Satellite-borne gnss-r land surface roughness and vegetation optical depth inversion method and related device

Abstract

The application discloses a kind of spaceborne GNSS-R ground roughness and vegetation optical thickness inversion method and related device, it is related to satellite remote sensing technical field, the method includes obtaining the reflection signal intensity of multiple observation points in mapped equal-area scalable geogrid, incident angle, longitude and latitude, soil moisture and clay content;The reflection signal intensity of each observation point is linearly calibrated, to obtain the calibrated reflection signal intensity;According to the soil moisture, clay content and incident angle of each observation point, the effective reflection factor of each observation point is calculated;Based on the effective reflection factor and incident angle of each observation point, the first regression variable and the second regression variable of each observation point are constructed, and least square linear fitting is carried out, to obtain the first fitting coefficient and the second fitting coefficient of each grid unit, so that ground roughness root mean square height and vegetation optical thickness are obtained by inversion.This application significantly improves the inversion accuracy and stability.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing and global navigation satellite system reflectance measurement technology, and in particular to a method and related apparatus for inverting surface roughness and vegetation optical thickness using a spaceborne GNSS-R system. Background Technology

[0002] Soil moisture retrieval technology based on Global Navigation Satellite System Reflectometry (GNSS-R) has become an important tool in current remote sensing observation due to its advantages such as wide coverage, high spatiotemporal resolution, and relatively low cost. This technology receives navigation signals reflected from the Earth's surface, extracts information such as reflected signal strength (SR) and incident angle, and then combines this information with physical models or empirical relationships to retrieve soil moisture. However, surface roughness (Root Mean Square Height, RMS) and vegetation cover (usually characterized by optical thickness τ) significantly alter the transmission path and scattering characteristics of the reflected signal, thus interfering with the retrieval results. Existing GNSS-R-based soil moisture retrieval methods (such as L1-level products from a GNSS-R reflection signal observation mission) mostly employ a single incident angle assumption or introduce empirical parameters for correction, making it difficult to effectively separate the coupling effects of surface roughness and vegetation cover, resulting in large retrieval errors under complex surface conditions.

[0003] To mitigate the interference of surface roughness and vegetation cover on soil moisture inversion, previous studies have attempted to jointly solve these parameters using multi-angle observation data. However, existing methods are typically limited to local regions or single-day observation timescales, failing to establish a gridded joint inversion framework applicable globally with a stable monthly cycle. Furthermore, current technologies have not effectively integrated high-precision auxiliary soil dielectric information from other independent observation sources, resulting in insufficient stability of parameter estimation during the inversion process and low computational efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a method and related apparatus for inverting surface roughness and vegetation optical thickness using a spaceborne GNSS-R system. This method enables joint inversion of global monthly-scale gridded surface roughness and vegetation optical thickness based on multi-incident angle observations from the cyclone-based global navigation satellite system reflectometry, significantly improving inversion accuracy and stability.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for inverting surface roughness and vegetation optical thickness using a spaceborne GNSS-R system, including: The study acquires observational data from a global navigation satellite system (GNSS) for cyclones over a period of one month, along with soil moisture and clay content from corresponding active and passive soil moisture detection satellite grids. This observational data is then mapped onto an equal-area scalable Earth grid from the active and passive soil moisture detection satellites, resulting in a mapped equal-area scalable Earth grid. The mapped equal-area scalable Earth grid comprises multiple grid cells, each containing observational data from multiple observation points, soil moisture, and clay content. The observational data includes reflected signal intensity, incident angle, and latitude and longitude. The reflected signal intensity at each observation point is linearly calibrated to obtain the calibrated reflected signal intensity at each observation point. Based on the soil moisture, clay content and incident angle of each observation point, the square of the Fresnel reflection coefficient of each observation point is calculated. Based on the square of the Fresnel reflection coefficient of each observation point and the calibrated reflected signal intensity, the effective reflection factor of each observation point is calculated. Based on the effective reflection factor and incident angle of each observation point, the first regression variable and the second regression variable of each observation point are constructed respectively. Least squares linear fitting is performed on the first and second regression variables of all observation points in each grid cell to obtain the first and second fitting coefficients of each grid cell. Based on the preset L-band wavenumber, the root mean square height of the surface roughness of each grid cell is inverted using the first fitting coefficient of each grid cell, and the vegetation optical thickness of each grid cell is inverted using the second fitting coefficient of each grid cell. A global distribution data product is generated based on the root mean square height of surface roughness and the optical thickness of vegetation for each grid cell. The global distribution data product consists of the root mean square height of surface roughness and the optical thickness of vegetation covering the entire scalable Earth grid of equal area, and is used for parameter calibration in soil moisture inversion.

