A method for extracting stable water bodies from land surface using multimodal remote sensing images integrating prior knowledge

Abstract

This application provides a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrate prior knowledge, including: acquiring Sentinel‑1 SAR remote sensing data and Sentinel‑2 optical remote sensing data and preprocessing them separately to obtain monthly time-series SAR remote sensing images covering the entire domain and monthly median optical remote sensing images covering the entire domain; calculating the SDWI water body index and removing outliers; acquiring multiple stable water body samples from the entire domain; extracting SDWI pixel values ​​from the areas covered by the stable water body samples and determining the SDWI water body segmentation threshold; calculating the monthly water body frequency based on the SDWI water body segmentation threshold; determining the monthly initial stable water body based on the monthly water body frequency calculation; calculating the optimization index based on the monthly median optical remote sensing image and removing the non-water body part in the monthly initial stable water body; calculating the MNDWI water body index based on the monthly median remote sensing image; merging and integrating the MNDWI water body index with the monthly initial stable water body with the non-water body part removed, and supplementing the missed water body part to obtain the final monthly stable water body.

Description

Technical Field

[0001] The present application relates to the technical field of water body identification, and in particular to a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge. Background Art

[0002] Water is an essential natural resource for human production and life, as well as for the sustainable development of nature. Surface water, a crucial freshwater resource, often exists in the form of natural or artificial rivers, lakes, reservoirs, and mountain ponds. The spatiotemporal distribution of surface water bodies facilitates dynamic water resource management, water ecosystem restoration, and emergency monitoring of flash floods. It holds significant scientific value in fields such as climate change, hydrological modeling, and water environment management. Therefore, the efficient and accurate extraction of surface water bodies over a wide area is of great importance.

[0003] The rapid development of multi-source satellite remote sensing has laid a solid foundation for the acquisition of land surface water information. The method based on remote sensing water index is widely used in water system mapping. It is easy to calculate and uses the spectral characteristics of water body reflection, absorption and scattering of solar radiation to construct an index through band combination. Combined with threshold segmentation, it can quickly obtain surface water information of small areas.

[0004] However, when using remote sensing water index to obtain land surface water information on a wide scale, the land surface water information obtained is inaccurate. Summary of the Invention

[0005] The embodiments of the present application provide a method for extracting stable water bodies on land surfaces from multimodal remote sensing images that integrates prior knowledge to accurately obtain wide-area stable water body information on land surfaces.

[0006] In a first aspect, embodiments of the present application provide a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge, including:

[0007] Acquire Sentinel-1 SAR remote sensing data and Sentinel-2 optical remote sensing data;

[0008] Preprocessing the Sentinel-1 SAR remote sensing data to obtain monthly time-series SAR remote sensing images covering the entire region;

[0009] Preprocessing the Sentinel-2 optical remote sensing data to obtain a monthly median optical remote sensing image covering the entire region;

[0010] Calculate the SDWI water index based on the monthly time-series SAR remote sensing images and remove outliers;

[0011] Using GSW water body products as prior information, global stable water body samples are extracted;

[0012] Based on the SDWI water body index and the stable water body sample, extracting the SDWI pixel value of the area covered by the stable water body sample, and determining the SDWI water body segmentation threshold;

[0013] Monthly water body frequency calculation based on SDWI water body segmentation threshold;

[0014] Calculate and determine the monthly initial stable water body based on the monthly water body frequency;

[0015] The optimization index is calculated based on the monthly median optical remote sensing image and the non-water part of the monthly initial stable water body is removed;

[0016] Calculate the MNDWI water index based on the monthly median remote sensing image;

[0017] The MNDWI water index is integrated with the monthly initial stable water body after removing the non-water body part, and the missed water body part is supplemented to obtain the final monthly stable water body.

[0018] In a feasible implementation, the preprocessing of the Sentinel-1 SAR remote sensing data to obtain monthly time-series SAR remote sensing images covering the entire region includes:

[0019] The Sentinel-1 SAR remote sensing data are subjected to terrain correction, Refined Lee filtering, splicing and cropping to form monthly time-series SAR images covering the entire region.

[0020] In a feasible implementation, the monthly time-series SAR image is a ground-range multi-view image in an interferometric wide-band mode (, with polarization modes of VV polarization and VH polarization, and an image spatial resolution of 10 m).

