A reflectivity correction method for retrieving vegetation optical depth from spaceborne GNSS-R

Abstract

This invention discloses a reflectance correction method for retrieving vegetation optical depth using spaceborne GNSS-R. The method includes the following steps: acquiring CYGNSS, SMAP, AMSR2, AMSRU, and MODIS data; performing quality control on the dataset and extracting necessary variables to calculate surface reflectance; spatiotemporally matching GNSS-R observation data with auxiliary data; eliminating non-vegetation reflectance point data using MODIS land cover type data; correcting reflectance for soil moisture, surface roughness, and incident angle; and performing sensitivity analysis and accuracy evaluation. The technical solution of this invention, by progressively separating the influence of soil moisture, surface roughness, and incident angle on reflectance, can achieve the separation of vegetation contribution to reflectance. This represents another breakthrough in vegetation parameter research using spaceborne GNSS-R technology, providing an operable technical solution for estimating parameters such as vegetation optical depth and vegetation water content.

Description

Technical Field

[0001] This invention belongs to the field of GNSS vegetation optical depth inversion technology, specifically, it relates to a reflectance correction method for inverting vegetation optical depth using spaceborne GNSS-R. Background Technology

[0002] Vegetation optical depth (VOD) is a parameter used to quantify the microwave transmittance of the vegetation layer and is increasingly used to study the impacts of global climate and environmental change on vegetation. It has proven to be a key ecological indicator in studies of plant hydraulics, carbon storage, and other ecological aspects. Therefore, long-term and accurate global monitoring of VOD is crucial for enhancing our understanding of the connections between water and carbon cycles in terrestrial ecosystems. Remote sensing, as an important observational tool, is one of the most effective methods for estimating vegetation optical depth. Traditional methods for retrieving VOD based on active and passive microwave remote sensing include: observing microwave radiation (brightness temperature) using passive sensors and combining it with radiative transfer models to retrieve VOD; commonly used sensors include the SMAP and AMSR series. Another method involves using radar to emit microwaves and receive backscattered signals, estimating VOD using scattering models (such as water cloud models); commonly used sensors include Sentinel-1 and ALOS-2. However, the spatial resolution of the aforementioned sensors is relatively low, typically 10-40 km, and they are highly dependent on frequency bands. The L-band is sensitive to high biomass and water content, while the C / X band is easily affected by weather. Most importantly, their temporal resolution is low, and the observation period is long. Therefore, there is still a lack of long-term, free, globally active VOD products. Thus, we need to develop a better inversion algorithm to obtain long-term, continuous global VOD products with high spatiotemporal resolution.

[0003] Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technology that retrieves surface parameters by receiving the delay and polarization information of navigation satellite (such as GPS and BeiDou) signals after reflection from the Earth's surface. Because GNSS-R uses the L-band signal, a band highly interactive with the natural medium, it is the optimal frequency range for monitoring soil moisture and vegetation. Spaceborne GNSS-R technology boasts unique advantages such as low observation costs (no signal transmitter required), wide coverage (global coverage), short revisit periods (multiple constellations, high orbit and signal update frequencies), and the ability to conduct all-weather observations. Currently, this technology has been widely applied in various fields, including land, ocean, and atmosphere. Furthermore, existing research indicates that using spaceborne GNSS-R technology to retrieve vegetation optical depth has great potential.

