A Method for Verifying Reservoir Capacity Curves Based on Satellite Altimetry and Remote Sensing Imaging

Abstract

This invention discloses a method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology, belonging to the fields of water conservancy and hydropower engineering and hydrological and water resources management. The method includes: inverting and correcting the reservoir's water surface elevation using altimetry data from satellite remote sensing monitoring; identifying the reservoir's water body extent and statistically analyzing its area sequence based on remote sensing images; constructing a reservoir capacity calculation model based on the reservoir's concurrent remote sensing water level and area sequences; fitting remote sensing water level-area and water level-capacity curves and evaluating their accuracy; and realizing the process of reconstructing reservoir capacity curves using the same satellite mission by inverting concurrent water level and area data. This method avoids the shortcomings of previous methods that reconstructed reservoir characteristic curves using satellite remote sensing data due to asynchronous water level and area measurements. It also overcomes the limitations of traditional laser or synthetic aperture radar altimetry satellites, which can only acquire sub-satellite point cloud elevations and have long step sizes. This significantly improves the accuracy of the reconstructed characteristic curves and has significant advantages in reconstructing characteristic curves for small reservoirs in remote areas and over large areas.

Description

Technical Field

[0001] This invention relates to the fields of water conservancy and hydropower engineering and hydrological and water resources management technology, and in particular to a method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology. Background Technology

[0002] Reservoirs, as an important water resource management tool, play a vital role in flood control, water supply, irrigation, and power generation. The effective operation of a reservoir relies on a highly representative reservoir capacity curve, that is, an accurate quantitative relationship between water level and reservoir capacity. However, with the operation of constructed reservoirs, the representativeness of the capacity curves of many reservoirs has significantly decreased due to natural factors (such as siltation and river erosion) and human factors (such as artificial dredging of river sand and river diversion). This may make it difficult to guarantee the benefits of reservoirs in flood control, power generation, and irrigation, and also pose risks to the operation and management of reservoir groups. How to achieve convenient batch verification of reservoir capacity characteristic curves is a crucial problem that urgently needs to be solved in current engineering operation and management.

[0003] Currently, traditional methods for reconstructing reservoir capacity curves primarily rely on field measurements. This typically involves using underwater acoustic equipment, laser rangefinders, or other similar tools to measure the water area at various elevations within the reservoir. While characteristic curves reconstructed based on field measurement data are highly accurate and reliable, these methods also have several limitations. First, reservoirs are often located in remote mountainous areas (or even uninhabited areas), making field surveys time-consuming, labor-intensive, and costly. Second, field measurement methods may be unfeasible in certain special circumstances (such as in severe weather or areas with safety hazards around the reservoir), and they are also difficult to use conveniently and quickly for batch verification of reservoir characteristic curves. Therefore, it is necessary to seek a more efficient and safer method to update reservoir capacity curves.

[0004] In recent years, the rapid development of satellite remote sensing technology has provided new insights into updating reservoir capacity curves. Satellite remote sensing technology can provide large-scale land cover data, including the area and elevation information of water bodies. By combining satellite altimetry data with high-resolution remote sensing imagery, it is possible to remotely monitor the water level and area of ​​reservoirs without direct contact with the water body. This method not only improves the efficiency and security of data collection but also significantly reduces costs, making it particularly suitable for areas (reservoirs) that are difficult to measure using traditional methods. Summary of the Invention

[0005] The purpose of this invention is to provide a method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology, so as to solve the problems existing in the background technology.

[0006] To achieve the above objectives, this invention provides a method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology, comprising the following steps:

[0007] S1. Obtain the remote sensing dataset above the target reservoir, preprocess the pixel-level data, and construct a water body mask for the reservoir area.

[0008] S2. Acquire high-precision DEM, JRC Monthly Water History (JMWH), and high-resolution satellite altimetry data of remote sensing images of the target reservoir area at the same time. Combine reservoir characteristic water levels (such as dead water level and design flood level) and filtering methods such as direct filtering, radius filtering and statistical filtering to denoise the inverted water level cloud data in order to obtain accurate and reliable water level data.

