A space-borne multi-baseline InSAR data vegetation height inversion method

Abstract

The present application belongs to the technical field of synthetic aperture radar interferometry, and provides a kind of spaceborne multi-baseline InSAR data vegetation height retrieval method, comprising: obtaining spaceborne multi-baseline InSAR data and external DEM data, and carrying out main auxiliary image registration operation to multi-baseline InSAR data;Based on spaceborne multi-baseline InSAR data, obtain the vertical wave number of the multiple interference baselines formed by main satellite and auxiliary satellite and interference complex coherence;Multi-baseline is respectively constructed RVoG model, and vertical wave number and interference complex coherence are substituted into the RVoG model of multi-baseline, and three-dimensional look-up table is formed respectively;The intersection of three-dimensional look-up table is obtained, and the unknown parameter at intersection is the vegetation scene parameter obtained by inversion.The present application utilizes the multiple independent single polarization interference data that can be provided by spaceborne multi-baseline InSAR satellite system, and through constructing three-dimensional look-up table between extinction coefficient, vegetation height and ground scattering ratio and interference complex coherence, the effective inversion of vegetation height is realized.

Description

Technical Field

[0001] This invention relates to the field of synthetic aperture radar interferometry technology, specifically to a method for inverting vegetation height from spaceborne multi-baseline InSAR data. Background Technology

[0002] Vegetation plays a vital role in ecological restoration and environmental protection, such as desertification control and water conservation. Vegetation height, as one of the key parameters describing vegetation characteristics, is widely used to estimate biomass distribution through effective inversion of its parameters. This is beneficial for studying the sustainable productivity of vegetation-covered areas and is of great significance for assessing the health status of ecosystems and guiding ecological restoration strategies.

[0003] Traditional vegetation height data is obtained through manual field surveys, which results in high economic costs and makes it difficult to measure vegetation height over large areas. Therefore, effectively combining all-weather, all-time synthetic aperture radar (SAR) technology with vegetation coherent scattering models to retrieve large-scale vegetation vertical spatial distribution information (vegetation height) has irreplaceable application value for forestry sustainable development and climate change research.

[0004] In SAR systems, radar antennas emit electromagnetic waves using side-looking geometry, continuously recording the backscattering information of observed targets. The received electromagnetic signals are processed to form a Single-Look Complex (SLC) image on the range (slant range)-azimuth plane. Interferometric SAR (InSAR) acquires two SLC images with slightly different viewpoints within the same scene, using the phase difference between corresponding pixels to obtain elevation information of ground features. It is widely used in the extraction of Digital Elevation Models (DEMs) or Digital Surface Models (DSMs). In vegetated areas, InSAR observations integrate the relative height information of vegetation canopy and understory scattering, providing a basis for vegetation height inversion. However, traditional single-baseline InSAR is limited by a limited number of observations and often relies on statistical regression methods for vegetation height inversion.

[0005] Coherent scattering models of vegetation establish a physical relationship between interferometric observations and structural parameters such as vegetation height, based on the scattering mechanism of electromagnetic waves in vegetated scenes. Due to their clear physical meaning and relatively rigorous theoretical support, these methods typically achieve high inversion accuracy. Among numerous coherent scattering models, the Random Volume over Ground (RVoG) model is the most representative two-layer medium model. This model simplifies the vegetation layer as a uniformly distributed volume scattering medium and, together with surface scattering, describes the coherent scattering process of the vegetation scene. Therefore, it is still widely used in vegetation height inversion research. However, the RVOG model contains multiple parameters to be determined, and its complete solution usually relies on sufficient observational constraints provided by Polarimetric SAR interferometry (PolInSAR) data. Due to the current state of development of on-orbit satellite systems, there is still a relative lack of satellites capable of operationally acquiring PolInSAR data, which to some extent restricts the application of vegetation parameter inversion based on the RVOG model.

[0006] Unlike traditional single-baseline InSAR, which relies on satellite re-orbiting (passing the same location at different times) to acquire interferometric data, spaceborne multi-baseline InSAR acquires interferometric data from multiple baselines in a single pass, effectively avoiding the effects of temporal decorrelation and improving the accuracy of ground feature (or vegetation height) retrieval from InSAR data. Figure 1 Taking the HT-1 distributed wheel-formation multi-baseline InSAR satellite system as an example, it consists of four X-band SAR satellites in a wheel-formation configuration, including one main satellite HT-1A and three auxiliary satellites HT-1B, HT-1C, and HT-1D. Multiple interferometric baselines can be formed between each satellite, providing rich interferometric observation information and necessary data support for RVOG model parameter inversion. Therefore, research on vegetation height inversion methods using spaceborne multi-baseline InSAR data, including HT-1 satellite data, is not only feasible but also has significant theoretical and practical value.

