A multi-source radar remote sensing monitoring method for lake reservoir storage capacity change in an african area without ground data

Abstract

The application discloses a kind of lake reservoir water storage variation multi-source radar remote sensing monitoring methods for african groundless data area, to solve the problem that african lack of ground data area traditional monitoring technology cost is high, difficult to carry out monitoring and analysis to lake reservoir water storage variation under extreme weather and dense time phase, steps include: collecting multi-source radar remote sensing data and carrying out data preprocessing and screening;Utilize SAR image data to obtain lake reservoir area change sequence, utilize spaceborne radar altimeter data to obtain lake reservoir water level change sequence;Establish the relationship model of lake reservoir area and water level change, reconstruct lake reservoir area, water level change time sequence;Construct lake reservoir water storage variation estimation model, monitor each lake reservoir water storage variation in the region to be measured, analyze lake reservoir water storage variation characteristics.The method of the application is suitable for lake reservoir water storage variation monitoring in groundless data area, can reduce data dependence and weather influence, obtain high time resolution lake and reservoir water storage variation information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing and water resource monitoring, and particularly relates to a multi-source radar remote sensing monitoring method for lake and reservoir storage capacity change in an African area without ground data. BACKGROUND

[0002] Although lakes and reservoirs only account for about 2% of the area of the earth's land surface, they are indispensable water resources for human social economy, and play an important role in flood regulation and storage, agricultural irrigation, aquaculture promotion, and ecological environment improvement. Traditional methods for monitoring lake and reservoir storage capacity change rely on field measurement data, which is not only time-consuming and labor-intensive, but also difficult to meet the monitoring requirements of a large range, high time frequency and low.

[0003] Remote sensing technology has the advantages of low cost, strong timeliness, and the ability to observe a large range synchronously, and has received more and more attention in the monitoring of surface water resources in recent years, especially in areas without ground observation data. At present, scholars have carried out research on monitoring the change of lake and reservoir storage capacity by using satellite remote sensing technology, but most of the existing methods are based on optical remote sensing images and satellite altimeter data. This method is greatly affected by weather, and it is difficult to obtain high time resolution information of lake and reservoir storage capacity change.

[0004] Synthetic aperture radar (SAR) and radar altimeter are not affected by weather, and have all-weather observation capability. The application of multi-source radar remote sensing technology including SAR and radar altimeter to lake and reservoir storage capacity monitoring can reduce the dependence on ground data and weather influence, and can provide a monitoring method for obtaining high time resolution information of lake and reservoir storage capacity change in areas without ground observation data. SUMMARY

[0005] The purpose of the present application is to provide a multi-source radar remote sensing monitoring method for lake and reservoir storage capacity change in an African area without ground data, which can accurately extract high time resolution information of lake and reservoir storage capacity change in an area without ground observation data.

[0006] The present application adopts the following technical scheme: a multi-source radar remote sensing monitoring method for lake and reservoir storage capacity change in an African area without ground data, comprising the following steps:

[0007] Step S1, collecting multi-source radar remote sensing data of each lake and reservoir in the to-be-measured area, including SAR image data and spaceborne radar altimeter data, and respectively performing data preprocessing;

[0008] Step S2, using the SAR image data of each lake and reservoir after preprocessing, introducing an SBGFRLS active contour model to obtain the area time series of the lake and reservoir;

[0009] Step S3: Using the preprocessed altimeter data of each lake and reservoir from the spaceborne radar, calculate the water level of the lake and reservoir on the day the spaceborne radar passes over and perform deviation correction to obtain the water level time series of the lake and reservoir.

[0010] Step S4: Based on the area time series obtained in step S3 and the water level time series obtained in step S4, establish a model of the relationship between lake / reservoir area and water level change, and reconstruct the lake / reservoir area and water level change time series.

[0011] Step S5: Based on the reconstructed time series of lake and reservoir area and water level changes, construct an estimation model for lake and reservoir water storage changes, monitor the water storage changes of each lake and reservoir in the area to be measured, and analyze the characteristics of lake and reservoir water storage changes.