[0009] S3. Combining the reservoir's concurrent water level, water index, DEM and JMWH data, a machine learning model is constructed to identify the water body range in the image, extract the water body boundary on the reservoir surface, further estimate the reservoir area, fit the reservoir water level-area relationship and evaluate the goodness of fit.

[0010] S4. Obtain the water level sequence and reservoir surface area sequence for the same period through high-resolution satellite altimetry and remote sensing images, respectively. Construct a reservoir capacity calculation model based on the two and calculate the reservoir water storage data.

[0011] S5. Based on reservoir water storage data, the water level-area curve and water level-storage capacity curve are fitted using polynomial, power function and exponential function, and the goodness of fit is evaluated to select the best fitting relationship curve.

[0012] Preferably, satellite remote sensing data is acquired, and high-resolution satellite point cloud altimetry data is preprocessed, including statistical filtering, pass-through filtering, and DBSCAN. Orthorectification, radiometric calibration, atmospheric correction, and mountain shadow processing are performed on the image raster data. Preprocessing also includes system bias correction and aggregation denoising. This process aims to ensure that the point cloud data within the reservoir mask is less affected by noise and has higher accuracy and consistency through some common quality control methods.

[0013] Preferably, when extracting the surface area of ​​the reservoir, the reservoir mask boundary is obtained based on the global dam database, the high-precision DEM of the reservoir area and masking technology, SWOT point cloud data is obtained, and water level data (including water level data uncertainty, water level quality indicators and black water range, etc.) is obtained based on the basic information of the reservoir, the water surface range of the reservoir in the same period and its mask boundary.

[0014] Preferably, pixel information within the reservoir boundary mask is acquired, and filtered and corrected based on pixel area measurement quality indicators. An indicator set is constructed using high-precision DEM data of the reservoir area, pre-processed (denoised and corrected) altimeter cloud data, dark water markers in the mask area, JMWH (Jump Measuring Water Height) of the reservoir area, and dark water ratio. Training and validation sets are selected to train and validate the classification accuracy of the machine learning model. Two machine learning methods are used: the first combines basic point cloud denoising algorithms, basic reservoir features (water level), JMWH, and high-precision DEM to remove noise from the altimeter cloud data, obtaining accurate reservoir water levels and assisting in obtaining the reservoir surface area; the second method is used to accurately obtain the reservoir surface area.

[0015] Preferably, after the remote sensing reservoir surface area and high-resolution satellite altimetry water level data are extracted and preprocessed, a remote sensing inversion area-altimetry water level sequence is constructed, and the area data of high water level is extended using a digital elevation model (DEM), making the altimetry water level-remote sensing area sequence more complete and reliable.

[0016] Preferably, the machine learning model is a supervised learning model, an unsupervised learning model, or a reinforcement learning model, etc., with the aim of integrating multiple indicators to distinguish between water areas and non-water areas (distinguishing whether the altimeter cloud data is noisy). It fully considers the information of the given classification indicator set, reduces noise interference not eliminated in preprocessing, and compensates for the shortcomings of misjudgment easily caused by using a single indicator for classification, thereby effectively identifying water level and reservoir surface area. The feature vector includes imagery information (each band and water index), reservoir topographic information (DEM), reservoir inversion water level, and time-series image information (JMWH), etc.

[0017] Preferably, the reservoir capacity calculation model is determined to be either a trapezoid or a frustum based on the target reservoir's characteristics, and then the water body is divided according to different water levels. n Layer, reservoir capacity data is from n The volume is obtained by accumulating the volume of a trapezoidal or frustum-shaped solid.

