High-temporal-spatial-resolution water level inversion method fusing satellite height measurement data and GNSS-R

Abstract

The invention discloses a high-temporal-spatial-resolution water level inversion method fusing satellite height measurement data and GNSS-R (global navigation satellite system-reflectometry). Firstly, a target area is determined, and GNSS data, satellite height measurement data and water body mask data of the target area are acquired; processing satellite altimetry data to obtain an observation water level in a research area, and eliminating system offset by comparing average water level deviations of different altimetry satellites in an overlapping period to obtain a multi-source altimetry satellite fusion water level; gNSS data are processed, a mathematical model between signal-to-noise ratio data and the water surface height is constructed, and GNSS-R water level data are obtained through a nonlinear fitting method; and finally, combining satellite height measurement data and GNSS-R data to construct a high-temporal-spatial-resolution water level inversion model based on XGBoost. By combining the satellite height measurement data and the GNSS-R data, water level monitoring with higher temporal-spatial resolution can be realized, the accuracy and reliability of water level inversion are remarkably improved, and the method has important application in disaster early warning and water resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of GNSS-R water level inversion, and in particular to a high temporal and spatial resolution water level inversion method integrating satellite altimetry data and GNSS-R. Background Art

[0002] Water level measurement is an important part of hydrological monitoring and plays an important role in flood prevention and control, river infrastructure construction, etc. How to accurately and efficiently observe water level changes in a large area of ​​a river basin has become a research hotspot in recent years. Traditional water level monitoring methods (such as water gauges and water level gauges) can meet general water level monitoring requirements, but these methods have problems such as high installation cost, difficult maintenance, and poor equipment stability in remote areas.

[0003] In recent years, the development of GNSS-R technology has provided a new technical path for water level monitoring. GNSS-R is a new remote sensing technology that uses GNSS reflected signals to invert geophysical parameters. Unlike traditional GNSS positioning, GNSS-R focuses on the characteristics of GNSS signals after reflection on the earth's surface (such as water surface, soil, ice surface, etc.). By analyzing the time delay, phase change, power attenuation and other characteristics of these reflected signals, a series of geophysical parameters can be inverted. GNSS-R technology has shown significant advantages in water level monitoring. Compared with traditional water level measurement methods, it has the characteristics of low cost, global coverage, passive measurement and low energy consumption.

[0004] Although ground-based GNSS-R technology can achieve dynamic water level monitoring around the clock, its coverage is limited and it is difficult to fully reflect water level changes in a large area. This limitation may lead to blind spots in monitoring, affecting the comprehensiveness and reliability of hydrological information, resulting in untimely warnings or false alarms. Especially in complex terrain and multi-water environments, these problems will reduce the effectiveness of disaster prevention and mitigation and increase the risk of loss of life and property.

[0005] Satellite altimetry technology has the characteristics of all-day, all-weather and wide coverage. Satellite altimetry can cover areas that are difficult to reach by ground observation, and can provide accurate water level data even in extreme weather conditions, providing key support for flood prevention. However, the temporal resolution of satellite altimetry data is low, which makes it difficult to meet the high timeliness requirements of water level data for flood prevention and control. Summary of the invention

[0006] Therefore, the present invention aims at the problem of limited coverage of ground-based GNSS-R, combines ground-based GNSS-R technology with satellite altimetry technology to achieve complementary advantages, and proposes a high-temporal and spatial resolution water level inversion method that integrates satellite altimetry data and GNSS-R, so as to effectively improve the comprehensiveness and reliability of hydrological information, thereby better achieving the goal of disaster prevention and mitigation. The purpose of the present invention is achieved through the following technical solutions:

[0007] A high temporal and spatial resolution water level inversion method integrating satellite altimetry data and GNSS-R comprises the following steps:

[0008] Step 1: Collect basic data: determine the target area, obtain GNSS observation data, satellite altimetry data and water mask data of the target area;

[0009] Step 2: Obtain multi-source altimetry satellite fusion water level data: Extract the vector boundary of the target area based on the water body mask data, then obtain multiple satellite altimetry data inside the target area based on the vector boundary, and perform error correction and outlier removal on the satellite altimetry data to finally obtain the along-track observed water level; convert the observation data of different altimetry satellites to a unified elevation benchmark, and eliminate the system offset by comparing the average water level deviation during the overlap period of different altimetry satellites, so as to obtain the multi-source altimetry satellite fusion water level;

