Method for monitoring and inverting vegetation water dynamics based on GNSS reflection geometry response index

Abstract

This invention discloses a method for dynamic monitoring and inversion of vegetation moisture based on the GNSS reflection geometric response index, belonging to the field of GNSS remote sensing inversion and surface ecological parameter monitoring technology. This invention solves the problems of existing GNSS-R vegetation moisture indices, such as reliance on empirical normalization, sensitivity to observation geometry, and difficulty in long-term stable comparative analysis. This invention utilizes direct sunlight and surface reflection signals observed by a ground-based GNSS receiver during continuous observation. Based on the response characteristics of the reflection signals to changes in satellite geometry, a vegetation reflection geometric response index is established to characterize the changing features of vegetation moisture status. This invention uses the establishment of a reflection response function and the extraction of geometric sensitivity parameters and stable historical reference states to quantitatively characterize vegetation moisture changes. This reduces the impact of noise interference or abnormal observations, eliminates the need for inversion using external remote sensing products or ground-based measured data, and is suitable for long-term continuous vegetation moisture monitoring in various climatic zones and vegetation types.

Description

Technical Field

[0001] This invention belongs to the field of GNSS remote sensing inversion and surface ecological parameter monitoring technology, specifically involving a method for dynamic monitoring and inversion of vegetation moisture based on GNSS reflection geometric response index. Background Technology

[0002] Vegetation water status serves as a crucial indicator of vegetation physiological activity and ecosystem water conditions. Changes in vegetation water status typically precede changes in vegetation structure and cover, playing a significant role in drought monitoring, ecological assessment, and climate response. Currently, plant water status is primarily obtained through optical remote sensing, thermal infrared remote sensing, and microwave remote sensing. However, optical remote sensing is severely affected by cloud cover and sunlight, making long-term continuous observation difficult. Thermal infrared remote sensing relies on energy balance equations for calculation, resulting in parameter uncertainties. Microwave remote sensing is less susceptible to cloud interference, but its spatial resolution is low and its models are complex, making it unsuitable for long-term continuous monitoring.

[0003] With the continuous construction and improvement of ground-based Global Navigation Satellite System (GNSS) observation networks, surface parameter inversion methods based on GNSS reflected signals have gradually attracted attention. GNSS signals contain both direct signals and signals reflected from the surface during propagation. These two types of signals superimpose at the receiver, and their interference characteristics change with variations in the dielectric properties of the surface medium, enabling the reflected signals to respond to changes in surface and vegetation moisture. Existing studies typically characterize vegetation moisture status by analyzing amplitude variations of reflected signals or constructing empirical normalized indices. However, these methods are sensitive to noise and anomalies, and the indices rely on empirical parameters, making them difficult to adapt to the long-term stable monitoring needs under different site conditions and vegetation types.

[0004] Furthermore, during continuous GNSS satellite observations, the spatial geometry of the observation station is constantly changing, and the reflected signal exhibits a significant geometric response as the satellite elevation angle changes. However, most current studies only focus on a single point in time or a specific amplitude at a single point in time, failing to fully utilize the geometric response characteristics reflected by the reflected signal, and thus failing to extract the necessary geometric structural features from the reflected signal. Summary of the Invention

[0005] To address the shortcomings of existing GNSS-R vegetation moisture monitoring methods, such as reliance on empirical normalization, sensitivity to observation geometry, and difficulty in long-term stable monitoring, this invention provides a dynamic monitoring and inversion method for vegetation moisture based on the GNSS Vegetation Reflective Geometry Index (GVRI). The aim is to achieve stable and continuous inversion of vegetation moisture status by characterizing the sensitivity of GNSS reflection signals to changes in satellite geometry, without relying on external remote sensing products or fixed empirical normalization parameters, and to improve the physical consistency and comparability of monitoring results. By introducing the reflection geometric response index, stable characterization and long-term continuous monitoring of vegetation moisture status are achieved, effectively solving the problems of existing GNSS-R vegetation moisture indicators relying on empirical normalization, sensitivity to observation geometry, and difficulty in long-term stable comparative analysis, thus improving the stability, comparability, and physical consistency of vegetation moisture monitoring results.