[0012] Preferably, the data preprocessing in step S1 is performed as follows:

[0013] Step S1.1: Perform orbit correction, radiometric correction, RefinedLee filtering to suppress speckle noise, topographic correction, and decibel reduction on the SAR image data of each lake and reservoir in sequence;

[0014] Step S1.2: Obtain the coordinates of the nadir point from the data of the satellite-borne radar altimeter based on latitude and longitude information, and extract the height measurement data of that point.

[0015] Preferably, step S2 uses spaceborne SAR imagery to obtain time series data on lake and reservoir areas, as follows:

[0016] Step S2.1: For the preprocessed SAR image, select the VV and VH bands with different polarization modes, calculate the water index SDWI, and obtain the SDWI image.

[0017] Step S2.2: Use the K-means clustering algorithm to classify the SDWI image and generate a binary image containing two major categories: water bodies and non-water bodies, to obtain the initial water body outline map;

[0018] Step S2.3: Use the water body contour map obtained in the previous step as the initial contour of the SBGFRLS active contour model, and determine the water body boundary through iterative formula;

[0019] Step S2.4: Perform connected component segmentation on the water body boundary, select the connected component with larger area as the final water body region, count the number of water body pixels in the final water body distribution map and calculate its area, and establish the area time series of each lake and reservoir.

[0020] Preferably, in step S3, the time series of lake and reservoir water levels are obtained using data from the spaceborne radar altimeter, as follows:

[0021] Step S3.1: For the preprocessed spaceborne radar altimeter data, use the water body boundary determined in step S2.3 as the vector boundary data of lakes and reservoirs, filter out the elevation points that fall on the water surface, and perform geophysical correction and waveform retracking correction on the elevation points within the water surface in sequence.

[0022] Step S3.2: Arrange the altimeter points retained in the previous step in ascending order of latitude to form a one-dimensional array, calculate the mean error σ of the data in the array, use the 3σ criterion to remove outliers, and average the remaining altimeter data to obtain the water level value;

[0023] Step S3.3: For different radar altimeter data, execute steps S3.1 and S3.2 respectively, classify the data according to different geoid surfaces, select reference benchmarks, and convert the water levels calculated by different radar altimeters into the same elevation benchmark.

[0024] Step S3.4: Use the data from the overlapping observation periods between satellites to sequentially correct the deviation of the measured water level data, combine the obtained data series after deviation correction, and take the arithmetic mean of the daily water level values ​​of the overlapping periods.

[0025] Step S3.5: Perform Gaussian filtering on all water level values ​​to establish a time series of lake and reservoir water levels.

[0026] Preferably, in step S4, a model is established to show the relationship between the area and water level of the lake / reservoir. Since it is difficult to obtain area and water level data for the same day, cubic spline curve interpolation is performed on the water level time series data based on the area time series to reconstruct the time series of lake / reservoir area and water level changes.

[0027] Preferably, in step S5, a model for estimating changes in lake and reservoir water storage is constructed based on the time series of changes in lake and reservoir area and water level, the changes in lake and reservoir water storage are monitored, and the characteristics of changes in lake and reservoir water storage are analyzed.

[0028] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0029] 1. This invention provides a method for monitoring changes in lake and reservoir water storage in areas without ground data, which can reduce data dependence and weather influence, and obtain high temporal resolution information on changes in lake and reservoir water storage.

[0030] 2. The method of this invention utilizes multi-source radar remote sensing data to achieve rapid acquisition of large-scale water body monitoring and change information, reducing the consumption of manpower and material resources and greatly saving monitoring costs. Attached Figure Description

[0031] Figure 1 This is a flowchart of the multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to the present invention.

[0032] Figure 2 A flowchart of a method for constructing time series data of lake and reservoir areas provided in an embodiment of the present invention;

[0033] Figure 3 A flowchart of a method for constructing time series data of lake and reservoir water levels provided in an embodiment of the present invention;

[0034] Figure 4 A rendering of the partial boundary extraction of Lake Victoria in 2021, provided for an embodiment of the present invention;

[0035] Figure 5 The image shows the results of constructing and reconstructing the water level, area, and time of Lake Victoria in 2021, as provided in this embodiment of the invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the application will be further described in detail below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments involved in this invention. All non-innovative embodiments based on these embodiments by other researchers in the art are within the protection scope of this invention. Furthermore, the step numbers in the embodiments of this invention are only set for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0037] This invention discloses a multi-source radar remote sensing method for monitoring changes in lake and reservoir water storage in areas of Africa where no ground data is available. Figure 1 As shown, it includes the following steps:

[0038] Step S1: Collect multi-source radar remote sensing data of each lake and reservoir in the area to be measured, including SAR image data and spaceborne radar altimeter data, and perform data preprocessing respectively.