[0007] In recent years, vegetation height retrieval methods have developed rapidly, forming various technical paths utilizing different data sources, different bands, and different baseline combinations. These methods each have their advantages in acquiring vegetation parameter characteristics, but they still have certain limitations. For example, lidar and tomographic SAR methods have high data requirements and are costly, resulting in significant limitations in practical engineering applications; polarimetric interferometric SAR vegetation height retrieval methods currently lack long-term, large-scale on-orbit baseline data, differing from the needs of widespread practical applications; the SINC model in the coherence coefficient retrieval method ignores the contributions of surface scattering and canopy extinction, resulting in a high degree of model simplification and low retrieval accuracy; the phase center difference method cannot accurately determine the phase center of canopy scattering and the phase center of surface scattering, leading to a systematic underestimation of vegetation height. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a method for vegetation height inversion from spaceborne multi-baseline InSAR data. This method aims to improve the accuracy and reliability of vegetation height inversion by addressing the limitations imposed by the performance of existing datasets, model parameter settings, and the accuracy of the research methods.

[0009] This invention provides a method for inverting vegetation height from spaceborne multi-baseline InSAR data, comprising: Acquire spaceborne multi-baseline InSAR data and external DEM data, and perform master-slave image registration on the multi-baseline InSAR data; Based on spaceborne multi-baseline InSAR data, the vertical wavenumber and interferometric complex coherence of multiple interferometric baselines formed by the main satellite and auxiliary satellites are obtained; RVOG models were constructed for each of the multiple baselines, and the vertical wavenumber and interferometric complex coherence were substituted into the RVOG models of the multiple baselines to form three-dimensional lookup tables for each of the multiple baselines; the three-dimensional lookup tables are three-dimensional surfaces formed by unknown parameters including ground scattering ratio, extinction coefficient and vegetation height. The intersection of the three-dimensional lookup table is obtained, and the unknown parameters at the intersection are the vegetation scene parameters obtained by inversion.

[0010] As can be seen from the above technical solution, the present invention provides a method for inverting vegetation height from spaceborne multi-baseline InSAR data. By utilizing multiple sets of independent single-polarization interferometric data provided by the spaceborne multi-baseline InSAR satellite system, and constructing a three-dimensional lookup table between extinction coefficient, vegetation height, ground scattering ratio, and interferometric complex coherence, the effective inversion of vegetation height is achieved.

[0011] Optionally, obtaining the vertical wavenumber and interferometric complex coherence of multiple interferometric baselines formed by the primary and secondary satellites based on spaceborne multi-baseline InSAR data includes: Interferometric processing was performed on the spaceborne multi-baseline InSAR data to obtain the interferometric phase between the main satellite and each auxiliary satellite. The main satellite and the auxiliary satellite form an interferometric baseline. The interferometric baseline of the main and auxiliary satellites is calculated based on the orbital information of the main and auxiliary satellites, and the vertical wavenumber of the interferometric baseline is obtained. The flat terrain phase, topographic phase, and error phase are removed from the interferometric phase to obtain the corrected interferometric phase; The complex interference coherence is obtained based on the corrected interference phase.

[0012] Optionally, the interferometric phase between the primary satellite and the secondary satellite is: s represents the pixel value, the superscript * indicates conjugate, P represents the primary satellite, and Ai represents the secondary satellite i.

[0013] Optionally, vertical wavenumber , Represents the estimated interferometric baseline The corresponding vertical baseline, Indicates the radar wave wavelength. Indicates the incident angle of the main satellite radar. Interference baseline The tilt angle, the value of m depends on the interferometric acquisition mode. When the InSAR system is using bistation data, m=1, and when the InSAR system is using heavy rail station data, m=2.

[0014] Optional, flat phase Topographic phase , Vertical height information of the target area in external DEM data; error phase , For orbital phase error, The phase error is caused by system factors; The corrected interference phase is then... .

[0015] Optionally, the interference complex coherence is ,in, The coefficient is the interference coherence coefficient.