[0006] The technical solution adopted in this invention is as follows:

[0007] A method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index includes the following steps:

[0008] S1: Acquire raw GNSS signal data and obtain an effective observation sequence for reflection analysis based on the raw GNSS signal data;

[0009] S2: Establish a reflection response function by utilizing the relationship between satellite observation geometry and the effective observation sequence obtained in S1. Obtain reflection geometric sensitivity parameters based on the reflection response function. Normalize the obtained reflection geometric sensitivity parameters relative to historical stable periods. Construct the dimensionless vegetation reflectance index GVRI based on the normalized reflection geometric sensitivity parameters.

[0010] S3: Construct the dimensionless vegetation reflectance index GVRI in continuous time series, process the time series GVRI to obtain the final GVRI time series.

[0011] S4: Analyze the final GVRI time series obtained in S3, and construct a vegetation moisture status correlation model in combination with meteorological factors to establish the response relationship between GVRI and meteorological factors, and invert the vegetation moisture content.

[0012] S5: Using the GVRI time series obtained in S3 and the vegetation moisture content obtained by inversion in S4, time series analysis of vegetation moisture status is performed to obtain long-term trend indicators and abnormal event identification results to characterize vegetation moisture status.

[0013] Preferably, the specific steps of S2 include:

[0014] S201: Based on the relationship between satellite observation geometry and the effective observation sequence obtained in S1, establish a reflection response function to characterize the response characteristics of the reflected signal to changes in satellite elevation angle, and obtain the sensitivity of the reflection response function to changes in satellite elevation angle;

[0015] S202: Based on the sensitivity of the reflection response function to changes in satellite elevation angle, determine the reflection geometric sensitivity parameters, and use the reflection geometric sensitivity parameters to measure the stability of the reflected signal and the degree of response to geometric changes;

[0016] S203: Use the average value of the reflection geometric sensitivity parameter during the historical stable period as the reference value, and normalize the reflection geometric sensitivity parameter in the current time window to convert it into a relatively stable reference value;

[0017] S204: Construct the dimensionless vegetation reflectance index GVRI based on the normalized reflectance geometric sensitivity parameters.

[0018] As a preferred embodiment, the reflection response function in S201 The calculation formula is:

[0019] (1)

[0020] in For the first The elevation angle of each satellite observation point The effective number of observation sequences for reflectance analysis, The mapping function is selected based on application requirements;

[0021] Reflection geometric sensitivity parameters in S202 The calculation formula is:

[0022] (2)

[0023] in, This represents the number of valid observation points within that time window. A larger value indicates a more sensitive response, while a smaller value indicates a more stable response.

[0024] The formula for calculating the average reflection geometric sensitivity during the historical stable period in S203 is as follows:

[0025] (3)

[0026] in, For the first Geometric sensitivity values ​​within a reference window This refers to the number of reference windows during historical periods of stability. This represents the average geometric sensitivity of reflection during a historical period of stability.

[0027] The formula for calculating the normalized geometric sensitivity value is:

[0028] (4)

[0029] For the current time window Internal reflection geometric sensitivity value, This is the normalized geometric sensitivity value for the current time window;

[0030] The dimensionless vegetation reflectance index (GVRI) in S204 is characterized as follows:

[0031] GVRI (5)

[0032] Characterize the current time window The relative change in the intensity of the internal reflection signal's response to changes in satellite geometry relative to the reference period, when A larger value indicates that the reflection response is stable to changes in geometric conditions, corresponding to high vegetation moisture content; when... The smaller the value, the more sensitive the reflection response is to changes in geometric conditions, corresponding to low vegetation moisture content.

[0033] As a preferred option, the specific steps of S3 are as follows:

[0034] S301: Calculate the GVRI for all time windows to obtain the GVRI time series on the time scale;

[0035] S302: Perform statistical analysis on the GVRI time series, identify outliers and correct them to obtain the final GVRI time series.

[0036] Preferably, the time scale in S301 is a daily scale or a weekly scale.

[0037] As a preferred option, the specific steps for constructing the vegetation moisture status association model in S4 include:

[0038] S401: Calculate statistical characteristics and rate of change based on the final GVRI time series obtained in S3;

[0039] S402: By combining statistical characteristics and rates of change with meteorological conditions, a vegetation water state correlation model is established to characterize the response relationship between GVRI and meteorological factors and to retrieve vegetation water content. The vegetation water state correlation model is characterized as follows:

[0040] (9)

[0041] in, This refers to the moisture content of the vegetation. For precipitation, This refers to evaporation.

