Water level monitoring method and device based on SWOT altimetry satellite, equipment and storage medium

Abstract

The application discloses a water level monitoring method and device based on a SWOT height measurement satellite, equipment and a storage medium, relates to the technical field of satellite remote sensing, and comprises the following steps: acquiring secondary high-resolution grid data of a water surface terrain observation satellite and boundary vector data of a lake to be monitored; extracting all water surface elevation pixel observation values located in the water area range of the lake to be monitored and corresponding pixel-level quality control indexes from the grid data based on the boundary vector data; judging the pixel-level quality control indexes based on a preset quality threshold, to obtain a primary screening data set; performing outlier rejection on each candidate pixel in the primary screening data set based on a median absolute deviation, to obtain a secondary screening data set; and performing aggregate calculation on water surface elevation values of each effective pixel in the secondary screening data set, to determine a representative water level of the lake to be monitored at an observation time, so that the accuracy and availability of high-resolution products of the water surface terrain observation satellite in inland lake water level monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing technology, and in particular to a method, apparatus, equipment and storage medium for water level monitoring based on SWOT altimetry satellites. Background Technology

[0002] Lake water levels are a core indicator reflecting regional hydrological cycles and water resource reserves. However, ground-based hydrological stations are sparsely distributed and limited by administrative divisions, making it difficult to cover small and medium-sized lakes in remote areas. While satellite altimetry technology can achieve large-scale monitoring, early radar altimetry satellites could only provide one-dimensional profile data and had large orbital spacing. Laser altimetry satellites, on the other hand, are severely affected by cloud cover and have low temporal resolution, failing to meet the needs of high-frequency monitoring.

[0003] For the first time, a water surface topography observation satellite has employed wide-swath interferometry technology, providing high-resolution two-dimensional water surface elevation data, representing a breakthrough in inland lake monitoring. However, the Ka-band is sensitive to water vapor and prone to tropospheric delay errors. Mixed pixels at the land-water interface contribute to land pollution, and multipath effects cause anomalous jumps, resulting in a long-tailed, non-normally distributed data. Existing methods that rely heavily on quality labels lose edge data, and traditional standard deviation methods, which assume a normal distribution, cannot effectively identify outliers and may even mistakenly delete true water level fluctuation signals. Therefore, a data quality control method adapted to non-Gaussian distribution characteristics is urgently needed.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a water level monitoring method, device, equipment, and storage medium based on SWOT altimeter satellites. This invention aims to solve the technical problem of accurately identifying and eliminating abnormal observations when SWOT wide-swath interferometric radar data is subject to high data dispersion and non-normal distribution due to multipath effects, ground object interference, and thermal noise.

[0006] To achieve the above objectives, the present invention provides a water level monitoring method based on SWOT altimetry satellites, the water level monitoring method based on SWOT altimetry satellites comprising the following steps: Acquire high-resolution secondary raster data from water surface topography observation satellites and boundary vector data of the lakes to be monitored; Based on the boundary vector data, extract all water surface elevation pixel observations and corresponding pixel-level quality control indicators located within the monitored lake area from the raster data; The pixel-level quality control indicators are judged based on preset quality thresholds to obtain a preliminary screening dataset; Outlier removal is performed on each candidate cell in the primary screening dataset based on the median absolute deviation to obtain the secondary screening dataset. The water surface elevation values ​​of each valid cell in the secondary screening dataset are aggregated and calculated to determine the representative water level of the lake to be monitored at the observation time.

[0007] In one embodiment, the step of judging the pixel-level quality control indicators based on a preset quality threshold to obtain a preliminary screening dataset includes: Read pixel-level quality control indicators; The value of the pixel-level quality control index is compared with the preset fully valid identifier to obtain the first comparison result; The value of the pixel-level quality control index is compared with a preset suspicious usable identifier to obtain a second comparison result; When the value of the pixel-level quality control index is equal to the preset fully valid identifier or the preset suspicious usable identifier in the first comparison result or the second comparison result, the water surface elevation pixel observation value with the same pixel position as the pixel-level quality control index is retained as a candidate pixel. A primary screening dataset is constructed based on all the retained candidate pixels.

[0008] In one embodiment, the step of removing outliers from each candidate pixel in the primary screening dataset based on the median absolute deviation to obtain the secondary screening dataset includes: Determine the first median of the water surface elevation values ​​for all candidate pixels in the primary screening dataset; Determine the absolute value of the difference between the water surface elevation value of each candidate pixel and the first median to obtain the first absolute deviation corresponding to each candidate pixel; Determine the median absolute deviation of the first absolute deviation for all candidate pixels; Multiply the absolute deviation of the median by a preset scaling factor to obtain the robust standard deviation; Calculate the lower limit and upper limit of the effective data retention interval based on the first median and the robust standard deviation; Construct an effective data retention interval based on the lower limit and the upper limit of the interval; Determine whether the water surface elevation value of each candidate pixel is within the valid data retention interval to obtain the location determination result; Based on the location determination result, candidate pixels located within the effective data retention interval are retained as effective pixels, and candidate pixels located outside the effective data retention interval are removed to obtain a secondary filtered dataset.

