River section water level multi-source satellite cooperative remote sensing monitoring method and system

Abstract

The invention relates to a river section water level multi-source satellite collaborative remote sensing monitoring method and system, and belongs to the field of river section water level remote sensing monitoring. The method comprises the following steps: classifying sections based on section morphological characteristics and gradient characteristics; based on the section water levels monitored by the satellites with different types of sections and different spatial resolutions, an error correction model for monitoring different types of section water levels by the satellites with different spatial resolutions is constructed; and on the basis of a water level error correction model for monitoring different types of sections by satellites with different spatial resolutions, and monitoring different types of sections and different time phase section water levels by satellites with different spatial resolutions, obtaining a multi-source satellite collaborative monitoring time sequence water level data set of different types of sections, namely finishing section water level multi-source satellite collaborative remote sensing monitoring. According to the method, the sections are classified, then the water levels of different types of sections are monitored through multi-source satellite cooperative remote sensing, and the requirement for high-frequency monitoring of river reach water level monitoring is met.

Description

Technical Field

[0001] The present invention belongs to the field of river section water level remote sensing monitoring, and in particular relates to a river section water level multi-source satellite collaborative remote sensing monitoring method and system. Background Art

[0002] River water level is closely related to river runoff and is an important indicator for evaluating river water conditions. Dynamic and high-precision monitoring of river cross-section water level is crucial for flood control in the basin, water conservancy project construction and management, river ecological protection and other businesses. Although traditional cross-section water level monitoring based on hydrological stations has high monitoring accuracy, it is limited by factors such as station setting costs and station setting conditions. At present, the number of river hydrological stations is limited, especially in some mountainous areas with complex terrain and inconvenient transportation, where water level monitoring data is seriously missing. With the rapid development of remote sensing technology, its high timeliness, large-scale coverage and continuous dynamic monitoring characteristics make river water level remote sensing monitoring show great potential in dynamically grasping river water level changes.

[0003] The remote sensing monitoring method of river water level based on river topography data and river water edge remote sensing monitoring is an effective means to dynamically obtain river water level. This method uses remote sensing to extract the river water edge and superimposes river topography data to obtain the river section water level. At present, river topography data mostly uses 30m spatial resolution SRTM DEM data, and river water edge remote sensing monitoring mostly uses a single satellite data source. In addition, the 30m spatial resolution DEM data is relatively coarse and it is difficult to meet the terrain accuracy requirements for water level monitoring in narrower river sections; in addition, the frequency of obtaining the river water edge using a single satellite data source is limited, which limits the frequency of river water level monitoring.

[0004] With the continuous development of satellite / UAV remote sensing technology, the technical means of remote sensing monitoring of river topography and waterside are becoming more and more abundant. For example, the use of domestically produced Ziyuan-3 satellites, Gaofen-7 satellites or UAVs equipped with oblique photography cameras can carry out high spatial resolution stereo mapping of river topography and obtain high spatial resolution and high precision riverbank topography data; for example, the use of Sentinel 1 / 2 satellites with 10m spatial resolution, domestically produced 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 can achieve high frequency monitoring of river waterside. The use of stereo mapping satellites, optics, radar and other multi-source satellites to coordinate river water level monitoring can significantly increase the frequency of river water level monitoring. However, the accuracy of extracting river water level information from river waterside monitoring using satellites with different spatial resolutions is closely related to the morphological characteristics and slope characteristics of the cross section, which makes it difficult for the water levels of different characteristic cross sections obtained by satellite monitoring with different spatial resolutions to form an effective time series, limiting the ability of multi-source satellites to coordinate remote sensing monitoring of river water levels. Summary of the invention

[0005] In response to the above problems, the present invention provides a method and system for multi-source satellite collaborative remote sensing monitoring of river section water levels. The method takes into account the error characteristics of obtaining water levels of different types of sections from terrain data by extracting river water lines using satellites with different spatial resolutions, constructs an error correction model for monitoring water levels of different types of sections using multi-source satellite images with different spatial resolutions, corrects the errors of water levels of different types of sections monitored by multi-source satellite images with different spatial resolutions, improves the consistency of water levels of different types of sections monitored by satellites with different spatial resolutions, and realizes the collaborative acquisition of high-frequency, high-precision, long-time series river section water level data sets using multi-source satellites.

