Spaceborne GNSS-R soil humidity inversion method based on Fresnel reflection coefficient

Abstract

The invention discloses a satellite-borne GNSS-R soil humidity inversion method based on a Fresnel reflection coefficient, and belongs to the field of satellite-borne GNSS-R soil humidity remote sensing monitoring, and the method comprises the steps: obtaining satellite-borne GNSS-R observation data, and carrying out the preprocessing; eliminating satellite-borne GNSS-R observation data influenced by a water body by adopting a water body elimination method; fitting to obtain a Fresnel reflection coefficient correction function based on different satellite signal incidence angles and environmental parameters; correcting the original surface reflectance by using the Fresnel reflection coefficient correction function to obtain the corrected surface reflectance; and establishing a linear inversion equation based on the corrected surface reflectance and soil humidity reference product data to realize soil humidity inversion. According to the method, water body interference is effectively eliminated, the influence of multiple factors on the Fresnel reflection coefficient is weakened, the soil humidity inversion precision and stability are improved, the sampling point number advantage is remarkable, and reliable technical support is provided for dynamic monitoring of the soil humidity.

Description

Technical Field

[0001] This invention belongs to the field of spaceborne GNSS-R soil moisture remote sensing monitoring / GNSS reflectance measurement technology, specifically involving a spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient. Background Technology

[0002] The Global Navigation Satellite System (GNSS) was initially developed for navigation, positioning, and timing (PNT) services. With the continuous development of GNSS technology and the expansion of its application scenarios, it has extended beyond traditional PNT services to multiple fields such as remote sensing, communications, and aerospace. Among these, utilizing GNSS reflected signals for remote sensing applications has become a significant innovative direction for GNSS technology. When GNSS signals propagate to the Earth's surface, reflection occurs. Onboard receivers can simultaneously capture both direct and reflected signals, and the resulting interference signal carries information about the Earth's physical properties. GNSS Reflectometry (GNSS-R) technology uses L-band signals emitted by GNSS satellites as an external radiation source. By extracting and analyzing the characteristic parameters of the reflected signals, it retrieves the physical properties of the reflecting surface, making it a core branch of GNSS remote sensing applications. Based on the differences in signal receiving carriers, GNSS-R is divided into three categories: spaceborne, airborne, and ground-based. Among them, spaceborne GNSS-R has shown great application potential in fields such as soil moisture monitoring, sea ice detection, and marine environmental monitoring due to its advantages such as wide coverage, high observation frequency, and significant cost-effectiveness.

[0003] Soil moisture, as a key hydrological parameter of the terrestrial surface system, directly influences numerous processes such as agricultural production, ecological environment evolution, regional water cycle, and climate change due to its spatiotemporal distribution characteristics. Accurate, real-time, and large-scale soil moisture monitoring is of significant practical importance for drought and flood early warning, agricultural irrigation optimization, and ecological environmental protection. While traditional soil moisture monitoring methods (such as field measurements and neutron spectrometer methods) offer high accuracy, they suffer from limitations such as limited observation range, time-consuming and labor-intensive processes, and difficulty in achieving large-scale dynamic monitoring. Among existing remote sensing technologies, microwave radiometers (such as those using the SMAP satellite) can achieve large-scale soil moisture retrieval, but they suffer from low temporal resolution and sparse spatial sampling points. Spaceborne GNSS-R technology (such as the CYGNSS constellation), with its advantages of multi-satellite and multi-channel observation, can provide high-frequency, high-density surface reflection signal data, offering a new approach for large-scale dynamic soil moisture monitoring. However, current spaceborne GNSS-R soil moisture inversion still faces key technical bottlenecks: First, water bodies significantly interfere with the power of reflected signals; even narrow water bodies can cause abnormal surface reflectivity. Traditional water body removal methods are suitable for low-resolution data, but easily erroneously remove valid observations from high-resolution CYGNSS data. Second, the Fresnel reflectance coefficient is affected by multiple factors such as incident angle, soil type, soil moisture, and temperature, leading to deviations in surface reflectance calculations and directly reducing the accuracy of soil moisture inversion. Furthermore, GNSS-R inversion results are also affected by factors such as vegetation cover and surface roughness, and data stability and inversion accuracy need further improvement. Therefore, developing a spaceborne GNSS-R soil moisture inversion method that can accurately remove water body interference and effectively correct for the influence of the Fresnel reflectance coefficient is of great significance for promoting the practical application of this technology. Summary of the Invention

