series polarization interferometry phase deviation correction, comprising:

[0041] The first unit is used to obtain multi-view time-series SAR remote sensing images of the study area, perform radiation correction, registration and filtering on the multi-view time-series SAR remote sensing images, and obtain polarization scattering information, incident angle information and vertical beam information of the SAR remote sensing images; generate interference pairs according to the relationship between the time baseline and the spatial baseline between the multi-view time-series SAR remote sensing images, and obtain spatial interference information between images of different dates;

[0042] The second unit is used to compare the phase gradient differences between different interference pair combinations spanning the same time interval, and establish a spatial phase closure model of three groups of SAR image interference pairs on three dates; perform spatial averaging processing on the spatial interference information, filter out high-frequency scattering components and low-frequency scattering components, and establish a phase gradient change model caused by soil moisture changes in different time spans;

[0043] The third unit is used to decompose the complex interference value of the full-resolution interference pattern into interference components caused by atmospheric changes, interference components generated by ground displacement deformation, interference components caused by ground noise, and interference components caused by soil moisture changes, and correct the correlation components and interference phase components caused by soil moisture changes; based on the correction results of the correlation components and interference phase components, the best fit of the spatial average value is calculated to obtain the optimal expected value of soil moisture sensitivity, and then the soil moisture inversion result in arid areas is obtained.

[0044] According to a third aspect of the embodiments of the present invention,

[0045] An electronic device is provided, comprising:

[0046] processor;

[0047] a memory for storing processor-executable instructions;

[0048] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0049] A fourth aspect of the embodiments of the present invention is:

[0050] A computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the aforementioned method when executed by a processor.

[0051] The construction of the model in the method provided by the present invention starts from the image interference mechanism, effectively removes the influence of low-frequency scattering components and high-frequency components in the original image interference phase diagram through spatial averaging, establishes the best fitting function of soil moisture sensitivity to solve the time series change of soil moisture, and does not require other auxiliary data. Reliable acquisition of soil moisture in arid areas is achieved based on a rigorous theoretical model. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic flow chart of a soil moisture inversion method based on time-series polarization interferometry phase deviation correction according to an embodiment of the present invention;

[0053] Figure 2 The present invention is a schematic diagram of the structure of a soil moisture inversion system based on time-series polarization interferometry phase deviation correction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0056] Figure 1 FIG. 1 is a flow chart of a soil moisture inversion method based on time-series polarization interferometry phase deviation correction according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0057] S101. Acquire multi-view time-series SAR remote sensing images of the study area, perform radiation correction, registration and filtering on the multi-view time-series SAR remote sensing images, and obtain polarization scattering information, incident angle information and vertical beam information of the SAR remote sensing images; generate interference pairs according to the relationship between the time baseline and the spatial baseline between the multi-view time-series SAR remote sensing images, and obtain spatial interference information between images of different dates;

[0058] S102. Compare the phase gradient differences between different interference pair combinations spanning the same time interval, and establish a spatial phase closure model of three groups of SAR image interference pairs on three dates; perform spatial averaging processing on the spatial interference information, filter out high-frequency scattering components and low-frequency scattering components, and establish a phase gradient change model caused by soil moisture changes in different time spans;

[0059] S103. Decompose the complex interference value of the full-resolution interference pattern into interference components caused by atmospheric changes, interference components generated by ground displacement deformation, interference components caused by ground noise, and interference components caused by soil moisture changes, and correct the correlation components and interference phase components caused by soil moisture changes; based on the correction results of the correlation components and interference phase components, calculate the best fit of the spatial average value, obtain the optimal expected value of soil moisture sensitivity, and then obtain the soil moisture inversion result in arid areas.

[0060] In an optional embodiment,

[0061] The method also includes calculating the spatially continuous phase gradient differences of all interferometric pairs:

[0062] ;

[0063] in, The term is the complex conjugate of the spatially averaged complex coherence over the longest time span, * Indicates conjugate calculation, the right side The term refers to the product of all interference pairs with time span n, Indicates a moment in time, Indicates the time span interval; Indicates the time span and The spatially continuous phase gradient differences of all interference pairs.