[0004] In GNSS-R surface parameter inversion, surface reflectance is directly related to surface parameters, and different surface parameters (such as soil moisture and vegetation cover) significantly affect reflectance. By analyzing changes in reflectance, vegetation parameters can be estimated. However, surface reflectance is contributed by multiple factors, and to obtain the reflectance contributed by vegetation, the interference of other factors must be isolated. First, soil moisture affects surface reflectance because moist soil reflects weaker signals, while dry soil reflects stronger signals. Ignoring soil moisture may incorrectly attribute signal changes caused by soil moisture variations to changes in vegetation parameters, leading to inversion errors. Second, surface roughness affects signal scattering. Rough surfaces increase scattering, weakening the reflected signal. If surface roughness is not corrected, vegetation parameter inversion may be inaccurate, especially in farmland or areas with significant natural topographic variations. Changes in the incident angle affect the path and intensity of the reflected signal. At different incident angles, the signal penetrates the vegetation layer and interacts with the surface in different ways. If the effect of the incident angle is ignored, the inversion model may fail to correctly resolve vegetation structure, leading to biased parameter estimation. For example, at low incident angles, the signal path is longer, vegetation attenuation is more significant, and vegetation thickness may be overestimated. However, current research focuses on correcting the reflectance of soil moisture inversion, with less attention paid to correcting the reflectance of vegetation parameters, especially vegetation optical depth.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] This invention aims to propose a reflectance correction method for inverting vegetation optical depth using spaceborne GNSS-R. This method corrects the effects of soil moisture, surface roughness, and incident angle on vegetation reflectance, thereby solving the problem of underestimation or overestimation of vegetation optical depth in spaceborne GNSS-R and improving the accuracy of VOD inversion.

[0007] This invention provides the following technical solution:

[0008] A reflectance correction method for retrieving vegetation optical depth using spaceborne GNSS-R, comprising the following steps:

[0009] Step S1, Data Preparation: First, download L1B data, SMAP data, AMSR2 data, AMSRU data, and MODIS data of CYGNSS version 3.2 from the official website.

[0010] Step S2: Calculate CYGNSS surface reflectance and extract other variables; preprocess and perform quality control on GNSS-R data, SMAP data and other auxiliary data.

[0011] Step S3: Unify the temporal and spatial resolution of GNSS-R observation data and auxiliary data (SMAP data, AMSR2 data, AMSRU data, MODIS data).

[0012] Step S4: Extract vegetation cover types (17 types: evergreen coniferous forest, evergreen broad-leaved forest, deciduous coniferous forest, deciduous broad-leaved forest, mixed forest, dense shrubland, sparse shrubland, wooded wasteland, grassland, water body, permanent wetland, farmland, urban and built-up area, farmland / vegetation mixed area, ice and snow, wasteland, barren land) using MODIS land cover type data. Reflection point data (non-water body, non-vegetation type) is retained, specifically by creating a mask: 0 for water body type, less than 12 for vegetation type, and 13, 15, 16, and 255 for non-vegetation type.

[0013] Step S5: Correct the CYGNSS surface reflectance for soil moisture, surface roughness, and incident angle.

[0014] Step S6 involves performing accuracy evaluation and sensitivity analysis on the results to further evaluate the performance of the method.

[0015] Preferably, the variable parameters extracted from the CYGNSS GNSS-R observation data in step S2 for calculation and inversion include: quality flag, power DDM, receiver-to-speech distance (rx_to_sp_range), GPS transmitter-to-speech distance (tx_to_sp_range), GNSS-R receiver antenna gain (sp_rx_gain), specular reflection latitude (sp_lat), specular reflection longitude (sp_lon), specular reflection angle (sp_inc_angle), GPS transmitter equivalent isotropic radiated power (gps_eirp), DDM signal-to-noise ratio (ddm_snr), bistatic radar cross section (BRCS), and effective scattering area (eff_scatter). The auxiliary data used include the following variables: latitude and longitude (longitude, latitude), soil moisture (soil_moisture, soil_moisture_pm), vegetation water content (vegetation_water_content), vegetation optical depth (vegetation_opacity_dca), surface roughness (roughness_coefficient), surface temperature (surface_temperature), quality flag (retrieval_qual_flag_pm) in SMAP data, vegetation optical depth (vod_10h) in AMSR2 data, and vegetation optical depth (data_VOD) in AMSRU data. Land cover types (17 types: water bodies, evergreen coniferous forests, evergreen broad-leaved forests, deciduous coniferous forests, deciduous broad-leaved forests, mixed forests, dense shrubs, loose shrubs, wooded wastelands, wastelands, grasslands, permanent wetlands, farmland, urban and built-up areas, farmland interspersed with natural vegetation, ice and snow, barren, no data or unclassified, mask 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 255) and NDVI in MODIS data.

