A method and system for multi-source satellite collaborative remote sensing monitoring of river cross-section water level

Abstract

This invention relates to a method and system for multi-source satellite collaborative remote sensing monitoring of river cross-section water levels, belonging to the field of river cross-section water level remote sensing monitoring. The method includes classifying cross-sections based on their morphological and slope characteristics; constructing error correction models for water levels of different types of cross-sections monitored by satellites at different spatial resolutions; and obtaining time-series water level datasets for different types of cross-sections through multi-source satellite collaborative monitoring, based on these error correction models and the water levels at different time phases of different types of cross-sections monitored by satellites at different spatial resolutions. This completes the multi-source satellite collaborative remote sensing monitoring of cross-section water levels. This invention achieves cross-section classification and then meets the high-frequency monitoring requirements for river section water levels through multi-source satellite collaborative remote sensing monitoring.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing monitoring of water level at river cross sections, and specifically relates to a method and system for multi-source satellite collaborative remote sensing monitoring of water level at river cross sections. Background Technology

[0002] River water level is closely related to river runoff and is an important indicator for evaluating river hydrological conditions. Dynamic and high-precision monitoring of river cross-section water levels is crucial for basin flood control, water conservancy project construction and management, and river ecological protection. While traditional hydrological station-based cross-section water level monitoring offers high accuracy, the number of such stations is currently limited due to factors such as station setup costs and conditions. This is particularly true in some mountainous areas with complex terrain and poor transportation, where water level monitoring data is severely lacking. With the rapid development of remote sensing technology, its high timeliness, wide coverage, and continuous dynamic monitoring capabilities have demonstrated enormous potential for dynamic monitoring of river water level changes.

[0003] Remote sensing monitoring of river water levels based on river topographic data and river waterline is an effective means of dynamically acquiring river water levels. This method utilizes remote sensing to extract the river waterline and overlays it with river topographic data to obtain the river cross-section water level. Currently, river topographic data mostly uses 30m spatial resolution SRTM DEM data, and remote sensing monitoring of river waterlines often relies on a single satellite data source. Furthermore, 30m spatial resolution DEM data is relatively coarse, making it difficult to meet the accuracy requirements for topographic data in monitoring narrow river sections; additionally, the frequency of acquiring river waterlines using a single satellite data source is limited, restricting the frequency of river water level monitoring.

[0004] With the continuous development of satellite / UAV remote sensing technology, the technical means for remote sensing monitoring of river topography and waterline have become increasingly rich. For example, using domestic ZY-3 satellite, Gaofen-7 satellite, or UAVs equipped with oblique photography cameras, high spatial resolution stereo mapping of river topography can be carried out to obtain high spatial resolution and high precision riverbank topographic data. High-frequency monitoring of river waterlines can be achieved using Sentinel 1 / 2 satellites with 10m spatial resolution, domestic GF-1 / 2 / 6 satellites with 1m / 2m / 8m / 16m spatial resolution, HJ-2A / 2B satellites with 16m spatial resolution, and Landsat series satellites with 30m spatial resolution. Utilizing multi-source satellites such as stereo mapping satellites, optical satellites, and radar satellites in collaboration to monitor river water levels can significantly increase the frequency of river water level monitoring. However, the accuracy of extracting river water level information using satellites with different spatial resolutions is closely related to the morphological and slope characteristics of the cross-section. This makes it difficult to form an effective time series of water levels from different characteristic cross-sections monitored by satellites with different spatial resolutions, limiting the ability of multi-source satellite collaborative remote sensing monitoring of river water levels. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a multi-source satellite collaborative remote sensing monitoring method and system for river cross-section water levels. It considers the error characteristics of extracting river waterline lines from topographic data using satellites with different spatial resolutions to obtain water levels for different types of cross-sections. An error correction model for monitoring water levels at different types of cross-sections using multi-source satellite images at different spatial resolutions is constructed. This model corrects the errors in monitoring water levels at different types of cross-sections using multi-source satellite images at different spatial resolutions, improving the consistency of water level monitoring at different types of cross-sections using satellites with different spatial resolutions. This enables the acquisition of high-frequency, consistent, long-term time-series river cross-section water level datasets using multi-source satellite collaboration.

[0006] The first objective of this invention is to provide a multi-source satellite collaborative remote sensing method for monitoring water levels at river cross-sections, comprising:

[0007] The cross sections are classified based on their morphological and slope characteristics.

[0008] Based on the water level of different types of cross sections monitored by satellites with different spatial resolutions, an error correction model for water level of different types of cross sections monitored by satellites with different spatial resolutions is constructed.

[0009] Based on the error correction model for water level at different types of cross sections monitored by satellites with different spatial resolutions, and the water level at different time phases of different types of cross sections monitored by satellites with different spatial resolutions, a multi-source satellite collaborative monitoring time series water level dataset for different types of cross sections is obtained, thus completing the multi-source satellite collaborative remote sensing monitoring of cross section water levels.