[0010] Step 3: Obtain GNSS-R water level data: extract the signal-to-noise ratio data of low satellite elevation angles in the GNSS observation data, and process it using a low-order polynomial to remove the interference of the direct signal; construct a mathematical model between the reflected signal and the water surface height, and select equally spaced sampling points to preliminarily determine the unknown parameters of the model, then fit and solve the parameters using the nonlinear least squares method, and use the established mathematical model to calculate the water level height of the sampling point;

[0011] Step 4: Convert GNSS-R water level data and satellite altimetry data to the same elevation benchmark, and then build an XGBoost-based model by combining the wide coverage of satellite altimetry data and the high temporal resolution of GNSS-R water level data. The multi-source altimetry satellite fusion water level, GNSS-R water level data of different stations, time, and longitude and latitude in the study area are used as sample data, and the XGBoost machine learning algorithm is combined to build a high spatiotemporal resolution water level inversion model. The model is as follows:

[0012] W alt =f(t,lon,lat,W gnss )

[0013] Among them, W alt is the fusion water level of multi-source altimetry satellites; t is time; lon is longitude; lat is latitude; W gnss is the GNSS-R water level.

[0014] For further optimization, the specific operation of step 1 is as follows: first, according to the research needs, define the target area, determine the scope of the water body of interest (such as lakes, rivers or reservoirs), and clarify its geographical scope and spatial coordinates. Then, obtain GNSS observation data from the GNSS receiving equipment or related databases in the target area; at the same time, extract satellite altimetry data (water level observation data) covering the target area from altimetry satellites (such as ICESat-2, SWOT, etc.) to ensure sufficient time and space coverage; extract water body distribution information in the target area through remote sensing images or public water body databases (such as GLCF or MODIS water body data), and generate water body mask data to limit the scope of application of the data and remove interference from non-water body areas.

[0015] Furthermore, in step 2: the satellite altimetry data is corrected for errors and the water surface height is calculated. The specific formula is as follows:

[0016] H=H alt -R range -(iono+wet+dry+solid+pole)-Giodt

[0017] In the formula, H represents the water level; H alt Represents the distance from the satellite to the reference ellipsoid; R range It represents the distance from the satellite to the earth's surface; iono represents the ionosphere correction (using the GIM global ionosphere model); wet represents the wet troposphere correction; dry represents the dry troposphere correction; solid represents the solid earth tide correction; pole represents the polar tide correction; Geoid is used to convert the vertical reference from the reference ellipsoid to the geoid (using the EGM2008 geoid model).

[0018] Furthermore, in step 2, the elimination of system offset by comparing the average water level deviation during the overlapping period of different altimetry satellites specifically includes: comparing the data of the overlapping observation period, calculating the mean and standard deviation of the deviation, and correcting the deviation using linear regression or other statistical methods.

[0019] Furthermore, the mathematical model constructed in step 3 is:

[0020]

[0021] Where δSNR is the signal-to-noise ratio after removing the direct signal; λ is the wavelength of the satellite signal; h is the reflection height; θ is the satellite altitude angle; k is the wave number; s is the roughness parameter of the reflection surface; C 1 With C 2 In-phase and out-of-phase components are used to replace the amplitude A and phase

[0022]

[0023] After the mathematical model is established, m equally spaced sampling points are selected and the initial values ​​of the undetermined parameters are determined. The undetermined parameters are set as C 1 , C 2 ,h,s 2 , the best fitting parameters are solved by the nonlinear least squares method, and the water level height of the sampling point can be calculated through the mathematical model.

[0024] The application of the technical solution of the present invention has the following beneficial effects:

[0025] (1) Improving the spatiotemporal resolution of water level monitoring

[0026] Satellite altimetry data has the characteristics of wide coverage and high spatial accuracy, while GNSS-R data has the advantage of high temporal resolution. After fusing the two, it can make up for the shortcomings of a single data source, generate water level data with high temporal and spatial resolution, and achieve a more refined description of dynamic hydrological processes.

[0027] (2) Enhance the accuracy and reliability of water level monitoring

[0028] By fusing different data sources and making full use of their respective advantages, such as the millimeter-level accuracy of satellite altimetry and the temporal continuity of GNSS-R, the impact of errors or missing data in a single data source can be reduced, thereby improving the accuracy and stability of overall water level monitoring. Through innovative data fusion and machine learning optimization, this method is superior to traditional single data source technology in terms of flexibility, adaptability and accuracy, and has strong real-time and reliability.