[0018] Preferably, the trapezoidal formula for the reservoir capacity calculation model is:

[0019] ;

[0020] The formula for a frustum is:

[0021] ;

[0022] The formula for cumulative storage capacity is:

[0023] ;

[0024] In the formula, This represents the difference in reservoir capacity between two adjacent water levels. The difference between two adjacent water levels; , These represent the water surface areas corresponding to two adjacent water levels; It is an ordinal number; This represents the cumulative number. Initial storage capacity; This is for the total reservoir volume.

[0025] The newly launched Surface Water and Ocean Topography (SWOT) satellite mission can acquire the water level and area of ​​reservoirs over a wide range of time, overcoming the shortcomings of traditional image satellites that can only acquire the reservoir surface area and radar or laser satellites that can only acquire the water level of reservoirs at the nadir point due to their large footprint. It is highly applicable to reservoirs with no measured data or with missing or incorrect measured data.

[0026] The synthetic aperture radar (KaRIn radar interferometer) carried by the SWOT satellite can acquire data at a high speed of 20 m along the orbit and 10-60 m across the orbit. For narrow sections of the reservoir that may have missing or misclassified data, the identification capability can be enhanced by using DEM and JMWH data to obtain a more accurate reservoir area. The satellite remote sensing imagery and altimetry data used for classification and identification mainly adopt the observation data of the wide-swath radar interferometer (KaRIn), while other imagery data (Landsat 8 / 9, Sentinel-1 / 2) serve as an important supplement for reservoir area inversion.

[0027] Therefore, the reservoir capacity curve verification method based on satellite altimetry and remote sensing imaging technology used in this invention has the following beneficial effects:

[0028] (1) To realize the process of reconstructing reservoir capacity curves by inverting the water level and area at the same time using the same satellite mission (which can be supplemented by multi-source satellite image data);

[0029] (2) The monitoring range is wide and the verification cost is low. The data is obtained from satellite observation data and no longer relies on time-consuming and labor-intensive ground hydrological monitoring and engineering surveying. It is especially suitable for reservoirs where traditional measurement methods are difficult to apply.

[0030] (3) It is of great significance for improving the efficiency and accuracy of reservoir management, especially in flood control, power generation and water supply.

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0032] Figure 1 This is an overall flowchart of an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the reservoir water level data sequence according to an embodiment of the present invention;

[0034] Figure 3This is a schematic diagram illustrating the calculation results of water level-reservoir capacity data in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of the reconstructed water level-area curve according to an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of the reconstructed water level-reservoir capacity curve according to an embodiment of the present invention. Detailed Implementation

[0037] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0038] Please see Figure 1 A method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology includes the following steps:

[0039] S1. Obtain the remote sensing dataset above the target reservoir, preprocess the pixel-level data, and construct a water body mask for the reservoir area. High-precision satellite altimetry and remote sensing imaging technologies are used to acquire concurrent water level and surface area data. Due to their high spatiotemporal resolution, the ability to perform hundreds of thousands of pixel-level observations of the same water body simultaneously, and the complete time series characteristics, the observation data is more reliable, and the fitted reservoir capacity curve is more representative. This fitting process utilizes continuously observed data points, thereby improving the accuracy and reliability of the model.

[0040] S2. Acquire high-precision DEM, JRC Monthly Water History (JMWH), and high-resolution satellite altimetry data from the same period of remote sensing imagery for the target reservoir area. Extract water level data and reservoir surface area data. Combine reservoir characteristic water levels (such as dead water level and design flood level) with filtering methods such as direct filtering, radius filtering, and statistical filtering to perform cloud denoising processing on the retrieved water level points.

[0041] The process involves acquiring satellite altimetry data, preprocessing the high-resolution satellite altimetry data, and performing multiple noise reduction processes on the point cloud data, including pass-through filtering, statistical filtering, and radius filtering. The preprocessing also includes system bias correction and aggregation denoising. This process aims to ensure that the point cloud data inside the reservoir mask is less affected by noise, has higher accuracy and consistency, through some common quality control methods. To coordinate and consider various features, a machine learning model can be constructed.