[0010] In a specific embodiment of the present invention, obtaining the cross-sectional morphology features and slope features includes:

[0011] Based on the cross-section location and the elevation information of the river section where the cross-section is located, the topographic data of the river section where the cross-section is located is obtained.

[0012] Based on the topographic data of the river section where the cross-section is located, the cross-sectional morphology and slope characteristics are obtained.

[0013] In a specific embodiment of the present invention, obtaining the elevation information data of the river section where the cross-section is located includes:

[0014] Draw the cross-section line vector based on the cross-section location;

[0015] The river section where the cross-section is located is determined based on the cross-sectional line vector and the historical maximum water volume range.

[0016] Based on the cross-section location and the river section where the cross-section is located, obtain the elevation information data of the river section where the cross-section is located.

[0017] In a specific embodiment of the present invention, the elevation information data of the river section where the cross-section is located is obtained by stereo mapping satellite monitoring and / or monitoring by an unmanned aerial vehicle equipped with an oblique photography camera.

[0018] This may be the elevation information data of the river section where the cross-section is located, obtained from monitoring by stereoscopic mapping satellites and / or by drones equipped with oblique photography cameras, after correction.

[0019] In a specific embodiment of the present invention, obtaining the topographic data of the river segment where the cross-section is located based on the cross-section location and the elevation information data of the river segment where the cross-section is located includes:

[0020] Based on the cross-section location and the river section where the cross-section is located, the vector of the water body range of the river section is extracted by stereo mapping satellite multispectral imagery.

[0021] The elevation information data of the river section where the cross-section is located is masked by the river section water body range vector to obtain the topographic data of the river section where the cross-section is located.

[0022] In a specific embodiment of the present invention, obtaining the cross-sectional morphology and slope characteristics based on the topographic data of the river section where the cross-section is located includes:

[0023] The cross-section line vector is trimmed using the water area vector of the river section where the cross-section is located, and the number of cross-section line vector elements after trimming is taken as the number of channels;

[0024] The number of branch channels represents the morphological characteristics of the cross section;

[0025] Calculate the slope using the topographic data of the river channel where the cross-section is located;

[0026] Slope is extracted and average slope of cross section is calculated using cross section line vectors;

[0027] The average slope of the cross section represents the slope characteristics of the cross section.

[0028] In a specific embodiment of the present invention, the classification of cross-sections based on cross-sectional morphology and slope characteristics includes:

[0029] The cross-sections are classified by combining cross-sectional morphological characteristics and slope characteristics. The number of cross-section types is the number of channels × the number of levels of the average slope of the cross-section.

[0030] In a specific embodiment of the present invention, the construction of an error correction model for water levels at different spatial resolutions of different cross-sections based on satellite monitoring of different types of cross-sections with different spatial resolutions includes:

[0031] In the same type of cross section, the cross section water level monitored by the satellite with the first spatial resolution is used as the benchmark to analyze the error characteristics between the cross section water level monitored by satellite images with other spatial resolutions and the benchmark.

[0032] Based on the error characteristics of similar cross sections, a correction model for water level error of similar cross sections monitored by satellite with different spatial resolutions is established.

[0033] Repeat the steps of "analyzing error characteristics" and "establishing error correction models" until the error correction models for water levels of satellite monitoring sections with different spatial resolutions for all types of sections are completed, thus obtaining error correction models for water levels of different types of sections monitored by satellite with different spatial resolutions.

[0034] In a specific embodiment of the present invention, obtaining the water level of a cross-section monitored by satellites of different types and spatial resolutions includes:

[0035] Based on satellite images of different spatial resolutions covering different types of cross sections, extract the waterline of the river section where the different types of cross sections are located;

[0036] Based on the intersection of the riverbank line and the cross-section vector of different types of cross sections, the elevation of the corresponding intersection point is extracted from the topographic data, and the average elevation of the pixels is calculated.

[0037] The average pixel elevation is used as the cross-sectional water level, thus obtaining the cross-sectional water level monitored by satellites at different spatial resolutions for different types of cross-sections.

[0038] In a specific embodiment of the present invention, the error correction model for water level at different types of cross sections monitored by satellites with different spatial resolutions, and the multi-source satellite collaborative monitoring time-series water level dataset for different types of cross sections monitored by satellites with different spatial resolutions at different time phases, to obtain the dataset includes:

[0039] The water level error correction model of the same type of cross section monitored by satellite with different spatial resolutions is used to correct the water level of the same type of cross section at different time phases monitored by satellite with different spatial resolutions, and the time series dataset of water level monitored by satellite with different spatial resolutions of the same type of cross section is obtained.