[0006] The first object of the present invention is to provide a multi-source satellite collaborative remote sensing monitoring method for river section water level, comprising:

[0007] Classify the sections based on their morphological and slope characteristics;

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

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

[0010] In a specific embodiment of the present invention, the cross-sectional morphological characteristics and slope characteristics are obtained, including:

[0011] Based on the cross-section location and the elevation information data 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 section is located, the cross-section morphological characteristics 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 section is located includes:

[0014] Draw the section line vector according to the section position;

[0015] Determine the river section where the section is located based on the section line vector and the historical maximum water body range;

[0016] According to the section location and the range of the river section where the section is located, the elevation information data of the river section where the section is located is obtained.

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

[0018] Or it is the corrected elevation information data of the river section where the section is located obtained by stereo mapping satellite monitoring and / or unmanned aerial vehicle equipped with an oblique photography camera.

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

[0020] According to the location of the section and the range of the river section where the section is located, the water range vector of the river section is extracted through the multispectral image of the stereo mapping satellite;

[0021] The elevation information data of the river section where the section is located is masked with the river section water body range vector to obtain the terrain data of the river section where the section is located.

[0022] In a specific embodiment of the present invention, the obtaining of cross-section morphological characteristics 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 clipped with the water area vector of the river section where the cross-section is located, and the number of cross-section line vector elements after clipping is taken as the number of branches;

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

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

[0026] Use the section line vector to extract the slope and calculate the average slope of the section;

[0027] The average slope of the section is used to represent the slope characteristics of the section.

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

[0029] The sections are classified by combining the section morphological characteristics and slope characteristics, where the number of section types is the number of branches × the number of levels of the average slope of the section.

[0030] In a specific embodiment of the present invention, the section water level monitored by satellites with different spatial resolutions at different sections is used to construct an error correction model for monitoring water levels at different sections with different spatial resolutions, including:

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

[0032] Based on the error characteristics of the same type of sections, a water level error correction model for the same type of sections monitored by satellites with different spatial resolutions is established;

[0033] Repeat the steps of “analyzing error characteristics” and “establishing error correction model” until the error correction models of water level in satellite monitoring sections with different spatial resolutions for all types of sections are completed, and the error correction models of water level in satellite monitoring of different types of sections with different spatial resolutions are obtained.

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

[0035] Based on satellite images with different spatial resolutions covering different types of sections, the waterside lines of the river sections where different types of sections are located are extracted;

[0036] According to the intersection points of the waterside lines and the cross-section vectors of different types of cross-sections, the corresponding intersection elevations are extracted from the terrain data, and the mean pixel elevation is calculated;

[0037] The mean value of pixel elevation is taken as the cross-section water level, thus obtaining the cross-section water level monitored by satellites with different spatial resolutions for different types of cross-sections.

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

[0039] The error correction model of water level of the same type of sections monitored by satellites with different spatial resolutions is used to correct the water level of the same type of sections at different phases monitored by satellites with different spatial resolutions, and the time series data set of water level monitored by satellites with different spatial resolutions of the same type of sections is obtained.

[0040] The satellite monitoring time series water level datasets with different spatial resolutions for the same type of sections are combined in chronological order to obtain the multi-source satellite collaborative monitoring water level time series datasets for the same type of river sections.

[0041] The second object of the present invention is to provide a multi-source satellite collaborative remote sensing monitoring system for river section water level, comprising:

[0042] Classification module: used to classify sections based on section morphology and slope characteristics;

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

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

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

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

[0047] The processor is used to execute the computer program stored in the memory so that the electronic device executes the method as described above.

[0048] A fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, and when the program or instruction is executed on a computer, the computer executes the method as described above.

[0049] Beneficial effects of the present invention:

[0050] The present invention discloses a method and system for multi-source satellite collaborative remote sensing monitoring of water level in a river section. The present invention classifies the sections by using the section positions and the elevation information data of the river section where the sections are located. Then, the cross-sectional water levels of different types of sections are monitored by multi-source satellite collaborative remote sensing, thereby meeting the requirements for terrain accuracy in water level monitoring in narrower river sections and the requirements for high frequency of obtaining data sets. At the same time, by establishing water level error correction models for different types of sections, the present invention overcomes the difficulty of achieving coordination and consistency between data obtained by monitoring different types of sections with satellites of different spatial resolutions in the multi-source satellite collaborative remote sensing monitoring technology.