[0006] Optionally, the calculation formula for linear calibration is as follows: ; in, The calibrated reflected signal strength; The decimal value of the reflected signal strength is obtained by converting the original reflected signal strength into a linear form.

[0007] Optionally, based on the soil moisture, clay content, and incident angle at each observation point, the square of the Fresnel reflectance coefficient for each observation point is calculated, specifically including: The soil dielectric constant of each observation point is calculated based on the soil moisture and clay content. Based on the soil dielectric constant and incident angle at each observation point, the square of the Fresnel reflection coefficient at each observation point is calculated using the Fresnel reflection coefficient formula for vertical or horizontal polarization.

[0008] Optionally, the formula for calculating the first regression variable is as follows: ; in, It is the first regression variable; Effective reflective factor; Angle of incidence; The specific formula for calculating the second regression variable is as follows: ; in, It is the second regression variable; The angle of incidence is denoted as .

[0009] Optionally, the least squares linear fitting is implemented using a pseudo-inverse matrix approach. The specific least squares linear fitting is as follows: ; in, It is the first regression variable; The first fitting coefficient is set to zero. If the calculated first fitting coefficient is greater than zero, then the first fitting coefficient is forced to be set to zero or a negative value. This is a regression matrix, where the first column represents the second regression variable, and the second column is a vector of all 1s. is the second fitting coefficient.

[0010] Optionally, the inversion formula for the root mean square height of surface roughness is: ; in, The root mean square height of the surface roughness; The first fitting coefficient; For L-band wavenumber, , For frequency, The speed of light; The inversion formula for vegetation optical thickness is: τ = -b / 2; Where τ is the optical thickness of the vegetation; b is the second fitting coefficient.

[0011] Optionally, the root mean square height of surface roughness is controlled within the range of 0 cm to 1.2 cm, and the optical thickness of vegetation is controlled within the range of 0 to 1.9 cm.

[0012] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the spaceborne GNSS-R surface roughness and vegetation optical thickness inversion method described in any one of the above.

[0013] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the spaceborne GNSS-R surface roughness and vegetation optical thickness inversion method described above.

[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the spaceborne GNSS-R surface roughness and vegetation optical thickness inversion method described above.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and related apparatus for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R. By acquiring observation data from a cyclone global navigation satellite system over multiple days within a month, as well as soil moisture and clay content from corresponding active and passive soil moisture detection satellite grids, and mapping the observation data to an equal-area scalable Earth grid of the active and passive soil moisture detection satellites, a mapped equal-area scalable Earth grid is obtained. This solves the problem in existing technologies where observation data is limited to local areas or single-day time scales and cannot form stable monthly-scale cumulative observations on a global scale. It realizes the systematic organization and accumulation of multi-day, multi-angle, and multi-observation-point data under a unified grid framework, laying a data foundation for subsequent global-scale joint inversion.

[0016] By linearly calibrating the reflected signal intensity at each observation point, the calibrated reflected signal intensity at each observation point is obtained. Based on the soil moisture, clay content, and incident angle at each observation point, the square of the Fresnel reflection coefficient at each observation point is calculated. Based on the square of the Fresnel reflection coefficient at each observation point and the calibrated reflected signal intensity, the effective reflection factor at each observation point is calculated. This solves the problem that existing technologies cannot effectively integrate external high-precision auxiliary soil dielectric information, and realizes the accurate calculation of the Fresnel reflection coefficient using soil moisture and clay content provided by active and passive soil moisture detection satellites, thereby significantly improving the physical authenticity of the effective reflection factor and the quality of the inversion input.

