Surface roughness inversion method and application for single-frequency, single-polarization spaceborne GNSS-R

Abstract

This invention provides a method for retrieving surface roughness using single-frequency, single-polarization spaceborne GNSS-R, comprising: obtaining the square of the Fresnel reflection modulus and vegetation optical thickness using SMAP data; obtaining the effective reflectivity of the surface based on spaceborne GNSS-R data; refining the incident angle, and grouping the obtained square of the reflection modulus and effective reflectivity according to the refined incident angle to obtain the square of the reflection modulus and effective reflectivity under different incident angles; using a theoretical model of effective reflectivity, regressing to obtain correction coefficients, and thus obtaining the surface roughness. This invention also provides a corresponding computer-readable storage medium. The method of this invention combines GNSS-R and SMAP data, and uses the grouped effective reflectivity of the surface after refining the incident angle to determine roughness, overcoming the errors caused by the different scattering mechanisms of GNSS-R and SMAP, thus improving the accuracy of surface roughness.

Description

Technical Field

[0001] This invention relates to a surface roughness inversion method, specifically to a surface roughness inversion method for single-frequency single-polarization spaceborne GNSS-R and its application. The method of this invention is applicable to, but not limited to, the inversion of GNSS-R satellite data such as CYGNSS, TDS-1, and China Fengyun GNOS-R. Background Technology

[0002] Surface roughness is closely related to physical properties such as surface runoff, water storage capacity, infiltration rate, soil erosion, ground heat balance, and soil evaporation, and is an important topic in soil science, especially agricultural soil science.

[0003] Surface roughness can be categorized by application into physical roughness, used in meteorology for aerodynamic applications, and geometric roughness, used in geosciences, particularly remote sensing. Physical roughness is the height at which the wind speed value is zero on a near-surface wind speed profile, while geometric roughness characterizes the degree of surface undulation, comprising the horizontal correlation length and the root-mean-square height in the vertical direction. Because surface roughness significantly affects electromagnetic wave reflection, it is a focus of attention in optics and remote sensing.

[0004] Surface roughness measurement is divided into contact measurement and non-contact measurement. The former mainly uses a needle profile measuring instrument to measure surface roughness, which is inexpensive and easy to use. However, this measurement technique interferes with the surface, has limited sampling length, and suffers from unstable errors due to manual measurement. Automated contact measurement technology in contact measurement increases costs.

[0005] Non-contact measurement uses lidar profile measurement technology, which can achieve millimeter and sub-millimeter levels in both horizontal and vertical directions, but this technology is not very portable.

[0006] Handheld photogrammetry in contact measurement has significant advantages in cost, portability, and data processing speed, but data processing time is relatively long.

[0007] The problem with surface station-based contact surveys is their limited coverage. For large-scale or global-scale surface studies, remote sensing technology is particularly important for obtaining surface roughness data.

[0008] GNSS-R technology is a novel method for remote sensing ground parameters using reflected signals from navigation satellites (GNSS). Its applications extend to the ocean surface, such as using GNSS-R to study sea surface wind fields, significant wave height, and wind speed. However, the application of GNSS-R on land surfaces is relatively lagging, although its scatterometer models can be used for monitoring and research related to soil moisture, vegetation biomass, surface freeze-thaw cycles, and wetlands.

[0009] Monitoring surface roughness using spaceborne GNSS-R is a relatively new research area. Summary of the Invention

[0010] The present invention aims to provide a method and application for surface roughness inversion for single-frequency single-polarization spaceborne GNSS-R, so as to improve the accuracy of surface roughness.

[0011] To achieve the above objectives, this invention provides a method for surface roughness inversion for single-frequency, single-polarization spaceborne GNSS-R, comprising:

[0012] S1: The square of the Fresnel reflection coefficient modulus and the vegetation optical thickness are obtained using SMAP data;

[0013] S2: Obtain the effective reflectivity of the Earth's surface based on data from the spaceborne GNSS-R;

[0014] S3: Refine the incident angle θ of GNSS-R, and group the obtained Fresnel reflection coefficient modulus square and the effective reflectivity of the ground surface according to the refined incident angle to obtain the Fresnel reflection coefficient modulus square and the effective reflectivity of different incident angles.

[0015] S4: Based on the square of the Fresnel reflection coefficient modulus of vegetation optical thickness at different incident angles and the effective reflectance at different incident angles, the roughness correction coefficient is obtained by regression using the theoretical model of effective reflectance.

