A method and system for retrieving GNSS-R microwave land surface emissivity

Abstract

The application discloses a kind of GNSS-R microwave surface emissivity inversion method and system.First, obtain spaceborne GNSS-R data, spaceborne brightness temperature data, soil temperature data;Surface reflectivity is calculated using spaceborne GNSS-R data, the multiple surface reflectivity calculated is aggregated, to generate single surface reflectivity observation value;Surface emissivity reference value is calculated using spaceborne brightness temperature data and soil temperature data;Then single surface reflectivity observation value is input into surface emissivity inversion model, to obtain surface emissivity numerical value;Finally, the difference between surface emissivity numerical value and surface emissivity reference value is calculated, and the surface emissivity numerical value result of surface emissivity inversion model when the difference satisfies least square is used as inversion result.The application introduces aggregation algorithm in the calculation of aggregated GNSS-R surface reflectivity, which can effectively solve the problem of aggregation error caused by the difference of GNSS-R data space-time position distribution in the current GNSS-R inversion microwave surface emissivity method, and improve the inversion accuracy of microwave surface emissivity.

Description

Technical Field

[0001] This invention relates to the fields of GNSS signal processing and microwave remote sensing technology, and in particular to a method and system for inverting GNSS-R microwave surface emissivity. Background Technology

[0002] Microwave surface emissivity is a crucial input parameter in the inversion of surface and atmospheric parameters, playing a vital role in numerical weather prediction, soil moisture estimation, snow depth retrieval, surface temperature retrieval, and vegetation change monitoring. Existing methods for obtaining microwave surface emissivity mainly fall into two categories. One is the physical inversion model, based on a radiative transfer model, calculated by inputting parameters such as satellite brightness temperature, atmospheric brightness temperature, surface temperature, atmospheric transmittance, and vegetation transmittance. The other is the empirical inversion model, which establishes a mathematical empirical model between brightness temperature and other data and surface emissivity, obtaining the emissivity through long-term series data inversion.

[0003] GNSS-R (Global Navigation Satellite System–Reflectometry) is a passive bistatic remote sensing technology that uses GNSS reflection signals to remotely sense surface parameters. The inversion of microwave surface emissivity using GNSS-R technology primarily leverages its ability to observe surface reflectivity attributes. GNSS-R surface reflectivity is obtained by processing GNSS-R observations using a Delay Doppler Map (DDM), and then correlated with existing surface emissivity products to achieve the purpose of inverting surface emissivity.

[0004] However, in current practical computing applications, the following problems and challenges exist:

[0005] 1) Currently, there are no studies using spaceborne GNSS-R technology to retrieve surface emissivity. Microwave surface emissivity is an important parameter in microwave remote sensing, and obtaining microwave surface emissivity data at global and regional scales is crucial. However, both of the current mainstream measurement methods have their shortcomings. For example, physical inversion models rely on a large amount of surface parameter knowledge, resulting in poor applicability over a wide range; while empirical inversion models rely on existing input satellite radiation observations with insufficient temporal or spatial resolution and lack low-frequency L-band surface emissivity data.

[0006] 2) Since the spatiotemporal resolution of existing GNSS-R datasets such as CYGNSS (Cyclone GNSS) is higher than that of brightness temperature data from traditional microwave remote sensing radiometers, there may be multiple observations on the same day at the selected 9km spatial scale. How to solve the differences between different observations caused by observation time and location, better reflect the changes at the 9km spatial scale, and improve the inversion accuracy of microwave surface emissivity has important practical value and research significance. Summary of the Invention

[0007] This invention provides a method and system for inverting GNSS-R microwave surface emissivity, which effectively solves the technical problem of aggregation error caused by the spatiotemporal location distribution differences of GNSS-R data, and provides a brand-new means for obtaining L-band microwave surface emissivity.

[0008] This invention provides a method for inverting GNSS-R microwave surface emissivity, comprising:

[0009] Acquire spaceborne GNSS-R data, spaceborne brightness temperature data, and soil temperature data;

[0010] The surface reflectance is calculated using the aforementioned spaceborne GNSS-R data. The calculated surface reflectance values ​​are then aggregated to generate a single surface reflectance observation value.