[0009] In one embodiment, the step of aggregating and calculating the water surface elevation values ​​of each valid cell in the secondary screening dataset to determine the representative water level of the lake to be monitored at the observation time includes: Obtain the total number of valid pixels in the secondary filtered dataset and the water surface elevation value of each valid pixel; The water surface elevation values ​​of each effective pixel are summed to obtain the total elevation. Dividing the sum of the elevations by the total number of effective pixels yields the representative water level of the lake to be monitored at the observation time.

[0010] In one embodiment, the method further includes: Obtain the lateral distance of the effective pixel relative to the satellite nadir point; Based on the preset distance interval length, the effective pixels are segmented and statistically analyzed to obtain the error distribution characteristics of each horizontal distance interval; Based on the error distribution characteristics, effective pixels located at the edge or center of the track are assigned weights or subjected to geometric filtering.

[0011] In one embodiment, the method further includes: Obtain raw altimetry data of the reference altimeter satellite for the lake to be monitored at the observation time, wherein the raw altimetry data includes the satellite orbital altitude and the altimeter observation distance; The original altimetry data is subjected to ionospheric delay correction, dry tropospheric delay correction, wet tropospheric delay correction, solid earth tide correction and polar tide correction to obtain various correction values; The reference water level is determined based on the satellite orbital altitude, the altimeter observation distance, and the various correction values. The representative water level is spatiotemporally matched with the reference water level to obtain a matching data pair; Based on the matching data, determine the deviation value, root mean square error value, and correlation coefficient; The effectiveness of the representative water level is evaluated based on the deviation value, the root mean square error value, and the correlation coefficient.

[0012] In one embodiment, the step of extracting all water surface elevation pixel observations and corresponding pixel-level quality control indicators located within the monitored lake area from the raster data based on the boundary vector data includes: Acquire secondary high-resolution raster data products from water surface topography observation satellites, wherein the raster data products include water surface elevation raster data and quality control identifier raster data; Construct a spatial mask based on the boundary vector file of the lake to be monitored; Align the geographic coordinate system of the spatial mask with the geographic coordinate system of the water surface elevation raster data to obtain the aligned spatial mask; Based on the aligned spatial mask, the water surface elevation raster data is cropped, and all water surface elevation pixel observations within the range of the aligned spatial mask are extracted. Pixel-level quality control indicators are extracted from the quality control identifier raster data corresponding to each water surface elevation pixel observation value.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a water level monitoring device based on a SWOT altimeter satellite, the device comprising: The data acquisition module is used to acquire secondary high-resolution raster data from water surface topography observation satellites and boundary vector data of the lake to be monitored; based on the boundary vector data, it extracts all water surface elevation pixel observations and corresponding pixel-level quality control indicators located within the water area of ​​the lake to be monitored from the raster data. The primary screening module is used to judge the pixel-level quality control indicators based on a preset quality threshold to obtain a primary screening dataset. The secondary filtering module is used to remove outliers from each candidate cell in the primary filtered dataset based on the median absolute deviation, thereby obtaining the secondary filtered dataset. The water level calculation module is used to aggregate and calculate the water surface elevation values ​​of each valid cell in the secondary screening dataset to determine the representative water level of the lake to be monitored at the observation time.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a water level monitoring device based on SWOT altimetry satellites. The device includes: a memory, a processor, and a water level monitoring program based on SWOT altimetry satellites stored in the memory and executable on the processor. The water level monitoring program based on SWOT altimetry satellites is configured to implement the steps of the water level monitoring method based on SWOT altimetry satellites as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a water level monitoring program based on SWOT altimetry satellites. When the SWOT altimetry satellite-based water level monitoring program is executed by a processor, it implements the steps of the water level monitoring method based on SWOT altimetry satellites as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the water level monitoring method based on SWOT altimeter satellite as described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: By combining pixel-level quality control indicators with a median absolute deviation filtering strategy, this method effectively addresses the problem that traditional standard deviation methods fail to effectively identify outliers when high-resolution satellite data for water surface topography exhibits non-Gaussian distribution characteristics due to multipath effects, ground object interference, and thermal noise in inland lakes. This is because the mean is easily skewed by extreme values. In the initial screening stage, this method retains marginally valid data through a lenient quality threshold. In the secondary filtering stage, it utilizes the median absolute deviation to construct a robust standard deviation and adaptive retention interval. This achieves the goal of effectively removing outliers caused by instrument thermal noise, multipath effects, and algorithm inversion errors while maximizing the retention of effective signals reflecting true hydrological dynamics. This avoids the data sparsity caused by strict filtering and the loss of true extreme water level information due to excessive removal in traditional methods. Verified by measured data, this method significantly reduces the root mean square error and bias of the observed data. It can accurately capture the seasonal variation characteristics and extreme hydrological events of lake water levels without relying on external auxiliary data such as ground hydrological stations, achieving an optimal balance between data integrity and monitoring accuracy. This significantly improves the availability and reliability of wide-swath interferometric radar data in inland hydrological dynamic monitoring. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the water level monitoring method based on SWOT altimeter satellites in this application. Figure 2 This is a schematic diagram of the water level monitoring process provided in Embodiment 1 of the water level monitoring method based on SWOT altimeter satellite of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the water level monitoring method based on SWOT altimeter satellites in this application. Figure 4 This is a schematic diagram of the module structure of the water level monitoring device based on the SWOT altimeter satellite according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the water level monitoring method based on SWOT altimeter satellite in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a water level monitoring device based on a SWOT altimeter satellite. The following description uses a water level monitoring device based on a SWOT altimeter satellite as an example to illustrate this embodiment and the subsequent embodiments.