[0040] By combining time-series water level datasets from satellite monitoring at different spatial resolutions for the same type of cross section in chronological order, a multi-source satellite collaborative monitoring water level time-series dataset for the same type of river section can be obtained.

[0041] The second objective of this invention is to provide a multi-source satellite collaborative remote sensing monitoring system for river cross-section water levels, comprising:

[0042] Classification module: Used to classify cross sections based on their morphological and slope characteristics;

[0043] Model building module: used to construct error correction models for water levels of different types of cross sections monitored by satellites with different spatial resolutions based on different types of cross sections;

[0044] Correction module: It is used to correct the error model of water level of different types of cross sections based on satellite monitoring of different spatial resolutions, and to obtain the multi-source satellite collaborative monitoring time series water level dataset of different types of cross sections by satellite monitoring of different spatial resolutions at different time phases, thus completing the multi-source satellite collaborative remote sensing monitoring of cross section water level.

[0045] A third object of the present invention is to provide an electronic device comprising: a processor coupled to a memory;

[0046] The memory is used to store computer programs;

[0047] The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the method described above.

[0048] A fourth object of the present invention is to provide a computer-readable storage medium storing a program or instructions that, when executed on a computer, cause the computer to perform the method described above.

[0049] The beneficial effects of this invention are:

[0050] This invention discloses a multi-source satellite collaborative remote sensing monitoring method and system for river cross-section water level. The invention classifies cross-sections using cross-section location and elevation information data of the river section where the cross-section is located. Then, it monitors the cross-section water level of different types of cross-sections through multi-source satellite collaborative remote sensing. This satisfies the requirements for terrain accuracy in water level monitoring of narrow river sections and the need for high-frequency acquisition of datasets. At the same time, by establishing error correction models for water level of different types of cross-sections, the invention overcomes the difficulty in achieving coordination and consistency between data obtained from satellites with different spatial resolutions monitoring different types of cross-sections in multi-source satellite collaborative remote sensing monitoring technology.

[0051] The method of this invention enables the acquisition of high-frequency, consistent, long-term time-series river cross-section water level datasets using multi-source satellite collaboration, greatly improving the remote sensing monitoring capability of river water levels.

[0052] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart of a multi-source satellite collaborative remote sensing monitoring method for river cross-section water level according to an embodiment of the present invention is shown;

[0055] Figure 2 A schematic diagram of the cross-sectional line and the center line of the river section where the Benzilan Hydrological Station is located is shown according to an embodiment of the present invention.

[0056] Figure 3 A schematic diagram of the cross-sectional line and the center line of the river section where the Shigu Hydrological Station is located is shown according to an embodiment of the present invention.

[0057] Figure 4 The matching of the water level monitored by Sentinel 2 at the Benzilan section with the reference water level and the water level correction model are shown according to an embodiment of the present invention.

[0058] Figure 5 The matching of the monitoring water level at the Benzilan cross section Landsat-8 / 9 with the reference water level and the water level correction model are shown according to an embodiment of the present invention.

[0059] Figure 6 The matching of the water level monitored by Sentinel 2 at the Shigu section with the reference water level and the water level correction model are shown according to an embodiment of the present invention.

[0060] Figure 7 The matching of the monitoring water level at the Landsat-8 / 9 cross-section of the Stone Drum section with the reference water level and the water level correction model are shown according to an embodiment of the present invention.

[0061] Figure 8 The diagram shows the long-term water level changes of the Benzilan section from 2018 to 2023, based on multi-source satellite collaborative monitoring according to an embodiment of the present invention.

[0062] Figure 9 The diagram illustrates the long-term water level changes of the Shigu section from 2018 to 2023, based on multi-source satellite collaborative monitoring according to an embodiment of the present invention.

[0063] Figure 10 A framework diagram of a multi-source satellite collaborative remote sensing monitoring system for river cross-section water level according to an embodiment of the present invention is shown.

[0064] Figure 11 A frame diagram of an electronic device according to an embodiment of the present invention is shown;

[0065] In the diagram: Classification module 1; Model building module 2; Correction module 3; Electronic device 300; Processor 301; Memory 302. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] like Figure 1 As shown, an example of a multi-source satellite collaborative remote sensing monitoring method for river cross-section water level according to the present invention includes:

[0068] S1. Classify cross sections based on their morphological and slope characteristics;

[0069] S2. Based on the cross-sectional water levels monitored by satellites with different spatial resolutions for different types of cross sections, construct error correction models for the water levels of different types of cross sections monitored by satellites with different spatial resolutions.

[0070] S3. Based on the error correction model of water level of different types of cross sections monitored by satellites with different spatial resolutions, and the water level of different types of cross sections at different time phases monitored by satellites with different spatial resolutions, a multi-source satellite collaborative monitoring time series water level dataset of different types of cross sections is obtained, thus completing the multi-source satellite collaborative remote sensing monitoring of cross section water levels.