[0064] In an optional embodiment,

[0065] The method also includes processing the image interference information by spatial averaging, filtering out the influence of low-pass scattering components and high-pass scattering components, and establishing an interference information change model caused by soil moisture changes in different time spans of SAR images:

[0066] It is assumed that there is no spatial correlation between the individual pixel values ​​in the SAR image, and each pixel value is associated with a value that represents the soil moisture sensitivity. Related, The asymmetric distribution of will lead to a non-zero phase closure model;

[0067] Define each date A dimensionless factor related to the soil moisture measurement When the soil is completely dry, the value is zero and increases with the increase of moisture. Soil moisture sensitivity and The linear correlation is calculated by the following formula:

[0068] ;

[0069] in, represents the complex interference value associated with soil moisture changes, and express and The soil moisture value at the time, Indicates soil moisture changes;

[0070] Establish soil moisture sensitivity distribution function, if is a random variable that follows an exponential distribution, and its probability density function Calculated by the following formula:

[0071] ;

[0072] in, represents the natural exponential function, is a random variable;

[0073] Calculate Date and In the time span N The spatial average of the complex interference value related to soil moisture changes within a pixel interval is calculated by the following formula:

[0074] ;

[0075] Given Date and Pixels in time span Related Interference Correlation Calculated by the following formula:

[0076] ;

[0077] The spatial average of its interference phase Calculated by the following formula:

[0078] .

[0079] In an optional embodiment,

[0080] The spatial complex interferometric mean of the uncorrected full-resolution time-series SAR image interferometric pair The interference component is mainly divided into the interference component caused by soil moisture change, the interference component caused by atmospheric change, the interference component caused by all other types of ground noise, and the interference component caused by ground deformation. , , , express.

[0081] In an optional embodiment,

[0082] use represents the spatial average of the atmospheric variation term and the ground displacement variation term of the spatial scale, and the noise term The spatial average value of N increases and approaches zero;

[0083] Derived components of soil moisture variation in full-resolution interferograms Calculated by the following formula:

[0084] .

[0085] In an optional embodiment,

[0086] Correction of the correlation component caused by soil moisture changes without interference from other noise sources and phase change component , soil moisture sensitivity The best expectation is calculated by the following formula:

[0087] ;

[0088] in, is the spatial phase mean caused by soil moisture variation, is the soil moisture change value.

[0089] Another embodiment of the present application is as follows:

[0090] (1) Data preparation;

[0091] The present invention realizes dynamic time-series monitoring of soil moisture inversion. It is necessary to obtain multi-scene full-resolution time-series SAR remote sensing images of the study area, perform data preprocessing such as radiation correction, registration, and filtering, and obtain SAR remote sensing image polarization scattering information, local incident angle, vertical beam and other information.

[0092] (2) Obtaining interferometric coherent information;

[0093] Assume that two SAR images are A pixel at a time The complex observation at is , then the complex interference value of the full resolution at two given moments is The specific calculation is as follows:

[0094] ;

[0095] The phase information is calculated by the following formula:

[0096] ;

[0097] The complex coherence is calculated by the following formula:

[0098] ;

[0099] in, Represents a variable For real data, the expected value of the complex coherence of two images is usually estimated as pixel A nearby point N The average value of the above formula is:

[0100] ;

[0101] Given a time interval and Spatial average of the inner winding phase Calculated by the following formula:

[0102] ;

[0103] Given a time interval and The spatial average of the inner true phase is calculated by the following formula:

[0104] ;

[0105] Given a time interval , and The cumulative closing phase within the time span is calculated by the following formula:

[0106] ;

[0107] Consider the set of all interfering pairs within a given time span (e.g., 12 days, 24 days, etc.). On two dates, and The spatially continuous phase gradient difference of all interference pairs in the time interval between Calculated by the following formula:

[0108] ;

[0109] in, The term is the complex conjugate of the spatially averaged (or filtered) interferogram of the longest time span, and the subsequent product includes all interferometric pairs with time span n.

[0110] (3) Use the spatial averaging method to process the complex interference information of the image;

[0111] In order to reduce the influence of high-frequency scattering components and low-frequency scattering components in the complex interference information of the image, this application uses the spatial averaging method to filter out the above factors, defines the exponential probability distribution of soil moisture sensitivity, and constructs the relationship between the spatial average value of the complex interference value related to soil moisture changes and the soil moisture changes.

[0112] The specific steps of spatial averaging of complex interference information of images are as follows:

[0113] 1) Assume that there is no spatial correlation between individual pixel values ​​in the SAR image and that each pixel value is associated with a value that represents soil moisture sensitivity. Related, The asymmetric distribution of will lead to a non-zero phase closure model.

[0114] 2) Define the date A dimensionless factor related to the soil moisture measurement , when the soil is completely dry, the value is zero and increases with the increase of moisture. Soil moisture sensitivity and The linear relationship is calculated by the following formula:

[0115] ;

[0116] in, represents the complex interference value associated with soil moisture changes, and Respectively and The soil moisture value at the time, Indicates soil moisture changes.