[0025] Based on this, the embodiments of this application provide a water level monitoring method based on SWOT altimeter satellites, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the water level monitoring method based on SWOT altimeter satellites in this application.

[0026] In this embodiment, the water level monitoring method based on SWOT altimeter satellite includes steps S10~S50: Step S10: Acquire secondary high-resolution raster data from the water surface topography observation satellite and boundary vector data of the lake to be monitored; like Figure 2 As shown, Figure 2This diagram illustrates the workflow for SWOT satellite inland lake water level monitoring based on a combination of quality labeling and MAD filtering. First, SWOT L2_HR_Raster data is acquired and combined with lake boundary vector data. Through masking, water surface elevation (WSE) pixels within the lake's water area are obtained. Next, the WSE pixels are initially screened based on quality labels to obtain a candidate pixel set (wse_qual ≤ 1). Then, the MAD filtering method is used to process the candidate pixel set. This process includes two parallel steps: calculating the median and absolute deviation of the water surface elevation, and constructing the robust standard deviation and retention interval, thus obtaining the effective pixels after removing outliers. Subsequently, spatial aggregation calculation (arithmetic mean) is performed on the effective pixels after outlier removal to obtain the representative water level elevation of the lake. Finally, Sentinel-3A altimeter data corrected for geophysical errors is used as validation data and compared with the representative water level elevation of the lake to evaluate accuracy (Bias, RMSE, correlation).

[0027] In its implementation, this solution collects L2_HR_Raster_100m water surface elevation data from the SWOT altimeter satellite Level 2 high-resolution hydrological data product. The water surface elevation pixels within the lake area are extracted using a lake boundary vector mask, and the initial data are preliminarily screened using the pixel-level quality control index (wse_qual) provided by the data product. The median absolute deviation method (MAD) was used to perform secondary filtering on the water surface elevation data after the initial screening. Outliers were identified and removed by constructing a robust standard deviation. Based on the effective water surface elevation pixels retained after MAD filtering, the representative water level of the lake at that moment is obtained by aggregation calculation. The effectiveness and accuracy of the proposed SWOT lake level monitoring method based on a combination of quality labeling and MAD strategy were evaluated using error-corrected Sentinel-3A altimeter data.

[0028] It should be noted that the surface topography observation satellite refers to the Surface Water and Ocean Topography (SWOT) satellite, launched in 2022. It was the first satellite to employ wide-swath interferometry technology, and its onboard Ka-band radar interferometer provides high spatial resolution two-dimensional water surface elevation data globally. Additionally, the Level 2 high-resolution raster data refers to the Level 2 High Resolution Raster data product, L2_HR_Raster data, generated after Level 1 processing. This data is stored in a 100-meter grid format and contains pre-processed water surface elevation information. Furthermore, the boundary vector data of the lake to be monitored refers to the geographic information data of the lake boundary stored in vector format, typically in Shapefile format, used to accurately define the water area of ​​the lake and distinguish the water body from the land boundary.

[0029] Understandably, step S10 obtains the aforementioned SWOT satellite secondary high-resolution raster data and the boundary vector data of the lake to be monitored from a data center, such as NASA Earthdata or CNES data center, to provide a basic data source for subsequent water level monitoring. In this embodiment, a typical lake is used as the study area, and continuous observation data from one year is selected for processing to ensure the temporal continuity and spatial integrity of the data.