[0042] As a preferred option, statistical characteristics include maximum value, minimum value, and mean value.

[0043] As a preferred option, the long-term trend indicators for S5 are: to obtain and analyze GVRI time series after multi-seasonal or interannual correction, and to obtain long-term trend indicators for vegetation moisture changes over long time scales and interannual differences.

[0044] The results of the abnormal event identification are as follows: by identifying abnormal drought or wet events in the final GVRI time series obtained by S3, a set of abnormal vegetation moisture events is obtained, and consistency analysis is performed in combination with the meteorological data of the same period (consistency refers to the degree of matching between the abnormal changes in GVRI and the meteorological factors (such as precipitation) of the same period in terms of change trend, occurrence time and change direction) to determine the rationality of abnormal drought or wet events.

[0045] As a preferred option, in S5, the GVRI time series obtained in S3 and the vegetation moisture content obtained by inversion in S4 are used to perform time series analysis on the vegetation moisture status, obtain long-term change trend indicators and abnormal event identification results, and output them in the form of time series curves, statistical indicators or spatial distribution maps to characterize the vegetation moisture status.

[0046] Preferably, the specific steps of S1 include:

[0047] S101: Acquire raw GNSS signal data;

[0048] S102: Screen and perform quality control on the raw GNSS signal data, and divide the time series into time window sequences. Quality control includes removing low signal-to-noise ratio observation records and limiting the range of satellite elevation angles.

[0049] S103: Within each time window, a filter is used to remove multipath noise and high-frequency interference, separate the direct signal and the reflected signal, and obtain an effective observation sequence for reflection analysis.

[0050] Preferably, in S101, a ground-based GNSS receiver receives and reads raw GNSS signal data, and subsequent reflection analysis is carried out based on the pseudorange, carrier phase, and signal strength information of the raw GNSS signal data.

[0051] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0052] 1. This invention starts from the response characteristics of GNSS reflected signals to changes in satellite geometry. By constructing the Geometric Response Index (GVRI), it quantifies the sensitivity of reflected signals to changes in satellite elevation angle. Compared with existing methods based on reflection amplitude or empirical normalization, this invention effectively reduces the impact of noise and abnormal observations on vegetation moisture retrieval results and improves the stability and consistency of monitoring results.

[0053] 2. This invention introduces a relative normalized reflectance geometric sensitivity based on a historical stable reference period. It does not rely on the limitations of external remote sensing products and does not require the use of fixed thresholds, thus achieving the goal of long-term continuous monitoring of vegetation moisture status and enhancing the applicability and comparability of the indicators under different stations, different vegetation types, and different climatic conditions. Attached Figure Description

[0054] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0055] Figure 2 This is a schematic diagram of the spatial distribution of GVRI in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0057] like Figure 1 As shown, the method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index includes the following steps:

[0058] S1: Acquire raw GNSS signal data and obtain an effective observation sequence for reflection analysis based on the raw GNSS signal data; specific steps include:

[0059] S101: Acquire raw GNSS signal data; deploy 9 ground-based GNSS receivers within the study area to receive and read raw GNSS signal data. In this embodiment, the deployment of GNSS receivers is mainly determined based on the spatial range of the study area and observation requirements, prioritizing effective spatial coverage of the study area; simultaneously, considering satellite observation geometry, observation locations with good visibility are selected; and considering terrain undulations, surface cover types, and potential multipath interference factors, obstruction and strong reflection environments are avoided as much as possible to improve the stability and reliability of the observation data. Subsequent reflection analysis is then conducted based on the acquired pseudorange, carrier phase, and signal strength information of the raw GNSS signal data.