[0011] The reference value of surface emissivity was calculated using the aforementioned spaceborne brightness temperature data and soil temperature data;

[0012] The individual surface reflectance observation is input into the surface emissivity inversion model to obtain the surface emissivity value;

[0013] Calculate the difference between the surface emissivity value and the surface emissivity reference value, and take the surface emissivity value of the surface emissivity inversion model when the difference satisfies least squares as the inversion result that meets the requirements.

[0014] Specifically, the process of calculating surface reflectance using the spaceborne GNSS-R data, aggregating multiple calculated surface reflectance values, and generating a single surface reflectance observation value includes:

[0015] Using the formula Γ=∑ i Γ(i)*w(i) calculates the aggregated surface reflectance Γ; where Γ(i) is the i-th surface reflectance and w(i) is the weight of the i-th surface reflectance.

[0016] Specifically, w(i) is expressed by the formula w(i)=(1 / C(i)) / ∑ i (1 / C(i)) is calculated; where C(i)=D(i)*T(i)*V(i), D(i)=d(i) / ∑ i d(i), T(i) = |T cygnss (i)-T smap | / ∑ i |T cygnss (i)-T smap |, d(i) is the distance from the location of the i-th surface reflectance to the center point of its corresponding grid cell, Tcygnss (i) represents the acquisition time of the i-th surface reflectance, T smap The acquisition time of the spaceborne brightness temperature data is given, and SNR(i) is the signal-to-noise ratio of the i-th surface reflectance. It is the average value of all signal-to-noise ratio data within the corresponding grid cell.

[0017] Specifically, the calculation of the surface emissivity reference value using the spaceborne brightness temperature data and soil temperature data includes:

[0018] Through formula The surface emissivity reference value ε was calculated. p Among them, TB p T represents the satellite-borne brightness temperature data, and T represents the soil temperature data.

[0019] Specifically, the surface emissivity inversion model is ε1(t)=α*(Γ1(t)-Γ2(t0))+ε2(t0), where ε1(t) is the surface emissivity of the first-scale grid calculated on day t, Γ1(t) is the aggregated surface reflectivity of the first-scale grid on day t, Γ2(t0) is the average of all calculated GNSS-R surface reflectivities in the second-scale grid on day t0, which is closest to day t, ε2(t0) is the reference value of surface emissivity in the second-scale grid, and α is the coefficient of the surface emissivity inversion model.

[0020] The present invention also provides a GNSS-R microwave surface emissivity inversion system, comprising:

[0021] The data acquisition module is used to acquire spaceborne GNSS-R data, spaceborne brightness temperature data, and soil temperature data.

[0022] The surface reflectance aggregation module is used to calculate the surface reflectance using the spaceborne GNSS-R data, aggregate the calculated surface reflectance values, and generate a single surface reflectance observation value.

[0023] The surface emissivity reference value calculation module is used to calculate the surface emissivity reference value using the satellite-borne brightness temperature data and soil temperature data;

[0024] The model inversion module is used to input the single surface reflectance observation value into the surface emissivity inversion model to obtain the surface emissivity value;

[0025] The inversion output module is used to calculate the difference between the surface emissivity value and the surface emissivity reference value, and to take the surface emissivity value of the surface emissivity inversion model when the difference satisfies least squares as the inversion result that meets the requirements.

[0026] Specifically, the surface reflectance aggregation module is used to aggregate surface reflectance using the formula Γ=∑ i Γ(i)*w(i) calculates the aggregated surface reflectance Γ; where Γ(i) is the i-th surface reflectance and w(i) is the weight of the i-th surface reflectance.

[0027] Specifically, it also includes: a weight calculation module, used to calculate the weight using the formula w(i)=(1 / C(i)) / ∑ i w(i) is calculated using (1 / C(i)); where C(i) = D(i) * T(i) * V(i), D(i) = d(i) / ∑ i d(i), T(i) = |T cygnss (i)-T smap | / ∑i|T cygnss (i)-T smap |, di is the distance from the location of the i-th surface reflectance to the center point of its grid cell, Tcygnssi is the acquisition time of the i-th surface reflectance, Tsmap is the acquisition time of the spaceborne brightness temperature data, SNRi is the signal-to-noise ratio of the i-th surface reflectance, and SNR is the average value of all signal-to-noise ratio data within its grid cell.