[0021] In one feasible implementation, the preprocessing of the Sentinel-2 optical remote sensing data to obtain a monthly median optical remote sensing image covering the entire region includes:

[0022] The Sentinel-2 optical remote sensing data is cloud-processed using the QA60 band, and a monthly median optical remote sensing image covering the entire area is formed through stitching, monthly median synthesis, and cropping.

[0023] In a feasible implementation, the calculation formula of the SDWI water index is:

[0024] ;

[0025] in, represents the SDWI water index, express Polarization band, express Polarization band.

[0026] In a feasible implementation, the calculation formula of the SDWI water body segmentation threshold is:

[0027] ;

[0028] Where, Represents the mean value of the SDWI water index in the area corresponding to the stable water sample point, Represents the standard deviation of the SDWI water index in the area corresponding to the stable water sample point.

[0029] In a feasible implementation, the monthly water body frequency calculation based on the SDWI water body segmentation threshold includes:

[0030] When the SDWI water index of a pixel is greater than the SDWI water body segmentation threshold, the pixel is marked as a water body. Based on this, the regional monthly time series water body mask is obtained in sequence. On this basis, the monthly water body frequency value is calculated, that is, the frequency of each pixel being identified as a water body in the monthly time series. The specific calculation formula is:

[0031]

[0032] Where, F Indicates the frequency of pixels being identified as water bodies in the monthly time series; Indicates the number of times the pixel is identified as a water body, Indicates the valid observation number of the pixel.

[0033] In a feasible implementation, pixels with a monthly water body frequency greater than 0.3 are determined as monthly initial stable water bodies;

[0034] An optimization index is calculated based on the monthly median optical remote sensing image and the non-water body portion of the monthly initial stable water body is removed. When the optimization index value is greater than 0, it indicates a non-water body. This is used to remove pixels in the low-scattering area of ​​the monthly initial stable water body that are mistakenly classified as water bodies.

[0035] The optimization index calculation formula is:

[0036]

[0037] Where, OI Indicates the optimization index result, Indicates the shortwave infrared 1 band reflectivity, Indicates the shortwave infrared 2-band reflectivity, represents the reflectivity of red light band, Indicates the reflectivity of the green light band.

[0038] In a feasible implementation method, the MNDWI water index is calculated based on the monthly median remote sensing image. When the MNDWI water index value is greater than 0, it is a water body.

[0039] The calculation formula of MNDWI water index is:

[0040]

[0041] Where, MNDWI represents the MNDWI water index, represents the reflectivity of the green light band, Indicates the shortwave infrared 1 band reflectivity.

[0042] The embodiment of the present application provides a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge, including: acquiring Sentinel-1 SAR remote sensing data and Sentinel-2 optical remote sensing data; SAR remote sensing data are preprocessed to obtain monthly time-series SAR remote sensing images covering the entire area; Sentinel-2 optical remote sensing data are preprocessed to obtain monthly median optical remote sensing images covering the entire area; the SDWI water body index is calculated based on the monthly time-series SAR remote sensing images and outliers are removed; multiple stable water body samples are obtained from the entire area; based on the SDWI water body index and stable water body samples, the SDWI pixel values ​​of the area covered by the stable water body samples are extracted, and the SDWI water body segmentation threshold is determined; the monthly water body frequency is calculated based on the SDWI water body segmentation threshold; the monthly initial stable water body is determined based on the monthly water body frequency calculation; the optimization index is calculated based on the monthly median optical remote sensing image and the non-water body part of the monthly initial stable water body is removed; the MNDWI water body index is calculated based on the monthly median remote sensing image; the MNDWI water body index is merged and integrated with the monthly initial stable water body with the non-water body part removed, and the missed water body part is supplemented to obtain the final monthly stable water body.

[0043] This method combines active and passive remote sensing, and can overcome the problems of interference from cloudy and rainy weather and insufficient effective ground observations. It greatly weakens the phenomena of imperfect water body edges and small patches in the results, missed urban water bodies, and misclassification of water bodies in low-scattering areas. Secondly, this method uses the sample points of existing remote sensing water body products as prior information to automatically and quickly obtain the remote sensing water body index segmentation threshold, reducing the uncertainty of threshold segmentation. In addition, this method also comprehensively applies multi-source remote sensing water body indices, and improves the robustness of land surface water body information extraction through time series frequency judgment, partition integration and result optimization integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present application and do not constitute improper limitations on the present invention.