[0051] The method of the present invention can realize the coordinated acquisition of high-frequency, high-precision, long-time series river section water level data sets by using multi-source satellites, which greatly improves the remote sensing monitoring capability of river water levels.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0055] Figure 2 A schematic diagram of a cross-section line and a river centerline of a river section where the Benzilan hydrological station is located according to an embodiment of the present invention is shown;

[0056] Figure 3 A schematic diagram showing a cross-sectional line and a river centerline of a river section where the Shigu hydrological station is located according to an embodiment of the present invention is shown;

[0057] Figure 4 The diagram shows the matching of the water level monitored by the sentinel 2 of the Benzilan section with the reference water level and the water level correction model according to an embodiment of the present invention;

[0058] Figure 5 The figure shows the matching of the water level monitored by Landsat-8 / 9 at the Benzilan section with the reference water level and the water level correction model according to an embodiment of the present invention;

[0059] Figure 6 The diagram shows the matching of the water level monitored by the Shigu section sentinel 2 with the reference water level and the water level correction model according to an embodiment of the present invention;

[0060] Figure 7 The figure shows the matching of the water level monitored by Landsat-8 / 9 at the Shigu section with the reference water level and the water level correction model according to an embodiment of the present invention;

[0061] Figure 8 A long-term river section water level change diagram of Benzilan section monitored by multi-source satellite collaboration from 2018 to 2023 according to an embodiment of the present invention is shown;

[0062] Fig. 9 A long-term river section water level change diagram of Shigu section monitored by multi-source satellite collaboration from 2018 to 2023 according to an embodiment of the present invention is shown;

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

[0064] Fig.11 A framework diagram of an electronic device according to an embodiment of the present invention is shown;

[0065] In the figure: classification module 1; model building module 2; correction module 3; electronic device 300; processor 301; memory 302. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

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

[0069] S2. Based on the water levels of sections monitored by satellites with different spatial resolutions at different sections, an error correction model for water levels of sections monitored by satellites with different spatial resolutions at different sections is constructed;

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

[0071] In the embodiment of the present invention, in step S1, the acquisition of the cross-sectional morphological features and the slope features includes:

[0072] S-1. Based on the cross-section location and the elevation information data 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 section is located, obtain the cross-section morphological characteristics and slope characteristics.

[0074] In certain embodiments of the present invention, the acquisition of elevation information data of the river section where the section is located in step S-1 specifically includes:

[0075] a1. Draw the section line vector according to the section position;

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

[0077] a3.According to the location of the section and the range of the river section where the section is located, obtain the elevation information data of the river section where the section is located;

[0078] Exemplarily, step a3 includes:

[0079] According to the location of the section and the range of the river section where the section is located, the elevation information data of the river section where the section is located is obtained through stereo mapping satellite monitoring;

[0080] At present, the stereo mapping satellite with high spatial resolution 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, height information of objects such as buildings and trees;

[0081] That is, in the embodiment of the present invention, the elevation information data obtained by the GF-7 satellite is used as the final elevation information data used in the next step;

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

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

[0084] According to the location of the section and the range of the river section where the section is located, the elevation information data of the river section where the section is located is obtained by using a drone equipped with an oblique camera;

[0085] That is, in the embodiment of the present invention, the elevation information data obtained by the oblique photography camera carried by the unmanned aerial vehicle is used as the elevation information data used in the next step.

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

[0087] 1. According to the location of the section and the range of the river section where the section is located, obtain the elevation information data of the river section where the section is located through stereo mapping satellite monitoring and / or unmanned aerial vehicle equipped with oblique photography cameras;

[0088] 2. The elevation information data of the control points within the river section where the section is located obtained by ICESat-2 laser altimeter satellite or RTK are used to correct the elevation information data of the river section where the section is located obtained by stereo mapping satellite monitoring and / or unmanned aerial vehicle equipped with oblique photography cameras to obtain the corrected elevation information data;

[0089] That is, in the embodiment of the present invention, the corrected elevation information data is used as the elevation information data used in the next step;

[0090] This is because in actual operation, in order to improve the accuracy of the final elevation information data, the accuracy of ICESat-2 laser altimeter or RTK is more accurate. The elevation information data of the control points within the river section where the section is located obtained by ICESat-2 laser altimeter or RTK is used to correct the elevation information data of the river section where the section is located obtained by stereo mapping satellite monitoring and / or drone-mounted oblique photography cameras.