[0028] Specifically, the surface emissivity reference value calculation module is used to calculate the reference value using the formula... The surface emissivity reference value ε was calculated. p Wherein, TBp is the satellite-borne brightness temperature data, and T is the soil temperature data.

[0029] Specifically, the surface emissivity inversion model is ε1(t)=α*(Γ1(t)-Γ2(t0))+ε2(t0), where ε1(t) is the surface emissivity of the first-scale grid calculated on day t, Γ1(t) is the aggregated surface reflectivity of the first-scale grid on day t, Γ2(t0) is the average of all calculated GNSS-R surface reflectivities in the second-scale grid on day t0, which is closest to day t, ε2(t0) is the reference value of surface emissivity in the second-scale grid, and α is the coefficient of the surface emissivity inversion model.

[0030] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0031] First, satellite-borne GNSS-R data, satellite-borne brightness temperature data, and soil temperature data are acquired. Then, the surface reflectance is calculated using the satellite-borne GNSS-R data, and the calculated surface reflectance values ​​are aggregated to generate a single surface reflectance observation value. A surface emissivity reference value is calculated using the satellite-borne brightness temperature data and soil temperature data. Next, the single surface reflectance observation value is input into the surface emissivity inversion model to obtain the surface emissivity numerical value. Finally, the difference between the surface emissivity numerical value and the surface emissivity reference value is calculated, and the surface emissivity numerical result of the surface emissivity inversion model when the difference satisfies least squares is taken as the acceptable inversion result. This invention introduces an aggregation algorithm in calculating aggregated GNSS-R surface reflectance, which can effectively solve the aggregation error problem caused by the spatiotemporal location distribution differences of GNSS-R data in current GNSS-R inversion methods for microwave surface emissivity, thus improving the inversion accuracy of microwave surface emissivity. Attached Figure Description

[0032] Figure 1 A flowchart illustrating the GNSS-R microwave surface emissivity inversion method provided in this embodiment of the invention;

[0033] Figure 2 This is a schematic diagram of the main observation DDM of a spaceborne GNSS-R in a certain location in an embodiment of the present invention;

[0034] Figure 3 This is a diagram showing the three-day distribution and three-day average distribution of surface emissivity in the United States calculated in this embodiment of the invention.

[0035] Figure 4 This is a diagram showing the distribution of the average surface emissivity of the Indian region at a 15-day scale, calculated in this embodiment of the invention.

[0036] Figure 5 A block diagram of a GNSS-R microwave surface emissivity inversion system provided in an embodiment of the present invention. Detailed Implementation

[0037] This invention provides a method and system for inverting GNSS-R microwave surface emissivity, effectively solving the technical problem of aggregation error caused by the spatiotemporal location distribution differences of GNSS-R data, and providing a brand-new means for obtaining L-band microwave surface emissivity.

[0038] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0039] See Figure 1 The GNSS-R microwave surface emissivity inversion method provided in this embodiment of the invention includes:

[0040] Step S110: Acquire spaceborne GNSS-R data, spaceborne radiometer brightness temperature data, and soil temperature data for the target area and target time period;

[0041] Step S120: The acquired spaceborne GNSS-R data, spaceborne radiometer brightness temperature data, and soil temperature data are spatiotemporally registered according to the first-scale grid and second-scale grid of EASE 2.0 to generate a data set with the same number of grid points as the target area. At the same time, quality control is performed on the data set to remove unqualified data sets. Specifically, when performing quality control on the data set, the preset rejection criteria include: GNSS-R observation DDM power peak value greater than -147 dB, GNSS-R receiver antenna gain less than 0 dB, GNSS-R observation DDM signal-to-noise ratio greater than receiver antenna gain by 14 dB, GNSS-R receiver received reflected signal incident angle greater than 40 degrees, and GNSS-R observation DDM power peak position located in any one or more combinations of columns 7 to 10.

[0042] In this embodiment, the first-scale grid is a 9km-scale grid, and the second-scale grid is a 36km-scale grid.

[0043] Step S130: Using qualified data sets as units, calculate the surface reflectance using the spaceborne GNSS-R data in the data sets, aggregate the multiple surface reflectance values ​​calculated in each data set, and generate a single surface reflectance observation value.