[0029] (3) Improving adaptability in complex hydrological environments

[0030] In areas with complex hydrological environments and difficult to cover by traditional monitoring methods (such as plateau lakes, remote reservoirs, etc.), this method can provide reliable data support and is suitable for areas with complex terrain and limited monitoring conditions to achieve accurate water level inversion. The present invention will be further described in detail below with reference to the figures. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of the high spatiotemporal resolution water level inversion method integrating satellite altimetry data and GNSS-R in Example 1;

[0032] Figure 2 The system deviations of different altimeters in Example 1 are (1) the changing trends of different height-measured water levels in the same period; (2) the linear fitting to construct the correlation equation;

[0033] Figure 3This is a schematic diagram of the GNSS-R water level inversion principle;

[0034] Figure 4 The process of constructing a high spatiotemporal resolution water level inversion model in Example 1;

[0035] Figure 5 Comparison between simulated water levels and measured water levels at four stations in Lake Huron in Example 1, where (a), (b), (c), and (d) represent the four stations respectively. DETAILED DESCRIPTION

[0036] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0037] Embodiment 1:

[0038] A high temporal and spatial resolution water level inversion method integrating satellite altimetry data and GNSS-R comprises the following steps:

[0039] Step 1: Determine the target area and obtain GNSS data, satellite altimetry data and water mask data of the target area;

[0040] According to the research needs, the target area is first delineated, the scope of the water body of interest (such as lakes, rivers or reservoirs) is determined, and its geographical scope and spatial coordinates are clarified. Subsequently, the GNSS observation data in the target area is obtained through the Canadian Active Control System and the National Geodetic Survey of the United States from the GNSS receiving equipment or related databases. At the same time, data from three radar altimetry satellites (Jason-3, Sentinel-3A and Sentinel-3B) and one laser altimeter (ICESat-2) in the Lake Huron area from January 2021 to December 2023 are selected to extract water level observation data covering the target area to ensure sufficient temporal and spatial coverage. In addition, the water body distribution information of the target area is extracted through remote sensing images or public water body databases (such as GLCF or MODIS water body data), and a water body mask is generated to limit the scope of application of the data and remove interference from non-water body areas.

[0041] Step 2: Extract the transit data of different altimetry satellites in the lake based on the water body mask data, and then correct the errors and remove the outliers to finally obtain the water level observed along the track. Then, convert the observation data of different altimetry satellites to a unified elevation benchmark, and eliminate the system offset by comparing the average water level deviation during the overlap period of different altimetry satellites, so as to obtain the multi-source altimetry satellite fusion water level.

[0042] Specifically, the water mask data is used to extract the vector boundary of the target area, and then the satellite altimetry data inside the target area is obtained based on the vector boundary. Then the satellite altimetry data is corrected for errors and the water surface height is calculated. The specific formula is as follows:

[0043] H=H alt -R range -(iono+wet+dry+solid+pole)-Geod (1)

[0044] In the formula, H represents the water level; H alt Indicates the distance from the satellite to the reference ellipsoid. m; R range Indicates the distance from the satellite to the earth's surface. m; iono indicates the ionosphere correction value (using the GIM global ionosphere model); wet indicates the wet troposphere correction value; dry indicates the dry troposphere correction value; solid indicates the solid earth tide correction value; pole indicates the polar tide correction value; Geoid is used to convert the vertical reference from the reference ellipsoid to the geoid (using the EGM2008 geoid model).

[0045] Before integrating multi-source satellite altimetry data, it is necessary to use relative calibration methods to eliminate their systematic bias. The specific steps include: comparing data from overlapping observation periods, calculating the mean and standard deviation of the bias, and correcting the bias using linear regression or other statistical methods.

[0046] For example, the data obtained by ICESat-2 and Sentinel-3B satellites in a certain area have 14 observation dates with the same observation date, and the trend of change is highly consistent, such as Figure 2 As shown in the figure, the ICESat-2 satellite data is unified to the Sentinel-3B measurement benchmark through linear fitting to eliminate the systematic deviation between the two.

[0047] Step 3: Extract the signal-to-noise ratio data of low satellite elevation angles in the GNSS data, use a low-order polynomial to remove the direct signal part in the signal-to-noise ratio data, and establish a mathematical model between it and the water surface height. The principle is as follows: Figure 3 This is the principle diagram of water level inversion using GNSS-R technology. As can be seen from the figure, the GNSS antenna receives the reflected signal from the water surface while receiving the direct signal from the GNSS satellite. Compared with the direct signal, the reflected signal has an additional path D when it reaches the antenna, which can be expressed as:

[0048] D=2hsinθ (2)

[0049] Where h is the vertical distance from the receiver antenna phase center to the water surface; θ is the satellite elevation angle. The path delay can be converted to phase delay using the following formula:

[0050]

[0051] In the formula, λ is the wavelength of the satellite signal; θ is the satellite altitude angle. From formula (3), it can be deduced that there is a relationship between frequency and reflection height:

[0052]

[0053] Then, the signal-to-noise ratio data after detrending is modeled as:

[0054]

[0055] Where A is the amplitude; is the phase; λ is the wavelength of the satellite signal; θ is the satellite altitude angle.