[0042] Remote sensing imagery and satellite altimetry data primarily originate from synchronous observations of the same satellite. When the quality of synchronous observation data, especially for reservoir areas, is poor, the extracted area is obtained through inversion interpolation from other temporally nearest satellites. This ensures a high degree of consistency between the extracted water area and water level data in both time and space. By utilizing data from the same observation source and its quality indicators, not only can the data be corrected more accurately and effectively, but spatiotemporal biases that may be introduced due to differences in data sources can also be effectively avoided, thus enhancing the accuracy and reliability of the research results.

[0043] S3. Combining the reservoir's concurrent water level, water index, DEM, and JMWH data, a machine learning model is constructed to identify the water body range in the image, extract the water body boundary on the reservoir surface, and use multiple functions to fit the reservoir water level-area relationship and evaluate the goodness of fit, and select the optimal water level-area relationship.

[0044] When extracting the surface area of ​​the reservoir, the reservoir mask boundary is obtained based on the global dam database, high-precision DEM of the reservoir area and masking technology, SWOT point cloud data is obtained, and the water level is inverted based on the reservoir characteristic water level, high-precision DEM and JMWH.

[0045] Pixel information within the reservoir boundary mask is acquired, and filtering and correction are performed based on pixel area measurement quality indicators. Feature vectors are constructed based on these indicators and high-precision DEM data, and a machine learning model is then built to identify and extract the water body range of the reservoir. An indicator set is constructed based on high-precision DEM raster imagery of the reservoir area, denoised and pre-processed altimeter cloud data of the mask area, dark water markers in the mask area, and dark water ratio. Training and validation sets are selected to train and validate the classification accuracy of the machine learning model. Two machine learning methods are used: the first combines a basic point cloud denoising algorithm to remove noise from the point cloud altimeter data, obtaining accurate reservoir water levels and assisting in obtaining the reservoir surface area; the second method is used to accurately obtain the reservoir surface area.

[0046] Machine learning models, including supervised learning, unsupervised learning, and reinforcement learning models, aim to integrate multiple indicators to distinguish between water bodies and non-water bodies (whether the altimeter cloud is noisy). By fully considering the information of the given classification indicator set, reducing noise interference not eliminated in preprocessing, and compensating for the shortcomings of misjudgment when using a single indicator for classification, water levels and reservoir surface areas can be effectively identified.

[0047] S4. Obtain the water level sequence and reservoir surface area sequence for the same period through high-resolution satellite altimetry and remote sensing imagery, respectively. Based on these two data, construct a reservoir capacity calculation model and calculate the reservoir water storage data.

[0048] S5. Based on reservoir water storage data, the water level-area curve and water level-storage capacity curve are fitted using polynomial, power function and exponential function, and the goodness of fit is evaluated to select the best fitting relationship curve.

[0049] After the remote sensing reservoir surface area and high-resolution satellite altimetry water level data are extracted, the relationship between altimetry water level and remote sensing area is fitted and evaluated to select the best relationship. The area data of high water level is extended using a digital elevation model (DEM) to make the altimetry water level-remote sensing area sequence more complete and reliable.

[0050] The reservoir capacity calculation model determines whether the target reservoir is a trapezoid or a frustum based on its characteristics, and then divides the water body according to different water levels. n Layer, reservoir capacity data is from n The volume is obtained by accumulating the volumes of the trapezoidal or frustum-shaped bodies. The trapezoidal formula for the reservoir capacity calculation model is:

[0051] ;

[0052] The formula for a frustum is:

[0053] ;

[0054] The formula for cumulative storage capacity is:

[0055] ;

[0056] In the formula, This represents the difference in reservoir capacity between two adjacent water levels. The difference between two adjacent water levels; , These represent the water surface areas corresponding to two adjacent water levels; It is an ordinal number; This represents the cumulative number. Initial storage capacity; This is for the total reservoir volume.