[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 section where the cross-section is located, the terrain data of the river section where the cross-section is located is obtained, including:

[0092] i. According to the cross-section location and the river section range where the cross-section is located, the river section water body range vector is extracted through stereo mapping satellite multispectral images, specifically:

[0093] And improve the accuracy of the obtained data, select a stereo mapping satellite image with high spatial resolution to extract the river section water body range vector, currently the stereo mapping satellite with high spatial resolution is the GF-7 satellite, and the stereo mapping satellite multispectral image in this step is the GF-7 satellite multispectral image;

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

[0095] In some embodiments of the present invention, step S-2, based on the topographic data of the river section where the section is located, obtaining the cross-section morphological characteristics and slope characteristics, includes:

[0096] i. Use the water area vector of the river section where the section is located to clip the section line vector, and use the number of clipped section line vector elements as the number of branches. Specifically, the number of branches takes values ​​of 1, 2, ..., n;

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

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

[0099] iv. Use the section line vector to extract the slope and calculate the average slope of the section;

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

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

[0102] The cross sections are classified by combining cross section morphological characteristics and slope characteristics, where the number of cross section types is the number of branch channels × the number of levels of the average cross section slope. Specifically:

[0103] The average slope of the cross section is exemplarily 4 levels, and the slope is divided into 4 categories with reference to the slope classification table in TD / T 1055-2019: ≤6°, (6°, 15°], (15°, 25°], >25°;

[0104] The corresponding section types are divided into n×4, see Table 1 for details.

[0105] Table 1

[0106] Section classification code 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 the embodiment of the present invention, the number of levels of the average cross-section slope is not specifically limited. To improve the accuracy of subsequent cross-section water level data, the number of levels of the average cross-section slope may be divided in more detail.

[0108] In the embodiment of the present invention, the acquisition of the cross-section water levels monitored by satellites with different spatial resolutions at different types of cross-sections in step S2 includes:

[0109] b1. Based on satellite images of different spatial resolutions covering different types of sections, extract the waterside lines of the river sections where different types of sections are located. Specifically:

[0110] Search and download cloud-free multi-source satellite images of different spatial resolutions covering river sections where different types of sections are located. Multi-source satellite images include but are not limited to GF-7DLC images (0.65 / 2.6m), GF-1 / B / C / D / GF-6PMS images (2 / 8m), GF-2PMS images (1 / 4m) and GF-1 / 6WFV / HJ2A / 2B CCD images (16m), Sentinel 1 / 2IW / MSI images (10m), and Landsat-8 / 9OLI images (30m). Use the threshold method or machine learning model to extract the range of water bodies and obtain the waterside lines of river sections where different types (1-1, 1-2, 2-1, …) of sections are dynamically extracted from images with different spatial resolutions (including but not limited to 0.65m, 1m, 2m, 10m, 16m and 30m).

[0111] b2. Extract the corresponding intersection elevation from the terrain data according to the intersection of the waterside line and the cross-section vector of the river section where the different types of cross-sections are located, and calculate the mean pixel elevation;

[0112] b3. The mean value of pixel elevation is used as the cross-section water level, and the cross-section water level monitored by satellites with different spatial resolutions of different types of cross-sections is obtained.

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

[0114] S2-1, in the same type of section, taking the section water level monitored by the satellite with the first spatial resolution as the benchmark, analyzing the error characteristics between the section water level monitored by the satellite images with other spatial resolutions and the benchmark, wherein the first spatial resolution refers to the higher spatial resolution among all satellite spatial resolutions. Exemplarily, step S2-1 includes:

[0115] The temporal water levels of different types of sections calculated by extracting water edges from satellite images with a spatial resolution better than 2m are used as a benchmark, and the error characteristics of water levels of different types of sections monitored by satellite images with other spatial resolutions are analyzed.