[0060] S102: The raw GNSS signal data is screened and quality controlled, and the time series is segmented to obtain a time window sequence. The quality control includes removing observation records with low signal-to-noise ratio and limiting the satellite elevation angle range. Specifically: (1) Remove observation records with signal quality lower than a preset threshold (the preset threshold needs to be determined by experience or statistical analysis based on the observation data quality, equipment performance and environmental conditions of the study area); in this embodiment, to ensure the quality of the observation data, the signal-to-noise ratio threshold is set to 25-45dB; at the same time, the satellite elevation angle threshold is set to 5°-25°. (2) Limit the satellite elevation angle range to To reduce the influence of non-specular reflection; (3) Divide the observation data into different time periods according to the chronological order and construct a time window sequence. ;

[0061] S103: Within each time window, a filter is used to remove multipath noise and high-frequency interference, separating the direct signal and the reflected signal to obtain an effective observation sequence that can be used for reflection analysis. ;

[0062] S2: Establish a reflection response function based on the relationship between satellite observation geometry and the effective observation sequence obtained in S1 for reflection analysis. Obtain reflection geometric sensitivity parameters based on the reflection response function. Normalize the obtained reflection geometric sensitivity parameters relative to historical stable periods, and construct the dimensionless vegetation reflectance index (GVRI) based on the normalized reflection geometric sensitivity parameters. Specific steps include:

[0063] S201: Based on the relationship between satellite observation geometry and the effective observation sequence obtained in S1 for reflection analysis, a reflection response function is established to characterize the response characteristics of the reflected signal to changes in satellite elevation angle.

[0064] (1)

[0065] in For the first The elevation angle of each satellite observation point This is the amount of reflected signal after preprocessing. The mapping function can be selected according to the application requirements. It can be a linear function, a nonlinear function (such as an exponential function, a logarithmic function, a power function, etc.), or other differentiable functions.

[0066] S202: Based on the sensitivity of the reflection response function to changes in satellite elevation angle, determine the reflection geometric sensitivity parameters, and use these parameters to measure the stability of the reflected signal and its responsiveness to geometric changes. The formula for calculating the sensitivity of the reflection response function to changes in satellite elevation angle is as follows:

[0067] (2)

[0068] in This represents the number of valid observation points within the time window (the time window is a time segment interval used to calculate the current reflection geometric sensitivity parameter). A larger value indicates a more sensitive response, while a smaller value indicates a more stable response.

[0069] The formula for calculating the average reflection geometric sensitivity during the historical stable period in S203 is as follows:

[0070] (3)

[0071] in, For the first The geometric sensitivity values ​​within a reference window (the reference window is a time segmentation interval within a historical stable period, used to provide a normalized baseline value). This refers to the number of reference windows during historical periods of stability. This represents the average geometric sensitivity of reflection during a historical period of stability.

[0072] The formula for calculating the normalized geometric sensitivity value is:

[0073] (4)

[0074] For the current time window Internal reflection geometric sensitivity value, This is the normalized geometric sensitivity value for the current time window;

[0075] The dimensionless vegetation reflectance index (GVRI) in S204 is characterized as follows:

[0076] GVRI (5)

[0077] GVRI represents the current time window. The relative change in the intensity of the internal reflection signal's response to changes in satellite geometry relative to the reference period. When A larger value indicates a more stable reflection response to changes in geometric conditions, corresponding to higher vegetation moisture content; when... The smaller the value (closer to 0 or negative), the more sensitive the reflection response is to changes in geometric conditions, corresponding to lower vegetation moisture content;

[0078] S3: Construct the dimensionless vegetation reflectance index (GVRI) for continuous time series, process the time series GVRI to obtain the final GVRI time series, and the specific steps include:

[0079] S301: Calculate the GVRI for all time windows to obtain the daily or weekly GVRI time series. series :

[0080] (6)

[0081] S302: Perform statistical analysis on the GVRI time series, identify outliers and correct them to obtain the final GVRI time series. Outlier identification is performed using the standard deviation threshold method, i.e. Exceeding the average Values ​​exceeding one standard deviation are considered outliers. For identified outliers, linear interpolation or smoothing filtering between adjacent time points is used for correction. Statistical analysis is performed on the GVRI time series, and the processed time series is denoted as GVRI. filtered (t), used as input for subsequent vegetation moisture analysis:

[0082] (7)

[0083] S4: Statistical features are extracted from the final GVRI time series, and a vegetation moisture status correlation model is constructed in conjunction with meteorological factors to establish the response relationship between GVRI and meteorological factors, and to invert vegetation moisture content, including:

[0084] S401: Calculating statistical characteristics and rate of change based on the final GVRI time series: Based on the corrected GVRI time series Calculate statistical characteristics (maximum value) Minimum value and mean ) and rate of change Vegetation moisture status is characterized based on statistical features and rates of change:

[0085] (8)

[0086] in The time interval between adjacent observations;

[0087] S402: By combining statistical characteristics and rates of change with meteorological conditions, a vegetation water state correlation model is established to characterize the response relationship between GVRI and meteorological factors and to retrieve vegetation water content. The vegetation water state correlation model is characterized as follows:

[0088] (9)

[0089] in, This refers to the moisture content of the vegetation. For precipitation, This refers to evaporation.

