A method for inverting farmland soil moisture using phase information of radar interferometry

Abstract

The present invention discloses a method for inverting farmland soil moisture in coordination with radar interference phase information, and relates to the technical field of quantitative inversion of synthetic aperture radar remote sensing. The method comprises the following steps: S1: interferometrically processing multi-phase SAR data to obtain initial interferometric phase information containing multiple phase components; S2: optimizing the initial interferometric phase information, eliminating phase components irrelevant to soil moisture, and optimizing the interferometric phase information characterizing soil moisture characteristics; S3: constructing a soil moisture inversion model, and combining the interferometric phase information and radar backscattering information to invert and obtain soil surface moisture information. The present invention fully exploits radar satellite phase observation information and radiation observation information, and through the coordinated application of multi-dimensional radar information, realizes the application of L-band radar data in soil surface moisture inversion, and achieves the technical effect of high-precision farmland soil moisture inversion.

Description

Technical Field

[0001] The invention belongs to the technical field of synthetic aperture radar remote sensing quantitative inversion, and in particular relates to a method for inverting farmland soil moisture using coordinated radar interference phase information. Background Art

[0002] Relevant research has been carried out on soil moisture monitoring using InSAR phase information. Based on the contribution of soil moisture changes to radar phase information, InSAR technology has been applied to soil surface moisture inversion research, and corresponding experimental analysis has been carried out on different theoretical assumptions. Based on the difference in the penetration ability of electromagnetic waves in soils with different moisture levels, the change information of soil moisture is obtained by using DInSAR phase monitoring. There is a systematic deviation of the interference phase in the short-time baseline multi-view interferogram. The interference phase caused by soil moisture changes will have a direct impact on deformation monitoring, especially for high-precision deformation monitoring of natural surfaces. The interference phase corresponding to soil moisture changes needs to be considered in deformation monitoring research and application. Correspondingly, if there is no deformation in the area, the interference phase information can be optimized in theory to obtain the InSAR phase component that characterizes soil moisture changes, which can then be used for quantitative inversion of soil moisture. In the time-series InSAR technology, it has been confirmed that the change of soil equivalent depth will lead to the change of interference phase. The non-zero closure error of the interferogram is the direct basis for the change of interference phase, and the reason for the non-zero closure error is the change of soil moisture content. Triangulated closure errors do not contain deformation, atmospheric and DEM error information, so they can effectively overcome the impact of the above factors on soil moisture estimation. However, since the number of triangulated closed loops in the short baseline interferogram is much smaller than the number of images, that is, the effective observations are less than the number of unknown parameters, soil moisture inversion belongs to the solution of an underdetermined set of equations, and there is a problem of non-unique solution. In the absence of surface deformation and effective removal of atmospheric phase delay, soil moisture information can be directly obtained through interferometric phase. This strategy has better universality than using closed phase differences and coherence models. However, this idea does not fully take into account the influence of ionospheric delay, especially L-band SAR is significantly affected by the ionosphere. It is urgent to develop interferometric SAR phase inversion soil moisture technology methods that can effectively take into account the influence of tropospheric and ionospheric delays and meet the requirement that the effective observations are more than the unknown parameters.

[0003] When the deformation signal is effectively avoided in the interferometric processing results, and the atmospheric delay error, ionospheric delay error, orbit, terrain and other errors are compensated to the maximum extent, the soil moisture can be separated from its residual phase. Therefore, whether reliable soil moisture information can be extracted from the interferometric phase directly depends on the degree of compensation for other components in the interferometric phase. In the current methods of extracting soil moisture using interferometric phase, the compensation of atmospheric delay error depends on external GNSS data. The atmospheric compensation scheme that does not rely on external data is mainly phase-elevation correlation compensation, that is, the atmospheric delay is estimated using the potential linear correlation between atmospheric phase and elevation. This method works well when the tropospheric dry delay is dominant, but it is difficult to express the atmospheric delay estimation dominated by wet delay through the linear relationship between phase and elevation. Other commonly used tropospheric correction techniques include correction algorithms using independent component analysis or machine learning, correction algorithms based on high-resolution atmospheric numerical models, and the general InSAR atmospheric correction online service system GACOS. GACOS combines atmospheric water vapor content data obtained by GNSS with ECMWF, and has been proven to be able to effectively improve tropospheric delay, especially long-wave components and parts related to terrain, becoming a mainstream InSAR atmospheric error correction system. In view of the influence of atmospheric delay on InSAR deformation monitoring, GACOS data products have been used in atmospheric phase processing. Under the premise of effectively removing atmospheric delay, high-precision surface deformation monitoring has been achieved. However, in the radar interferometric phase inversion soil moisture technology, the GACOS model has not been effectively applied, and the radar phase has not been effectively processed due to the influence of the ionosphere, resulting in the limited accuracy of radar interferometric phase inversion soil moisture and cannot be widely used. Summary of the invention