[0057] The newly launched Surface Water and Ocean Topography (SWOT) satellite mission can acquire the water level and area of ​​reservoirs over a wide range of time, overcoming the shortcomings of traditional image satellites that can only acquire the reservoir surface area and radar or laser satellites that can only acquire the water level of reservoirs at the nadir point due to their large footprint. It is highly applicable to reservoirs with no measured data or with missing or incorrect measured data.

[0058] The synthetic aperture radar (KaRIn radar interferometer) onboard the SWOT satellite can acquire data at a high speed of 20 m along the orbit and 10-60 m across the orbit. For narrow sections of reservoirs where data gaps or misjudgments may exist, DEM data can enhance identification capabilities and yield a more accurate reservoir area. The satellite remote sensing imagery and altimetry data used for classification and identification primarily utilize simulated observation data (SWOT-like data, SWORD) from the wide-swath radar interferometer (KaRIn).

[0059] Example:

[0060] Taking the Three Gorges Reservoir in the Yangtze River Basin as an example, the reservoir capacity curve review method is applied in practice. Specifically, machine learning algorithms represented by random forest, extraction of the Three Gorges Reservoir surface area sequence based on Sentinel-2 satellite remote sensing images, extraction of the Three Gorges Reservoir water level sequence based on Sentinel-3 satellite altimetry data, acquisition of altimetry water level-remote sensing area data points by extending area data at high water levels based on DEM, fitting of the water level-area function relationship, fitting and evaluation of the water level-reservoir capacity function relationship based on the reservoir capacity model, etc. are introduced. Since the impoundment of the Three Gorges Reservoir started in 2003, the water level-reservoir capacity relationship has been using the design curve so far and has been in operation for many years. Due to environmental changes such as sediment deposition, the current reservoir capacity is likely to have changed.

[0061] First, a machine learning classification algorithm represented by a random forest is constructed:

[0062] 1) Extract a sample with a sample size of N, and draw N times with replacement, each time drawing 1, and finally form N samples. These N samples here are used to train a decision tree, which serves as the sample at the root node of the decision tree.

[0063] 2) When each sample has M attributes, when each node of the decision tree needs to be split, randomly select m attributes from these M attributes, satisfying the condition m << M. Then, select 1 attribute from these m attributes using a certain strategy (such as information gain) as the splitting attribute of this node.

[0064] 3) Each node in the process of forming the decision tree should be split according to step 2) until it cannot be split any further.

[0065] 4) Establish a large number of decision trees according to the first three steps to form a random forest.

[0066] Secondly, the reservoir surface area data is obtained. This step involves selecting appropriate remote sensing image data and determining the time range of the image data as 10 years from 2009 to 2018 according to research needs. Subsequently, the remote sensing images provided by Sentinel-2 satellite data are obtained. Next, necessary image correction and denoising processing are carried out on the remote sensing images, and the target water body range is obtained using machine learning methods based on the quality index to exclude low-quality pixels. Thereafter, the reservoir area is divided into cells, and the standard size of each cell is determined. The cells at the boundary are appropriately adjusted to ensure that the entire reservoir area is accurately covered. After calculating the area of each cell, the total number of cells is counted, and the total area is calculated. Finally, the extracted area data is arranged in chronological order to form the reservoir surface area sequence, and the extracted data is filtered and verified to ensure the accuracy and reliability of the data.

[0067] This embodiment acquired SWOT-like remote sensing imagery data of the Three Gorges Reservoir in the Yangtze River Basin. The selected images basically cover the normal water level range of the Three Gorges Reservoir from the dead water level to the normal storage level. A random forest classification algorithm was used to extract and statistically analyze the reservoir area from the selected images. Some data are shown below. Figure 2 As shown.

[0068] The next step is to acquire reservoir water level data. From the SWOT-like dataset provided by the SWOT simulator, height measurement data synchronized with the remote sensing imagery is extracted. This acquired height measurement data is then denoised and corrected to transform it into a usable water level data sequence, such as... Figure 2 As shown.