[0117] 3) Establish soil moisture sensitivity distribution function, assuming is a random variable that follows an exponential distribution, and its probability density function The specific manifestations are as follows:

[0118] ;

[0119] 4) Given a date and In the time span N The spatial average of the complex interference value related to soil moisture changes within a pixel interval is calculated by the following formula:

[0120] ;

[0121] The real part With the imaginary part Calculated by the following formula:

[0122] ;

[0123] ;

[0124] Given Date and Pixels in time span Related Interference Correlation and the spatial average of the interference phase Calculated by the following formulas:

[0125] ;

[0126] ;

[0127] (4) Coherent information correction;

[0128] Complex interferometry value of full resolution interferogram Calculated by the following formula:

[0129] ;

[0130] in, is the interference component caused by soil moisture changes, is the interference component caused by atmospheric changes, is the interference component caused by all other types of noise on the ground, It is the interference component caused by ground deformation.

[0131] Spatial complex interferometry mean of uncorrected full-resolution time-series SAR images Calculated by the following formula:

[0132] ;

[0133] in, represents the spatial average of the atmospheric term and displacement term at a larger spatial scale, represents the spatial average of the noise term, as N The increase of is close to zero, so the above formula can be changed to:

[0134] ;

[0135] Derivative components of soil moisture variation in full-resolution polarimetric SAR interferograms Calculated by the following formula:

[0136] ;

[0137] The phase change caused by soil moisture change is calculated using the following formula:

[0138] ;

[0139] (5) Use the calibration model to invert soil moisture value changes;

[0140] Use the phase correction model and soil moisture obtained in step (4) The time series change law of soil moisture sensitivity The best fit of the phase correction model for each pixel of each interferogram is calculated by the following formula:

[0141] ;

[0142] Among them, it means Corrected interferometric phase gradient for a given date span.

[0143] Best Expectations for Soil Moisture Sensitivity Calculated by the following formula:

[0144] ;

[0145] Finally, the soil moisture change value is inverted , and obtain the soil moisture change results in arid areas.

[0146] A second aspect of an embodiment of the present invention provides a soil moisture inversion system based on time-series polarization interferometry phase deviation correction, comprising:

[0147] The first unit is used to obtain multi-view time-series SAR remote sensing images of the study area, perform radiation correction, registration and filtering on the multi-view time-series SAR remote sensing images, and obtain polarization scattering information, incident angle information and vertical beam information of the SAR remote sensing images; generate interference pairs according to the relationship between the time baseline and the spatial baseline between the multi-view time-series SAR remote sensing images, and obtain spatial interference information between images of different dates;

[0148] The second unit is used to compare the phase gradient differences between different interference pair combinations spanning the same time interval, and establish a spatial phase closure model of three groups of SAR image interference pairs on three dates; perform spatial averaging processing on the spatial interference information, filter out high-frequency scattering components and low-frequency scattering components, and establish a phase gradient change model caused by soil moisture changes in different time spans;

[0149] The third unit is used to decompose the complex interference value of the full-resolution interference pattern into interference components caused by atmospheric changes, interference components generated by ground displacement deformation, interference components caused by ground noise, and interference components caused by soil moisture changes, and correct the correlation components and interference phase components caused by soil moisture changes; based on the correction results of the correlation components and interference phase components, the best fit of the spatial average value is calculated to obtain the optimal expected value of soil moisture sensitivity, and then the soil moisture inversion result in arid areas is obtained.

[0150] According to a third aspect of the embodiments of the present invention,

[0151] An electronic device is provided, comprising:

[0152] processor;

[0153] a memory for storing processor-executable instructions;

[0154] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0155] A fourth aspect of the embodiments of the present invention is:

[0156] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0157] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A soil moisture inversion method based on time-series polarization interferometry phase deviation correction is characterized in that: include: Acquire multi-view time-series SAR remote sensing images of the study area, perform radiation correction, registration and filtering on the multi-view time-series SAR remote sensing images, and obtain polarization scattering information, incident angle information and vertical beam information of the SAR remote sensing images; generate interference pairs according to the relationship between the time baseline and the spatial baseline between the multi-view time-series SAR remote sensing images, and obtain spatial interference information between images of different dates; The phase gradient differences between different interference pairs across the same time interval were compared to establish spatial phase closure models of three groups of SAR image interference pairs on three dates; the spatial interference information was spatially averaged to filter out high-frequency scattering components and low-frequency scattering components, and a phase gradient change model caused by soil moisture changes in different time spans was established; The complex interference value of the full-resolution interferogram is decomposed into interference components caused by atmospheric changes, interference components generated by ground displacement deformation, interference components caused by ground noise, and interference components caused by soil moisture changes, and the correlation components and interference phase components caused by soil moisture changes are corrected; based on the correction results of the correlation components and interference phase components, the best fit of the spatial average value is calculated to obtain the best expected value of soil moisture sensitivity, and then the soil moisture inversion result in arid areas is obtained; The method also includes calculating the spatially continuous phase gradient differences of all interferometric pairs: in, The term is the complex conjugate of the spatially averaged complex coherence over the longest time span, * Indicates conjugate calculation, the right side The term refers to the product of all interference pairs with a time span of n, where k represents a certain moment and n represents the time span interval; T n (t a ,t b ) represents the time span t a and t b The spatially continuous phase gradient differences of all interference pairs.

2. The method according to claim 1, characterized in that The method also includes processing the image interference information by spatial averaging, filtering out the influence of low-pass scattering components and high-pass scattering components, and establishing an interference information change model caused by soil moisture changes in different time spans of SAR images: It is assumed that there is no spatial correlation between the individual pixel values ​​in the SAR image, and each pixel value is associated with a value s that represents the soil moisture sensitivity. j Related, j The asymmetric distribution of will lead to a non-zero phase closure model; Define for each date t k The dimensionless factor m related to the measured soil moisture k When the soil is completely dry, the value is zero and increases with the increase of moisture. Soil moisture sensitivity s j With m k The linear correlation is calculated by the following formula: in, represents the complex interference value associated with soil moisture changes, m a With m b Indicates t a and t b Soil moisture value at time, Δm ab Indicates soil moisture changes; Establish soil moisture sensitivity distribution function, if s j is a random variable following an exponential distribution, and its probability density function f s Calculated by the following formula: Among them, f s,exp represents the natural exponential function, s is a random variable; Calculate date t a and t b The spatial average of the complex interference values ​​related to soil moisture changes in N pixel intervals within the time span is calculated by the following formula: Given a date t a and t b Interference correlation of pixel j in the time span Calculated by the following formula: The spatial average of its interference phase Calculated by the following formula:

3. The method according to claim 1, characterized in that The spatial complex interferometric mean of the uncorrected full-resolution time-series SAR image interferometric pair The interference component is mainly divided into the interference component caused by soil moisture change, the interference component caused by atmospheric change, the interference component caused by all other types of ground noise, and the interference component caused by ground deformation. express.

4. The method according to claim 1, characterized in that: use represents the spatial average of the atmospheric variation term and the ground displacement variation term of the spatial scale, and the noise term The spatial average value of approaches zero as N increases; Derived components of soil moisture variation in full-resolution interferograms Calculated by the following formula:

5. The method according to claim 1, characterized in that: Correction of the correlation component γ and phase change component caused by soil moisture change without interference from other noise sources Soil moisture sensitivity The best expectation is calculated by the following formula: in, is the spatial phase mean caused by soil moisture variation, Δm ab is the soil moisture change value.

6. A soil moisture inversion system based on time-series polarization interferometry phase deviation correction, used to implement the method as described in any one of claims 1 to 5, characterized in that: include: The first unit is used to obtain multi-view time-series SAR remote sensing images of the study area, perform radiation correction, registration and filtering on the multi-view time-series SAR remote sensing images, and obtain polarization scattering information, incident angle information and vertical beam information of the SAR remote sensing images; generate interference pairs according to the relationship between the time baseline and the spatial baseline between the multi-view time-series SAR remote sensing images, and obtain spatial interference information between images of different dates; The second unit is used to compare the phase gradient differences between different interference pair combinations spanning the same time interval, and establish a spatial phase closure model of three groups of SAR image interference pairs on three dates; perform spatial averaging processing on the spatial interference information, filter out high-frequency scattering components and low-frequency scattering components, and establish a phase gradient change model caused by soil moisture changes in different time spans; The third unit is used to decompose the complex interference value of the full-resolution interference pattern into interference components caused by atmospheric changes, interference components generated by ground displacement deformation, interference components caused by ground noise, and interference components caused by soil moisture changes, and correct the correlation components and interference phase components caused by soil moisture changes; based on the correction results of the correlation components and interference phase components, the best fit of the spatial average value is calculated to obtain the optimal expected value of soil moisture sensitivity, and then the soil moisture inversion result in arid areas is obtained.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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