[0004] The purpose of the present invention is to provide a method for inverting farmland soil moisture in coordination with radar interferometric phase information. Based on the interferometric phase closure principle and the characteristics that changes in soil moisture lead to different penetration capabilities of electromagnetic waves, a quantitative inversion model of soil moisture is constructed using interferometric phase information. By introducing interferometric phase characteristic parameters that characterize soil moisture information, reliable acquisition of soil moisture is achieved in combination with radar backscattering information.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a method for inverting farmland soil moisture using coordinated radar interferometric phase information, comprising the following steps:

[0007] S1: Perform interferometric processing on multi-phase SAR data to obtain initial interferometric phase information containing multiple phase components;

[0008] S2: Optimize the initial interferometric phase information, remove the phase components irrelevant to soil moisture, and optimize the interferometric phase information that characterizes soil moisture characteristics;

[0009] S3: Construct a soil moisture inversion model and combine the interferometric phase information and radar backscattering information to invert and obtain soil surface moisture information.

[0010] As a preferred technical solution of the present invention, the S1 further comprises:

[0011] S1.1: Process multi-phase SAR data, combine them with corner reflector data, perform image geometry calibration, and obtain time-series SAR images with high planar positioning accuracy;

[0012] S1.2: Using the multi-phase SAR data after geometric calibration as input, interferometric processing is performed to obtain interferometric phase information including deformation, terrain, atmosphere, ionosphere, orbit error, noise phase and soil moisture change.

[0013] S1.3: Use satellite precise orbit data and interferometric baseline information to remove orbit errors and flat earth phases, remove terrain phases through external DEM, and combine with adaptive filtering methods to suppress the influence of noise phases.

[0014] As a preferred technical solution of the present invention, the image geometry calibration processing in S1.1 includes: using the distance formula from the ground point to the satellite sensor, the Doppler frequency shift formula and the earth ellipsoid equation to solve the ground point coordinates, combining the ground corner reflector control data, obtaining the satellite geometry calibration parameters, and performing geometry calibration processing on the multi-phase SAR data;

[0015] The specific relevant formula is as follows:

[0016] The distance formula from the ground point to the satellite sensor is obtained through the position vector of the satellite sensor and the ground point, which is specifically expressed as:

[0017]

[0018] In the formula, R gc =[X g Y g Z g ] T Represents the position vector of the ground point, R sc =[X s Y s Z s ] T represents the position vector of the satellite sensor, and R represents the slant range, i.e., the distance from the satellite sensor to the ground point;

[0019] The Doppler shift formula is:

[0020]

[0021] Where λ represents the radar wavelength, V sc =[V sx V sy V sz ] Τ represents the velocity vector of the satellite;

[0022] The equation of the Earth's ellipsoid is:

[0023]

[0024] In the formula, R e represents the major axis of the Earth ellipsoid, R p H represents the minor semi-axis of the Earth ellipsoid. t It is the elevation value of the ground point, which is obtained through the Digital Elevation Model (DEM).

[0025] As a preferred technical solution of the present invention, the elimination of phase components irrelevant to soil moisture in S2 specifically includes the following processing methods:

[0026] S2.1: Suppress the DEM error phase, obtain high-precision and high-timeliness external DEM through L-SAR dual-satellite flyby data, and further reduce the impact of DEM error on the interferometric phase;

[0027] S2.2: Suppressing orbit error phase. L-SAR satellites use dual-frequency differential GNSS to achieve precise orbit determination, improve satellite orbit positioning accuracy, and reduce the impact of orbit error on interferometric phase.