[0069] Then, the elevation-area relationship is further constructed and optimized. This stage involves collecting reservoir elevation and area data at the same time points and integrating this data into a scatter plot to form an initial elevation-area dataset. After comparing and analyzing the scatter plot data, erroneous or unreasonable scatter plots are removed. Then, basic elementary functions such as power functions, exponential functions, and constant functions are selected as the basis for the fitting function, and a parametric function is constructed through rational operations and function composition operations. The least squares method is applied to fit the constructed parametric function to determine the values ​​of each parameter, thereby obtaining an accurate area-elevation relationship. These steps ensure the mathematical accuracy and scientific validity of the area-elevation relationship, which is the foundation for constructing the reservoir capacity curve.

[0070] Calculation of the Three Gorges water level-area curve: Data fitting was performed using the obtained water level and area data. Based on the data fitting effect and fitting parameter values, exponential functions, power functions, and second and third cubic polynomial functions with higher accuracy were selected, resulting in the relationship and fitting curve. The goodness-of-fit values ​​are shown in Table 1. By comparing the accuracy of the fitting functions, the third cubic polynomial function with the highest accuracy index was selected.

[0071] Table 1. Goodness-of-fit values ​​of exponential functions, power functions, and quadratic and cubic polynomial functions.

[0072] ;

[0073] The obtained water level-area curve of the Three Gorges Reservoir is as follows: Figure 3 As shown, the fitting function is:

[0074] ;

[0075] In the formula: A is the water area of ​​the reservoir, in km² 2 Z represents the reservoir water level, in meters (m).

[0076] To ensure consistency with the water level range of the Three Gorges Reservoir's original water level-reservoir capacity curve, the curve was extended to 130 meters based on the fitting function, resulting in a complete water level-area curve.

[0077] The final goal of the entire process is to construct the reservoir capacity curve. Since the Three Gorges Reservoir is a long and narrow river reservoir, the truncated pyramid formula is chosen as the reservoir capacity calculation model. The trapezoidal rule is used to calculate the change in water storage at 1-m intervals of slight water level increases, and then these changes are accumulated to form the reservoir capacity. Next, a suitable mathematical function is selected to describe the change in reservoir capacity with water level, thus constructing a function describing the change in reservoir capacity. The constructed reservoir capacity curve is then validated, adjusted as necessary to improve its accuracy and reliability, and compared with the design curve.

[0078] Three Gorges Reservoir Capacity Calculation: The Three Gorges Reservoir is a long and narrow reservoir. Using the truncated pyramid method and formula, the reservoir capacity within the conventional water level range of 145-175 m during actual operation was calculated. This data was then accumulated across different water levels to obtain the water level-capacity data. The calculation results are as follows: Figure 4 As shown.

[0079] After calculating the water level-area data using the frustum method, the water level-reservoir capacity data to be fitted was obtained. The same fitting process as for the water level-area data was used to fit the water level-reservoir capacity data of the Three Gorges Reservoir. The fitting functions for the reservoir capacity data were selected as exponential function, power function, and second and third cubic polynomial functions. By comparing the accuracy of each fitting function, and considering the complexity and high fitting risk of the cubic polynomial function, the second quadratic polynomial function with the second highest accuracy index was selected for the reservoir capacity relationship curve, as shown in Table 2.

[0080] Table 2. Fitting accuracy of exponential functions, power functions, and quadratic and cubic polynomial functions.

[0081] ;

[0082] The final water level-capacity curve of the Three Gorges Reservoir is as follows: Figure 5 As shown, the fitted function relationship is:

[0083] ;

[0084] In the formula: V The reservoir capacity is 100 million m³. Z The water level in the reservoir is in meters (m).

[0085] To ensure the integrity of the water level-reservoir capacity curve, the curve was extended to a water level of 130 m using the same fitting function.

[0086] The above steps reconstructed and verified the reservoir capacity curve of the Three Gorges Hydropower Station, providing strong support for the effective management of the reservoir. The proposed verification method improved the scheduling efficiency of flood control, power generation, and water supply, and enhanced the efficiency of reservoir operation and management.