[0039] Step S2: Using the preprocessed SAR image data of each lake and reservoir, the area time series of the lake and reservoir is obtained by introducing the SBGFRLS (Sparse Bregman Gradient Flow with Reinitialization and Level Set) active contour model.

[0040] Step S3: Using the preprocessed altimeter data of each lake and reservoir from the spaceborne radar, calculate the water level of the lake and reservoir on the day the spaceborne radar passes over and perform deviation correction to obtain the water level time series of the lake and reservoir.

[0041] Step S4: Based on the area time series obtained in step S3 and the water level time series obtained in step S4, establish a model of the relationship between lake / reservoir area and water level change, and reconstruct the lake / reservoir area and water level change time series.

[0042] Step S5: Based on the reconstructed time series of lake and reservoir area and water level changes, construct an estimation model for lake and reservoir water storage changes, monitor the water storage changes of each lake and reservoir in the area to be measured, and analyze the characteristics of lake and reservoir water storage changes.

[0043] In one embodiment of the present invention, Lake Victoria, a tectonic lake located in east-central Africa, is selected as the target area. Sentinel-1 remote sensing imagery is used as SAR imagery for area time series construction, and Sentinel-3 and Jason-3 data are used as satellite-borne radar altimeter data for water level time series construction. The specific process is as follows:

[0044] Step 1: Obtain Sentinel-1 data for the entire year of 2021 as SAR imagery, such as... Figure 2 As shown, orbit correction, radiometric correction, speckle noise suppression using the Refined Lee filter method, topographic correction, and decibel reduction are performed sequentially to obtain the preprocessed SAR image.

[0045] Acquire Sentinel-3 and Jason-3 data for the entire year of 2021 as data for the spaceborne radar altimeter, such as... Figure 3 As shown, the coordinates of the sub-satellite point are obtained based on latitude and longitude information, and the altimeter data of that point is extracted.

[0046] Step 2: Based on SAR image data from January 2021, such as... Figure 4 As shown, for the preprocessed SAR image, the VV and VH bands with different polarization modes are selected, and the water index SDWI is calculated according to the following formula to obtain the SDWI image.

[0047] SDWI = ln(10·VV·VH) - 8

[0048] In the formula, VV and VH represent the co-polarized backscattering value and the cross-polarized backscattering value corresponding to the SAR image, respectively.

[0049] Since lakes and reservoirs are closed and have boundaries, the K-means clustering algorithm is used to classify SDWI images, generating binary images containing two main categories: water bodies and non-water bodies, thus obtaining the initial water body outline map.

[0050] Furthermore, this embodiment introduces the SBGFRLS active contour model to optimize the water body boundary extraction method and improve the accuracy of the water body contour.

[0051] Use the initial water body profile as the initial profile of the SBGFRLS active profile model, and initialize the level set function Φ according to the following formulas; calculate the inner and outer means c1 and c2 of the profile curve; evolve the level set function; regularize the level set function using Gaussian filtering; check whether the evolution of the level set function has converged. If not, return to recalculate the inner and outer means of the profile curve.

[0052] The final water body boundary is obtained when the iteration stops.

[0053]

[0054]

[0055] In the formula, I(x, y) represents the original image; Ω represents the feature space of the image; c1 is the average gray value of the image inside the curve; c2 is the average gray value of the image outside the curve; α is the constant velocity; SPF is the pressure sign function; The gradient of the pressure sign function; φ represents the gradient of the level set function φ; div represents the divergence operator; The equation representing the evolution of the level set function φ.

[0056] Since in addition to the lakes and reservoirs that need to be extracted, many rivers, puddles and other water bodies in the surrounding area will also be extracted in the image, this embodiment further divides the final water body boundary into connected components, selects the connected components with larger areas, and retains them as the final water body area to form a water body distribution map.

[0057] Then, the number of water body pixels in the water distribution map is calculated and multiplied by the ground resolution of a single pixel to estimate the actual area of ​​the water body.