[0016] The SMAP soil moisture and surface roughness are used as auxiliary input parameters for reflectance correction, while VOD from SMAP, AMSR2, and AMSRU data is used as validation data. Other data are used as auxiliary parameters to analyze the influence of multiple factors on surface reflectance.

[0017] Preferably, step S2 involves preprocessing the acquired data, including the following sub-steps:

[0018] Step S2.1, CYGNSS data quality control. CYGNSS data filtering uses a single QC flag bit for data filtering, instead of the entire QC flag bit. The variable `quality_flags` has a total of 31 quality flag bits. In this invention, these 31 quality flags are generated into 31-bit binary numbers. These bits correspond to different quality control flags, and the combined condition indicates "only selecting points that meet specific quality requirements." If the condition is met, the corresponding latitude and longitude coordinates are recorded in an array. Simultaneously, to ensure that all used data is valid, all observations containing NaN values ​​are deleted; all observations less than 0 are discarded; RCG values ​​are greater than 3; if the gain of the receiving antenna in the direction of the reflection point is less than 0 dBi, it needs to be discarded; if the uncertainty of BRCS is greater than 1, it also needs to be discarded; the incident angle sampling point is greater than 65°; sampling points with a signal-to-noise ratio less than or equal to 0 dB are discarded; to ensure that the error of the specular reflection point caused by the terrain is within a reasonable range, sampling points whose delay row of the DDM peak power is outside [7, 10] are removed.

[0019] SMAP data quality control assigns a value of NAN to soil moisture less than 0.02 and equal to -9999, as well as to vegetation water content, vegetation optical depth, surface roughness, and surface temperature equal to -9999. Furthermore, the data is filtered based on the quality labels provided within the dataset.

[0020] Step S2.2, calculate the effective surface reflectance of GNSS-R.

[0021]

[0022] In the formula The coherent components are shown in equation (3); It is the right-hand circularly polarized (RHCP) power emitted; It is the gain of the transmitting antenna; and Represents equivalent isotropic radiated power (EIRP); The distance between the specular reflection point and the receiver; The gain of the receiving antenna; It is the distance between the transmitter and the point of reflection on the mirror. It is the GPS L1 wavelength (19cm). The DDM noise floor is defined as the power mean of a specified noise region, and is calculated using the following formula:

[0023]

[0024] In the formula, and Define the delay chamber boundary for the specified noise region; and The Doppler frequency boundary of the specified noise region; M is the number of pixels in the noise region; The DDM power value for the specified location.

[0025] When it is assumed that the reflected signal on land is mainly determined by coherent reflection from the surface, the coherent component of the bistatic radar received power in this case... It can be represented as:

[0026]

[0027] Convert surface reflectance to dB:

[0028]

[0029] Preferably, step S3, which unifies the temporal and spatial resolution of GNSS-R data and auxiliary data, includes the following:

[0030] S3.1 Align GNSS-R with auxiliary data (SMAP data, AMSR2 data, AMSRU data, MODIS data) on a daily scale using the data acquisition time.

[0031] S3.2 Unify AMSR2 vegetation optical depth data, AMSRU vegetation optical depth data, MODIS land cover type data, and NDVI data into an SMAP 9 x 9 km grid.

[0032] In step S3.3, using a two-dimensional bilinear interpolation method, the soil moisture, vegetation water content, vegetation optical depth, surface temperature, and surface roughness of the SMAP lift-track are matched to the quality-controlled CYGNSS specular reflection points using the AMSR2 lift-track vegetation optical depth, AMSRU vegetation optical depth, and MODIS NDVI data meshed in step S3.2. Finally, the lift-track data are averaged.

[0033] Preferably, step S5 involves correcting the CYGNSS surface reflectance for soil moisture, surface roughness, and incident angle. This includes the following:

[0034] S5.1, Derivation of Vegetation Effective Reflectance. GNSS land reflectance signals contain information such as soil moisture, surface roughness, and vegetation. CYGNSS effective reflectance can be used for surface parameter inversion. Assuming that soil moisture, vegetation, and surface roughness dominate the contribution of reflectance, the effective reflectance of vegetation can be obtained by separating the effects of soil moisture and surface roughness.