[0044] This step is explained in detail: Surface reflectance is calculated using spaceborne GNSS-R data. Multiple calculated surface reflectance values ​​are then aggregated to generate a single surface reflectance observation, including:

[0045] Using the formula Γ=∑ i The aggregated surface reflectance Γ is calculated using Γ(i)*w(i); where Γ(i) is the i-th surface reflectance and w(i) is the weight of the i-th surface reflectance. Specifically, this is achieved using the formula w(i)=(1 / C(i)) / ∑ i w(i) is calculated using (1 / C(i)); where C(i) = D(i) * T(i) * V(i), and D(i) = d(i) / ∑ i d(i), T(i) = |T cygnss (i)-Tsmap / iTcygnssi-Tsmap, Vi=SNRi-SNR / SNRi-SNR2, di is the distance from the location of the i-th surface reflectance to the center point of its grid cell, X is the longitude of the center point of its grid cell, Y is the latitude of the center point of its grid cell, x(i) is the longitude of the location where the i-th surface reflectance is obtained, y(i) is the latitude of the location where the i-th surface reflectance is obtained, Tcygnss (i) represents the acquisition time of the i-th surface reflectance, T smap is the acquisition time of the brightness temperature data from the spaceborne radiometer, and SNR(i) is the signal-to-noise ratio of the i-th surface reflectance. It is the average value of all signal-to-noise ratio data within the corresponding grid cell.

[0046] Step S140: Using qualified data sets as units, calculate the surface emissivity reference value using the brightness temperature data from the spaceborne radiometer and the soil temperature data in the data sets;

[0047] This step is explained in detail, using brightness temperature data from a spaceborne radiometer and soil temperature data to calculate a reference value for surface emissivity, including:

[0048] Through formula The surface emissivity reference value ε was calculated. p Among them, TB p T represents the brightness temperature data from the spaceborne radiometer, and T represents the soil temperature data.

[0049] Step S150: Input a single surface reflectance observation into the surface emissivity inversion model to obtain the surface emissivity value;

[0050] Step S160: Calculate the difference between the surface emissivity value and the surface emissivity reference value, and take the surface emissivity value of the surface emissivity inversion model when the difference satisfies the least squares condition as the inversion result that meets the requirements.

[0051] Specifically, the surface emissivity inversion model is ε1(t)=α*(Γ1(t)-Γ2(t0))+ε2(t0), where ε1(t) is the surface emissivity of the first-scale grid calculated on day t, Γ1(t) is the aggregated surface reflectivity of the first-scale grid on day t, Γ2(t0) is the average of all calculated GNSS-R surface reflectivities in the second-scale grid on day t0, which is closest to day t, ε2(t0) is the reference value of surface emissivity in the second-scale grid, and α is the coefficient of the surface emissivity inversion model.

[0052] The method provided in the embodiments of the present invention will be described in detail below.

[0053] Step 1: Acquire spaceborne GNSS-R data, spaceborne radiometer brightness temperature data, and soil temperature data for the target area and time period. Spaceborne GNSS-R data refers to data acquired by a specific spaceborne GNSS-R satellite receiver, such as the product from the US CYGNSS (Cyclone GNSS) satellite. Spaceborne radiometer brightness temperature data refers to brightness temperature data acquired by a radiometer onboard a satellite, such as the brightness temperature product from the US SMAP (Soil Moisture Active and Passive) satellite. Soil temperature data refers to soil temperature data used to calculate reference values ​​for surface emissivity, such as the soil temperature product from the US MODIS (Moderate-resolution Imaging Spectroradiometer). Figure 2 These are DDM observations obtained using spaceborne GNSS-R technology.

[0054] Step 2, Data Preprocessing and Quality Control. The three types of data acquired in the previous step are registered to the EASE 2.0 36km and 9km grids based on their time and spatial information. The time scale is 1 day, and the spatial scale is determined according to standard EASE 2.0 36km and 9km grid information. Then, quality control is performed on the registered data sets, removing data sets that do not meet quality standards. Specific quality removal criteria include: GNSS-R observation DDM power peak value greater than -147 dB, GNSS-R receiver antenna gain less than 0 dB, GNSS-R observation DDM signal-to-noise ratio greater than receiver antenna gain by 14 dB, GNSS-R receiver received reflected signal incident angle greater than 40 degrees, and GNSS-R observation DDM power peak position located in columns 7 to 10. The above quality removal criteria are only examples and are not limited to this.