[0090] S5: Long-term trend indicators: Analyze GVRI time series data after seasonal or interannual corrections to obtain long-term changes and interannual differences in vegetation moisture.

[0091] (10)

[0092] in, The number of valid observations within the corresponding season or year. This represents the average level of vegetation moisture status within that timescale. It is calculated by analyzing different seasons or years. By comparing the data, we can assess the long-term trends in vegetation moisture and its interannual differences.

[0093] Anomaly event identification results: Abnormal drought / abnormal wet events are identified based on extreme points and abrupt change points in the time series: a significant decrease in the vegetation water deficit rate within a short period indicates an extreme vegetation water deficit; a significant increase in the vegetation water deficit rate within a short period indicates an abnormal increase in vegetation water. Based on these results, meteorological data such as precipitation, temperature, or soil moisture during the same period are compared to determine whether an abnormal drought / wet event has occurred.

[0094] By combining the GVRI time series obtained in S3 and the vegetation moisture content obtained from S4 inversion, a time series analysis of vegetation moisture status is conducted. Long-term trend indicators and abnormal event identification results are obtained and output in the form of time series curves, statistical indicators or spatial distribution maps to characterize vegetation moisture status, providing a data foundation for subsequent ecological environment assessment, hydrological analysis and climate change research.

[0095] In this embodiment, based on the GVRI time series results calculated from multiple GNSS observation stations, a spatial distribution map of the GVRI in the study area is generated using spatial interpolation methods. For example... Figure 2As shown, there are significant differences in GVRI values ​​in different regions. Regions with high GVRI correspond to regions with higher vegetation moisture, while regions with low GVRI correspond to regions with lower vegetation moisture. This indicates that the method can effectively characterize the distribution characteristics of vegetation moisture status on a spatial scale, providing support for regional-scale drought monitoring and ecological environment assessment.

[0096] 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 method for monitoring and inverting vegetation moisture dynamics based on GNSS reflection geometric response index, characterized in that: Includes the following steps: S1: Acquire raw GNSS signal data and obtain an effective observation sequence for reflection analysis based on the raw GNSS signal data; S2: Establish a reflection response function by utilizing the relationship between satellite observation geometry and the effective observation sequence obtained in S1. Obtain reflection geometric sensitivity parameters based on the reflection response function. Normalize the obtained reflection geometric sensitivity parameters relative to historical stable periods. Construct the dimensionless vegetation reflectance index GVRI based on the normalized reflection geometric sensitivity parameters. S3: Construct the dimensionless vegetation reflectance index GVRI in continuous time series, process the time series GVRI to obtain the final GVRI time series. S4: Analyze the final GVRI time series obtained in S3, and construct a vegetation moisture status correlation model in combination with meteorological factors to establish the response relationship between GVRI and meteorological factors, and invert the vegetation moisture content. S5: Using the GVRI time series obtained in S3 and the vegetation moisture content obtained by inversion in S4, time series analysis of vegetation moisture status is performed to obtain long-term trend indicators and abnormal event identification results to characterize vegetation moisture status.

2. The method for monitoring and inverting vegetation moisture dynamics based on GNSS reflection geometric response index according to claim 1, characterized in that: The specific steps of S2 include: S201: Based on the relationship between satellite observation geometry and the effective observation sequence obtained in S1, establish a reflection response function to characterize the response characteristics of the reflected signal to changes in satellite elevation angle, and obtain the sensitivity of the reflection response function to changes in satellite elevation angle; S202: Based on the sensitivity of the reflection response function to changes in satellite elevation angle, determine the reflection geometric sensitivity parameters, and use the reflection geometric sensitivity parameters to measure the stability of the reflected signal and the degree of response to geometric changes; S203: Use the average value of the reflection geometric sensitivity parameter during the historical stable period as the reference value, and normalize the reflection geometric sensitivity parameter in the current time window to convert it into a relatively stable reference value; S204: Construct the dimensionless vegetation reflectance index GVRI based on the normalized reflectance geometric sensitivity parameters.