[0071] In this embodiment of the invention, step S1, obtaining the cross-sectional morphology features and slope features, includes:

[0072] S-1. Based on the cross-section location and the elevation information of the river section where the cross-section is located, obtain the topographic data of the river section where the cross-section is located;

[0073] S-2. Based on the topographic data of the river section where the cross-section is located, obtain the cross-sectional morphology and slope characteristics.

[0074] In some embodiments of the present invention, obtaining the elevation information data of the river segment where the interrupted surface is located in step S-1 specifically includes:

[0075] a1. Draw the cross-section line vector according to the cross-section location;

[0076] a2. Determine the river section range where the cross section is located based on the cross section line vector and the historical maximum water volume range. The historical maximum water volume range can be obtained from the GSW water area product.

[0077] a3. Based on the cross-section location and the river section where the cross-section is located, obtain the elevation information data of the river section where the cross-section is located;

[0078] For example, step a3 includes:

[0079] Based on the location of the cross section and the extent of the river section where the cross section is located, elevation information data of the river section where the cross section is located is obtained through three-dimensional mapping satellite monitoring.

[0080] Currently, the high spatial resolution stereo mapping satellite is the Gaofen-7 (GF-7) satellite. The elevation information obtained by the GF-7 satellite is DSM (Digital Surface Model) data, which is a type of elevation information data, including surface elevation information and the height information of objects such as buildings and trees.

[0081] In this embodiment of the invention, elevation information data obtained from the GF-7 satellite is used as the final elevation information data for the next step.

[0082] To improve the coverage of the river section topographic data obtained in step a3, try to select the Gaofen-7 (GF-7) satellite image corresponding to the minimum water area of ​​the river section;

[0083] In some other embodiments of the present invention, step a3 includes:

[0084] Based on the cross-section location and the river section where the cross-section is located, elevation information data of the river section where the cross-section is located is obtained by using a drone equipped with an oblique photography camera.

[0085] In this embodiment of the invention, elevation information data obtained by an oblique photography camera mounted on a drone is used as the elevation information data for the next step.

[0086] In some other embodiments of the present invention, step a3 includes:

[0087] 1. Based on the cross-section location and the river section where the cross-section is located, obtain elevation information data of the river section where the cross-section is located through stereo mapping satellite monitoring and / or UAV equipped with oblique photography cameras;

[0088] 2. The elevation information data of the control points within the river section where the cross section is located, obtained by the ICESat-2 laser altimetry satellite or RTK, is used to correct the elevation information data of the river section where the cross section is located, obtained by the stereo mapping satellite monitoring and / or the UAV equipped with an oblique photography camera, to obtain the corrected elevation information data.

[0089] In this embodiment of the invention, the corrected elevation information data is used as the elevation information data for the next step.

[0090] This is because, in practical operation, to improve the accuracy of the final elevation information data, the ICESat-2 laser altimetry satellite or RTK is more accurate. The elevation information data of the control points within the river section where the cross section is located is obtained by using the elevation information data of the control points within the river section where the cross section is located, obtained by the stereo mapping satellite monitoring and / or the UAV equipped with the oblique photography camera, to correct the elevation information data of the river section where the cross section is located.

[0091] In step S-1 of the model embodiment of the present invention, based on the cross-section location and the elevation information data of the river segment where the cross-section is located, the topographic data of the river segment where the cross-section is located is obtained, including:

[0092] i. Based on the cross-section location and the river segment where the cross-section is located, extract the water body range vector of the river segment using stereoscopic mapping satellite multispectral imagery. Specifically:

[0093] Furthermore, to improve the accuracy of the obtained data, high spatial resolution stereo mapping satellite imagery is selected to extract the water body range vector of the river section. Currently, the high spatial resolution stereo mapping satellite is the GF-7 satellite, and the stereo mapping satellite multispectral imagery in this step is the GF-7 satellite multispectral imagery.

[0094] ii. Use the river section water body range vector to mask the elevation information data of the river section where the cross section is located, and obtain the topographic data of the river section where the cross section is located.

[0095] In some embodiments of the present invention, step S-2, based on the topographic data of the river segment where the cross-section is located, obtains the cross-sectional morphology and slope characteristics, including:

[0096] i. Use the water area vector of the river section where the cross-section is located to trim the cross-section line vector, and take the number of cross-section line vector elements after trimming as the number of channels. Specifically, the number of channels takes the values ​​1, 2, ..., n.

[0097] ii. The number of branch channels represents the morphological characteristics of the cross section. As the number of branch channels increases, the cross section morphology becomes more complex.