[0017] By constructing the first and second regression variables for each observation point based on the effective reflectance factor and incident angle, respectively, the first and second regression variables for each observation point are obtained. Least square linear fitting is performed on the first and second regression variables of all observation points within each grid cell to obtain the first and second fitting coefficients for each grid cell. Based on the preset L-band wavenumber, the root mean square height of surface roughness of each grid cell is inverted using the first fitting coefficient, and the vegetation optical thickness of each grid cell is inverted using the second fitting coefficient. This solves the problems of severe coupling between surface roughness and vegetation effect, large inversion error caused by single-angle assumption, and poor parameter estimation stability in existing methods. It realizes linear physical inversion of roughness and vegetation contribution in one go using multi-angle observations within a monthly-scale gridded framework, which greatly improves the robustness and computational efficiency of parameter estimation.

[0018] Globally distributed data products are generated based on the root mean square height of surface roughness and the optical thickness of vegetation in each grid cell. These products, consisting of root mean square height of surface roughness and optical thickness of vegetation covering the entire scalable Earth grid of equal area, are used for parameter calibration in soil moisture retrieval. This solves the problem that existing technologies cannot output global regular gridded monthly-scale roughness and vegetation products, and enables high-quality data products that can be directly used for global drought monitoring, agricultural remote sensing, and climate research. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is an application environment diagram of a spaceborne GNSS-R surface roughness and vegetation optical thickness inversion method according to an embodiment of this application; Figure 2 A schematic flowchart of a method for inverting surface roughness and vegetation optical thickness using a spaceborne GNSS-R system, provided as an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1 The spaceborne GNSS-R surface roughness and vegetation optical thickness inversion method provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the reflected signal intensity, incident angle, latitude and longitude, soil moisture, and clay content of multiple observation points in a mapped, scalable, equal-area Earth grid to server 104. Server 104 performs linear calibration on the reflected signal intensity of each observation point to obtain the calibrated reflected signal intensity; calculates the effective reflectance factor of each observation point based on its soil moisture, clay content, and incident angle; constructs a first regression variable and a second regression variable for each observation point based on its effective reflectance factor and incident angle, and performs least-squares linear fitting to obtain the first and second fitting coefficients for each grid cell, thereby retrieving the root mean square height of surface roughness and the optical thickness of vegetation. Server 104 can feed back the obtained root mean square height of surface roughness and optical thickness of vegetation to terminal 102.

[0024] The terminal 102 can be, but is not limited to, various desktop computers, laptops, and IoT devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0025] In one exemplary embodiment, such as Figure 2 As shown, a method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 207. Wherein: Step 201: Obtain observation data from the cyclone global navigation satellite system for multiple days within a month, as well as soil moisture and clay content from the corresponding active and passive soil moisture detection satellite grid. Map the observation data to the equal-area scalable Earth grid of the active and passive soil moisture detection satellite to obtain the mapped equal-area scalable Earth grid. The mapped equal-area scalable Earth grid includes multiple grid cells, and each grid cell includes observation data, soil moisture, and clay content from multiple observation points. The observation data includes reflected signal intensity, incident angle, and latitude and longitude.

[0026] Step 202: Perform linear calibration on the reflected signal intensity of each observation point to obtain the calibrated reflected signal intensity of each observation point.

[0027] Step 203: Based on the soil moisture, clay content and incident angle of each observation point, calculate the square of the Fresnel reflection coefficient of each observation point, and calculate the effective reflection factor of each observation point based on the square of the Fresnel reflection coefficient of each observation point and the calibrated reflection signal intensity.

[0028] Step 204: Based on the effective reflection factor and incident angle of each observation point, construct the first regression variable and the second regression variable for each observation point.

[0029] Step 205: Perform least squares linear fitting on the first and second regression variables of all observation points in each grid cell to obtain the first and second fitting coefficients for each grid cell.

[0030] Step 206: Based on the preset L-band wavenumber, the root mean square height of the surface roughness of each grid cell is inverted using the first fitting coefficient of each grid cell, and the optical thickness of the vegetation of each grid cell is inverted using the second fitting coefficient of each grid cell.

[0031] Step 207: Generate a global distribution data product based on the root mean square height of surface roughness and the optical thickness of vegetation for each grid cell; the global distribution data product consists of the root mean square height of surface roughness and the optical thickness of vegetation covering the entire scalable Earth grid of equal area, and is used for parameter calibration in soil moisture inversion.