[0016] Optionally, the multi-baseline RVOG model is , where the parameters Indicates ground phase, Indicates the ground scattering ratio. This indicates that pure volume scattering is incoherent. , Indicates the extinction coefficient. Indicates the angle of incidence. Indicates the vertical wavenumber. Indicates vegetation height; Substituting the vertical wavenumber and interferometric complex coherence into the RVOG model respectively and In this process, the three-dimensional surface equation with respect to the unknown parameters is obtained.

[0017] By adopting the above technical solution, this application has the following beneficial effects: This invention leverages the unique advantage of spaceborne multi-baseline InSAR satellite systems, which can acquire multi-baseline interferometric SAR (InSAR) data in a single pass, to obtain data from currently in-orbit spaceborne multi-baseline InSAR satellite systems. By integrating multi-baseline interferometric information, it achieves high-precision inversion of vegetation height. Compared with traditional methods, this algorithm significantly improves data acquisition efficiency and computational accuracy, not only greatly reducing observation time but also effectively reducing the dependence of traditional vegetation height inversion algorithms on airborne InSAR data.

[0018] Based on the classic RVOG model, this invention fully considers the correlation between parameters such as incident angle, extinction coefficient, ground scattering ratio, ground phase, vertical wavenumber, and vegetation height. By utilizing multiple sets of independent single-polarization interferometric data provided by the spaceborne multi-baseline InSAR satellite system, a three-dimensional lookup table is constructed between the extinction coefficient, vegetation height, ground scattering ratio, and interferometric complex coherence to achieve effective inversion of vegetation height. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0020] Figure 1 This illustrates one of the flowcharts for the satellite-borne multi-baseline InSAR data vegetation height inversion method provided by an embodiment of the present invention; Figure 2 This is the second flowchart of the satellite-borne multi-baseline InSAR data vegetation height inversion method provided by an embodiment of the present invention; Figure 3 The macros provided in the embodiments of the present invention are shown. Figure 1 Schematic diagram of the HT-1 distributed wheel formation multi-baseline InSAR satellite system; Figure 4 A schematic diagram of a three-dimensional lookup table for a multi-baseline RVOG model provided in an embodiment of the present invention is shown. Figure 5 This figure shows the vegetation height inversion results of the experimental area provided by the method of the present invention. Figure 6 A scatter plot of the evaluation indicators provided in an embodiment of the present invention is shown. Detailed Implementation

[0021] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore merely examples, and should not be construed as limiting the scope of protection of the present invention. It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0022] In one embodiment, such as Figure 1-2 As shown, a method for inverting vegetation height from spaceborne multi-baseline InSAR data is provided, including: S1. Acquire spaceborne multi-baseline InSAR data and external DEM data, and perform master-slave image registration on the multi-baseline InSAR data.

[0023] This embodiment uses macros. Figure 1 Multi-baseline InSAR data from the HT-1 distributed wheel formation satellite and SRTM 30 m resolution DEM data from the same time period as the InSAR data were used to study the Chilcotin Plateau ecoregion in the central interior of British Columbia, Canada.

[0024] The SAR data used comes from the HT-1 interferometric (IM) mode. In this mode, the four satellites synchronously receive radar echoes emitted by the primary satellite. The temporal baselines of the primary and secondary image pairs are approximately zero, and the impact of temporal decorrelation on interferometry is negligible, facilitating the inversion of vegetation height parameters over large spatial scales. Multiple interferometric baselines can be formed between each satellite, thus providing rich interferometric observation information.

[0025] Furthermore, master-slave image registration is performed on the multi-baseline InSAR images. Master-slave SAR image registration consists of two steps: coarse registration and fine registration.

[0026] Coarse registration is performed using metadata from primary and secondary satellite images, employing a "primary satellite as the reference, multiple secondary satellites in parallel registration" mode. This means that the image of each secondary satellite needs to be independently registered with the image of the primary satellite. In this embodiment, a total of three high-precision registration pairs are generated.

[0027] In the fine registration step, precise registration is performed based on the backscattering intensity or interferometric coherence image.

[0028] S2. Based on spaceborne multi-baseline InSAR data, obtain the vertical wavenumber and interferometric complex coherence of multiple interferometric baselines formed by the main satellite and auxiliary satellites.

[0029] Step S2 includes: S210. Perform interferometric processing on the spaceborne multi-baseline InSAR data to obtain the interferometric phase between the main satellite and each auxiliary satellite.