[0056] The nonlinear fitting method introduces an attenuation factor into formula (5), and in order to ensure a stable numerical solution, it is transformed to obtain formula (6):

[0057]

[0058] In the formula, δSNR is the signal-to-noise ratio data after removing the direct signal; is the attenuation factor; λ is the wavelength of the satellite signal; h is the reflection height; θ is the satellite altitude angle; k is the wave number; s is the roughness parameter of the reflection surface; C 1 With C 2 In-phase and out-of-phase components are used to replace the amplitude A and phase

[0059]

[0060] After the mathematical model is established, m equally spaced sampling points are selected and the initial values ​​of the undetermined parameters are determined. The undetermined parameters are set as C 1 , C 2 ,h,s 2 , the best fitting parameters are solved by the nonlinear least squares method, and the water level height of the sampling point can be calculated through the mathematical model.

[0061] Step 4: The multi-source altimetry satellite fusion water level, GNSS-R water level data of different stations, time, and longitude and latitude in the study area are used as sample data. Then, a high spatiotemporal resolution water level inversion model is constructed in combination with the XGBoost machine learning algorithm. The model is as follows:

[0062] W alt =f(t,lon,lat,W gnss ) (8)

[0063] Among them, Walt is the fusion water level of multi-source altimetry satellites; t is time; lon is longitude; lat is latitude; W gnss is the GNSS-R water level.

[0064] Water level data in multiple target areas are randomly selected according to longitude and latitude, and the correlation and accuracy of the inverted water level and the measured water level are statistically analyzed to complete the accuracy verification of the high temporal and spatial resolution water level inversion model.

[0065] The input parameters of the high spatiotemporal resolution water level inversion model built with the XGBoost machine learning algorithm are GNSS-IR water level data, time, longitude and latitude, temperature and rainfall at different stations, and the output parameters are satellite height measurement water level data. The process of building a high spatiotemporal resolution water level inversion model is as follows: Figure 4 shown.

[0066] To construct a high spatiotemporal resolution water level inversion model, the overall data set must first be divided into a training set and a test set in a ratio of 7:3. During the training process, a 5-fold cross-validation was implemented to enhance the generalization ability of the model. At the same time, the Bayesian optimization method was used to adjust the three key hyperparameters in the model, and the determination coefficient R obtained by cross-validation was used. 2 The average value of is taken as the optimization target. After setting an appropriate number of iterations, the optimization process will produce a set of optimal hyperparameter configurations. Using this set of parameters, the high spatiotemporal resolution water level inversion model is trained on the training set. Finally, the prediction performance of the model is verified by the test set to evaluate its effectiveness in practical applications.

[0067] The measured data of four stations numbered 11690, 11070, 9075014 and 9075099 in the Lake Huron area were selected to evaluate the effectiveness of the model. Due to the vast area of ​​Lake Huron, the water level may vary significantly even in different areas of the same lake (by comparing the measured data of different stations, it is found that the average deviation between different stations in the same lake is between 2 and 5 cm). Therefore, with each hydrological station as the center, the fused data within a range of 0.4 degrees around the station are collected. This radius not only ensures that sufficient elevation observation data is obtained, but also ensures that the lake surface fluctuations are within a small range, so that each station is regarded as an independent small lake for research.

[0068] By fusing multi-source data, the model effectively overcomes the limitations of a single data source, can achieve dynamic water level monitoring over a large area, and reduce the risk of false alarms and missed reports caused by local data anomalies, thereby significantly improving the accuracy and real-time performance of water situation monitoring. To verify the effectiveness of the model, the simulated water levels with a time resolution of 1 hour at stations 11070, 11690, 9075014, and 9075099 from January 1, 2021 to December 31, 2023 were compared with the measured water levels. The statistical results are shown in the following table:

[0069] Table 1

[0070]

[0071] Figure 5 The simulated water level (blue line), measured data (red line) and satellite altimetry data (yellow scattered points) obtained from January 1, 2021 to December 31, 2023 are plotted together. Combining the graph and table, it can be found that the change trend of the simulated water level at each station is relatively consistent with the measured water level. The RMSE of the simulated water level and the measured water level at each station are 7.46cm, 5.15cm, 6.10cm and 7.34cm respectively, indicating that the simulated water level obtained by the model has a high accuracy.