[0035] When considering soil moisture, surface roughness, and vegetation effects, effective reflectance can be defined as follows:

[0036]

[0037] Right now , .in The Fresnel reflectance of the surface; Indicates the dielectric constant of the soil; It is transmittance, which is the optical depth of vegetation. and angle of incidence The function represents the signal attenuation as the signal propagates through the vegetation canopy; The signal wavenumber; This represents the root mean square height of the surface.

[0038] S5.2, Soil Moisture Correction. When an L-band signal is reflected from a vegetation surface, it can penetrate the vegetation and be reflected from the soil surface. The reflected signal contains signal components reflected from both the vegetation and soil surfaces. Soil moisture is corrected using SMAP alone based on empirical formulas. The reflectivity is corrected. The power reflectivity is estimated based on SNR. It refers to soil moisture.

[0039] S5.3, Surface Roughness Correction. Surface roughness is one of the main elements affecting signal reflection. When the surface is sufficiently smooth, the coherent scattering component of the signal dominates; as surface roughness increases, the coherent scattering component decreases. This is achieved by using the surface roughness coefficient provided by SMAP alone. According to the formula To correct the effect of surface roughness on reflectivity.

[0040] S5.4 Surface reflectance is affected not only by soil moisture and surface roughness, but also by the angle of incidence, similar to the effect of the angle of incidence on backscattering observations. To correct for the influence of local angles of incidence on surface reflectance, an alternative formula can be used. To correct for the influence of local incident angles. It is the angle of incidence. It is a parameter that typically varies between 0 and 2.

[0041] Taking the logarithm of equation (5) reveals the effective reflectance contributed by vegetation. :

[0042]

[0043] Preferably, the sensitivity analysis and accuracy assessment described in step S6 involves performing sensitivity analysis on the VOD product with uncorrected, individually corrected soil moisture and surface roughness, pairwise corrected, and fully corrected reflectance. The selected accuracy indicator is the correlation coefficient (CC). Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the implementation cases will be briefly introduced below.

[0045] Figure 1 This is a flowchart of a reflectance calibration method for retrieving vegetation optical depth using a spaceborne GNSS-R, as provided in an embodiment of the present invention.

[0046] Figure 2 This invention provides the relationship between reflectivity and SM, surface roughness, and incident angle at different VOD levels in the embodiments of the present invention.

[0047] Figure 3 This is a scatter plot comparing the reflectance of a single correction with AMSRU2 VOD data provided in this embodiment of the invention.

[0048] Figure 4 This is a scatter plot comparing the reflectance of the pairwise combined correction with the AMSRU2 VOD data provided in this embodiment of the invention.

[0049] Figure 5 This is a scatter plot comparing the fully corrected reflectance with three types of VOD data provided in this embodiment of the invention. Detailed Implementation

[0050] The present invention will be described in detail below with reference to specific embodiments and examples, thereby making the advantages and various effects of the present invention more clearly apparent. Those skilled in the art should understand that these specific embodiments and examples are for illustrative purposes only and are not intended to limit the present invention.

[0051] Throughout this specification, unless otherwise specified, the terminology used herein should be understood as having the meaning commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the event of any conflict, this specification shall prevail.

[0052] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0053] This invention provides a reflectance correction method for retrieving vegetation optical depth using spaceborne GNSS-R, which will be described in detail below with reference to the accompanying drawings:

[0054] Example 1

[0055] To verify the feasibility and reliability of the reflectance correction method for vegetation optical depth retrieved by spaceborne GNSS-R in this invention, CYGNSS GNSS-R data (CYGNSS L1B v3.2), SMAP data (soil moisture, surface roughness, surface temperature, VOD, vegetation water content (VWC), AMSRU data (VOD), AMSR2 data (VOD), and MODIS data (MCD12C1 land cover, MOD13C2 NDVI)) were downloaded from relevant public websites. The data period was from January 1, 2021 to December 31, 2021. SMAP, AMSRU, and AMSR2 VOD data were used as reference data, while SMAP soil moisture, surface roughness, and VWC data were used as auxiliary data for reflectance correction. The temporal and spatial resolution information and parameters used in this invention for the six types of data are shown in Table 1.