[0004] The purpose of this invention is to address the significant interference from water bodies and the poor adaptability of traditional rejection methods in spaceborne GNSS-R soil moisture inversion, as well as the problem that Fresnel reflectance coefficient is affected by multiple factors such as incident angle, soil type, humidity, and temperature. This invention provides a spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient. It employs a square grid screening mechanism, constructing a grid centered on specular reflection points to accurately identify and reject observations affected by water bodies, thus improving the accuracy and stability of spaceborne GNSS-R soil moisture inversion. Simultaneously, leveraging the advantages of the spaceborne platform, it obtains soil moisture results with higher temporal and spatial resolution, facilitating comparison and joint application with existing soil moisture reference product data.

[0005] According to one aspect of the present invention, a method for inverting soil moisture in spaceborne GNSS-R based on Fresnel reflectance coefficient is provided, comprising:

[0006] Step 1: Acquire spaceborne GNSS-R observation data and perform preprocessing;

[0007] Step 2: Use the water body removal method to remove the spaceborne GNSS-R observation data affected by water bodies;

[0008] Step 3: Based on different satellite signal incident angles and environmental parameters, the Fresnel reflection coefficient correction function is fitted and obtained;

[0009] Step 4: Based on the spaceborne GNSS-R observation data, the original surface reflectance is corrected using the Fresnel reflectance correction function to obtain the corrected surface reflectance;

[0010] Step 5: Based on the corrected surface reflectance and the corresponding soil moisture reference product data, establish a linear inversion equation for soil moisture to achieve soil moisture inversion.

[0011] Furthermore, step 2 employs an improved water body removal method to remove spaceborne GNSS-R observation data affected by water bodies, including:

[0012] Using the latitude and longitude of the mirror reflection points of the spaceborne GNSS-R observation data as the center, a square grid of a set size is constructed in the acquired global surface water dataset;

[0013] The system iterates through all mirror reflection points in the satellite-borne GNSS-R observation data and uses spatial overlay to determine whether there are water pixels in each square grid of a set size: if there is at least one water pixel with a label value greater than or equal to 1 in the grid, the observation data corresponding to that mirror reflection point is removed; if there is no water pixel in the grid, the observation data corresponding to that mirror reflection point is retained.

[0014] Furthermore, the expression for the Fresnel reflection coefficient correction function is:

[0015]

[0016] in, , , These are empirical parameters. This is the angle of incidence of the satellite signal.

[0017] Furthermore, in step 4, the original surface reflectance is corrected using the Fresnel reflectance correction function to obtain the corrected surface reflectance, including:

[0018] The original surface reflectance was obtained by combining the GNSS-R land scattering model with spaceborne GNSS-R observation data.

[0019] The original surface reflectance is multiplied by the Fresnel reflectance correction function, and the result is smoothed to obtain the corrected surface reflectance.

[0020] Furthermore, in step 5, based on the corrected surface reflectance and the corresponding soil moisture reference product data, a linear inversion equation for soil moisture is established, including:

[0021] The corrected surface reflectance and soil moisture reference product data were resampled to the set grid, and the average corrected surface reflectance and average soil moisture within the set grid were calculated.

[0022] The difference between the soil moisture reference product data and the average soil moisture value during the modeling period, the difference between the corrected surface reflectance and the average corrected surface reflectance value, and the slope is obtained through linear fitting.

[0023] Based on the slope, and combining the corrected surface reflectance, the average corrected surface reflectance, and the average soil moisture, a linear inversion equation for soil moisture is established.

[0024] Furthermore, the expression for the linear inversion equation of soil moisture is:

[0025]

[0026] in, Indicates soil inversion value, Indicates the slope. This represents the corrected surface reflectance. This represents the average surface reflectance after correction. This represents the average soil moisture.

[0027] Further, step 1 involves acquiring and preprocessing the spaceborne GNSS-R observation data, including:

[0028] The acquired spaceborne GNSS-R observation data is processed using the set temporal and spatial resolutions, and outlier data is removed from the spaceborne GNSS-R observation data.

[0029] Furthermore, the environmental parameters include soil moisture, soil temperature, and soil type.

[0030] According to one aspect of the present invention, a spaceborne GNSS-R soil moisture inversion system based on Fresnel reflectance coefficient is provided for implementing the aforementioned spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient, comprising:

[0031] The first processing module is used to acquire and preprocess spaceborne GNSS-R observation data.