[0055] Step 3: Calculate aggregated surface reflectance. First, based on the spaceborne GNSS-R data in the dataset, calculate the surface reflectance within the 9km grid. The calculation assumes that coherent reflection is dominant for land, and based on the bistatic radar equations, the surface reflectance calculation formula is as follows:

[0056]

[0057] Where Γ is the surface reflectance, P r For GNSS-R observation of DDM power peak, R ts and R rs P represents the distance from the surface mirror reflection point to the GNSS transmitter and GNSS-R receiver, respectively. t G t G represents the equivalent isotropic radiated power of a GNSS transmitter. r λ is the antenna gain of the GNSS-R receiver, and λ is the wavelength of the GNSS signal.

[0058] Step 4: Calculate the reference value for surface emissivity. Based on a simplified radiative transfer model, and using the acquired brightness temperature data from the spaceborne radiometer and soil temperature data, the reference value for surface emissivity is calculated using the following formula:

[0059]

[0060] Where, ε p For reference value of surface emissivity, TB p T represents the brightness temperature data from the spaceborne radiometer, and T represents the soil temperature data.

[0061] Step 5: Calculate the microwave surface emissivity at a 9km scale. Based on the aggregated surface reflectivity calculated in Step 3 and the surface emissivity reference value calculated in Step 4, calculate the surface emissivity according to the surface emissivity inversion model (linear model), and determine the model coefficients according to the least squares principle. The model is as follows:

[0062] ε 9km (t)=α*(Γ 9km (t)-Γ 36km (t0))+ε 36km (t0)

[0063] Where, ε 9km (t) represents the surface emissivity calculated on day t, Γ 9km (t) represents the aggregated surface reflectance of a 9km grid on day t, Γ 36km (t0) is the average of all calculated GNSS-R reflectivities within a 36km grid on the nearest day t0, ε 36km (t0) is the reference value of surface emissivity within a 36km grid, and α is the model coefficient. In order to find the optimal solution of the linear coefficient, according to the least squares principle, when the difference between the calculated surface emissivity and the reference value of surface emissivity satisfies the least squares condition, the linear coefficient α obtained at this time is considered to be the optimal solution.

[0064] Figure 3 This is a calculation of the three-day distribution and three-day mean distribution of surface emissivity in the United States. Figure 4 This shows the calculated mean distribution of surface emissivity over a 15-day timescale in the Indian region. The method provided in this embodiment of the invention achieves a spatial resolution of 9 km for the calculated surface emissivity, indicating that the method has high spatial resolution performance in calculating surface emissivity. Furthermore, its temporal resolution is superior to the example SMAP data, demonstrating good temporal resolution performance in calculating surface emissivity.

[0065] See Figure 5The GNSS-R microwave surface emissivity inversion system provided in this embodiment of the invention includes:

[0066] Data acquisition module 100 is used to acquire spaceborne GNSS-R data, spaceborne radiometer brightness temperature data, and soil temperature data within the target area and target time period;

[0067] The spatiotemporal registration module 200 is used to spatiotemporally register the acquired spaceborne GNSS-R data, spaceborne radiometer brightness temperature data, and soil temperature data according to the first-scale grid and second-scale grid of EASE 2.0, generating a data set with the same number of grid points as the target area. At the same time, the module performs quality control on the data set to remove unqualified data sets. Specifically, when performing quality control on the data set, the preset rejection criteria include: GNSS-R observation DDM power peak value greater than -147 dB, GNSS-R receiver antenna gain less than 0 dB, GNSS-R observation DDM signal-to-noise ratio greater than receiver antenna gain by 14 dB, GNSS-R receiver received reflected signal incident angle greater than 40 degrees, and GNSS-R observation DDM power peak position located in any one or more combinations of columns 7 to 10.

[0068] In this embodiment, the first-scale grid is a 9km-scale grid, and the second-scale grid is a 36km-scale grid.

[0069] The surface reflectance aggregation module 300 is used to calculate the surface reflectance using the spaceborne GNSS-R data in the qualified data group as a unit, and to aggregate the multiple surface reflectances calculated from each data group to generate a single surface reflectance observation value.