[0056] Table 1. Temporal and spatial resolution information of the six types of data used in this invention.

[0057]

[0058] A reflectance correction method for retrieving vegetation optical depth using spaceborne GNSS-R, the implementation process of which includes the following steps:

[0059] Step S1, Data Preparation: First, download L1B data, SMAP data, AMSR2 data, AMSRU data, and MODIS data of CYGNSS version 3.2 from the official website.

[0060] Step S2: Calculate CYGNSS surface reflectance and extract other variables; preprocess and perform quality control on GNSS-R data, SMAP data and other auxiliary data.

[0061] Step S3: Unify the temporal and spatial resolution of GNSS-R observation data and auxiliary data (SMAP data, AMSR2 data, AMSRU data, MODIS data).

[0062] Step S4: Extract vegetation cover types (17 types: evergreen coniferous forest, evergreen broad-leaved forest, deciduous coniferous forest, deciduous broad-leaved forest, mixed forest, dense shrubland, sparse shrubland, wooded wasteland, grassland, water body, permanent wetland, farmland, urban and built-up area, farmland / vegetation mixed land, ice and snow, wasteland, barren land) using MODIS land cover type data. Reflection point data (non-water body, non-vegetation type) is retained. Specifically, masks are created to remove reflection points on water bodies (mask 0) and non-vegetation types (masks 13, 15, 16, 255); reflection points on vegetation types (mask less than 12) are retained.

[0063] Step S5: Correct the CYGNSS surface reflectance for soil moisture, surface roughness, and incident angle.

[0064] Step S6 involves performing accuracy evaluation and sensitivity analysis on the results to further evaluate the performance of the method.

[0065] As one implementation of this embodiment, the parameter extraction and data preprocessing in step S2 includes the following steps:

[0066] Step S2.1, the variable parameters extracted from the CYGNSS GNSS-R observation data for calculation and inversion include: quality flag, power DDM (power_analog), receiver-to-specular reflection point distance (rx_to_sp_range), GPS transmitter-to-specular reflection point distance (tx_to_sp_range), GNSS-R receiver antenna gain (sp_rx_gain), specular reflection point latitude (sp_lat), specular reflection point longitude (sp_lon), specular reflection point incident angle (sp_inc_angle), GPS transmitter equivalent isotropic radiated power (gps_eirp), DDM signal-to-noise ratio (ddm_snr), bistatic radar cross section (BRCS), and effective scattering area (eff_scatter). The auxiliary data used include the following variables: latitude and longitude, soil moisture, vegetation water content, vegetation optical depth (DCA), surface roughness coefficient, surface temperature, retrieval qual flag, and AMSR2 number from the SMAP data. The data includes vegetation optical depth (vod_10h) from the data, vegetation optical depth (data_VOD) from the AMSRU data, land cover type (17 land cover types: water body, evergreen coniferous forest, evergreen broad-leaved forest, deciduous coniferous forest, deciduous broad-leaved forest, mixed forest, dense shrub, loose shrub, tree-covered wasteland, wasteland, grassland, permanent wetland, farmland, urban and built-up areas, farmland intersecting with natural vegetation, ice and snow, barren, no data or unclassified, with masks of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 255) and NDVI from the MODIS data.

[0067] The SMAP soil moisture and surface roughness are used as auxiliary input parameters for reflectance correction, while VOD from SMAP, AMSR2, and AMSRU data is used as validation data. Other data are used as auxiliary parameters to analyze the influence of multiple factors on surface reflectance.