[0030] The interferometric phase between the primary satellite and the secondary satellite is: , s represents the pixel value, a represents the magnitude of the pixel value, and the superscript * indicates conjugate. The wavelength of the radar wave is represented by j, which is the imaginary unit. P represents the primary satellite, and Ai represents the secondary satellite i. This represents the distance difference between the primary satellite and the secondary satellite relative to the ground target point. The distance from the main satellite to the ground target point. This refers to the distance from the auxiliary satellite to the ground target point.

[0031] like Figure 3 As shown, in macro Figure 1 Taking the HT-1 distributed wheel formation multi-baseline InSAR satellite system as an example, to facilitate the correspondence between the main satellite (HT-1A) and the three auxiliary satellites (HT-1B, HT-1C, HT-1D), the subscripts and superscripts of the main satellite will be directly represented as A, and the subscripts and superscripts of the auxiliary satellites as B, C, and D, respectively. The corresponding pixel values ​​for the SLC data of the onboard multi-baseline InSAR system will then be... , , , Both are complex numbers and can be represented as the product of amplitude and phase terms. The pixel values ​​corresponding to the SLC data of its primary satellite (HT-1A) and three secondary satellites (HT-1B, HT-1C, HT-1D) are shown below. , , , They are respectively: , , , , in, , , , These represent the distances from HT-1A, HT-1B, HT-1C, and HT-1D to the target point, respectively. When measuring ground vegetation elevation information, it is assumed that the scattering characteristics of each satellite in the spaceborne multi-baseline InSAR system have high stability within the observation time interval; therefore, it can be assumed that... .

[0032] Furthermore, interferometric processing is performed on the spaceborne multi-baseline InSAR data. For the multi-baseline InSAR satellite system (HT-1) used in this embodiment, the interferometric phases corresponding to the three sets of interferometric data are obtained, as follows: , , , in, 、 、 .

[0033] S220. The main satellite and the auxiliary satellite form an interferometric baseline. The interferometric baseline of the main and auxiliary satellites is calculated based on the orbital information of the main and auxiliary satellites, and the vertical wavenumber of the interferometric baseline is obtained.

[0034] Furthermore, baseline estimation is performed using the flight orbital state vector information of the interferometry platform to estimate the initial parameters of multiple interferometric baselines formed by pairwise connections between the primary and secondary satellites. Each baseline... Corresponding parallel baseline and vertical baseline , is represented as: , in, Indicates the incident angle of the main satellite radar. Indicates the spatial baseline between the primary and secondary satellites. Baseline tilt angle (baseline) (The angle between the horizontal direction and the horizontal direction).

[0035] Furthermore, vertical wavenumber ( By calculating the baseline parameters and geometric relationships obtained from the above-derived spaceborne multi-baseline InSAR data observations, the vertical wavenumbers of multiple interferometric baselines can be calculated. , is represented as: , in, It is the vertical wavenumber corresponding to the interferometric baseline. Indicates the radar wave wavelength. R P Indicates the angle of incidence of the main satellite The slant range length for side-looking imaging of the ground target point is the distance between the main satellite and the target point; the value of m depends on the interferometric acquisition mode. When the InSAR system uses bistation data, m=1; when the InSAR system uses heavy orbit station data, m=2. Based on the vertical wavenumber of each interferometric baseline, three sets of interferometric pairs with the required vertical wavenumbers are selected as the original dataset.

[0036] In this embodiment, taking the three sets of interferometric baselines AB, AC, and AD formed by the main satellite (HT-1A) and three auxiliary satellites (HT-1B, HT-1C, and HT-1D) of HT-1 as an example, the vertical wavenumbers are as follows: , in, They represent the estimated interferometric baselines, respectively. , , The corresponding vertical baseline, Indicates the incident angle of the main satellite radar. , , Interference baselines , , The angle of inclination.

[0037] S230. Remove the flat phase, terrain phase, and error phase from the interferometric phase to obtain the corrected interferometric phase.

[0038] Assuming no change in surface and atmospheric conditions during the imaging process of the primary and secondary satellite images, the flat-ground phase is the phase change caused by the flat ground, expressed as: ; Topographic phase refers to the phase change caused by topographic relief, expressed as: , in, This refers to the vertical height information of the target area in external DEM data. The phase error is the phase error caused by factors such as inaccurate orbital parameters and system thermal noise, and is expressed as: , in, For orbital phase error, The phase error is caused by factors such as system thermal noise; The corrected interference phase is then... .

[0039] S240. Based on the corrected interference phase, obtain the complex interference coherence.