[0098] iii. Calculate the slope using the topographic data of the river channel where the cross-section is located;

[0099] iv. Extract the slope using the cross-sectional line vector and calculate the average cross-sectional slope;

[0100] V. The average slope of the cross section represents the slope characteristics of the cross section.

[0101] In this embodiment of the invention, step S1, classifying the cross-section based on its morphological and slope characteristics, includes:

[0102] The cross-sections are classified based on their combined morphological and slope characteristics. The number of cross-section types is calculated as the number of distributaries multiplied by the number of average slope levels. Specifically:

[0103] For example, the average slope of the cross section is taken as 4 levels. Referring to the slope classification table in TD / T 1055—2019, the slope is divided into 4 categories: ≤6°, (6°, 15°], (15°, 25°], >25°.

[0104] The corresponding cross-section types are divided into n×4, as detailed in Table 1.

[0105] Table 1

[0106] Cross-section classification coding Morphological characteristics Slope characteristics 1-1 Number of branches=1 Average slope ≤ 6° 1-2 Number of branches=1 Average slope ∈ (6°, 15°) 1-3 Number of branches=1 Average slope ∈ (15°, 25°) 1-4 Number of branches=1 Average slope > 25° 2-1 Number of branches=2 Average slope ≤ 6° … … …

[0107] In this embodiment of the invention, the number of levels of the average slope of the cross section is not specifically limited. In order to improve the accuracy of subsequent cross section water level data, the number of levels of the average slope of the cross section can be divided in more detail.

[0108] In this embodiment of the invention, obtaining the water level of different types of cross-sections monitored by satellites with different spatial resolutions in step S2 includes:

[0109] b1. Based on satellite imagery of different spatial resolutions covering different types of cross sections, extract the waterline of the river segment where each cross section is located. Specifically:

[0110] Find and download multi-source satellite images of different spatial resolutions covering river sections of different types, including but not limited to GF-7DLC imagery (0.65 / 2.6m), GF-1 / B / C / D / GF-6PMS imagery (2 / 8m), GF-2PMS imagery (1 / 4m), GF-1 / 6WFV / HJ2A / 2B CCD imagery (16m), Sentinel 1 / 2IW / MSI imagery (10m), and Landsat-8 / 9OLI imagery (30m). Use thresholding or machine learning models to extract the water body extent and obtain the water boundary lines of river sections of different types (1-1, 1-2, 2-1, ...) dynamically extracted from images of different spatial resolutions (including but not limited to 0.65m, 1m, 2m, 10m, 16m, and 30m).

[0111] b2. Extract the corresponding intersection point elevation from the terrain data based on the intersection point of the riverbank line and the cross-section vector in the river section where different types of cross-sections are located, and calculate the average pixel elevation.

[0112] b3. The average pixel elevation is used as the cross-sectional water level, thus obtaining the cross-sectional water level monitored by satellites at different spatial resolutions for different types of cross-sections.

[0113] In this embodiment of the invention, step S2, based on the water levels of different types of cross-sections monitored by satellites with different spatial resolutions, constructs an error correction model for the water levels of different types of cross-sections monitored by satellites with different spatial resolutions, including:

[0114] S2-1. For cross-sections of the same type, using the cross-sectional water level monitored by a satellite with the first spatial resolution as a benchmark, analyze the error characteristics between the cross-sectional water level monitored by satellite images of other spatial resolutions and the benchmark. Here, the first spatial resolution refers to the higher spatial resolution among all satellite spatial resolutions. For example, step S2-1 includes:

[0115] Using the time-series water levels of different types of cross sections calculated from waterline extracted from satellite images with a spatial resolution better than 2m as a benchmark, the error characteristics of monitoring water levels of different types of cross sections using satellite images with other spatial resolutions are analyzed.

[0116] S2-2. Based on the error characteristics of similar cross-sections, establish a water level error correction model for similar cross-sections monitored by satellites at different spatial resolutions. Specifically:

[0117] Select one type of cross section from different types, such as the 1-1 type cross section, and proceed to step S2-2, that is, based on the error characteristics of the 1-1 type cross section obtained in step S2-1, construct the satellite monitoring water level error correction model of the 1-1 type cross section with different spatial resolutions, as shown in Equation (1).

[0118]

[0119] In equation (1), o represents a high spatial resolution satellite (≤2m); j represents satellites with other spatial resolutions; and Water levels at section 1-1 are monitored by high spatial resolution satellite and other spatial resolution satellites j, respectively. f is an error correction model between the water level at section 1-1 monitored by satellite j and the water level monitored by the high spatial resolution satellite. f can be a traditional linear or nonlinear fitting model, or a machine learning model such as support vector machine or neural network can be selected.

[0120] S2-3. Repeat the steps of “analyzing error characteristics” and “establishing error correction model”, that is, repeat steps S2-1 and S2-2 until the water level error correction model of satellite monitoring section with different spatial resolutions for all types of sections is completed, and the water level error correction model of different types of sections for satellite monitoring with different spatial resolutions is obtained, as shown in Equation (2).