[0070] Specifically, the surface reflectance aggregation module 300 is used to apply the formula Γ=∑ i Γ(i)*w(i) calculates the aggregated surface reflectance Γ; where Γ(i) is the i-th surface reflectance and w(i) is the weight of the i-th surface reflectance.

[0071] It also includes: a weight calculation module, used to calculate the weights using the formula w(i)=(1 / C(i)) / ∑ i w(i) is calculated using (1 / C(i)); where C(i) = D(i) * T(i) * V(i), and D(i) = d(i) / ∑ i d(i), T(i) = |T cygnss (i)-T smap | / ∑ i |T cygnss (i)-T smap |, d(i) is the distance from the location of the i-th surface reflectance to the center point of its corresponding grid cell, X is the longitude of the center point of its corresponding grid cell, Y is the latitude of the center point of its corresponding grid cell, x(i) is the longitude of the location where the i-th surface reflectance was obtained, y(i) is the latitude of the location where the i-th surface reflectance was obtained, and T cygnss (i) represents the acquisition time of the i-th surface reflectance, T smap is the acquisition time of the brightness temperature data from the spaceborne radiometer, and SNR(i) is the signal-to-noise ratio of the i-th surface reflectance. It is the average value of all signal-to-noise ratio data within the corresponding grid cell.

[0072] The surface emissivity reference value calculation module 400 is used to calculate the surface emissivity reference value using the brightness temperature data of the spaceborne radiometer and the soil temperature data in the qualified data set as a unit.

[0073] Specifically, the surface emissivity reference value calculation module 400 is used to calculate the reference value using the formula... The surface emissivity reference value ε was calculated. p Where TBp is the brightness temperature data of the spaceborne radiometer and T is the soil temperature data.

[0074] The model inversion module 500 is used to input a single surface reflectance observation into the surface emissivity inversion model to obtain the surface emissivity value;

[0075] The inversion output module 600 is used to calculate the difference between the surface emissivity value and the surface emissivity reference value, and to take the surface emissivity value of the surface emissivity inversion model when the difference satisfies the least squares condition as the inversion result that meets the requirements.

[0076] Specifically, the surface emissivity inversion model is ε1(t)=α*(Γ1(t)-Γ2(t0))+ε2(t0), where ε1(t) is the surface emissivity of the first-scale grid calculated on day t, Γ1(t) is the aggregated surface reflectivity of the first-scale grid on day t, Γ2(t0) is the average of all calculated GNSS-R surface reflectivities in the second-scale grid on day t0, which is closest to day t, ε2(t0) is the reference value of surface emissivity in the second-scale grid, and α is the coefficient of the surface emissivity inversion model.

[0077] This invention discloses a method and system for inverting 9km-scale spaceborne GNSS-R microwave surface emissivity. The method specifically includes: (1) acquiring spaceborne GNSS-R data, spaceborne radiometer brightness temperature data, and soil temperature data for the target area and target time period; (2) data processing and quality control; (3) calculating aggregated surface reflectivity; (4) calculating a reference value for surface emissivity; and (5) calculating the 9km-scale microwave surface emissivity. This invention is the first to realize the calculation of 9km-scale microwave surface emissivity based on spaceborne GNSS-R data, overcoming the shortcomings of existing observation methods and providing a new method for obtaining L-band microwave surface emissivity. Furthermore, it implements a spaceborne GNSS-R reflectivity aggregation method that considers location and time differences, effectively solving the technical problem of aggregation errors caused by differences in the spatiotemporal location distribution of GNSS-R data, and improving the inversion accuracy of 9km-scale spaceborne GNSS-R microwave surface emissivity.

[0078] Any aspects of this invention not described in detail in the embodiments are well-known techniques to those skilled in the art. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this invention and not to limit it. Although this invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this invention without departing from the spirit and scope of this invention, and all such modifications and substitutions should be covered within the scope of the claims of this invention.