[0032] By implementing steps 201 to 207 above, this embodiment has the following beneficial effects: (1) By acquiring observation data from the cyclone global navigation satellite system for multiple days within a month, as well as the soil moisture and clay content of the corresponding active and passive soil moisture detection satellite grid, and mapping the observation data to the equi-area scalable Earth grid of the active and passive soil moisture detection satellite, the mapped equi-area scalable Earth grid is obtained. This solves the problem that the observation data in the existing technology is limited to local areas or single-day time scales and cannot form stable monthly-scale cumulative observations on a global scale. It realizes the systematic organization and accumulation of multi-day, multi-angle, and multi-observation point data under a unified grid framework, laying the data foundation for subsequent global-scale joint inversion.

[0033] (2) By linearly calibrating the reflection signal intensity of each observation point, the calibrated reflection signal intensity of each observation point is obtained; based on the soil moisture, clay content and incident angle of each observation point, the square of the Fresnel reflection coefficient of each observation point is calculated, and based on the square of the Fresnel reflection coefficient of each observation point and the calibrated reflection signal intensity, the effective reflection factor of each observation point is calculated. This solves the problem that the existing technology fails to effectively integrate external high-precision auxiliary soil dielectric information, and realizes the accurate calculation of the Fresnel reflection coefficient using the soil moisture and clay content provided by the active and passive soil moisture detection satellite, thereby significantly improving the physical authenticity of the effective reflection factor and the quality of the inversion input.

[0034] (3) Based on the effective reflectance factor and incident angle of each observation point, the first regression variable and the second regression variable of each observation point are constructed respectively; the first regression variable and the second regression variable of all observation points in each grid cell are subjected to least square linear fitting to obtain the first fitting coefficient and the second fitting coefficient of each grid cell; based on the preset L-band wavenumber, the root mean square height of the surface roughness of each grid cell is inverted using the first fitting coefficient of each grid cell, and the vegetation optical thickness of each grid cell is inverted using the second fitting coefficient of each grid cell. This solves the problems of severe coupling between surface roughness and vegetation effect, large inversion error caused by single angle assumption and poor parameter estimation stability in the existing methods. It realizes the linear physical inversion of separating roughness and vegetation contribution in one go using multi-angle observation under the monthly scale grid framework, which greatly improves the robustness and computational efficiency of parameter estimation.

[0035] (4) A global distribution data product is generated based on the root mean square height of surface roughness and the optical thickness of vegetation in each grid cell. The global distribution data product consists of the root mean square height of surface roughness and the optical thickness of vegetation covering the entire scalable Earth grid of equal area. It is used for parameter calibration in soil moisture inversion. This solves the problem that existing technologies cannot output global regular gridded monthly scale roughness and vegetation products, and realizes high-quality data products that can be directly used for global drought monitoring, agricultural remote sensing and climate research.

[0036] In summary, this application overcomes the shortcomings of existing technologies, such as limited observation scale, severe parameter coupling, insufficient utilization of external information, and low computational efficiency, by constructing a complete technical chain of "multi-day accumulation - grid mapping - fusion of auxiliary dielectric information - multi-angle linear fitting - parameter separation and inversion". It is the first to realize the joint inversion of global monthly-scale gridded surface roughness and vegetation optical thickness based on multi-incident angle observations of the Cyclone Global Navigation Satellite System reflectometry. This significantly improves the inversion accuracy and stability, and has the computational efficiency and data product production capabilities for operational use.

[0037] Furthermore, the calculation formula for linear calibration in step 202 is as follows: ; in, The calibrated reflected signal strength; The decimal value of the reflected signal strength is obtained by converting the original reflected signal strength into a linear form.

[0038] Further, in step 203, based on the soil moisture, clay content, and incident angle at each observation point, the square of the Fresnel reflection coefficient at each observation point is calculated, specifically including: Step 2031: Calculate the soil dielectric constant for each observation point based on the soil moisture and clay content.

[0039] Step 2032: Based on the soil dielectric constant and incident angle at each observation point, the square of the Fresnel reflection coefficient at each observation point is calculated using the Fresnel reflection coefficient formula for vertical or horizontal polarization.

[0040] Furthermore, the specific formula for calculating the first regression variable in step 204 is as follows: ; in, It is the first regression variable; Effective reflective factor; Angle of incidence; The specific formula for calculating the second regression variable is as follows: ; in, It is the second regression variable; The angle of incidence is denoted as .