[0045] In the attached figure:

[0046] Figure 1 This is a flow chart of a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge, provided in one embodiment of the present application;

[0047] Figure 2 This is a schematic diagram of the monthly stable water extraction results of the demonstration area provided in one embodiment of the present application;

[0048] Figure 3 This is a partial schematic diagram of the water extraction results of the cultivated area of ​​the demonstration area provided by one embodiment of the present application;

[0049] Figure 4 This is a partial schematic diagram of the water body extraction results in the built-up area of ​​the demonstration zone provided by one embodiment of the present application;

[0050] Figure 5 This is a partial schematic diagram of the water body extraction results in the lake area of ​​the demonstration area provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0052] In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "plurality" is at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

[0053] In this application, unless otherwise specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0054] In this application, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it can mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Furthermore, when a first feature is "above," "above," or "above" a second feature, it can mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it can mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0055] Water is an essential natural resource for human production and life, as well as for the sustainable development of nature. Surface water, a crucial freshwater resource, often exists in the form of natural or artificial rivers, lakes, reservoirs, and mountain ponds. The spatiotemporal distribution of surface water bodies facilitates dynamic water resource management, water ecosystem restoration, and emergency monitoring of flash floods. It holds significant scientific value in fields such as climate change, hydrological simulation, and water environment management. Therefore, the efficient and accurate extraction of surface water bodies over a wide area is of great importance.

[0056] The rapid development of multi-source satellite remote sensing has laid a solid foundation for the acquisition of land surface water information. The method based on remote sensing water index is widely used in water system mapping. It is easy to calculate and uses the spectral characteristics of water body reflection, absorption and scattering of solar radiation to construct an index through band combination. Combined with threshold segmentation, it can quickly obtain surface water information of small areas.

[0057] However, remote sensing water indices are inaccurate when used to obtain information on terrestrial surface water bodies over large areas. As the monitoring area expands, climatic conditions become more complex and susceptible to interference from clouds and rain. This is especially true during the rainy season in southern China, when weather conditions fluctuate frequently within a short period of time. Optical remote sensing imaging conditions and revisit cycles severely limit the number of effective ground observations. Consequently, large-scale water body extraction based on optical remote sensing cannot meet the needs of current monitoring. Furthermore, information on water body edges and small patches is incomplete in complex scenes. Synthetic aperture radar (SAR) satellite imagery offers all-day, all-weather observation capabilities and is immune to interference from clouds and rain. However, the strong echo diffusion effect of buildings can easily lead to missed water in built-up areas, and water bodies can be misclassified in low-scattering areas such as smooth surfaces and bare soil. Large-scale regions present complex geographic scenes, diverse landscape types, and variable water body morphology and water quality. Consequently, the temporal and spatial information presented in imagery varies significantly. Single-source or single-type water indices face challenges in terms of versatility and universality, and the effectiveness of threshold segmentation is uncertain.

[0058] In order to solve the above problems, an embodiment of the present application provides a method and system for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge. The solution provided by the embodiment of the present application will be described in detail below in conjunction with the accompanying drawings in the specification.

[0059] Figure 1 This is a flow chart of a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge, as provided in one embodiment of the present application.

[0060] Reference Figure 1 As shown, the embodiment of the present application provides a method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge, including:

[0061] S100: Acquire Sentinel-1 SAR remote sensing data and Sentinel-2 optical remote sensing data.