Claims

1. A method for inverting GNSS-R microwave surface emissivity, characterized in that, include: Acquire spaceborne GNSS-R data, spaceborne brightness temperature data, and soil temperature data; The surface reflectance is calculated using the aforementioned spaceborne GNSS-R data. The calculated surface reflectance values ​​are then aggregated to generate a single surface reflectance observation value. The reference value of surface emissivity was calculated using the aforementioned spaceborne brightness temperature data and soil temperature data; The individual surface reflectance observation is input into the surface emissivity inversion model to obtain the surface emissivity value; Calculate the difference between the surface emissivity value and the surface emissivity reference value, and take the surface emissivity value of the surface emissivity inversion model when the difference satisfies least squares as the inversion result that meets the requirements; The surface emissivity inversion model is ,in, Let be the surface emissivity of the first-scale grid calculated on day t. Let be the aggregated surface reflectance of the first-scale grid on day t. The closest to day t The average value of all calculated GNSS-R surface reflectances within the second-scale grid. This is a reference value for the surface emissivity within the second-scale grid. The coefficients of the surface emissivity inversion model are given.

2. The GNSS-R microwave surface emissivity inversion method as described in claim 1, characterized in that, The process of calculating surface reflectance using the spaceborne GNSS-R data, aggregating multiple calculated surface reflectance values, and generating a single surface reflectance observation value includes: Through formula The aggregated surface reflectance was calculated. ;in, Let i be the surface reflectance. is the weight of the i-th surface reflectance.

3. The GNSS-R microwave surface emissivity inversion method as described in claim 2, characterized in that, The Through formula Calculated; where, , , , Let be the distance from the location of the i-th surface reflectance to the center point of its corresponding grid cell. Let be the acquisition time for the i-th surface reflectance. The acquisition time of the spaceborne brightness temperature data. Let be the signal-to-noise ratio of the i-th surface reflectance. It is the average value of all signal-to-noise ratio data within the corresponding grid cell.

4. The GNSS-R microwave surface emissivity inversion method as described in claim 1, characterized in that, The calculation of the surface emissivity reference value using the spaceborne brightness temperature data and soil temperature data includes: Through formula The surface emissivity reference value was calculated. ;in, For the aforementioned spaceborne brightness temperature data, The soil temperature data is as described.

5. A GNSS-R microwave surface emissivity inversion system, characterized in that, include: The data acquisition module is used to acquire spaceborne GNSS-R data, spaceborne brightness temperature data, and soil temperature data. The surface reflectance aggregation module is used to calculate the surface reflectance using the spaceborne GNSS-R data, aggregate the calculated surface reflectance values, and generate a single surface reflectance observation value. The surface emissivity reference value calculation module is used to calculate the surface emissivity reference value using the satellite-borne brightness temperature data and soil temperature data; The model inversion module is used to input the single surface reflectance observation value into the surface emissivity inversion model to obtain the surface emissivity value; The inversion output module is used to calculate the difference between the surface emissivity value and the surface emissivity reference value, and to take the surface emissivity value of the surface emissivity inversion model when the difference satisfies least squares as the inversion result that meets the requirements. The surface emissivity inversion model is as follows: ,in, Let be the surface emissivity of the first-scale grid calculated on day t. Let be the aggregated surface reflectance of the first-scale grid on day t. The closest to day t The average value of all calculated GNSS-R surface reflectances within the second-scale grid. This is a reference value for the surface emissivity within the second-scale grid. The coefficients of the surface emissivity inversion model are given.

6. The GNSS-R microwave surface emissivity inversion system as described in claim 5, characterized in that, The surface reflectance aggregation module is specifically used to aggregate data through formulas. The aggregated surface reflectance was calculated. ;in, Let i be the surface reflectance. is the weight of the i-th surface reflectance.

7. The GNSS-R microwave surface emissivity inversion system as described in claim 6, characterized in that, Also includes: The weight calculation module is used to calculate weights using formulas. The calculation yielded the above ;in, , , , , Let be the distance from the location of the i-th surface reflectance to the center point of its corresponding grid cell. Let be the acquisition time for the i-th surface reflectance. The acquisition time of the spaceborne brightness temperature data. Let be the signal-to-noise ratio of the i-th surface reflectance. It is the average value of all signal-to-noise ratio data within the corresponding grid cell.

8. The GNSS-R microwave surface emissivity inversion system as described in claim 5, characterized in that, The surface emissivity reference value calculation module is specifically used to calculate the reference value using the formula... The surface emissivity reference value was calculated. ;in, For the aforementioned spaceborne brightness temperature data, The soil temperature data is as described.

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