[0041] Furthermore, in step 205, the least squares linear fitting is implemented using a pseudo-inverse matrix approach. Specifically, the least squares linear fitting is as follows: ; in, It is the first regression variable; The first fitting coefficient is set to zero. If the calculated first fitting coefficient is greater than zero, then the first fitting coefficient is forced to be set to zero or a negative value. This is a regression matrix, where the first column represents the second regression variable, and the second column is a vector of all 1s. The second fitting coefficient is .

[0042] Furthermore, the inversion formula for the root mean square height of surface roughness in step 206 is as follows: ; in, The root mean square height of the surface roughness; The first fitting coefficient; For L-band wavenumber, , For frequency; The speed of light; The inversion formula for vegetation optical thickness is: τ = -b / 2; Where τ is the optical thickness of the vegetation; b is the second fitting coefficient.

[0043] Furthermore, the root mean square height of surface roughness is controlled within the range of 0 cm to 1.2 cm, and the optical thickness of vegetation is controlled within the range of 0 to 1.9. When the inversion values ​​(i.e., the root mean square height of surface roughness and the optical thickness of vegetation) exceed the preset range, a truncation process is adopted, truncating the root mean square height of surface roughness to the range of 0 cm to 1.2 cm and the optical thickness of vegetation to the range of 0 to 1.9.

[0044] Example 2 In another exemplary embodiment of this application, a method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R is provided. This method utilizes multi-incident angle observation data from the Cyclone Global Navigation Satellite System (CYGNSS) Global Navigation Satellite System-Reflectometry (GNSS-R), combined with auxiliary information such as soil moisture and clay content provided by the Soil Moisture Active Passive (SMAP) satellite, to achieve a globally gridded monthly-scale joint inversion. The implementation steps of the method are detailed using CYGNSS observation data and SMAP auxiliary information from a specific month and year (days 001 to 031 of the year).

[0045] Step 1: Data Acquisition and Preprocessing CYGNSS observation data for multiple days over a month (31 days) was acquired. This data included the signal reflection intensity (SR), incidence angle (Inc), and latitude and longitude of each observation point. Simultaneously, soil moisture and clay content were obtained from the corresponding SMAP equal-area scalable Earth grid (EASE-Grid, 964 rows × 406 columns). Missing values ​​(such as NaN) were filled using multi-day averages.

[0046] Specifically, a CYGNSS calibration data file (e.g., CalMatday*.mat format) is loaded daily, from which latitude and longitude, reflected signal intensity, and incident angle are extracted.

[0047] Step 2: Spatial mapping: Based on the latitude and longitude of each observation point, a spatial mapping function (such as geo2easeGrid) is used to map each observation point to a grid cell in the SMAP EASE-Grid. This allows each grid cell to obtain the reflected signal intensity and incident angle corresponding to all observation points mapped to that grid cell. The mapped EASE-Grid consists of multiple grid cells, and each grid cell includes observation data from multiple observation points, along with the corresponding soil moisture and clay content.

[0048] Step 3: Linear calibration of reflected signal intensity: Linear calibration is performed on the reflected signal intensity at each mapped observation point. First, the original reflected signal intensity SR is converted into a decimal linear form. The conversion formula is: ; Then, the calibrated reflected signal strength is calculated using the following linear calibration formula. : ; Step 4: Calculation of the square of the Fresnel reflection coefficient and the effective reflection factor: Based on the soil moisture, clay content, and incident angle at each observation point, the squared Fresnel reflection coefficient (denoted as FresSqr) for each observation point is calculated, specifically including: First, based on the soil moisture and clay content at each observation point, the soil dielectric constant ε at that observation point is calculated using the dielectric constant model; Then, based on the soil dielectric constant and the angle of incidence at the observation point Using the Fresnel reflection coefficient formula for vertical or horizontal polarization, the square of the Fresnel reflection coefficient at the observation point is calculated. Among them, the Fresnel reflectance coefficient It can be , , , The Fresnel reflection coefficient is calculated using the following formulas (represented by vertical polarization, horizontal polarization, co-polarization, and cross-polarization): Vertically polarized Fresnel reflection coefficient: ; Horizontal polarization Fresnel reflection coefficient: ; Co-polarized Fresnel reflection coefficient: ; Cross-polarized Fresnel reflection coefficient: ; Select the corresponding FresSqr based on the actual observed polarization mode.

[0049] Then, the calibrated reflected signal strength at that observation point is used. Dividing by the square of the Fresnel reflection coefficient FresSqr, we obtain the effective reflectivity factor (RV) for each observation point: RV=CalSR / FresSqr.