[0028] S2.3: Suppress the atmospheric delay phase, use the GACOS model combined with the atmospheric water vapor content data obtained by GNSS and the European Center for Medium-Range Weather Forecasts data to generate a high spatial resolution zenith total delay map, and perform atmospheric delay correction on the interferometric phase;

[0029] S2.4: Suppress the ionospheric delay phase, obtain the ionospheric density in the SAR line of sight through the space environment monitoring system carried by the L-SAR satellite, extract the ionospheric phase delay component, and separate the ionospheric phase delay component from the interference phase.

[0030] As a preferred technical solution of the present invention, the GACOS model in S2.3 uses a tropospheric iterative decomposition model to separate stratification and turbulence signals from the total tropospheric delay, generates a high spatial resolution zenith total delay map, and performs atmospheric delay correction on the interferometric phase to overcome the influence of tropospheric atmospheric delay on the interferometric phase inversion soil moisture.

[0031] As a preferred technical solution of the present invention, the ionospheric delay phase suppression in S2.4 obtains the SAR line-of-sight ionospheric density through the space environment monitoring system carried by the L-SAR satellite, and then extracts the ionospheric phase delay component, separates the ionospheric phase delay component in the interference phase, and realizes effective optimization of InSAR phase information.

[0032] As a preferred technical solution of the present invention, S3 specifically includes:

[0033] S3.1: Soil moisture inversion based on interferometric phase information, taking N L-SAR satellite data of different phases as input, the unknown parameter is the soil moisture time series corresponding to N time periods, and forming a Interference pairs are used to obtain the corresponding interference phase information φ that characterizes the soil moisture characteristics. smc , the number of interferometric phase observations is greater than the number of unknown parameters, thus realizing the time series inversion of soil moisture;

[0034] S3.2: Based on the radar backscattering mechanism, the constraints of soil moisture inversion are improved by adding a group of effective observation equations between soil moisture and radar observation information. Then, the least squares solution strategy is used to solve the soil moisture information under the joint constraints of the interference phase and radar backscattering coefficient to obtain high-precision surface soil moisture information.

[0035] The present invention has the following beneficial effects:

[0036] In the process of constructing a soil moisture model using InSAR phase inversion, the present invention uses the GACOS model to correct the atmospheric delay of the interference phase for the atmospheric delay component in the interference phase, thereby obtaining reliable interference phase information with the atmospheric phase component removed, and optimizing the interference phase information. In addition, in view of the characteristics of the SAR interference phase being affected by the ionosphere, the ionosphere probe and photometer data are combined to obtain the ionosphere delay information, which is used to eliminate the influence of the ionosphere on the L-band SAR interference phase, thereby obtaining the optimized interference phase information characterizing the soil moisture characteristics, which is used for quantitative inversion of soil moisture, and accurate monitoring of soil surface moisture is achieved.

[0037] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 Schematic diagram of the process of the present invention;

[0040] Figure 2 It is a schematic diagram of the process of step S1 in the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0042] S1: Obtain multi-phase SAR images, combine them with the corner reflector coordinate information, perform geometric calibration on satellite images, and improve the plane positioning accuracy of SAR images. Interferometrically process SAR images of different phases to obtain the corresponding interference phase information φ int =φ flat +φ topo +φ def +φ atm +φ orb +φ noise +φ ion +φ smc , including the reference ellipsoid phase, terrain phase, radar line of sight deformation phase, atmospheric phase, orbit error, noise phase, ionospheric delay phase and interference phase caused by soil moisture change. The interference phase information is fully decomposed, and the interference phase information characterizing soil moisture characteristics is obtained by suppressing other phase components.

[0043] The specific implementation steps are as follows:

[0044] S1.1 Multi-phase SAR data processing, combined with corner reflector data, performs image geometry calibration and obtains time-series SAR images with high planar positioning accuracy.

[0045] The distance from the ground point to the satellite sensor is obtained through the position vector of the satellite sensor and the ground point, which is specifically expressed as:

[0046]

[0047] In the formula, Rgc =[X g Y g Z g ] T Represents the position vector of the ground point, R sc =[X s Y s Z s ] T represents the position vector of the satellite sensor, and R represents the slant range, that is, the distance from the satellite sensor to the ground point.