[0068] Step S2.2, CYGNSS data quality control. CYGNSS data filtering uses a single QC flag bit instead of the entire QC flag bit. The variable `quality_flags` has a total of 31 quality flag bits. In this invention, these 31 quality flags are generated into 31 binary numbers. These bits correspond to different quality control flags, and the combined condition indicates "only selecting points that meet specific quality requirements." If the condition is met, the corresponding latitude and longitude coordinates are recorded in an array. Simultaneously, to ensure all used data is valid, all observations containing NaN values ​​are deleted; all observations less than 0 are discarded; RCG values ​​greater than 3 are discarded; if the gain of the receiving antenna in the direction of the reflection point is less than 0 dBi, it is discarded; if the uncertainty of BRCS is greater than 1, it is also discarded; the incident angle sampling point is greater than 65°; sampling points with a signal-to-noise ratio less than or equal to 0 dB are discarded; to ensure that the error of the specular reflection point caused by terrain is within a reasonable range, sampling points whose delay row of the DDM peak power is outside [7, 10] are removed.

[0069] SMAP data quality control assigns a value of NAN to soil moisture less than 0.02 and equal to -9999, as well as to vegetation water content, vegetation optical depth, surface roughness, and surface temperature equal to -9999. Furthermore, the data is filtered based on the quality labels provided within the dataset.

[0070] Step S2.3, calculate the effective surface reflectance of GNSS-R:

[0071]

[0072] In the formula The coherent components are shown in equation (3); It is the right-hand circularly polarized (RHCP) power emitted; It is the gain of the transmitting antenna; and Represents equivalent isotropic radiated power (EIRP); The distance between the specular reflection point and the receiver; The gain of the receiving antenna; It is the distance between the transmitter and the point of reflection on the mirror. It is the GPS L1 wavelength (19cm). The DDM noise floor is defined as the power mean of a specified noise region, and is calculated using the following formula:

[0073]

[0074] In the formula, and Define the delay chamber boundary for the specified noise region; and The Doppler frequency boundary of the specified noise region; M is the number of pixels in the noise region; The DDM power value for the specified location.

[0075] When it is assumed that the reflected signal on land is mainly determined by coherent reflection from the surface, the coherent component of the bistatic radar received power in this case... It can be represented as:

[0076]

[0077] Convert surface reflectance to dB:

[0078]

[0079] Preferably, step S3, which unifies the temporal and spatial resolution of GNSS-R data and auxiliary data, includes the following:

[0080] S3.1 Align GNSS-R with auxiliary data (SMAP data, AMSR2 data, AMSRU data, MODIS data) on a daily scale using the data acquisition time.

[0081] S3.2 Unify AMSR2 vegetation optical depth data, AMSRU vegetation optical depth data, MODIS land cover type data, and NDVI data into an SMAP 9 x 9 km grid.

[0082] In step S3.3, using a two-dimensional bilinear interpolation method, the soil moisture, vegetation water content, vegetation optical depth, surface temperature, surface roughness of the SMAP lift-track, as well as the vegetation optical depth of the AMSR2 lift-track, AMSRU vegetation optical depth, and MODIS NDVI (gridged from step S3.2), are matched to the quality-controlled CYGNSS specular reflection points. Finally, the lift-track data are averaged.

[0083] Preferably, step S5 involves correcting the CYGNSS surface reflectance for soil moisture, surface roughness, and incident angle. This includes the following:

[0084] S5.1, Derivation of Vegetation Effective Reflectance. GNSS land reflectance signals contain information such as soil moisture, surface roughness, and vegetation. CYGNSS effective reflectance can be used for surface parameter inversion. Assuming that soil moisture, vegetation, and surface roughness dominate the contribution of reflectance, the effective reflectance of vegetation can be obtained by separating the effects of soil moisture and surface roughness.

[0085] When considering soil moisture, surface roughness, and vegetation effects, effective reflectance can be defined as follows:

[0086] Right now , .in The Fresnel reflectance of the surface; Indicates the dielectric constant of the soil; It is transmittance, which is the optical depth of vegetation. and angle of incidence The function represents the signal attenuation as the signal propagates through the vegetation canopy; The signal wavenumber; This represents the root mean square height of the surface.