[0116] S2-2. Based on the error characteristics of the same type of sections, a water level error correction model for the same type of sections monitored by satellites with different spatial resolutions is established. Specifically:

[0117] Select one type of section from different types of sections, such as type 1-1 section, and proceed to step S2-2, that is, according to the error characteristics of type 1-1 section obtained in step S2-1, construct a satellite monitoring water level error correction model with different spatial resolutions for type 1-1 section, as specifically shown in formula (1);

[0118]

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

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

[0121]

[0122] In formula (2), i is the section type code; o is a high spatial resolution satellite (≤2m); j is a satellite with other spatial resolutions; and The high spatial resolution satellite and other spatial resolution satellite j monitor the water level of the i-th section respectively; f is the error correction model between the water level of the i-th section monitored by the j satellite and the water level monitored by the high spatial resolution satellite. f can be a traditional linear or nonlinear fitting, or a machine learning model such as a support vector machine or a neural network can be selected.

[0123] It can be seen from step S2-3 that the water level error correction models of satellite monitoring sections with different spatial resolutions of different types of sections are independent of each other, that is, the water level error correction models of different characteristic sections are different. The modeling samples are the water levels of different types of sections monitored by high spatial resolution satellites and other spatial resolution satellites on the same day. The modeling samples try to cover the extreme values ​​of water levels of different types of sections to increase the representativeness of the samples.

[0124] In the embodiment of the present invention, step S3, based on the water level error correction model of different types of sections monitored by satellites with different spatial resolutions, and the water level of different types of sections in different phases monitored by satellites with different spatial resolutions, a multi-source satellite collaborative monitoring time series water level data set of different types of sections is obtained, including:

[0125] S3-1. Use the water level error correction model of the same type of sections monitored by satellites with different spatial resolutions to correct the water levels of the same type of sections at different time phases monitored by satellites with different spatial resolutions, and obtain the water level time series data set of the same type of sections monitored by satellites with different spatial resolutions;

[0126] Specifically, S3-1 includes:

[0127] For the same type of section, such as type 1-1 section, the corresponding correction model is shown in formula (1). Formula (1) is used to correct the error of the water level of the multi-source satellite monitoring section with different spatial resolutions and spatial resolution>2m. For details, see formula (3), that is, the section water level monitoring data set obtained by multi-source satellite monitoring with different spatial resolutions of the same type of section (type 1-1 section) is obtained.

[0128]

[0129] In formula (3), Obtain the water level correction value of type 1-1 section for satellite j.

[0130] S3-2, combining satellite monitoring time series water level data sets of different spatial resolutions of the same type of sections in chronological order to obtain multi-source satellite collaborative monitoring water level time series data sets of the same type of river sections, that is, completing multi-source satellite collaborative remote sensing monitoring of river section water levels;

[0131] The satellite monitoring time series water level datasets with different spatial resolutions of the same type of sections are combined in chronological order to obtain the multi-source satellite coordinated monitoring water level time series dataset of the same type of river section, that is, the water level correction values ​​of the same type of sections monitored by multi-source satellites with different spatial resolutions are used to form the water level time series dataset L of this type of section. i , as shown in formula (4).

[0132]

[0133] In formula (4), i is the number of different types of sections; t1, t2 and t k Indicates different dates. The section water levels corresponding to different dates are determined according to the multi-source satellite monitoring situation. If there is only one type of spatial resolution satellite monitoring water level for a certain type of section on a certain day, the satellite monitoring water level correction value is taken as the monitoring water level of this type of section; if there are multiple types of satellite monitoring water levels with different spatial resolutions on the same day, the high spatial resolution (≤2m) satellite monitoring water level or the satellite monitoring water level correction value with the highest spatial resolution among other spatial resolution (>2m) satellites is taken as the water level of the characteristic section on that day.

[0134] According to steps S1-S3 provided in the embodiment of the present invention, the multi-source satellite collaborative remote sensing monitoring of the cross-section water level at the Benzilan Hydrological Station and the Shigu Hydrological Station is performed in a manner with the highest accuracy.