[0121]

[0122] In equation (2), i represents the cross-section type code; o represents high spatial resolution satellites (≤2m); j represents satellites with other spatial resolutions; and Water levels at the i-th cross-section are monitored by both high spatial resolution satellite and other spatial resolution satellites j; f is an error correction model between the water level at the i-th cross-section monitored by satellite j and the water level monitored by the high spatial resolution satellite. f can be a traditional linear or nonlinear fitting model, or a machine learning model such as support vector machine or neural network can be selected.

[0123] As can be seen from steps S2-3, the water level error correction models for different types of cross sections monitored by satellites with different spatial resolutions are independent of each other. That is, the water level error correction models for different characteristic cross sections are different. The modeling samples are water levels of different types of cross sections monitored by high spatial resolution satellites and other spatial resolution satellites on the same day. The modeling samples should cover the extreme values ​​of water levels of different types of cross sections as much as possible to increase the representativeness of the samples.

[0124] In this embodiment of the invention, step S3, based on the error correction model for water level at different types of cross sections monitored by satellites with different spatial resolutions, and the water level at different time phases of different types of cross sections monitored by satellites with different spatial resolutions, obtains a multi-source satellite collaborative monitoring time-series water level dataset for different types of cross sections, including:

[0125] S3-1. Using the error correction model of satellite monitoring of the same type of cross section with different spatial resolutions, the water level of the same type of cross section at different time phases is corrected by satellite monitoring of the same type of cross section with different spatial resolutions, and the time series dataset of water level monitoring by satellite with different spatial resolutions of the same type of cross section is obtained.

[0126] Specifically, S3-1 includes:

[0127] For the same type of cross section, such as the 1-1 type cross section, the corresponding correction model is shown in Equation (1). Equation (1) is used to correct the error of the water level of the cross section with different spatial resolutions (>2m) by multi-source satellite monitoring. Specifically, see Equation (3), that is, to obtain the cross section water level monitoring dataset obtained by multi-source satellite monitoring of the same type of cross section (1-1 type cross section) with different spatial resolutions.

[0128]

[0129] In equation (3), Obtain the corrected water level value for section 1-1 for satellite J.

[0130] S3-2. Combine the time series water level datasets of satellite monitoring at different spatial resolutions for the same type of cross section in chronological order to obtain the time series water level dataset of multi-source satellite collaborative monitoring for the same type of river section, thus completing the multi-source satellite collaborative remote sensing monitoring of water level in the river section.

[0131] By combining time-series water level datasets from satellite monitoring of the same type of cross section at different spatial resolutions in chronological order, a multi-source satellite collaborative monitoring water level time-series dataset for the same type of river section is obtained. Specifically, this dataset L is composed of water level correction values ​​from multi-source satellite monitoring of the same type of cross section at different spatial resolutions. i As shown in equation (4).

[0132]

[0133] In equation (4): i represents the cross-sectional number of different types; t1, t2 and t k The water level at different sections on different dates is determined based on multi-source satellite monitoring. If a certain type of section has only one type of satellite monitoring water level on a certain day, then the correction value of that satellite monitoring water level is taken as the monitoring water level of that type of section. If a certain type of section has multiple different types of satellite monitoring water levels on the same day, then the correction value of the highest spatial resolution satellite monitoring water level among the high spatial resolution (≤2m) satellite monitoring water level or other spatial resolution (>2m) satellite monitoring water level is taken as the water level of that characteristic section on that day.

[0134] According to steps S1-S3 provided in the embodiments of the present invention, the most accurate method is selected to perform multi-source satellite collaborative remote sensing monitoring of the cross-sectional water levels at Benzilan Hydrological Station and Shigu Hydrological Station.

[0135] Specifically, through step S-1, DSM topographic data of the river segments where the cross-sections of the two embodiments are located are obtained based on GF-7DLC satellite imagery, as follows: Figure 2 , Figure 3 As shown, Figure 2 The diagram shows the cross-sectional line and centerline of the river section where the Benzilan Hydrological Station is located. Figure 3 The diagram shows the cross-sectional line and centerline of the river section where the Shigu Hydrological Station is located. Figures 2-3 In the diagram, the blue line represents the centerline, and the red line represents the cross-sectional line.

[0136] Through steps S1-2, the cross-sections of the embodiments are classified by combining cross-sectional morphology and slope characteristics. The classification of the Benzilan cross-section and the Shigu cross-section is shown in Table 2.

[0137] Table 2

[0138] Cross-section name Cross-section classification coding Morphological characteristics Slope characteristics Benzilan cross section 1-4 Number of branches=1 Average slope = 28.4° Stone Drum Cross Section 2-1 Number of branches=2 Average slope = 9.6°

[0139] In step b1, the remote sensing image information obtained from the Benzilan and Shigu cross sections in the embodiment is shown in Table 3.