[0032] The second processing module is used to remove spaceborne GNSS-R observation data affected by water bodies using a water body removal method.

[0033] The third processing module is used to fit the Fresnel reflection coefficient correction function based on different satellite signal incident angles and environmental parameters.

[0034] The fourth processing module is used to correct the original surface reflectance based on the spaceborne GNSS-R observation data using the Fresnel reflectance coefficient correction function, so as to obtain the corrected surface reflectance.

[0035] The fifth processing module is used to establish a linear inversion equation for soil moisture based on the corrected surface reflectance and the corresponding soil moisture reference product data, thereby realizing the inversion of soil moisture.

[0036] According to one aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. In order to accurately remove the interference of water bodies on the reflected signal and reduce the influence of multiple factors such as incident angle, soil type, humidity, and temperature on the Fresnel reflection coefficient, this invention proposes an improved water body removal method adapted to high-resolution data. It adopts a square grid screening mechanism, constructs a grid with the specular reflection point as the center, accurately identifies and removes observations affected by water bodies, avoids the problem of erroneous removal of effective observations caused by traditional large-scale grids, and maximizes the retention of useful data while ensuring the removal effect.

[0039] 2. This invention normalizes Fresnel reflectance coefficients at different incident angles to a unified benchmark, and then obtains a Fresnel reflectance coefficient correction function through function fitting. This function is used to correct the original surface reflectance, significantly reducing interference from multiple factors and improving the accuracy of surface reflectance calculation, thus laying the foundation for subsequent soil moisture inversion. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 The flowchart of a spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient is provided for this invention.

[0042] Figure 2This is a schematic diagram illustrating the removal of observations affected by water bodies, as provided by the present invention.

[0043] Figure 3 The graph showing the relationship between the corrected Fresnel reflection coefficient and the incident angle provided by this invention.

[0044] Figure 4 This is a schematic diagram of the soil moisture inversion results provided by the satellite-based GNSS-R system of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] like Figure 1 As shown in the figure, this invention proposes a method for inverting soil moisture using spaceborne GNSS-R based on Fresnel reflectance coefficient, comprising: Step 1, acquiring spaceborne GNSS-R observation data and preprocessing it; Step 2, using a water body removal method to remove spaceborne GNSS-R observation data affected by water bodies; Step 3, fitting a Fresnel reflectance coefficient correction function based on different satellite signal incident angles and environmental parameters; Step 4, correcting the original surface reflectance using the Fresnel reflectance coefficient correction function based on the spaceborne GNSS-R observation data to obtain the corrected surface reflectance; Step 5, establishing a linear inversion equation for soil moisture based on the corrected surface reflectance and the corresponding soil moisture reference product data to achieve soil moisture inversion.

[0047] Specifically, this embodiment of the invention collects multi-source data from the experimental area, including spaceborne GNSS-R observation data, soil moisture reference product data, auxiliary geographic data, and measured verification data. The spaceborne GNSS-R observation data uses L1-level data from the CYGNSS satellite, with a temporal resolution of 500ms incoherent accumulation mode and a spatial resolution of 3.5×0.5km. The soil moisture reference product data uses L3-level soil moisture reference product data from the SMAP satellite, with a spatial resolution of 36×36km, fused from data from 6:00 AM when the satellite descends and at 6:00 PM when it ascends, with the average taken for overlapping areas. Auxiliary geographic data includes STATSGO2 soil type data and the GSWE global surface water dataset. The measured verification data uses ISMN measured soil moisture data (i.e., the soil moisture reference product data used for soil moisture inversion during the verification period).

[0048] Specifically, the embodiments of the present invention provide the content of step 1:

[0049] The acquired data was divided into a modeling period and a validation period (without overlap) according to time sequence. The modeling period accounted for 50% of the total data (covering diverse scenarios), and the validation period accounted for 50%. The modeling period was used to fit the correlation equation between the corrected surface reflectance and SMAP soil moisture data, and the validation period was used to invert the soil moisture results and evaluate the accuracy of the model.

[0050] Quality control and preliminary preprocessing were performed on L1-level data from CYGNSS satellites: data marked as anomalous were removed, and sampling points with SNR < 2dB, receiving antenna gain < 0 or > 13, and incident angle > 65° at mirror points were removed, while GPSBlockIIF satellite data were retained; the data were converted to a standard format, and key parameters such as DDM signal-to-noise ratio, receiving antenna gain, incident angle, GPSEIRP, and DDM peak power were extracted. Combined with STATSGO2 soil type data, the experimental area range was cropped and the data was matched.