[0058] S = 10 * 10 * N

[0059] In the formula, S represents the final water area, and N represents the total number of pixels occupied by the lake.

[0060] Furthermore, based on SAR image data from February to December 2021, step 2 was repeated to obtain monthly area data of Lake Victoria in 2021, thus establishing a time series of Lake Victoria's area in 2021.

[0061] Step 3: For the Sentinel 3 satellite-borne radar altimeter data, use the vector boundary data of Lake Victoria to filter out the elevation points that fall on the water surface, and then perform geophysical correction and waveform retracking correction on the selected elevation point data in sequence.

[0062] The corrected elevation point data are arranged in ascending order of latitude to form a one-dimensional array. Outliers are removed using the 3σ criterion, and the average value is obtained to get the water level value for that day.

[0063]

[0064] In the formula, σ is the mean square error; H i is a single water level value within a period; n is the number of water level values ​​within that period; X is the average water level within that period; and m is the number of water level values ​​remaining after removing outliers.

[0065] The same correction and data removal steps were performed on the data obtained from the radar altimeter on the Jason-3 to obtain a set of water level values.

[0066] It should be noted that different radar altimeter elevation systems are different, and there are certain differences between them. Therefore, it is necessary to classify them according to the elevation system to facilitate the subsequent conversion of the height measurement data to the same elevation system.

[0067] Specifically, the water level data are classified according to different elevation systems. The first type of altimetry data is preprocessed to obtain altimetry water level values ​​based on the EGM2008 (Global Ultra-High Order Earth Gravity Field Model) geoid model. The second type of altimetry data is processed to obtain altimetry water level values ​​based on non-EGM2008 geoid models. The first type of altimetry water level values ​​based on the EGM2008 geoid model are used as the reference benchmark to convert water levels observed by different satellites into the same elevation benchmark.

[0068] Since both altimeters in this embodiment are based on the EGM2008 geoid model, reference system conversion is not required. However, it is necessary to consider the systematic errors between different altimeters. Using data from overlapping observation periods between satellites, the altimeter data are sequentially corrected for deviations according to the following formula to eliminate systematic errors:

[0069]

[0070] In the formula, The water level measured by satellite A on day i after eliminating systematic errors; The water level was measured by satellite A on day i before systematic errors were eliminated. The water level value monitored by satellite A on day j within the overlapping observation period; is the water level value monitored by satellite B on day j within the overlapping observation period; n is the sample size of paired data during the overlapping observation period.

[0071] Furthermore, the data series obtained in the previous step, after deviation correction, are combined to obtain the daily water level for the cross-period:

[0072]

[0073] In the formula, H i This represents the final water level value on day i during the cross-period time period; The water level value of satellite A on day i within the cross-time period after bias correction; Let be the water level value of satellite B on the i-th day within the cross-time period after bias correction.

[0074] Next, Gaussian filtering was applied to all daily water levels to establish a time series of Lake Victoria's water levels.

[0075] Step 4: Since different satellites arrived above Lake Victoria on different dates to collect data, the collection times of Sentinel-1 remote sensing image data and Sentinel-3 and Jason-3 satellite radar altimeter data are also different. It is necessary to establish a model of the relationship between area and water level changes based on the lake area and water level values ​​of Lake Victoria in different time periods.

[0076] like Figure 5 As shown, the area time series of Lake Victoria obtained in step 3 is used to interpolate the water level time series of Lake Victoria obtained in step 4 using cubic spline curves to obtain the water level value of Lake Victoria for the corresponding date of the area image, thus reconstructing the area and water level change time series of Lake Victoria.

[0077] Step 5: Since the size of the lake surface and the lake bottom are different, based on the area and water level change time series of the reconstructed Lake Victoria, Lake Victoria is divided into multiple units from the lake surface to the lake bottom according to the height. The volume of each unit corresponds to the water storage over a period of time. By adding up all the units, the total water storage of Lake Victoria can be estimated.

[0078] Specifically, assuming each unit has the geometric characteristics of a frustum or prism, the water level changes between adjacent time intervals, and the change in water storage is reflected in the change in water level. The change before and after the change corresponds to the volume of one unit. The monthly change in the reservoir's water storage is calculated using the following volume calculation formula:

[0079]

[0080] In the formula, ΔV i S represents the change in water storage between two adjacent dates. i-1 and S i The water area on two adjacent dates, h i and h i-1 These are the water level elevations for the corresponding days of two adjacent dates.