[0040] Specifically, interference complex coherence is ,in, The coefficient is the interference coherence coefficient.

[0041] Specifically applied to the macros used in this embodiment Figure 1 The complex interferometric coherence of the three baselines formed by pairwise combinations of the primary satellite (HT-1A) and any one of the three secondary satellites (HT-1B, HT-1C, HT-1D) in the distributed wheel formation multi-baseline InSAR satellite system (HT-1) is defined as follows: , , , in, The image (main image) is the image from which the main satellite (HT-1A) participates in the interferometry. , , Images (sub-images) from three auxiliary satellites (HT-1B, HT-1C, and HT-1D) participating in the interferometry. , , This indicates that the main image and the secondary image are multiplied by their conjugate. This indicates the calculation of the expected value, and the interference complex coherence. , , For a complex number, it is represented as: , , , in, , , These are the interference coherence coefficients corresponding to the three baselines AB, AC, and AD, respectively. The imaginary unit, 、 、 These represent the interference phases obtained after removing the flat terrain phase, terrain phase, and phase error from the three baselines AB, AC, and AD, respectively.

[0042] S3. Construct RVOG models for multiple baselines and substitute the vertical wavenumber and interferometric complex coherence into the RVOG models of the multiple baselines. The multiple baselines form three-dimensional lookup tables. The three-dimensional lookup tables are three-dimensional surfaces formed by unknown parameters including ground scattering ratio, extinction coefficient and vegetation height.

[0043] Specifically, the RVOG model views a vegetation scene as consisting of the ground and a layer of thickness. The vegetation consists of two layers composed of randomly oriented particles. The multi-baseline RVOG model is as follows: ; Among them, parameters Indicates ground phase, The scattering ratio of the ground body is expressed as follows: Pure volume scattering decoherence is expressed as: , in, , Indicates the extinction coefficient. Indicates the angle of incidence. Indicates the vertical wavenumber. This represents vegetation height; in summary, these constitute the six parameters necessary for solving the RVOG model. Among them, ground phase... After the preceding terrain phase removal operation, its terrain phase is considered to be 0; vertical wavenumber and angle of incidence All parameters are known.

[0044] In this embodiment, the three sets of vertical wavenumbers and interferometric complex coherence obtained in step S2 are... , and Substitute them into the RVOG model respectively and In the process, three sets of three-dimensional surface equations with respect to unknown parameters are obtained, such as Figure 4 As shown.

[0045] S4. Obtain the intersection of the 3D lookup table. The unknown parameters at the intersection are the vegetation scene parameters obtained by inversion.

[0046] For the satellite system (HT-1) used in this embodiment, the first step in solving the multi-baseline RVOG model is to convert the three sets of complex coherent lines into a single image. , , The corresponding three-dimensional lookup tables are constructed. Further, by finding the intersections of these three sets of three-dimensional lookup tables, the parameter combinations that simultaneously satisfy the constraints of each set of complex coherence and RVOG models are determined. Finally, under certain conditions... and Within the range, such as Figure 4 In part (d), at the intersection , and These are the vegetation scene parameters obtained through inversion, where This is the final vegetation height to be determined, and the final inversion result is as follows: Figure 5 As shown.

[0047] like Figure 6As shown, a scatter plot is used to characterize the consistency relationship between the sample true value height and the model inversion height, and color coding is used to further reflect the degree of local clustering of sample points in the scatter space. Figure 6 The horizontal axis represents the vegetation height (InSAR CHM) obtained by the multi-baseline 3D lookup table method, and the vertical axis represents the sample values ​​of the lidar canopy height model (Lidar CHM). Each scatter point corresponds to a sample point, and its position indicates the distribution of the sample in the two-dimensional space of "reference ground truth - model inversion value". The color of the scatter point indicates the degree of clustering of the sample in the two-dimensional scatter point space; the warmer the color, the more concentrated the samples are in that area, which is the main sample distribution range; the cooler the color, the more isolated the sample point is. The vegetation height obtained by the multi-baseline 3D lookup table method has a good positive correlation with the overall LiDAR CHM. The sample points are generally distributed along the 1:1 dashed line, indicating that the InSAR inversion results can reflect the overall trend of vegetation height change well. The evaluation indicators given in the figure show that the root mean square error (RMSE) is 1.47 m, the accuracy (Acc.) is 83.3%, and the coefficient of determination (R²) is [missing data]. 2 The value of 0.68 indicates that the method has a good ability to retrieve vegetation height in the experimental area. Overall, this figure shows that the multi-baseline 3D lookup table method can effectively retrieve vegetation height in the experimental area with high overall accuracy and relatively stable performance within the main sample distribution range.