[0048] Doppler frequency shift f d It can be expressed as:

[0049]

[0050] Where λ represents the radar wavelength, V sc =[V sx V sy V sz ] Τ represents the velocity vector of the satellite.

[0051] The ground points are in the geocentric rectangular coordinate system, so they satisfy the earth ellipsoid equation:

[0052]

[0053] In the formula, R e represents the major axis of the Earth ellipsoid, R p H represents the minor semi-axis of the Earth ellipsoid. t It is the elevation value of the ground point, which is obtained through the Digital Elevation Model (DEM).

[0054] The above three equations are used to solve the ground point coordinates. Combined with the ground corner reflector control data, the satellite geometric calibration parameters are obtained, and the multi-temporal SAR data are geometrically corrected to obtain multi-temporal SAR images with high planar positioning accuracy.

[0055] S1.2 uses the multi-phase SAR data after geometric calibration as input and performs interferometric processing to obtain interferometric phase information. The interferometric phase includes deformation, terrain, atmosphere, ionosphere, orbit error, soil moisture and noise phase components, as shown in the formula:

[0056] φ int =φ flat +φ topo +φ def +φ atm +φ orb +φ noise +φ ion +φsmc

[0057] In the formula, φ int is the interference phase, φ flat is the reference ellipsoid phase, φ topo is the terrain phase, φ def is the deformation phase of the radar line of sight, φ atm is the atmospheric phase, φ orb is the orbit error, φ noise is the noise phase. Compared with the traditional interference phase expression formula, the above formula adds φ ion and φ smc , represent the ionospheric delay phase and the interference phase caused by soil moisture changes, respectively.

[0058] S1.3 uses satellite precise orbit data and baseline information of interferometer pairs to remove orbit errors and flat earth phases. It can effectively remove terrain phases through high-precision and high-timeliness external DEMs, and combine adaptive filtering methods to suppress the influence of noise phases.

[0059] The interference phase is thus expressed as the superposition of the phase caused by atmospheric delay, ionospheric delay, deformation phase and soil moisture change.

[0060] In the area without deformation, or using a short-time baseline combination (4 days) to avoid the deformation phase, the interference phase can be expressed as:

[0061] φ int =φ atm +φ ion +φ smc

[0062] Therefore, the quantitative inversion of soil moisture using interferometric phase requires solving the problems of atmospheric delay and ionospheric delay.

[0063] S2: Interference phase optimization processing. For several phase components contained in the interference phase, the satellite's precise orbit data and the baseline information of the interference pair are used to remove the orbit error and the flat earth phase. The terrain phase is effectively removed through a high-precision and high-timeliness external DEM, and the influence of the noise phase is suppressed by combining the adaptive filtering method.

[0064] Specific operation: DEM error phase suppression, external DEM error will have a certain impact on the interference phase. When the image pair interference baseline is short, the corresponding elevation ambiguity is large, and the phase error caused by the corresponding elevation error is small, that is, the shorter the spatial baseline, the less the interference phase is affected by the external DEM error. L-SAR satellites use strict regression orbit control, the heavy orbit interference baseline control is better, and the spatial baseline index is kept within 700 meters. During the actual operation of the satellite, the L-SAR satellite interference baseline index was statistically analyzed. 95% of the image pairs have a vertical baseline length of no more than 400 meters, and the L-band wavelength is longer, and the corresponding elevation ambiguity is larger, so the interference phase is less affected by the external DEM error. And through the L-SAR dual-satellite flyby data to obtain high-precision and high-timeliness external DEM, the DEM error phase is further suppressed.

[0065] Orbit error phase suppression: L-SAR satellite uses dual-frequency differential GNSS to achieve precise orbit determination. The satellite orbit positioning accuracy is high, and the post-precision orbit determination accuracy is better than 10cm. Precise orbit data is uniformly used as input during the interference processing process, thereby suppressing the impact of orbit errors to the greatest extent in the interference phase.