[0051] Preprocessing was performed on the auxiliary geographic data and measured verification data: The GSWE global surface water dataset used the "Seasonality" product, and the surface pixels were labeled with the months of the year when they were flooded with water at a spatial resolution of 30m. Pixels with a labeled value >1 were identified as water bodies; physical parameters of four typical soil types, sandy loam, fertile soil, silty loam, and silty soil, were extracted from the STATSGO2 soil type data; meaningless values ​​of <0cm³ / cm³ or >1cm³ / cm³ were removed from the ISMN measured soil moisture data, and the observation data at a soil depth of 5cm were retained.

[0052] Specifically, this embodiment of the invention provides the content of step 2, which proposes a 3×3km square grid water body removal method to take advantage of the high resolution characteristics of CYGNSS data (3.5×0.5km). Figure 2 This is a diagram illustrating the removal of observations affected by water bodies. The steps are as follows:

[0053] 1. Centered on the latitude and longitude coordinates of each CYGNSS data mirror reflection point.

[0054] 2. Construct a 3×3km square grid in the preprocessed GSWE global surface water dataset to ensure that the grid area completely covers the area where the reflection point may be disturbed by water.

[0055] 3. Traverse all CYGNSS mirror reflection points and use spatial overlay analysis to determine whether there are water pixels in each 3×3km grid: If there is at least one water pixel with a label value ≥1 in the grid, the observation value of the reflection point is determined to be affected by water and is removed; if there are no water pixels in the grid, the observation value is retained.

[0056] 4. Repeat the above process to complete the removal of CYGNSS specular reflection points affected by water.

[0057] Through the above screening, an effective GNSS-R observation dataset that is not disturbed by water bodies is obtained, which is used as the basic input data for solving the original surface reflectance in step 4, thus avoiding the problem of erroneous rejection of effective observations caused by traditional large-scale grid water body removal methods.

[0058] Specifically, this embodiment of the invention provides the content of step 3:

[0059] Calculate the Fresnel reflection coefficient correction function under different incident angles and environmental parameters, including soil moisture, soil temperature, and soil type (soil type affects the soil complex dielectric constant); based on the existing GNSS-R land scattering model, describe the scattering and propagation characteristics of GNSS signals on the land surface, and the formula for solving the Fresnel reflection coefficient is:

[0060] (1)

[0061] in, The Fresnel reflection coefficient is a core parameter describing the reflection characteristics of electromagnetic waves in the vertical polarization direction at the soil surface. The angle of incidence of the satellite signal. and The angle of incidence of the satellite signal trigonometric functions, The complex permittivity of soil, is the vacuum permittivity.

[0062] The environmental parameters are set to the following ranges: soil moisture ranges from 1% to 100% (in 1% increments), soil temperature ranges from 1 to 60℃ (in 1℃ increments), and four typical soil types are selected: sandy loam, fertile soil, silty loam, and silty soil. The incident angle is set from 1 to 90° (in 1° increments). Based on the step size combinations of the above factors, a total of 2,160,000 Fresnel reflection coefficients are generated, with the incident angle as the independent variable. .

[0063] The Fresnel reflection coefficients of different combinations of influence quantities are reduced to the correction amount when the incident angle is 0° using the following formula:

[0064] (2)

[0065] in, This is the corrected Fresnel reflection coefficient. This is the Fresnel reflection coefficient corresponding to an incident angle of 0°.

[0066] Figure 3This is a graph showing the relationship between the corrected Fresnel reflection coefficient and the incident angle. To express the functional relationship between them, the incident angle is used as the independent variable and the corrected Fresnel reflection coefficient is used as the dependent variable. The Fresnel reflection coefficient correction function is obtained by fitting the graph using the least squares method, and the expression is as follows:

[0067] (3)

[0068] in, , , These are empirical parameters, obtained by solving for the parameters using the least squares method, and are respectively set to values ​​of... , , .

[0069] Specifically, this embodiment of the invention provides the content of step 4:

[0070] First, using the GNSS-R land scattering model, combined with key parameters from CYGNSS observation data (satellite signal incident angle)... Signal beam ), and setting parameters (vegetation correction factor) Surface roughness The original surface reflectance can be obtained by solving the problem. :

[0071] (4)

[0072] Correction function between original surface reflectance and Fresnel reflectance coefficient Multiply to obtain the corrected surface reflectance. :

[0073] (5)

[0074] The corrected surface reflectance is smoothed to remove isolated outliers, ensuring sequence continuity and stability, thus forming a high-precision surface reflectance dataset that can be used for soil moisture inversion.