[0048] In summary, by utilizing the multiple sets of independent single-polarization interferometric data provided by the spaceborne multi-baseline InSAR satellite system, extinction coefficients can be constructed. Vegetation height Scattering ratio with Earth A three-dimensional lookup table between these methods enables effective inversion of vegetation height. Compared to lidar methods, multi-baseline tomography SAR methods, phase center difference methods, and polarimetric interferometric SAR inversion methods, this invention can use macro-... Figure 1 In-orbit satellite InSAR data, including the HT-1 distributed wheel formation multi-baseline InSAR satellite system, is readily available and can meet the needs of large-scale vegetation height monitoring. Furthermore, experimental results show that the root mean square error and coefficient of determination of the method presented in this invention are superior to the commonly used SINC model method for InSAR satellites. This demonstrates that this invention can effectively improve the accuracy of vegetation height inversion based on existing interferometric observations.

[0049] The above embodiments are only used to provide a detailed description of the technical solutions of this application. However, the descriptions of the above embodiments are only for the purpose of helping to understand the methods of the embodiments of the present invention and should not be construed as limiting the embodiments of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art should be covered within the protection scope of the embodiments of the present invention.

Claims

1. A method for inverting vegetation height from spaceborne multi-baseline InSAR data, characterized in that, include: Acquire spaceborne multi-baseline InSAR data and external DEM data, and perform master-slave image registration on the multi-baseline InSAR data; Based on spaceborne multi-baseline InSAR data, the vertical wavenumber and interferometric complex coherence of multiple interferometric baselines formed by the main satellite and auxiliary satellites are obtained; RVOG models were constructed for multiple baselines, and vertical wavenumber and interferometric complex coherence were substituted into the RVOG models of the multiple baselines to form three-dimensional lookup tables. The three-dimensional lookup tables are three-dimensional surfaces formed by unknown parameters including ground scattering ratio, extinction coefficient and vegetation height. The intersection of the three-dimensional lookup table is obtained, and the unknown parameters at the intersection are the vegetation scene parameters obtained by inversion.

2. The method according to claim 1, characterized in that, The method, based on spaceborne multi-baseline InSAR data, obtains the vertical wavenumber and interferometric complex coherence of multiple interferometric baselines formed by the primary and secondary satellites, including: Interferometric processing was performed on the spaceborne multi-baseline InSAR data to obtain the interferometric phase between the main satellite and each auxiliary satellite. The main satellite and the auxiliary satellite form an interferometric baseline. The interferometric baseline of the main and auxiliary satellites is calculated based on the orbital information of the main and auxiliary satellites, and the vertical wavenumber of the interferometric baseline is obtained. The flat terrain phase, topographic phase, and error phase are removed from the interferometric phase to obtain the corrected interferometric phase; The complex interference coherence is obtained based on the corrected interference phase.

3. The method according to claim 2, characterized in that, The interferometric phase between the primary satellite and the secondary satellite is: s represents the pixel value, the superscript * indicates conjugate, P represents the primary satellite, and Ai represents the secondary satellite i.

4. The method according to claim 3, characterized in that, Vertical wavenumber , Represents the estimated interferometric baseline The corresponding vertical baseline, Indicates the incident angle of the main satellite radar. Indicates the radar wave wavelength. Interference baseline The tilt angle, the value of m depends on the interferometric acquisition mode. When the InSAR system is using bistation data, m=1, and when the InSAR system is using heavy rail station data, m=2.

5. The method according to claim 4, characterized in that, Flat phase Topographic phase , This refers to the vertical height information of the target area in external DEM data. Error phase , For orbital phase error, The phase error is caused by system factors; The corrected interference phase is then... .

6. The method according to claim 5, characterized in that, Interferometric complex coherence is ,in, The coefficient is the interference coherence coefficient.

7. The method according to claim 5, characterized in that, Multi-baseline RVOG model for , where the parameters Indicates ground phase, Indicates the ground scattering ratio. This indicates that pure volume scattering is incoherent. , Indicates the extinction coefficient. Indicates the angle of incidence. Indicates the vertical wavenumber. Indicates vegetation height; Substituting the vertical wavenumber and interferometric complex coherence into the RVOG model respectively and In this process, the three-dimensional surface equation with respect to the unknown parameters is obtained.

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