3. The method for monitoring and inverting vegetation moisture dynamics based on GNSS reflection geometric response index according to claim 2, characterized in that: Reflection response function in S201 The calculation formula is: （1） in For the first The elevation angle of each satellite observation point The effective number of observation sequences for reflectance analysis, The mapping function is selected based on application requirements; Reflection geometric sensitivity parameters in S202 The calculation formula is: （2） in, This represents the number of valid observation points within that time window. A larger value indicates a more sensitive response, while a smaller value indicates a more stable response. The formula for calculating the average reflection geometric sensitivity during the historical stable period in S203 is as follows: （3） in, For the first Geometric sensitivity values ​​within a reference window This refers to the number of reference windows during historical periods of stability. This represents the average geometric sensitivity of reflection during a historical period of stability. The formula for calculating the normalized geometric sensitivity value is: （4） For the current time window Internal reflection geometric sensitivity value, This is the normalized geometric sensitivity value for the current time window; The dimensionless vegetation reflectance index (GVRI) in S204 is characterized as follows: GVRI (5) Characterize the current time window The relative change in the intensity of the internal reflection signal's response to changes in satellite geometry relative to the reference period, when A larger value indicates that the reflection response is stable to changes in geometric conditions, corresponding to high vegetation moisture content; when... The smaller the value, the more sensitive the reflection response is to changes in geometric conditions, corresponding to low vegetation moisture content.

4. The method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index according to any one of claims 1-3, characterized in that: The specific steps for S3 are as follows: S301: Calculate the GVRI for all time windows to obtain the GVRI time series on the time scale; S302: Perform statistical analysis on the GVRI time series, identify outliers and correct them to obtain the final GVRI time series.

5. The method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index according to any one of claims 1-3, characterized in that: The specific steps for constructing a vegetation water status association model in S4 include: S401: Calculate statistical characteristics and rate of change based on the final GVRI time series obtained in S3; S402: By combining statistical characteristics and rates of change with meteorological conditions, a vegetation water state correlation model is established to characterize the response relationship between GVRI and meteorological factors and to retrieve vegetation water content. The vegetation water state correlation model is characterized as follows: （9） in, This refers to the moisture content of the vegetation. For precipitation, This refers to evaporation.

6. The method for monitoring and inverting vegetation moisture dynamics based on GNSS reflection geometric response index according to claim 5, characterized in that: The statistical characteristics in S402 include the maximum value, minimum value, and mean value.

7. The method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index according to any one of claims 1-3, characterized in that: The long-term trend indicators for S5 are: to obtain and analyze GVRI time series after multi-seasonal or interannual correction, and to obtain long-term trend indicators for vegetation moisture changes over long time scales and interannual differences. The results of the abnormal event identification are as follows: by identifying abnormal drought or wet events in the final GVRI time series obtained by S3, a set of abnormal vegetation moisture events is obtained, and consistency analysis is performed in combination with the meteorological data of the same period to determine the rationality of abnormal drought or wet events.

8. The method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index according to any one of claims 1-3, characterized in that: In S5, the GVRI time series obtained in S3 and the vegetation moisture content obtained by inversion in S4 are used to perform time series analysis on the vegetation moisture status, obtain long-term change trend indicators and abnormal event identification results, and output them in the form of time series curves, statistical indicators or spatial distribution maps to characterize the vegetation moisture status.

9. The method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index according to any one of claims 1-3, characterized in that: The specific steps of S1 include: S101: Acquire raw GNSS signal data; S102: Screen and perform quality control on the raw GNSS signal data, and divide the time series into time window sequences. Quality control includes removing low signal-to-noise ratio observation records and limiting the range of satellite elevation angles. S103: Within each time window, a filter is used to remove multipath noise and high-frequency interference, separate the direct signal and the reflected signal, and obtain an effective observation sequence for reflection analysis.

10. The method for monitoring and inverting vegetation moisture dynamics based on the GNSS reflection geometric response index according to claim 9, characterized in that: In S101, the ground-based GNSS receiver receives and reads raw GNSS signal data. Based on the pseudorange, carrier phase, and signal strength information of the raw GNSS signal data, subsequent reflection analysis is carried out.

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