[0072] The working principle and technical effect of the above technical solution are as follows: satellite altimetry data and GNSS-R water level data of the same time and the same area are matched, and their common time and space characteristics are used as input data to train the model, that is, to complete the construction of a high-temporal and spatial resolution water level inversion model that integrates satellite altimetry data and GNSS-R. This model can combine the advantages of satellite altimetry data and GNSS-R to improve the temporal and spatial resolution of water level monitoring.

[0073] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high temporal and spatial resolution water level inversion method integrating satellite altimetry data and GNSS-R, characterized in that: The following steps are involved: Step 1: Collect basic data: determine the target area, obtain GNSS observation data, satellite altimetry data and water mask data of the target area; Step 2: Obtain multi-source altimetry satellite fusion water level data: Extract the vector boundary of the target area based on the water body mask data, obtain multiple satellite altimetry data inside the target area based on the vector boundary, and perform error correction and outlier removal on the satellite altimetry data; Convert the observation data of different altimetry satellites to a unified elevation benchmark, and eliminate the system offset by comparing the average water level deviation during the overlap period of different altimetry satellites, so as to obtain the multi-source altimetry satellite fusion water level; Step 3: Obtain GNSS-R water level data: extract the signal-to-noise ratio data from the GNSS observation data, and process it to remove the interference of the direct signal; construct a mathematical model between the reflected signal and the water surface height, and select equally spaced sampling points to preliminarily determine the unknown parameters of the model, then fit and solve the parameters through the nonlinear least squares method, and use the established mathematical model to calculate the water level height of the sampling point; Step 4: The multi-source altimetry satellite fusion water level, GNSS-R water level data of different stations, time, and longitude and latitude in the study area are used as sample data, and the high spatiotemporal resolution water level inversion model is constructed in combination with the XGBoost machine learning algorithm. The model is as follows: W alt =f(t,lon,lat,W gnss ) Among them, W alt is the fusion water level of multi-source altimetry satellites; t is time; lon is longitude; lat is latitude; W gnss is the GNSS-R water level.

2. The high temporal and spatial resolution water level inversion method of integrating satellite altimetry data and GNSS-R according to claim 1 is characterized in that: The specific operations of step one are as follows: according to research needs, define the target area, determine the scope of the water body of interest, and clarify its geographical scope and spatial coordinates; obtain GNSS observation data from GNSS receiving equipment or related databases in the target area; extract satellite altimetry data covering the target area from altimetry satellites; extract water body distribution information of the target area through remote sensing images or public water body databases, and generate water body mask data.

3. The high temporal and spatial resolution water level inversion method integrating satellite altimetry data and GNSS-R according to claim 1, characterized in that: In step 2: perform error correction on the satellite altimetry data and calculate the water surface height. The specific formula is as follows: H=H alt -R range -(iono+wet+dry+solid+pole)-Geiod In the formula, H represents the water level; H alt Represents the distance from the satellite to the reference ellipsoid; R range It represents the distance from the satellite to the earth's surface; iono represents the ionosphere correction; wet represents the wet troposphere correction; dry represents the dry troposphere correction; solid represents the solid earth tide correction; pole represents the polar tide correction; Geoid is used to convert the vertical reference from the reference ellipsoid to the geoid.

4. The high temporal and spatial resolution water level inversion method of integrating satellite altimetry data and GNSS-R according to claim 1, characterized in that: In step 2, eliminating the system offset by comparing the average water level deviations during the overlapping periods of different altimetry satellites specifically includes: comparing the data during the overlapping observation period, calculating the mean value and standard deviation of the deviations, and correcting the deviations using linear regression.

5. The high temporal and spatial resolution water level inversion method integrating satellite altimetry data and GNSS-R according to claim 1, characterized in that: The mathematical model constructed in step 3 is: In the formula, δSNR is the signal-to-noise ratio data after removing the direct signal; λ is the wavelength of the satellite signal; h is the reflection height; θ is the satellite altitude angle; k is the wave number; s is the roughness parameter of the reflecting surface; C1 and C2 are the in-phase and out-of-phase components, which are used to replace the amplitude A and phase After the mathematical model is established, m equally spaced sampling points are selected and the initial values ​​of the undetermined parameters are determined. The undetermined parameters are set as C1, C2, h, s 2 , the best fitting parameters are solved by the nonlinear least squares method, and the water level height of the sampling point can be calculated through the mathematical model.

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