[0135] Specifically, through step S-1, the DSM terrain data of the river section where the sections of the above two embodiments are located is obtained based on the GF-7DLC satellite image. Figure 2 , Figure 3 As shown, Figure 2 The diagram shows the cross section line and river centerline of the river section where the Benzilan hydrological station is located. Figure 3 The diagram shows the cross section line and river centerline of the river section where the Shigu hydrological station is located. Figure 2-Figure 3 In the figure, the blue line is the center line and the red line is the section line;

[0136] Through step S1-2, the cross sections of the embodiment are classified in combination with the cross section morphological characteristics and slope characteristics. The classification of the Benzilan cross section and the Shigu cross section is shown in Table 2.

[0137] Table 2

[0138] Section Name Section classification code Morphological characteristics Slope characteristics Benzilan 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 searched and obtained for the Benzilan and Shigu sections in the embodiment is shown in Table 3.

[0140] Table 3 Example cross-section remote sensing image acquisition information table

[0141] Section Name Section classification code Multi-source satellite imagery Spatial resolution Benzilan 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] Through step S2, the matching of the reference water levels of the Benzilan and Shigu sections and the water level correction model in the embodiment are obtained. Figure 4-Figure 7 ,in, Figure 4 and Figure 5 The matching of the water level monitored by Sentinel-2 and Landsat-8 / 9 with the benchmark water level at Benzilan section and the water level correction model were shown respectively; Figure 6 and Figure 7 The matching situation of the water level 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] Through step S3, the long-term river section water level changes of Benzilan and Shigu sections from 2018 to 2023 in the embodiment are obtained, as shown in detail. Figure 8-Figure 9 ,in, Figure 8 It shows the long-term river section water level changes in the Benzilan section from 2018 to 2023. Fig. 9 The long-term river section water level changes at the Shigu section from 2018 to 2023 are shown.

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

[0145] Classification module 1: used to classify sections based on section morphological characteristics and slope characteristics;

[0146] Model building module 2: used to build error correction models for water levels of different types of sections monitored by satellites with different spatial resolutions based on water levels of sections of different types of sections monitored by satellites with different spatial resolutions;

[0147] Correction module 3: It is used to obtain the time series water level data set of multi-source satellite collaborative monitoring of different types of sections based on the error correction model of water level monitoring of different types of sections by satellites with different spatial resolutions, and the water level of sections with different phases monitored by satellites with different spatial resolutions, thus completing the multi-source satellite collaborative remote sensing monitoring of section water level.

[0148] like Fig.11 As shown, some embodiments of the present invention provide an electronic device, the electronic device 300, comprising: a processor 301, the processor 301 is 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 executes the method described in the above embodiment.

[0151] In certain embodiments of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program or instruction. When the program or instruction is executed on a computer, the computer executes the method described in the above embodiments.

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

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

Claims

1. A multi-source satellite collaborative remote sensing monitoring method for river section water level, characterized in that: include: Classify the sections based on their morphological and slope characteristics; Based on the water levels of different sections monitored by satellites with different spatial resolutions, an error correction model for water levels of different sections monitored by satellites with different spatial resolutions is constructed. Based on the water level error correction model of different types of sections monitored by satellites with different spatial resolutions, and the water level of different types of sections at different phases monitored by satellites with different spatial resolutions, a multi-source satellite collaborative monitoring time-series water level dataset of different types of sections is obtained, thus completing the multi-source satellite collaborative remote sensing monitoring of section water levels.

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

3. The method for multi-source satellite coordinated remote sensing monitoring of river section water level according to claim 2 is characterized in that: The acquisition of elevation information data of the river section where the section is located includes: Draw the section line vector according to the section position; Determine the river section where the section is located based on the section line vector and the historical maximum water body range; According to the section location and the range of the river section where the section is located, the elevation information data of the river section where the section is located is obtained.

4. The method for multi-source satellite coordinated remote sensing monitoring of river section water level according to claim 3 is characterized in that: The elevation information data of the river section where the section is located is the elevation information data of the river section where the section is located obtained by monitoring with a stereo mapping satellite and / or a tilt photography camera mounted on an unmanned aerial vehicle; Or it may be the corrected elevation information data of the river section where the section is located obtained by stereo mapping satellite monitoring and / or unmanned aerial vehicle equipped with an oblique photography camera.