[0016] S5: Surface roughness is obtained based on the roughness correction coefficient.

[0017] Preferably, the roughness correction coefficient is a regression coefficient A, and the theoretical model for the effective reflectivity is:

[0018]

[0019] γ=e (-τ×csc(θ)) ,

[0020] Where SR(θ) is the effective reflectivity of the Earth's surface; θ is the angle of incidence; γ is the square of the Fresnel reflectance modulus; γ is the vegetation transmittance; τ is the vegetation optical thickness; A is the regression coefficient.

[0021] Preferably, in step S5, the surface roughness s is:

[0022]

[0023] Where k is the free-space beam, which is a known constant; s is the surface roughness coefficient to be calculated; and A is the regression coefficient.

[0024] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed in a computer, it causes the computer to execute the surface roughness inversion method for single-frequency single-polarization spaceborne GNSS-R as described above.

[0025] The surface roughness inversion method for single-frequency, single-polarization spaceborne GNSS-R of this invention combines GNSS-R and SMAP data, and uses refined incident angle grouping of the effective reflectivity of the surface to determine roughness. This overcomes the errors caused by the different scattering mechanisms of GNSS-R and SMAP, thus improving the accuracy of surface roughness inversion. Furthermore, this method utilizes angular information instead of traditional normalization processing, resulting in higher accuracy in surface roughness inversion. Attached Figure Description

[0026] Figure 1 This is a flowchart of a surface roughness inversion method for single-frequency, single-polarization spaceborne GNSS-R according to an embodiment of the present invention. Detailed Implementation

[0027] The present invention will be further described below with reference to specific embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0028] This invention uses a zero-order equation to determine the effective reflectance of the Earth's surface. Therefore, the effective reflectance SR of the Earth's surface roughness obtained based on spaceborne GNSS-R satisfies the following theoretical model for effective reflectance:

[0029]

[0030] γ=e (-τ×csc(θ)) (2)

[0031] Wherein, SR(θ) is the effective reflectance of the Earth's surface, which can be directly calculated from the L1 data of the spaceborne GNSS-R;

[0032] θ is the angle of incidence, which can be obtained from the L1 data of the spaceborne GNSS-R;

[0033] The square of the Fresnel reflection coefficient modulus can be directly calculated using soil moisture and angle information from SMAP.

[0034] γ is the vegetation transmittance, and the calculation method is shown in formula (2);

[0035] τ represents the optical thickness of the vegetation, which can be obtained from SMAP data;

[0036] A is the regression coefficient, which is obtained through calculation.

[0037] The surface roughness inversion algorithm of this invention is applicable to the case where the receiver type is a single-frequency, single-polarization spaceborne GNSS-R receiver. For example... Figure 1 As shown, the surface roughness inversion method for single-frequency, single-polarization spaceborne GNSS-R of the present invention includes the following steps:

[0038] Step S1: Use SMAP data to obtain the square of the Fresnel reflection coefficient modulus. and vegetation optical thickness τ;

[0039] As mentioned above, τ is the square of the Fresnel reflectance modulus, which can be directly calculated using soil moisture and angle information from SMAP. τ is the vegetation optical thickness, which can be obtained from SMAP data.

[0040] Step S2: Obtain the effective reflectivity of the Earth's surface based on the L1 data from the spaceborne GNSS-R;

[0041] In step S2, the effective reflectivity SR(θ) of the Earth's surface is:

[0042]

[0043] Where θ is the incident angle of GNSS-R, SR(θ) is the effective reflectivity of GNSS-R at the incident angle θ, and R r R is the distance between the GNSS-R receiver and the mirror point on the Earth's surface. t P is the distance from the GNSS-R transmitter to the mirror image point on the Earth's surface. DDM denoted as the peak energy of the DDM waveform, N as the noise term, and F as the DDM bistatic radar cross section factor.

[0044] It should be noted that the effective reflectance SR(θ) in formula (1) actually varies with the angle; it is a formula affected by the angle. However, when using formula (3) above for calculation, the aforementioned R... r R t P DDM The parameters N and F can be directly read from the data acquired by GNSS-R, and these parameters are specific values ​​that do not change with the angle.

[0045] Furthermore, step S2 may also include: gridding the spaceborne GNSS-R data to conform to the SMAP projection method. In this embodiment, the SMAP projection method refers to each grid cell having a length of 36×36km. This is because the projection methods of GNSS-R data and SMAP are different, therefore it is necessary to convert them into a compatible projection method, i.e., gridding to the 36×36km SMAP size.