[0087] S5.2, Soil Moisture Correction. When an L-band signal is reflected from a vegetation surface, it can penetrate the vegetation and be reflected from the soil surface. The reflected signal contains signal components reflected from both the vegetation and soil surfaces. Soil moisture is corrected using SMAP alone based on empirical formulas. The reflectivity is corrected. The power reflectivity is estimated based on SNR. It refers to soil moisture.

[0088] S5.3, Surface Roughness Correction. Surface roughness is one of the main elements affecting signal reflection. When the surface is sufficiently smooth, the coherent scattering component of the signal dominates; as surface roughness increases, the coherent scattering component decreases. This is achieved by using the surface roughness coefficient provided by SMAP alone. According to the formula To correct the effect of surface roughness on reflectivity.

[0089] S5.4 Surface reflectance is affected not only by soil moisture and surface roughness, but also by the angle of incidence, similar to the effect of the angle of incidence on backscattering observations. To correct for the influence of local angles of incidence on surface reflectance, an alternative formula can be used. To correct for the influence of local incident angles. It is the angle of incidence. It is a parameter that typically varies between 0 and 2.

[0090] Taking the logarithm of equation (5) allows us to separate the effective reflectance contributed by vegetation. :

[0091]

[0092] Preferably, step S6 involves sensitivity analysis and accuracy assessment. The selected accuracy metric is the correlation coefficient (CC).

[0093]

[0094] In the formula, The number of data samples, For the corrected reflectivity, For vegetation optical depth from the reference dataset, They are respectively and The average value.

[0095] Appendix Figure 1 The relationship between reflectance and soil moisture (SM), surface roughness, and angle of incidence (ARI) at different VOD levels is shown. The figures reveal that surface reflectance increases with increasing soil moisture at different VOD levels. Regarding the effect of the angle of incidence, surface reflectance tends to increase between 20° and 50°, reaching its maximum at lower surface roughness. This indicates that surface reflectance is affected by other factors regardless of the VOD level.

[0096] Figure 2 (a) and Figure 3 (a) are scatter plots comparing the original reflectance with AMSR2 VOD. Figure 2 (b) and (c) are scatter plots comparing reflectance with AMSR2 VOD after adjusting for soil moisture and surface roughness only. Figure 3 (b), (c), and (d) are scatter plots comparing reflectance and AMSR2 VOD under different correction combinations. Because only 30 days of data were used, the SMAP sampling rate was poor, containing many zero values; therefore, AMSR2 VOD was used as a reference. Figure 2 and Figure 3 As can be seen, compared with the uncorrected reflectance scatter plot, the scatter points of other correction methods are more concentrated and exhibit a negative correlation. This indicates that the corrected reflectance is more effective for vegetation optical depth. Furthermore, the correction combination of soil moisture and irradiance angle shows the best correlation.

[0097] Figure 4(a) is a scatter plot comparing the original reflectance with AMSR2 VOD; (b), (c), and (d) are scatter plots comparing the fully corrected reflectance with SMAP VOD, AMSRU VOD, and AMSR2 VOD. It can be seen that the fully corrected reflectance is effective for all three reference data and shows a clear regularity. As shown in Table 2, the correlation coefficients between reflectance and reference VOD for all possible combinations were calculated, and combinations with poor data quality were removed. The results in the table show that, except for the low correlation of the surface roughness correction method, the CC values ​​of other correction methods are greater than the original reflectance. Therefore, this reflectance correction method is effective. However, the overestimation of data during single correction may be due to differences in correction caused by surface cover type. Figure 3 and Figure 4 In terms of scatter plot clustering, the scatter plot with all points corrected is denser and more regular, demonstrating that the correction method is sensitive to vegetation optical depth. Furthermore, the most suitable combination for inversion can be explored in future large-sample modeling and inversion.

[0098] Table 2 Comparison of reflectance and VOD data for different correction methods.