[0075] Specifically, this embodiment of the invention provides the content of step 5:

[0076] First, L1-level data from CYGNSS satellites and SMAP soil moisture data for the modeling period are extracted for modeling. The corrected surface reflectance and soil moisture reference product data are resampled to the same resolution grid. The resampling method is bilinear interpolation. The average surface reflectance and average SMAP soil moisture during the modeling period are calculated.

[0077] For each grid, extract all corresponding SMAP soil moisture data and corrected surface reflectance during the modeling period, and calculate... and :

[0078] (6)

[0079] (7)

[0080] in, This represents the difference between the SMAP soil moisture data and the average SMAP soil moisture during the modeling period. This represents the difference between the corrected CYGNSS surface reflectance and the average corrected surface reflectance during the modeling period. Here is the SMAP soil moisture data (reference value) at time t. This represents the average SMAP soil moisture content of the grid during the modeling period. Let be the corrected surface reflectance at time t. This represents the average corrected surface reflectance of the grid during the modeling period.

[0081] Then, the slope is calculated using the linear regression formula. The formula is as follows:

[0082] (8)

[0083] If the number of valid samples m during the modeling period within the grid is less than 3, then the grid's [data / data] will not be calculated. Values ​​marked as invalid grids are given. The following equation is the linear inversion equation for soil moisture:

[0084] (9)

[0085] Get the grid After calculating the values, L1-level data from the CYGNSS satellite during the validation period were extracted for soil moisture inversion. The corrected surface reflectance of each grid during the validation period was calculated, and the resulting values ​​were substituted into the linear soil moisture inversion equation to obtain the soil moisture inversion value for each grid. For invalid grids, the values ​​of neighboring valid grids were used. The values ​​are interpolated to ensure the spatial continuity of the inversion results. Finally, the values ​​are aggregated and averaged within the grid range to obtain the final soil moisture reference product data.

[0086] (10)

[0087] Where n is the number of sampling points within the grid during the validation period. To calculate the soil moisture inversion value for each sampling point within the grid during the validation period.

[0088] Specifically, the schematic diagram of the inversion results of the embodiments of the present invention is as follows: Figure 4As shown, the root mean square error between the retrieved soil moisture and the results from the soil moisture meters at the actual measurement sites is within 0.061 cm. 3 / cm 3 This experiment verified the feasibility of a spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance normalization.

[0089] The implementation of the various embodiments of the present invention is based on programmed processing through a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a spaceborne GNSS-R soil moisture inversion system based on Fresnel reflectance coefficients. This system is used to execute a spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficients from the above method embodiments.

[0090] The system includes: a first processing module for acquiring and preprocessing spaceborne GNSS-R observation data; a second processing module for removing spaceborne GNSS-R observation data affected by water bodies using a water body removal method; a third processing module for fitting a Fresnel reflection coefficient correction function based on different satellite signal incident angles and environmental parameters; a fourth processing module for correcting the original surface reflectance using the Fresnel reflection coefficient correction function based on the spaceborne GNSS-R observation data to obtain the corrected surface reflectance; and a fifth processing module for establishing a linear inversion equation for soil moisture based on the corrected surface reflectance and the corresponding soil moisture reference product data to achieve soil moisture inversion.

[0091] The spaceborne GNSS-R soil moisture inversion system based on Fresnel reflectance provided in this invention addresses the significant water body interference and poor adaptability of traditional removal methods in spaceborne GNSS-R soil moisture inversion, as well as the problem that Fresnel reflectance is affected by multiple factors such as incident angle, soil type, humidity, and temperature. It employs several modules and a square grid filtering mechanism, constructing a grid centered on the specular reflection point to accurately identify and remove observations affected by water bodies. This improves the accuracy and stability of spaceborne GNSS-R soil moisture inversion. Simultaneously, leveraging the advantages of the spaceborne platform, it obtains soil moisture results with higher temporal and spatial resolution, facilitating comparison and joint application with existing soil moisture reference product data.

[0092] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides an electronic device, including a memory and a processor. The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient as proposed in the above embodiments.