[0046] Step S3: Refine the incident angle θ of the GNSS-R, and group the square of the obtained Fresnel reflection coefficient modulus and the effective reflectivity of the ground surface according to the refined incident angle to obtain the square of the Fresnel reflection coefficient modulus under different incident angles. And the effective reflectivity SR(θ) at different incident angles;

[0047] The incident angle θ (i.e., the geometric information of the observation angle) is angle information directly provided by GNSS-R, which can be directly read from the data of the spaceborne GNSS-R. The effective reflectance obtained in step S2 is a mixture of data from various angles, while step S3 refines the effective reflectance according to the angle, that is, retains the effective reflectance for each angle. Therefore, by calculating different SRs under the same refined incident angle θ and then averaging them, the effective reflectance under different incident angles is obtained.

[0048] Current domestic and international algorithms ignore angle information. This invention improves the accuracy of the processing results by utilizing angle information.

[0049] Step S4: Based on the square of the Fresnel reflection coefficient modulus at different incident angles... The vegetation optical thickness τ and the effective reflectance SR(θ) at different incident angles were obtained by regression using the theoretical model of effective reflectance.

[0050] The theoretical models for effective reflectivity are shown in formulas (1) and (2):

[0051]

[0052] γ=e (-τ×csc(θ)) (2)

[0053] Step S5: Obtain the surface roughness s based on the regression coefficient A.

[0054] Wherein, the surface roughness s is:

[0055]

[0056] Where k is the free-space beam, which is a known constant; s is the surface roughness coefficient to be calculated; and A is the regression coefficient.

[0057] The surface roughness *s* calculated using traditional methods is derived from radar calculations based on the radiometer in SMAP. This roughness has accuracy issues when applied to GNSS-R, as its scattering mechanism differs, necessitating the calculation of a roughness specific to GNSS-R. This invention presents a surface roughness inversion method for single-frequency, single-polarization spaceborne GNSS-R that combines GNSS-R and SMAP data. By grouping the effective reflectivity of the surface using refined incident angles, the method determines the roughness, overcoming the errors caused by the different scattering mechanisms of GNSS-R and SMAP, thus improving the accuracy of surface roughness calculations.

[0058] According to another embodiment of the present invention, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed in a computer, causes the computer to perform the steps described above in the surface roughness inversion method for single-frequency single-polarization spaceborne GNSS-R. Since the steps performed are the same as those in the above-described surface roughness inversion method for single-frequency single-polarization spaceborne GNSS-R, they will not be described again here.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the invention. Various variations can be made to the above embodiments of the present invention. All simple and equivalent changes and modifications made in accordance with the claims and description of this application fall within the protection scope of the claims of this patent. All aspects not described in detail in this invention are conventional technical content.

Claims

1. A method for surface roughness inversion for single-frequency, single-polarization spaceborne GNSS-R, characterized in that, include: Step S1: Use SMAP data to obtain the square of the Fresnel reflection coefficient modulus and the vegetation optical thickness; Step S2: Obtain the effective reflectivity of the Earth's surface based on data from the spaceborne GNSS-R; Step S3: Set the incident angle of the GNSS-R The values ​​are refined, and the squares of the Fresnel reflection coefficient modulus and the effective reflectance of the ground surface are grouped according to the refined incident angle to obtain the squares of the Fresnel reflection coefficient modulus and the effective reflectance at different incident angles. Step S4: Based on the square of the Fresnel reflection coefficient modulus of vegetation optical thickness at different incident angles and the effective reflectance at different incident angles, the roughness correction coefficient is obtained by regression using the theoretical model of effective reflectance. Step S5: Obtain the surface roughness based on the roughness correction coefficient; The roughness correction coefficient is a regression coefficient A, and the theoretical model for the effective reflectivity is: ， ， Wherein, SR(θ) is the effective reflectance of the Earth's surface; Angle of incidence; The square of the Fresnel reflection coefficient modulus; Vegetation transmittance; The optical thickness of the vegetation is represented by A; A is the regression coefficient. In step S5, the surface roughness s is: ， in, is the free-space beam, is a known constant; s is the surface roughness to be calculated; A is the regression coefficient.

2. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, it causes the computer to perform the surface roughness inversion method for single-frequency single-polarization spaceborne GNSS-R as described in claim 1.

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