[0062] Specifically, based on the entire area, Sentinel-1 SAR remote sensing data and Sentinel-2 optical remote sensing data are acquired for a specified area. Sentinel-1 SAR remote sensing data is collected by the Sentinel-1 satellite, while Sentinel-2 optical remote sensing data is collected by the Sentinel-2 satellite. It should be noted that Sentinel-1 and Sentinel-2 are two Sentinel satellites. Sentinel-1 is a series of Earth observation satellites under the European Space Agency (ESA) Copernicus program. The primary mission of this satellite series is to provide all-weather, all-day radar imaging services for land and ocean observations. The Sentinel-1 satellite is equipped with a C-band synthetic aperture radar (SAR), capable of penetrating clouds and darkness to acquire high-resolution radar imagery. Sentinel-2 is part of the European Space Agency's Copernicus program, a series of high-resolution multispectral imaging satellites. Its primary mission is land monitoring, providing imagery of vegetation, soil and water cover, inland waterways, and coastal areas, and can also be used by emergency services. The Sentinel-2 satellite is equipped with a multispectral imager (MSI), capable of providing imagery data in 13 spectral bands, covering the visible, near-infrared, and shortwave infrared regions, with spatial resolutions of 10 meters, 20 meters, and 60 meters, respectively. All of this is known to those skilled in the art.

[0063] S200: Preprocess the Sentinel-1 SAR remote sensing data to obtain monthly time-series SAR remote sensing images covering the entire region.

[0064] Specifically, Sentinel-1 SAR remote sensing data were terrain corrected, filtered with Refined Lee filtering, and then stitched and cropped to produce monthly time-series SAR images covering the entire region. These images are multi-look, ground-range images generated in interferometric wide-swath mode with VV and VH polarizations, and a spatial resolution of 10 meters. Refined Lee filtering is an adaptive filtering algorithm for speckle noise in synthetic aperture radar (SAR) images. It improves upon the classic Lee filter and suppresses noise while better preserving edges and details.

[0065] S300: Preprocess Sentinel-2 optical remote sensing data to obtain monthly median optical remote sensing images covering the entire region.

[0066] Specifically, the QA60 band is used to perform cloud processing on Sentinel-2 optical remote sensing data, and through stitching, monthly median synthesis and cropping, a monthly median optical remote sensing image covering the entire area is formed.

[0067] The optical remote sensing image is an L2A surface reflectance product. The image used consists of 10 spectral bands, namely, blue band, green band, red band, vegetation red edge 1 band, vegetation red edge 2 band, vegetation red edge 3 band, near-infrared band, narrow-edge near-infrared band, shortwave infrared 1 band, and shortwave infrared 2 band. Among them, the spatial resolution of the blue band, green band, red band, and near-infrared band is 10 m; the spatial resolution of vegetation red edge 1 band, vegetation red edge 2 band, vegetation red edge 3 band, narrow-edge near-infrared band, shortwave infrared 1 band, and shortwave infrared 2 band is 20 m. The spatial resolution of bands with a spatial resolution of 20 m needs to be resampled to 10 m through nearest neighbor interpolation.

[0068] S400: Calculating the SDWI water index based on the monthly time-series SAR remote sensing images and removing outliers.

[0069] The SDWI water index is calculated based on the monthly time series SAR remote sensing images, and the abnormally large and small values ​​in the calculation results are set to 0. The calculation formula of the SDWI water index is:

[0070]

[0071] in, represents the SDWI water index, express Polarization band, express Polarization band.

[0072] S500: Using the GSW water body product as prior information, extract global stable water body samples.

[0073] Based on the GSW (Global Surface Water) remote sensing water product, we obtained information on permanent water bodies. Using stratified random sampling, we randomly selected several permanent water sample points across the entire coverage area as stable water samples. GSW is a global water time series coverage product produced by the Joint Research Centre of the European Commission under the Copernicus program using Landsat data. This data product provides information on the spatial distribution, variability, seasonality, and maximum extent of water bodies.

[0074] S600: Based on the SDWI water body index and the stable water body sample, extract the SDWI pixel value of the area covered by the stable water body sample, and determine the SDWI water body segmentation threshold.

[0075] Based on the SDWI water index obtained in S400 and the stable water samples obtained in S500, the SDWI pixel values ​​of the area covered by the stable water samples are extracted, and the SDWI segmentation threshold is determined by statistical methods.

[0076] The calculation formula of SDWI water body segmentation threshold is:

[0077]

[0078] Where, represents the SDWI water body segmentation threshold, Represents the mean value of the SDWI water index in the area corresponding to the stable water sample point, Represents the standard deviation of the SDWI water index in the area corresponding to the stable water sample point.