[0081] Furthermore, based on the monthly changes in lake and reservoir water storage, the monthly changes in Lake Victoria's water storage in 2021 can be monitored. It can be found that there are obvious seasonal changes in Lake Victoria's water storage. In May and October each year, the water storage increases, corresponding to the local rainy season. Although it fluctuates from June to September, the water storage change remains in a negative state, indicating that the water storage continues to decrease, corresponding to the local dry season. The extracted results are relatively reasonable.

[0082] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-source radar remote sensing method for monitoring changes in lake and reservoir water storage in areas of Africa without ground data, characterized in that, Includes the following steps: Step S1: Collect multi-source radar remote sensing data of each lake and reservoir in the area to be measured, including SAR image data and spaceborne radar altimeter data, and perform data preprocessing respectively. Step S2: Using the preprocessed SAR image data of each lake and reservoir, the area time series of the lakes and reservoirs is obtained by introducing the SBGFRLS active contour model, as follows: Step S2.1: For the preprocessed SAR image, select the VV and VH bands with different polarization modes, calculate the water index SDWI, and obtain the SDWI image: SDWI=ln(10·VV·VH)-8 (1) In the formula, VV and VH represent the same polarization backscattering value and cross polarization backscattering value corresponding to the SAR image, respectively; Step S2.2: Use the K-means clustering algorithm to classify the SDWI image and generate a binary map containing two categories: water bodies and non-water bodies, to obtain the initial water body outline map; Step S2.3: Use the initial water body contour as the initial contour of the SBGFRLS active contour model, and determine the water body boundary through iteration; Step S2.4: Perform connected component segmentation on the water body boundary, select connected components with an area greater than the preset lake / reservoir area threshold as water body regions, form a water body distribution map, count the number of water body pixels in the water body distribution map and estimate the actual area of ​​the water body, and establish a time series of lake / reservoir area. Step S3: Using the preprocessed altimeter data from the spaceborne radar of each lake and reservoir, calculate the water level values ​​of the lakes and reservoirs on the day the spaceborne radar passes over, and perform deviation correction to obtain the water level time series of the lakes and reservoirs. The method is as follows: Step S3.1: For the preprocessed spaceborne radar altimeter data, use the water body boundary determined in step S2.3 as the vector boundary data of lakes and reservoirs, filter out the elevation points that fall within the water surface, and perform geophysical correction and waveform retracking correction on the elevation points within the water surface in sequence to obtain the corrected elevation point data. Step S3.2: Arrange the corrected elevation point data into a one-dimensional array according to latitude from smallest to largest, calculate the standard error σ of the data in the array, use the 3σ criterion to remove outliers, and average the remaining elevation point data to obtain the water level of the lake on the day the spaceborne radar passes over. Step S3.3: For altimeter data from different spaceborne radars, execute steps S3.1 and S3.2 respectively to obtain the water level values ​​of the lakes and reservoirs on the day each spaceborne radar passes over. Classify and preprocess the water level data according to the different spaceborne radar altimeter elevation systems to obtain the altimeter water level values ​​based on the EGM2008 model and the altimeter water level values ​​based on non-EGM2008 models. Using the altimeter water level values ​​based on the EGM2008 model as the benchmark, the altimeter water level values ​​of different spaceborne radars are converted to the same elevation system. Step S3.4: Correct the deviation of the water level values ​​during the overlapping observation period between each satellite radar, and combine the corrected height and water level data to calculate the final water level value for the day during the overlapping observation period. Step S3.5: Perform Gaussian filtering on all daily final water level values ​​to establish a time series of lake and reservoir water levels; Step S4: Based on the area time series obtained in step S3 and the water level time series obtained in step S4, establish a model of the relationship between lake / reservoir area and water level change, and reconstruct the lake / reservoir area and water level change time series. Since the SAR image data and the spaceborne radar altimeter data are acquired at different times, cubic spline curve interpolation is performed on the lake and reservoir water level time series obtained in step S3.5 based on the time of the lake and reservoir area time series obtained in step S2.4 to obtain the lake and reservoir area and water level data corresponding to the same time, and reconstruct the lake and reservoir area and water level change time series. Step S5: Based on the reconstructed time series of lake and reservoir area and water level changes, construct an estimation model for lake and reservoir water storage changes, monitor the water storage changes of each lake and reservoir in the area to be measured, and analyze the characteristics of lake and reservoir water storage changes.