5. The method for multi-source satellite coordinated remote sensing monitoring of river section water level according to claim 2 is characterized in that: The method of obtaining the topographic data of the river section where the section is located based on the section position and the elevation information data of the river section where the section is located includes: According to the location of the section and the range of the river section where the section is located, the water range vector of the river section is extracted through the multispectral image of the stereo mapping satellite; The elevation information data of the river section where the section is located is masked with the river section water body range vector to obtain the terrain data of the river section where the section is located.

6. The method for multi-source satellite coordinated remote sensing monitoring of river section water level according to claim 5, characterized in that: The cross-section morphological characteristics and slope characteristics are obtained based on the topographic data of the river section where the cross-section is located, including: The cross-section line vector is clipped with the water area vector of the river section where the cross-section is located, and the number of cross-section line vector elements after clipping is taken as the number of branches; The number of branches represents the morphological characteristics of the cross section; Calculate the slope using the topographic data of the river where the section is located; Use the section line vector to extract the slope and calculate the average slope of the section; The average slope of the section is used to represent the slope characteristics of the section.

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

8. The method for multi-source satellite coordinated remote sensing monitoring of river section water level according to claim 1, characterized in that: The cross-section water level monitored by satellites with different spatial resolutions at different sections is based on constructing an error correction model for water level monitoring of different types of sections by satellites with different spatial resolutions, including: In the same type of cross section, the cross section water level monitored by satellite with the first spatial resolution is used as the benchmark, and the error characteristics between the cross section water level monitored by satellite images with other spatial resolutions and the benchmark are analyzed; Based on the error characteristics of the same type of sections, a water level error correction model for the same type of sections monitored by satellites with different spatial resolutions is established; Repeat the steps of "analyzing error characteristics" and "establishing error correction model" until the error correction models of water level in satellite monitoring sections with different spatial resolutions for all types of sections are completed, and the error correction models of water level in satellite monitoring of different types of sections with different spatial resolutions are obtained.

9. The method for multi-source satellite coordinated remote sensing monitoring of water level in a river section according to claim 8, characterized in that: The acquisition of cross-sectional water levels monitored by satellites with different spatial resolutions at different cross-section types includes: Based on satellite images with different spatial resolutions covering different types of sections, the waterside lines of the river sections where different types of sections are located are extracted; According to the intersection points of the waterside lines and the cross-section vectors of different types of cross-sections, the corresponding intersection elevations are extracted from the terrain data, and the mean pixel elevation is calculated; The mean value of pixel elevation is taken as the cross-section water level, thus obtaining the cross-section water level monitored by satellites with different spatial resolutions for different types of cross-sections.

10. A method for multi-source satellite coordinated remote sensing monitoring of water level in a river section according to any one of claims 1 to 9, characterized in that: The error correction model for monitoring water levels of different types of sections based on satellites with different spatial resolutions, and the water levels of different types of sections at different time phases monitored by satellites with different spatial resolutions, obtain a multi-source satellite collaborative monitoring time series water level data set of different types of sections, including: The error correction model of water level of the same type of sections monitored by satellites with different spatial resolutions is used to correct the water level of the same type of sections at different phases monitored by satellites with different spatial resolutions, and the time series data set of water level monitored by satellites with different spatial resolutions of the same type of sections is obtained. The satellite monitoring time series water level datasets with different spatial resolutions for the same type of sections are combined in chronological order to obtain the multi-source satellite collaborative monitoring water level time series datasets for the same type of river sections.

11. A multi-source satellite collaborative remote sensing monitoring system for river section water level, characterized in that: include: Classification module: used to classify sections based on section morphology and slope characteristics; Model building module: used to build error correction models for water level monitoring of different types of sections by satellites with different spatial resolutions based on water levels of sections monitored by satellites with different spatial resolutions; Correction module: It is used to obtain the time series water level data set of multi-source satellite collaborative monitoring of different types of sections based on the error correction model of water level monitoring of different types of sections by satellites with different spatial resolutions, and the water level monitoring of different types of sections at different phases by satellites with different spatial resolutions, thus completing the multi-source satellite collaborative remote sensing monitoring of section water levels.

12. An electronic device, characterized in that: include: a processor coupled to the memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, and when the program or the instruction is executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 10.

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