[0093] Finally, it should be noted that the above specific embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and many variations are possible. Any simple modifications, equivalent changes, and alterations made to the above specific embodiments based on the technical essence of the present invention should be considered within the protection scope of the present invention.

Claims

1. A method for inverting soil moisture using spaceborne GNSS-R based on Fresnel reflectance coefficient, characterized in that, include: Step 1: Acquire spaceborne GNSS-R observation data and perform preprocessing; Step 2: Use the water body removal method to remove the spaceborne GNSS-R observation data affected by water bodies; Step 3: Based on different satellite signal incident angles and environmental parameters, the Fresnel reflection coefficient correction function is fitted and obtained; Step 4: Based on the spaceborne GNSS-R observation data, the original surface reflectance is corrected using the Fresnel reflectance correction function to obtain the corrected surface reflectance; Step 5: Based on the corrected surface reflectance and the corresponding soil moisture reference product data, establish a linear inversion equation for soil moisture to achieve soil moisture inversion.

2. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, Step 2 employs an improved water body removal method to remove spaceborne GNSS-R observation data affected by water bodies, including: Using the latitude and longitude of the mirror reflection points of the spaceborne GNSS-R observation data as the center, a square grid of a set size is constructed in the acquired global surface water dataset; The system iterates through all mirror reflection points in the satellite-borne GNSS-R observation data and uses spatial overlay to determine whether there are water pixels in each square grid of a set size: if there is at least one water pixel with a label value greater than or equal to 1 in the grid, the observation data corresponding to that mirror reflection point is removed; if there is no water pixel in the grid, the observation data corresponding to that mirror reflection point is retained.

3. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, The expression for the Fresnel reflection coefficient correction function is: , in, , , These are empirical parameters. This is the angle of incidence of the satellite signal.

4. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, In step 4, the original surface reflectance is corrected using the Fresnel reflectance correction function to obtain the corrected surface reflectance, including: The original surface reflectance was obtained by combining the GNSS-R land scattering model with spaceborne GNSS-R observation data. The original surface reflectance is multiplied by the Fresnel reflectance correction function, and the result is smoothed to obtain the corrected surface reflectance.

5. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, Step 5 establishes a linear inversion equation for soil moisture based on the corrected surface reflectance and the corresponding soil moisture reference product data, including: The corrected surface reflectance and soil moisture reference product data were resampled to the set grid, and the average corrected surface reflectance and average soil moisture within the set grid were calculated. The difference between the soil moisture reference product data and the average soil moisture value during the modeling period, the difference between the corrected surface reflectance and the average corrected surface reflectance value, and the slope is obtained through linear fitting. Based on the slope, and combining the corrected surface reflectance, the average corrected surface reflectance, and the average soil moisture, a linear inversion equation for soil moisture is established.

6. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, The expression for the linear inversion equation of soil moisture is: , in, Indicates soil inversion value, Indicates the slope. This represents the corrected surface reflectance. This represents the average surface reflectance after correction. This represents the average soil moisture.

7. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, Step 1 involves acquiring and preprocessing spaceborne GNSS-R observation data, including: The acquired spaceborne GNSS-R observation data is processed using the set temporal and spatial resolutions, and outlier data is removed from the spaceborne GNSS-R observation data.

8. The method for soil moisture inversion based on Fresnel reflectance coefficient in spaceborne GNSS-R according to claim 1, characterized in that, The environmental parameters include soil moisture, soil temperature, and soil type.

9. A spaceborne GNSS-R soil moisture inversion system based on Fresnel reflectance coefficient, characterized in that, To implement the spaceborne GNSS-R soil moisture inversion method based on Fresnel reflectance coefficient as described in any one of claims 1-8, the method comprises: The first processing module is used to acquire and preprocess spaceborne GNSS-R observation data. The second processing module is used to remove spaceborne GNSS-R observation data affected by water bodies using a water body removal method. The third processing module is used to fit the Fresnel reflection coefficient correction function based on different satellite signal incident angles and environmental parameters. The fourth processing module is used to correct the original surface reflectance based on the spaceborne GNSS-R observation data using the Fresnel reflectance coefficient correction function, so as to obtain the corrected surface reflectance. The fifth processing module is used to establish a linear inversion equation for soil moisture based on the corrected surface reflectance and the corresponding soil moisture reference product data, thereby realizing the inversion of soil moisture.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the spaceborne GNSS-R soil moisture inversion method based on Fresnel reflection coefficient as described in any one of claims 1 to 8.

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