[0140] Table 3 Information on Cross-Sectional Remote Sensing Image Acquisition in Examples

[0141] Cross-section name Cross-section classification coding Multi-source satellite imagery Spatial resolution Benzilan cross section 1-4 GF-7 / Sentinel 2 / Landsat-8 / Landsat-9 0.65m / 10m / 30m / 30m Stone Drum Cross Section 2-1 GF-7 / Sentinel 2 / Landsat-8 / Landsat-9 0.65m / 10m / 30m / 30m

[0142] Step S2 obtained the matching of the reference water levels of the Benzilan and Shigu sections in the embodiment, as well as the water level correction model. (See details in step S2.) Figures 4-7 ,in, Figure 4 and Figure 5 The matching results of the water levels monitored by Sentinel 2 and Landsat-8 / 9 at the Benzilan section with the benchmark water level and the water level correction model are shown respectively. Figure 6 and Figure 7 The matching results of the water levels monitored by Sentinel 2 and Landsat-8 / 9 at the Shigu section with the benchmark water level and the water level correction model are shown respectively.

[0143] Step S3 yielded the long-term river cross-sectional water level changes from 2018 to 2023 at the Benzilan and Shigu sections in the embodiment, as detailed in [link to example]. Figures 8-9 ,in, Figure 8 This shows the long-term water level changes at the Benzilan section from 2018 to 2023. Figure 9 It shows the long-term water level changes of the Shigu section from 2018 to 2023.

[0144] like Figure 10 As shown, a multi-source satellite collaborative remote sensing monitoring system for river cross-section water level is characterized by comprising:

[0145] Classification Module 1: Used to classify cross sections based on their morphological and slope characteristics;

[0146] Model Module 2: Used to construct error correction models for water levels of different types of cross sections monitored by satellites with different spatial resolutions based on different types of cross sections and different spatial resolutions;

[0147] Correction Module 3: This module is used to correct the water level error model of different types of cross sections based on satellite monitoring with different spatial resolutions, and to obtain the multi-source satellite collaborative monitoring time series water level dataset for different types of cross sections by satellite monitoring of different types of cross sections at different time phases with different spatial resolutions. This completes the multi-source satellite collaborative remote sensing monitoring of cross section water levels.

[0148] like Figure 11 As shown, in some embodiments of the present invention, an electronic device is provided, the electronic device 300 including: a processor 301 coupled to a memory 302;

[0149] The memory 302 is used to store computer programs;

[0150] The processor 301 is configured to execute the computer program stored in the memory 302, so that the electronic device performs the method described in the above embodiments.

[0151] In some embodiments of the present invention, a computer-readable storage medium is provided that stores a program or instructions that, when executed on a computer, cause the computer to perform the methods described in the above embodiments.

[0152] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, electronic device, or apparatus.

[0153] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multi-source satellite collaborative remote sensing monitoring of water levels at river cross-sections, characterized in that, include: The cross sections are classified based on their morphological and slope characteristics. Based on the water level of different types of cross sections monitored by satellites with different spatial resolutions, an error correction model for water level of different types of cross sections monitored by satellites with different spatial resolutions is constructed. Based on the error correction model of water level of different types of cross sections monitored by satellites with different spatial resolutions, and the water level of different types of cross sections at different time phases monitored by satellites with different spatial resolutions, a multi-source satellite collaborative monitoring time series water level dataset of different types of cross sections is obtained, thus completing the multi-source satellite collaborative remote sensing monitoring of cross section water levels. The method for constructing error correction models for water levels at different types of cross sections based on satellite monitoring at different spatial resolutions includes: In the same type of cross section, the cross section water level monitored by the satellite with the first spatial resolution is used as the benchmark to analyze the error characteristics between the cross section water level monitored by satellite images with other spatial resolutions and the benchmark. Based on the error characteristics of similar cross sections, a correction model for water level error of similar cross sections monitored by satellite with different spatial resolutions is established. Repeat the steps of "analyzing error characteristics" and "establishing error correction models" until the error correction models for water levels of satellite monitoring sections with different spatial resolutions for all types of sections are completed, thus obtaining error correction models for water levels of different types of sections monitored by satellite with different spatial resolutions.

2. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 1, characterized in that, The acquisition of the cross-sectional morphology and slope characteristics includes: Based on the cross-section location and the elevation information of the river section where the cross-section is located, the topographic data of the river section where the cross-section is located is obtained. Based on the topographic data of the river section where the cross-section is located, the cross-sectional morphology and slope characteristics are obtained.

3. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 2, characterized in that, Obtaining the elevation information data of the river section where the cross-section is located includes: Draw the cross-section line vector according to the cross-section location; The river section where the cross-section is located is determined based on the cross-sectional line vector and the historical maximum water volume range. Based on the cross-section location and the river section where the cross-section is located, obtain the elevation information data of the river section where the cross-section is located.

4. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 3, characterized in that, The elevation information data of the river section where the cross-section is located is obtained by stereo mapping satellite monitoring and / or monitoring by an oblique photography camera mounted on a drone. This may be the elevation information data of the river section where the cross-section is located, obtained from monitoring by stereoscopic mapping satellites and / or by drones equipped with oblique photography cameras, after correction.

5. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 2, characterized in that, The acquisition of topographic data for the river segment where the cross-section is located, based on the cross-section location and the elevation information data of the river segment where the cross-section is located, includes: Based on the cross-section location and the river section where the cross-section is located, the vector of the water body range of the river section is extracted by stereo mapping satellite multispectral imagery. The elevation information data of the river section where the cross-section is located is masked by the river section water body range vector to obtain the topographic data of the river section where the cross-section is located.

6. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 5, characterized in that, The cross-section morphology and slope characteristics are obtained based on the topographic data of the river segment where the cross-section is located, including: The cross-section line vector is trimmed using the water area vector of the river section where the cross-section is located, and the number of cross-section line vector elements after trimming is taken as the number of channels; The number of channels represents the morphological characteristics of the cross section; Calculate the slope using the topographic data of the river channel where the cross-section is located; Slope is extracted and average slope of cross section is calculated using cross section line vectors; The average slope of the cross section represents the slope characteristics of the cross section.

7. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 1, characterized in that, The classification of cross sections based on cross-sectional morphology and slope characteristics includes: The cross-sections are classified by combining cross-sectional morphological characteristics and slope characteristics. The number of cross-section types is the number of channels × the number of levels of the average slope of the cross-section.

8. The method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to claim 1, characterized in that, Obtaining water levels at different types of cross sections from satellite monitoring at different spatial resolutions, including: Based on satellite images of different spatial resolutions covering different types of cross sections, extract the waterline of the river section where the different types of cross sections are located; Based on the intersection points of the riverbank line and the cross-section vector in the river section where different types of cross-sections are located, the corresponding intersection point elevations are extracted from the topographic data, and the average pixel elevation is calculated. The average pixel elevation is used as the cross-sectional water level, thus obtaining the cross-sectional water level monitored by satellites at different spatial resolutions for different types of cross-sections.

9. A method for multi-source satellite collaborative remote sensing monitoring of river cross-section water level according to any one of claims 1-8, characterized in that, The aforementioned error correction model based on satellite monitoring of different types of cross-sections with different spatial resolutions, and the multi-source satellite collaborative monitoring time-series water level datasets for different types of cross-sections at different time phases based on satellite monitoring of different types of cross-sections with different spatial resolutions, include: The water level error correction model of the same type of cross section monitored by satellite with different spatial resolutions is used to correct the water level of the same type of cross section at different time phases monitored by satellite with different spatial resolutions, and the time series dataset of water level monitored by satellite with different spatial resolutions of the same type of cross section is obtained. By combining time-series water level datasets from satellite monitoring at different spatial resolutions for the same type of cross section in chronological order, a multi-source satellite collaborative monitoring water level time-series dataset for the same type of river section can be obtained.

10. A multi-source satellite collaborative remote sensing monitoring system for river cross-section water level, characterized in that, include: Classification module: Used to classify cross sections based on their morphological and slope characteristics; Model building module: used to construct error correction models for water levels of different types of cross sections monitored by satellites with different spatial resolutions based on different types of cross sections; Correction module: It is used to correct the error model of water level of different types of cross sections based on satellite monitoring of different spatial resolutions, and to obtain the multi-source satellite collaborative monitoring time series water level dataset of different types of cross sections by satellite monitoring of different spatial resolutions at different time phases, thus completing the multi-source satellite collaborative remote sensing monitoring of cross section water level. The method for constructing error correction models for water levels at different types of cross sections based on satellite monitoring at different spatial resolutions includes: In the same type of cross section, the cross section water level monitored by the satellite with the first spatial resolution is used as the benchmark to analyze the error characteristics between the cross section water level monitored by satellite images with other spatial resolutions and the benchmark. Based on the error characteristics of similar cross sections, a correction model for water level error of similar cross sections monitored by satellite with different spatial resolutions is established. Repeat the steps of "analyzing error characteristics" and "establishing error correction models" until the error correction models for water levels of satellite monitoring sections with different spatial resolutions for all types of sections are completed, thus obtaining error correction models for water levels of different types of sections monitored by satellite with different spatial resolutions.

11. An electronic device, characterized in that, include: Processor, the processor being coupled to memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 9.

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