2. The multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to claim 1, characterized in that: In step S1, SAR image data is acquired and preprocessed, including orbit correction, radiometric correction, terrain correction, and the Refined Lee filtering method is used to suppress speckle noise and perform decibel reduction. Altimeter data from different spaceborne radars are acquired and preprocessed. The coordinates of the nadir point are obtained based on latitude and longitude information, and the altimeter data of the nadir point is extracted.

3. The multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to claim 1, characterized in that: In step S2.3, the water body boundary is determined iteratively, as follows: S2.3.1 Initialize the level set function Φ and calculate the inner and outer mean values ​​of the contour curve; S2.3.2 Evolution level set function, using Gaussian filtering to regularize the level set function; S2.3.3 Check if the evolution of the level set function has converged. If it has not converged, return to steps S2.3.1 to S2.3.2 to recalculate the internal and external means of the contour curve. When the iteration stops, the water boundary of the lake / reservoir is obtained. The iterative formula is as follows: In the formula, I(x,y) is the original image; Ω is the feature space of the image; c1 is the average gray level of the image inside the curve; c2 is the average gray level of the image outside the curve; α is a constant velocity; SPF is the pressure sign function. The gradient of the pressure sign function; φ represents the gradient of the level set function φ; div represents the divergence operator; The equation representing the evolution of the level set function φ.

4. The multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to claim 3, characterized in that: In step S2.4, the actual area of ​​the water body is estimated by multiplying the number of water body pixels in the water distribution map by the ground resolution of a single pixel, using the following formula: S = 10 * 10 * N (4) In the formula, S is the estimated water area and N is the total number of pixels occupied by the lake / reservoir water body; the number of water body pixels is converted into the actual water body area through formula (4) to establish the time series of lake / reservoir area.

5. The multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to claim 4, characterized in that: In step S3.2, the mean error σ of the data in the array is calculated according to formula (5), and the elevation point data after removing outliers is averaged according to formula (6) to obtain the water level H of the lake on the day the spaceborne radar passes over. avg : In the formula, σ is the mean square error; H i X is a single water level value within a period; n is the average water level within that period; m is the number of water level values ​​within that period; and m is the number of water level values ​​remaining after removing outliers.

6. The multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to claim 5, characterized in that: In step S3.4, the measured water level data is corrected for deviation using the following formula: In the formula: The water level measured by satellite A on day i before eliminating elevation system errors; The water level value monitored by satellite A on day j within the overlapping observation period; The value of the water level monitored by satellite B on day j within the overlapping observation period (reference baseline); n is the sample size of paired data within the overlapping observation period, denoted as nj. Using this as a reference, we obtain The water level value measured by satellite A on day i after eliminating elevation system errors; The formula for calculating the final water level value for a single day during the overlapping observation period is as follows: In the formula, H i This represents the final water level value on day i during the overlapping observation period; The water level value on day i within the overlapping observation period of satellite A after bias correction; The water level value on day i within the overlapping observation period after bias correction by satellite B.

7. The multi-source radar remote sensing monitoring method for changes in lake and reservoir water storage according to claim 6, characterized in that: In step S5, a model for estimating changes in lake and reservoir water storage is constructed, as follows: Step S5.1: Based on the time series of lake and reservoir area and water level changes, divide the lake and reservoir into several units from the lake surface to the lake bottom according to height. Assuming that each unit has the geometric characteristics of a frustum or prism, calculate the volume ΔV of each unit. i : In the formula, S i-1 and S i The water area on two adjacent dates; h i and h i-1 These represent the water level elevations on two adjacent dates; ΔV i This represents the change in water storage between two adjacent dates; Step S5.2: Calculate the volume ΔV of each unit. i As a measure of the change in water storage in a lake or reservoir between two adjacent dates, the volumes of all units are added together to estimate the total water storage of the entire lake or reservoir.

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