[0079] S700: Calculate monthly water body frequency based on SDWI water body segmentation threshold.

[0080] Based on the SDWI water body segmentation threshold obtained in S700, when the SDWI water body index of a pixel is greater than the SDWI water body segmentation threshold, the pixel is marked as a water body. Based on this, the regional monthly time series water body mask is obtained in sequence. On this basis, the monthly water body frequency value is calculated, that is, the frequency of each pixel being identified as a water body in the monthly time series. The specific calculation formula is:

[0081]

[0082] Where, F Indicates the frequency of pixels being identified as water bodies in the monthly time series; Indicates the number of times the pixel is identified as a water body, Indicates the valid observation number of the pixel.

[0083] S800: Determine the monthly initial stable water body based on the monthly water body frequency calculation.

[0084] Based on the monthly water body frequency obtained in step S700, pixels with a monthly water body frequency greater than 0.3 are determined to be stable water bodies, and the regional monthly initial stable water body is obtained accordingly.

[0085] S900: Calculate the optimization index based on the monthly median optical remote sensing image and remove the non-water part of the monthly initial stable water body.

[0086] An optimization index is calculated based on the monthly median optical remote sensing image and the non-water body portion of the monthly initial stable water body is removed. When the optimization index value is greater than 0, it indicates a non-water body. This is used to remove pixels in the low-scattering area of ​​the monthly initial stable water body that are mistakenly classified as water bodies.

[0087] The optimization index calculation formula is:

[0088]

[0089] Where, OI Indicates the optimization index result, Indicates the shortwave infrared 1 band reflectivity, Indicates the shortwave infrared 2-band reflectivity, represents the reflectivity of red light band, Indicates the reflectivity of the green light band.

[0090] S1000: Calculates the MNDWI water index based on monthly median remote sensing images.

[0091] Specifically, the MNDWI water index is calculated based on the monthly median remote sensing image. When the MNDWI water index value is greater than 0, it means that the part is a water body.

[0092] The calculation formula of MNDWI water index is:

[0093]

[0094] Where, MNDWI represents the MNDWI water index, represents the reflectivity of the green light band, Indicates the shortwave infrared 1 band reflectivity.

[0095] S1100: Merge and integrate the MNDWI water index with the monthly initial stable water body after removing the non-water body part, and supplement the missed water body part to obtain the final monthly stable water body.

[0096] Figure 2 This is a schematic diagram of the monthly stable water extraction results of the demonstration area provided in one embodiment of the present application; Figure 3 This is a partial schematic diagram of the water extraction results of the cultivated area of ​​the demonstration area provided by one embodiment of the present application; Figure 4 This is a partial schematic diagram of the water body extraction results in the built-up area of ​​the demonstration zone provided by one embodiment of the present application; Figure 5 This is a partial schematic diagram of the water extraction results of the lake area in the demonstration area provided by an embodiment of the present application. Figures 2 to 5 As shown in the figure, after comparison, it can be seen that this method can accurately obtain information about stable water bodies.

[0097] This method combines active and passive remote sensing, and can overcome the problems of interference from cloudy and rainy weather and insufficient effective ground observations. It greatly weakens the phenomena of imperfect water body edges and small patches in the results, missed urban water bodies, and misclassification of water bodies in low-scattering areas. Secondly, this method uses the sample points of existing remote sensing water body products as prior information to automatically and quickly obtain the remote sensing water body index segmentation threshold, reducing the uncertainty of threshold segmentation. In addition, this method also comprehensively applies multi-source remote sensing water body indices, and improves the robustness of land surface water body information extraction through time series frequency judgment, partition integration and result optimization integration.

[0098] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0099] It is easy to understand that those skilled in the art can combine, split, reorganize, etc. the embodiments of the present application based on the several embodiments provided in the present application to obtain other embodiments, and these embodiments do not exceed the scope of protection of the present application.