[0066] The focus is on interferometric phase optimization processing for atmospheric phase delay and ionospheric phase delay. First, the atmospheric delay phase is suppressed. The GACOS model combines the atmospheric water vapor content data obtained by GNSS and the data of the European Center for Medium-Term Weather Forecasts. It uses the tropospheric iterative decomposition model to separate the stratification and turbulence signals from the total tropospheric delay, and generates a high spatial resolution zenith total delay, which can effectively improve the tropospheric delay, especially the long-wave component and the part related to the terrain. By introducing the GACOS model, the atmospheric delay component φ in the interferometric phase is suppressed. atm , the interferometric phase information is corrected using the temporally consistent atmospheric delay information to overcome the influence of tropospheric atmospheric delay on the interferometric phase inversion soil moisture.

[0067] For the ionospheric delay component in the interferometric phase, the space environment monitoring system carried by the L-band SAR satellite is used to obtain the ionospheric density N in the SAR line of sight. e (s), and then extract the ionospheric phase delay component φ ion .

[0068] The total number of free electrons in the electromagnetic wave propagation path TEC is:

[0069] TEC = ∫ S N e (s)ds

[0070] Among them, S is the propagation path of the signal in the ionosphere. Then the propagation phase delay of the electromagnetic wave signal in the ionosphere is:

[0071]

[0072] Where f is the frequency of radar electromagnetic waves. Taking the L-band SAR satellite as an example, the corresponding frequency f is 1.26 GHz. The ionospheric delay phase component is obtained through the ionospheric density information obtained by the space environment monitoring system. The ionospheric phase delay component is separated from the interference phase to achieve effective optimization of the InSAR phase information. By eliminating the interference phase component irrelevant to soil moisture, the interference phase information φ that characterizes the soil moisture characteristics is obtained. smc , which is used for quantitative inversion of soil moisture to obtain reliable soil surface moisture information.

[0073] S3: Soil moisture inversion based on joint interferometric phase information and radar backscattering information. The interferometric phase information and radar backscattering information that characterize soil moisture characteristics are used as input to invert the soil surface moisture content.

[0074] m v =F[φ smc ,σ)]

[0075] In the formula, m v represents soil moisture content, F represents the inversion relationship between radar interferometry phase and backscattering information and soil moisture, and σ represents the radar backscattering coefficient.

[0076] The specific implementation includes the following two aspects:

[0077] S3.1 Soil moisture inversion based on interferometric phase information takes N L-SAR satellite data of different phases as input, and the unknown parameter is the soil moisture time series corresponding to N time periods. Through different time baseline combinations, Interference pairs are used to obtain the corresponding interference phase information φ that characterizes the soil moisture characteristics. smc , the number of interferometric phase observations is greater than the number of unknown parameters, thus realizing the time series inversion of soil moisture;

[0078] S3.2 is based on the radar backscattering mechanism. By increasing the effective observation equations between soil moisture and radar observation information, the constraints of soil moisture inversion are improved, and then the least squares solution strategy is used to solve the soil moisture information under the joint constraints of interference phase and radar backscattering coefficient to obtain high-precision surface soil moisture information.

[0079] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does 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 more embodiments or examples in a suitable manner.

[0080] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for inverting farmland soil moisture using cooperative radar interferometry phase information, characterized in that: The following steps are involved: S1: Interferometric processing of multi-temporal SAR data to obtain initial interferometric phase information of multiple phase components including deformation, terrain, atmosphere, ionosphere, orbit error, noise phase and soil moisture change; S2: Optimize the initial interferometric phase information, remove phase components irrelevant to soil moisture, including flat ground phase, terrain phase, noise phase, orbit error phase, atmospheric delay phase and ionospheric delay phase, and optimize the interferometric phase information that characterizes soil moisture characteristics; The S2 specifically includes: S2.1: Use satellite precise orbit data and interferometric baseline information to remove orbit errors and flat ground phases, use high-precision and high-time external DEM to remove terrain phases, and combine adaptive filtering methods to suppress the influence of noise phases; S2.2: Suppress the DEM error phase, obtain high-precision and high-timeliness external DEM through L-SAR dual-satellite flyby data, and further reduce the impact of DEM error on the interferometric phase; S2.3: Suppressing orbit error phase, L-SAR satellites use dual-frequency differential GNSS to achieve precise orbit determination, improve satellite orbit positioning accuracy, and reduce the impact of orbit error on interferometric phase; S2.4: Suppress the atmospheric delay phase, use the GACOS model combined with the atmospheric water vapor content data obtained by GNSS and the European Center for Medium-Range Weather Forecasts data to generate a high spatial resolution zenith total delay map, and perform atmospheric delay correction on the interferometric phase; S2.5: Suppress the ionospheric delay phase, obtain the ionospheric density in the SAR line of sight through the space environment monitoring system carried by the L-SAR satellite, extract the ionospheric phase delay component, and separate the ionospheric phase delay component from the interference phase; S3: Construct a soil moisture inversion model and combine the interferometric phase information and radar backscattering information to invert and obtain soil surface moisture information.