[0050] Step 5: Construct regression variables: Based on the effective reflectance factor of each observation point and angle of incidence Construct the first regression variable for each observation point. Second regression variable The calculation formula is as follows: First regression variable: ; Second regression variable: ; For each grid cell, the first regression variable A and the second regression variable B1 corresponding to all observation points within that grid cell over one month are accumulated. The number of accumulated observation points within each grid cell is no less than 10.

[0051] Step 6: Least squares linear fitting: For all observation points within each grid cell, a least-squares linear fit is performed on the first regression variable A and the second regression variable B1 to obtain the first fitting coefficient a and the second fitting coefficient b for each grid cell. The fitting model is as follows: ; Specifically, a pseudo-inverse matrix approach is used: For each grid cell, a regression matrix B is constructed. The first column of regression matrix B is a column vector composed of the second regression variable B1 of all observation points within that grid cell, and the second column of regression matrix B is a vector of all 1s with the same length as B1. The fitting coefficient vector is then calculated. : ; in, This represents the pseudo-inverse operation. The first element in coeff is the first fitting coefficient 'a', and the second element is the second fitting coefficient 'b'. If the calculated first fitting coefficient 'a' is greater than zero, then the first fitting coefficient 'a' is forced to be set to zero or a negative value (in this embodiment, a ≤ 0 is forced; if a > 0, then a = 0 is set).

[0052] Step 7: Invert the root mean square height of surface roughness and the optical thickness of vegetation: Based on the preset L-band wavenumber Using the first fitting coefficient of each grid cell The root mean square height of surface roughness (σ) of the grid cell is inverted, and the vegetation optical thickness (τ) of the grid cell is inverted using the second fitting coefficient b of each grid cell.

[0053] L-band wavenumber The calculation formula is: ; Where the frequency f = 1.575 GHz (gigahertz) and the speed of light c = 30 cm / GHz (cm per gigahertz), therefore: ; The inversion formula for the root mean square height σ of surface roughness is: ; The inversion formula for vegetation optical thickness τ is: τ = -b / 2; To ensure the physical validity of the inversion results, the root mean square height of surface roughness σ was controlled within the range of 0 cm to 1.2 cm, and the optical thickness of vegetation τ was controlled within the range of 0 to 1.9. When the inversion values ​​exceeded the corresponding ranges, truncation was performed: σ was truncated to the interval [0, 1.2] cm, and τ was truncated to the interval [0, 1.9].

[0054] Step 8: Generate globally distributed data products: Based on the root mean square height of surface roughness σ and the optical thickness of vegetation τ of each grid cell obtained from the inversion, a global distribution data product covering the entire EASE-Grid is generated. For grid cells with fewer than 10 observation points within a month, or grid cells where the least squares linear fit does not meet the condition of the first fitting coefficient a≤0, the σ and τ of the grid cell are filled with missing values ​​(such as NaN or -9999) to ensure the integrity of the output grid matrix and the numerical stability of subsequent applications.

[0055] The final output consists of two grid matrices: the root mean square height grid for surface roughness (RMSGrid) and the optical thickness grid for vegetation (TauGrid). A global distribution map can be plotted using fastscatter. The color scale for surface roughness RMS height ranges from 0 to 1.2 cm, and the color scale for vegetation optical thickness ranges from 0 to 1.9 cm.

[0056] The method described in this embodiment can be executed sequentially by computer program modules. These computer program modules include: a data acquisition module, a grid mapping module, a signal calibration module, a Fresnel calculation module, a regression variable construction module, a least squares fitting module, and a parameter inversion module.

[0057] Specifically, this embodiment uses MATLAB 2021b software to write the core program. This program runs in less than 30 minutes on a single machine (CPU 3.0 GHz, memory 16 GB) and can efficiently process one month's worth of global data.

[0058] As an alternative, those skilled in the art can also use the Python language to achieve the same functionality using linear algebra functions (such as numpy.linalg.pinv) in the NumPy and SciPy libraries.

[0059] The beneficial effects of this embodiment: 1. Achieved multi-angle joint inversion at the global lunar scale, improving RMS accuracy by more than 15%; 2. Eliminate single-angle assumption errors and support dynamic vegetation and roughness monitoring; 3. Highly efficient in calculations, it can be directly embedded into the CYGNSS soil moisture business process; 4. The inversion results are highly consistent with the actual measurements.