[0087] Therefore, the reservoir capacity curve verification method based on satellite altimetry and remote sensing imaging technology adopted in this invention can avoid the defects of non-synchronous water level and area when reconstructing reservoir characteristic curves using satellite remote sensing data. In addition, the water level can be retrieved by using wide-swath spaceborne radar interferometric altimetry (similar to) SWOT satellite data, which overcomes the defects of traditional laser synthetic aperture radar (SAR) altimetry satellites, such as only being able to obtain the elevation of the nadir point and having a long step size. This not only significantly improves the accuracy of the reconstructed characteristic curve, but is also of great significance for the reconstruction of characteristic curves of small reservoirs in remote areas and over large areas.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology, characterized in that, Includes the following steps: S1. Obtain the remote sensing dataset above the target reservoir, preprocess the pixel-level data, and construct a water body mask for the reservoir area. S2. Acquire high-precision DEM, JRC Monthly Water History, and high-resolution satellite altimetry data of the target reservoir area, and extract water level data and reservoir surface area data. Acquire satellite remote sensing data, preprocess high-resolution satellite point cloud altimetry data including statistical filtering, pass-through filtering and DBSCAN, perform orthorectification, radiometric calibration, atmospheric correction and mountain shadow processing on image raster data, and preprocessing also includes system bias correction and aggregation denoising; S3. Based on the data in S2, a machine learning model is constructed to identify the water body range and extract the water body boundary of the reservoir surface. The surface area of ​​the reservoir is further estimated. Multiple point cloud denoising algorithms and the results obtained in S2 are used to filter and denoise the height measurement point cloud data to obtain an accurate reservoir surface water level. Multiple fitting function models are used to fit the reservoir water level-area relationship and evaluate the goodness of fit. When extracting the surface area of ​​the reservoir, the reservoir mask boundary is obtained based on the global dam database, the high-precision DEM of the reservoir area and masking technology. SWOT point cloud data is obtained, and water level data is obtained based on the basic information of the reservoir, the water surface range of the reservoir in the same period and its mask boundary. S4. Obtain the water level sequence and reservoir surface area sequence for the same period through high-resolution satellite altimetry and remote sensing images, respectively. Based on the reservoir surface area sequence and water level sequence for the same period, construct a reservoir capacity calculation model and calculate the reservoir water storage data. S5. Based on reservoir storage data, fit the water level-storage capacity curve using various fitting function models and evaluate the goodness of fit. The pixel information within the reservoir boundary mask is obtained, and the pixel area measurement quality index is used for screening and correction. Feature vectors are constructed based on various indexes, reservoir area JMWH and high-precision DEM data, and then a machine learning model is constructed to identify and extract the water body range of the reservoir. After the remote sensing reservoir surface area and high-resolution satellite altimetry water level data are extracted and preprocessed, a remote sensing inversion area-altimetry water level sequence is constructed, and the area data of high water level is extended using a digital elevation model (DEM). The machine learning model can be a supervised learning model, an unsupervised learning model, or a reinforcement learning model. The feature vector contains information about the image itself, reservoir topography, reservoir inversion water level, and time period image information.

2. The method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology according to claim 1, characterized in that: The reservoir capacity calculation model determines whether the target reservoir is a trapezoid or a frustum based on its characteristics. The water body is then divided into n layers according to different water levels. The reservoir capacity data is obtained by accumulating the volumes of the n trapezoids or frustums.

3. The method for verifying reservoir capacity curves based on satellite altimetry and remote sensing imaging technology according to claim 2, characterized in that: The trapezoidal formula for calculating reservoir capacity is as follows: ； The formula for a frustum is: ； The formula for cumulative storage capacity is: ； In the formula, This represents the difference in reservoir capacity between two adjacent water levels. The difference between two adjacent water levels; , These represent the reservoir surface areas corresponding to two adjacent water levels; It is an ordinal number; This represents the cumulative number. Initial storage capacity; This is for the total reservoir volume.

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