2. The method for inverting farmland soil moisture using cooperative radar interferometry phase information according to claim 1, characterized in that: Said S1 further comprises: S1.1: Process multi-phase SAR data, combine them with corner reflector data, perform image geometry calibration, and obtain time-series SAR images with high planar positioning accuracy; S1.2: Using the geometrically calibrated multi-phase SAR data as input, perform interferometric processing to obtain initial interferometric phase information containing multiple phase components.

3. The method for inverting farmland soil moisture using cooperative radar interferometry phase information according to claim 2, characterized in that: The image geometry calibration processing in S1.1 includes: using the distance formula from the ground point to the satellite sensor, the Doppler frequency shift formula and the earth ellipsoid equation to solve the ground point coordinates, combining the ground corner reflector control data, obtaining the satellite geometry calibration parameters, and performing geometry calibration processing on the multi-phase SAR data; The specific relevant formula is as follows: The distance formula from the ground point to the satellite sensor is obtained through the position vector of the satellite sensor and the ground point, which is specifically expressed as: In the formula, R gc =[X g Y g Z g ] T Represents the position vector of the ground point, R sc =[X s Y s Z s ] T represents the position vector of the satellite sensor, and R represents the slant range, i.e., the distance from the satellite sensor to the ground point; The Doppler shift formula is: Where λ represents the radar wavelength, V sc =[V sx V sy V sz ] Τ represents the velocity vector of the satellite; The equation of the Earth's ellipsoid is: In the formula, R e represents the major axis of the Earth ellipsoid, R p H represents the minor semi-axis of the Earth ellipsoid. t It is the elevation value of the ground point, obtained through the digital elevation model (DEM).

4. The method for inverting farmland soil moisture using cooperative radar interferometry phase information according to claim 1, characterized in that: The GACOS model in S2.4 uses the tropospheric iterative decomposition model to separate the stratification and turbulence signals from the tropospheric total delay, generate a high spatial resolution zenith total delay map, and perform atmospheric delay correction on the interferometric phase to overcome the influence of the tropospheric atmospheric delay on the interferometric phase inversion soil moisture.

5. The method for inverting farmland soil moisture using cooperative radar interferometry phase information according to claim 1, characterized in that: The ionospheric delay phase suppression in S2.5 obtains the SAR line-of-sight ionospheric density through the space environment monitoring system carried by the L-SAR satellite, and then extracts the ionospheric phase delay component, separates the ionospheric phase delay component in the interference phase, and realizes the effective optimization of InSAR phase information.

6. The method for inverting farmland soil moisture using cooperative radar interferometry phase information according to claim 1, characterized in that: The S3 specifically includes: S3.1: Soil moisture inversion based on interferometric phase information, with N L-SAR satellite data of different phases as input, the unknown parameter is the soil moisture time series corresponding to N time periods, and different time baselines are combined to form Interference pairs are used to obtain the corresponding interference phase information φ that characterizes the soil moisture characteristics. smc , satisfying the requirement that the number of interferometric phase observations is greater than the number of unknown parameters, and realizing the time series inversion of soil moisture; S3.2: Based on the radar backscattering mechanism, the constraints of soil moisture inversion are improved by adding a group of effective observation equations between soil moisture and radar observation information. Then, the least squares solution strategy is used to solve the soil moisture information under the joint constraints of the interference phase and radar backscattering coefficient to obtain high-precision surface soil moisture information.

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