[0060] Example 3 In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores processed data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a spaceborne GNSS-R method for inverting surface roughness and vegetation optical thickness.

[0061] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0062] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0066] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R, characterized in that, The method includes: The study acquires observational data from a global navigation satellite system (GNSS) for cyclones over a period of one month, along with soil moisture and clay content from corresponding active and passive soil moisture detection satellite grids. This observational data is then mapped onto an equal-area scalable Earth grid from the active and passive soil moisture detection satellites, resulting in a mapped equal-area scalable Earth grid. The mapped equal-area scalable Earth grid comprises multiple grid cells, each containing observational data from multiple observation points, soil moisture, and clay content. The observational data includes reflected signal intensity, incident angle, and latitude and longitude. The reflected signal intensity at each observation point is linearly calibrated to obtain the calibrated reflected signal intensity at each observation point. Based on the soil moisture, clay content and incident angle of each observation point, the square of the Fresnel reflection coefficient of each observation point is calculated. Based on the square of the Fresnel reflection coefficient of each observation point and the calibrated reflected signal intensity, the effective reflection factor of each observation point is calculated. Based on the effective reflection factor and incident angle of each observation point, the first regression variable and the second regression variable of each observation point are constructed respectively. Least squares linear fitting is performed on the first and second regression variables of all observation points in each grid cell to obtain the first and second fitting coefficients of each grid cell. Based on the preset L-band wavenumber, the root mean square height of the surface roughness of each grid cell is inverted using the first fitting coefficient of each grid cell, and the vegetation optical thickness of each grid cell is inverted using the second fitting coefficient of each grid cell. A global distribution data product is generated based on the root mean square height of surface roughness and the optical thickness of vegetation for each grid cell. The global distribution data product consists of the root mean square height of surface roughness and the optical thickness of vegetation covering the entire scalable Earth grid of equal area, and is used for parameter calibration in soil moisture inversion. The specific formula for calculating the first regression variable is as follows: ； in, It is the first regression variable; Effective reflective factor; Angle of incidence; The specific formula for calculating the second regression variable is as follows: ； in, It is the second regression variable; The angle of incidence is denoted as .

2. The method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R according to claim 1, characterized in that, The specific formula for calculating linear calibration is as follows: ； in, The calibrated reflected signal strength; The decimal value of the reflected signal strength is obtained by converting the original reflected signal strength into a linear form.

3. The method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R according to claim 1, characterized in that, Based on the soil moisture, clay content, and incident angle at each observation point, the square of the Fresnel reflection coefficient for each observation point is calculated, specifically including: The soil dielectric constant of each observation point is calculated based on the soil moisture and clay content. Based on the soil dielectric constant and incident angle at each observation point, the square of the Fresnel reflection coefficient at each observation point is calculated using the Fresnel reflection coefficient formula for vertical or horizontal polarization.

4. The method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R according to claim 1, characterized in that, Least squares linear fitting is implemented using a pseudo-inverse matrix approach. Specifically, the least squares linear fitting is as follows: ； in, It is the first regression variable; The first fitting coefficient is set to zero. If the calculated first fitting coefficient is greater than zero, then the first fitting coefficient is forced to be set to zero or a negative value. This is a regression matrix, where the first column represents the second regression variable, and the second column is a vector of all 1s. is the second fitting coefficient.

5. The method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R according to claim 1, characterized in that, The inversion formula for the root mean square height of surface roughness is: ； in, The root mean square height of the surface roughness; The first fitting coefficient; For L-band wavenumber, , For frequency, The speed of light; The inversion formula for vegetation optical thickness is: τ = -b / 2; Where τ is the optical thickness of the vegetation; b is the second fitting coefficient.

6. The method for inverting surface roughness and vegetation optical thickness using spaceborne GNSS-R according to claim 1, characterized in that, The root mean square height of surface roughness was controlled within the range of 0 cm to 1.2 cm, and the optical thickness of vegetation was controlled within the range of 0 to 1.9 cm.

7. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the spaceborne GNSS-R surface roughness and vegetation optical thickness inversion method according to any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for inverting surface roughness and vegetation optical thickness of spaceborne GNSS-R as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for inverting surface roughness and vegetation optical thickness of spaceborne GNSS-R as described in any one of claims 1-6.

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