[0099]

[0100] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A reflectivity correction method for spaceborne GNSS-R inversion of vegetation optical depth, characterized in that, Includes the following steps: Step S1: Acquire multi-source data, including CYGNSS data, SMAP soil moisture and surface parameter data, AMSR2 vegetation optical depth data, AMSRU vegetation optical depth data, MODIS land cover type and NDVI data; Step S2: Preprocess and quality control are performed on the CYGNSS data, SMAP soil moisture and surface parameter data, AMSR2 vegetation optical depth data, AMSRU vegetation optical depth data, and MODIS land cover type and NDVI data, and the CYGNSS surface reflectance is calculated. Step S3: Unify the temporal and spatial resolution of GNSS-R observation data with auxiliary data such as SMAP data, AMSR2 data, AMSRU data, and MODIS data; Step S4: Extract vegetation cover type using MODIS land cover type data, generate a mask, and retain valid reflective point data; Step S5: Correct the CYGNSS surface reflectance for soil moisture, surface roughness, and incident angle; Step S6: Evaluate the correlation between the corrected reflectance and the optical depth of vegetation through sensitivity analysis and accuracy verification.

2. The reflectance correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 1, wherein, Step S2 involves quality control of the CYGNSS data, including: removing invalid data, noisy areas, abnormal incident angles, and observation points with insufficient signal-to-noise ratio; and filtering outliers in the SMAP data for soil temperature, vegetation moisture content, surface roughness, and temperature.

3. The reflectivity correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 1, wherein, Step S2 involves quality control of the CYGNSS data, including: calculating the effective surface reflectivity of the CYGNSS data. The calculation method is based on the dual radar cross section and coherent component power model to calculate the effective surface reflectivity.

4. The reflectance correction method for retrieving vegetation optical depth using spaceborne GNSS-R according to claim 3, characterized in that, Surface effective reflectivity The formula for calculating the surface effective reflectivity is: ； in This represents the coherent reflection power. As a noise floor; It is the right-hand circularly polarized power of the emitted signal; It is the gain of the transmitting antenna; The distance between the specular reflection point and the receiver; The gain of the receiving antenna; It is the distance between the transmitter and the point of reflection on the mirror. It is the GPS L1 wavelength.

5. The reflectance correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 4, characterized in that, The noise floor The calculation method is: select the delay and Doppler boundary of the noise region in the DDM, and take the mean of the power of all pixels in the region.

6. The reflectivity correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 5, wherein, Noise floor formula The formula for calculating the noise floor is: ； in, and Define the delay chamber boundary for the specified noise region; and The Doppler frequency boundary of the specified noise region; M is the number of pixels in the noise region; The DDM power value for the specified location.

7. The reflectivity correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 1, wherein, Step S3, which unifies the spatiotemporal resolution, includes: resampling the vegetation optical depth data of AMSR2 and AMSRU and the MODIS land cover type to the 9×9 grid of SMAP; matching the SMAP soil moisture, surface roughness and vegetation parameters to the CYGNSS reflection point through two-dimensional bilinear interpolation, and taking the average value of the ascending and descending orbit data.

8. The reflectivity correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 1, wherein, The specific method for generating the mask in step S4 is as follows: set the water body and non-vegetation types in the MODIS land cover types to invalid values; retain the reflection point data corresponding to the vegetation cover type.

9. The reflectivity correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 1, wherein, The correction process in step S5 includes: Soil moisture correction formula: where is the SMAP soil moisture; Surface roughness correction formula: wherein is the roughness coefficient; Incidence angle correction formula: , wherein the parameter has a value range of 0-2; The effective reflectance of the final vegetation is isolated by the following equation: .

10. The reflectance correction method for retrieving vegetation optical depth from spaceborne GNSS-R according to claim 1, wherein, The accuracy verification in step S6 includes: performing a correlation analysis between the corrected reflectance and the vegetation optical depth products of SMAP, AMSR2, and AMSRU, using the correlation coefficient as an evaluation index; and comparing the sensitivity of uncorrected, single-factor corrected, and multi-factor jointly corrected reflectance to vegetation optical depth.

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