[0100] The above specific implementation methods further explain in detail the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above are only specific implementation methods of the embodiments of the present application and are not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A method for extracting stable water bodies from land surfaces using multimodal remote sensing images that integrates prior knowledge, characterized in that: include: Acquire Sentinel-1 SAR remote sensing data and Sentinel-2 optical remote sensing data; Preprocessing the Sentinel-1 SAR remote sensing data to obtain monthly time-series SAR remote sensing images covering the entire region; Preprocessing the Sentinel-2 optical remote sensing data to obtain a monthly median optical remote sensing image covering the entire region; Calculate the SDWI water index based on the monthly time-series SAR remote sensing images and remove outliers; Using GSW water body products as prior information, global stable water body samples are extracted; Based on the SDWI water body index and the stable water body sample, extracting the SDWI pixel value of the area covered by the stable water body sample, and determining the SDWI water body segmentation threshold; Monthly water body frequency calculation based on SDWI water body segmentation threshold; Calculate and determine the monthly initial stable water body based on the monthly water body frequency; The optimization index is calculated based on the monthly median optical remote sensing image and the non-water part of the monthly initial stable water body is removed; Calculate the MNDWI water index based on the monthly median remote sensing image; The MNDWI water index is integrated with the monthly initial stable water body after removing the non-water body part, and the missed water body part is supplemented to obtain the final monthly stable water body; The calculation formula of the SDWI water index is: in, express Water index, express Polarization band, express Polarization band; The calculation formula of the SDWI water body segmentation threshold is: Where, represents the SDWI water body segmentation threshold, Represents the mean value of the SDWI water index in the area corresponding to the stable water sample point, Indicates the standard deviation of the SDWI water index in the area corresponding to the stable water sample point; The monthly water body frequency calculation based on the SDWI water body segmentation threshold includes: When the SDWI water index of a pixel is greater than the SDWI water body segmentation threshold, the pixel is marked as a water body. Based on this, the regional monthly time series water body mask is obtained in sequence. On this basis, the monthly water body frequency value is calculated, that is, the frequency of each pixel being identified as a water body in the monthly time series. The specific calculation formula is: Where, F Indicates the frequency of pixels being identified as water bodies in the monthly time series; Indicates the number of times a pixel is identified as a water body; Indicates the number of valid observations of a pixel; Pixels with a monthly water body frequency greater than 0.3 were identified as monthly initial stable water bodies; An optimization index is calculated based on the monthly median optical remote sensing image and the non-water body portion of the monthly initial stable water body is removed. When the optimization index value is greater than 0, it indicates a non-water body. This is used to remove pixels in the low-scattering area of ​​the monthly initial stable water body that are mistakenly classified as water bodies. The optimization index calculation formula is: Where, OI Indicates the optimization index result, Indicates the shortwave infrared 1 band reflectivity, Indicates the shortwave infrared 2-band reflectivity, represents the reflectivity of red light band, Indicates the reflectivity of the green light band.

2. The method for extracting stable water bodies from land surfaces using multimodal remote sensing images with integrated prior knowledge according to claim 1, characterized in that: The preprocessing of the Sentinel-1 SAR remote sensing data to obtain monthly time-series SAR remote sensing images covering the entire region includes: The Sentinel-1 SAR remote sensing data are subjected to terrain correction, Refined Lee filtering, splicing and cropping to form monthly time-series SAR images covering the entire region.

3. The method for extracting stable water bodies from land surfaces using multimodal remote sensing images with integrated prior knowledge according to claim 2, characterized in that: The monthly time-series SAR images are ground-range multi-view images in the interferometric wide-swath mode, with VV polarization and VH polarization, and an image spatial resolution of 10 m.

4. The method for extracting stable water bodies from land surfaces using multimodal remote sensing images with integrated prior knowledge according to claim 2, characterized in that: The preprocessing of the Sentinel-2 optical remote sensing data to obtain a monthly median optical remote sensing image covering the entire region includes: The Sentinel-2 optical remote sensing data is cloud-processed using the QA60 band, and a monthly median optical remote sensing image covering the entire area is formed through stitching, monthly median synthesis, and cropping.

5. The method for extracting stable water bodies from land surfaces using multimodal remote sensing images with integrated prior knowledge according to claim 1, characterized in that: Based on the monthly median remote sensing image, the MNDWI water index is calculated. When the MNDWI water index value is greater than 0, it is a water body. The calculation formula of MNDWI water index is: Where, MNDWI represents the MNDWI water index, represents the reflectivity of the green light band, Indicates the shortwave infrared 1 band reflectivity.

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