[0030] Step S20: Based on the boundary vector data, extract all water surface elevation pixel observations and corresponding pixel-level quality control indicators from the raster data within the area of ​​the lake to be monitored. It should be noted that the water surface elevation pixel observation value refers to the water surface elevation measurement value corresponding to each grid cell (pixel) in the raster data. This elevation result is obtained from satellite radar interferometry inversion, with each pixel representing the water surface elevation within a 100-meter spatial resolution range. Additionally, the pixel-level quality control index refers to the quality evaluation label, wse_qual, individually marked for each pixel in the SWOT data product. This label indicates the reliability level of the pixel observation value and considers factors such as interferometric phase quality, radar echo characteristics, and the confidence level of the inversion algorithm.

[0031] Understandably, step S20 constructs a spatial mask based on the boundary vector data obtained in step S10, extracts all water surface elevation pixel observations within the lake area and their corresponding pixel-level quality control indicators from the raster data, and achieves accurate positioning and data cropping of the study area, removing background data of land and non-lake water areas.

[0032] In one feasible implementation, step S20 includes steps A11 to A15: Step A11: Obtain the secondary high-resolution raster data product from the water surface topography observation satellite, which includes water surface elevation raster data and quality control identifier raster data; It should be noted that water surface elevation raster data refers to raster format data containing two-dimensional water surface elevation information, with each cell storing an elevation value reflecting the water surface elevation at the corresponding geographical location. Additionally, quality control label raster data refers to raster data that spatially corresponds to the water surface elevation raster data and stores the quality level label for each cell. Its spatial resolution is consistent with the water surface elevation raster data, and it is used to identify the quality level of the elevation data at the corresponding location.

[0033] Understandably, step A11 retrieves the complete SWOT Level 2 high-resolution product, namely the L2_HR_Raster_100m product, from the data center, while also acquiring water surface elevation raster data and quality control label raster data to ensure the spatial correspondence between elevation values ​​and quality labels, laying the foundation for subsequent joint processing.

[0034] Step A12: Construct a spatial mask based on the boundary vector file of the lake to be monitored; It should be noted that a spatial mask is a mask layer generated based on vector boundaries to define a spatial range. It is used to distinguish between the target area and the background area. Areas within the mask range are marked as valid, while areas outside the range are marked as invalid.

[0035] Understandably, step A12 generates a spatial mask based on the boundary vector file of the lake to be monitored, converts the vector boundary into a mask format that can be used for raster operations, and determines the spatial range for subsequent processing.

[0036] Step A13: Align the geographic coordinate system of the spatial mask with the geographic coordinate system of the water surface elevation raster data to obtain the aligned spatial mask; Understandably, step A13 matches and aligns the geographic coordinate system of the spatial mask with the geographic coordinate system of the water surface elevation raster data to ensure that the two correspond completely in spatial location, eliminate positional deviations caused by coordinate system differences, and obtain an aligned spatial mask that can be used for precise cropping.

[0037] Step A14: Crop the water surface elevation raster data based on the aligned spatial mask and extract all water surface elevation pixel observations within the aligned spatial mask range; Understandably, step A14 uses the aligned spatial mask to crop the water surface elevation raster data, extracts all water surface elevation pixel observations falling within the mask range, removes land and non-target water area data outside the lake range, and retains elevation observations located within the lake area to be monitored.

[0038] Step A15: Extract pixel-level quality control indicators corresponding to the observed values ​​of each water surface elevation pixel based on the quality control identifier raster data.

[0039] Understandably, step A15 extracts pixel-level quality control indicators corresponding to the location of each water surface elevation pixel observation value in step A14 based on the quality control identifier raster data, establishes a one-to-one correspondence between elevation values ​​and quality identifiers, and forms a data pair containing elevation values ​​and quality identifiers.

[0040] Step S30: Judge the pixel-level quality control indicators based on the preset quality threshold to obtain the initial screening dataset; It should be noted that the preset quality threshold refers to a pre-set critical value for the quality level used to filter data. In this embodiment, the threshold is set to 1, meaning that pixels with quality labels of 0 and 1 are retained, while pixels with quality labels of 2 and above are removed. This strategy aims to solve the problems of insufficient data volume due to retaining only pixels with quality labels of 0 and excessive noise introduced by retaining pixels with quality labels of 2. Furthermore, the primary screening dataset refers to the set of candidate pixels retained after the first round of quality screening, containing high-quality water surface elevation observations and their spatial distribution information, which serve as input for subsequent robust statistical filtering.

[0041] Understandably, step S30 uses a preset quality threshold to judge pixel-level quality control indicators, achieving a balance between data integrity and signal-to-noise ratio. This eliminates obvious measurement errors caused by severe land cover or land pollution while retaining valid observations in the lake's edge area, thus avoiding excessively sparse data.

[0042] It should be understood that the specific conditions for performing primary data filtering are as follows: Cells with a quality control index wse_qual value less than or equal to 1 are selected and retained; that is, cells with wse_qual = 0 (fully valid) and wse_qual = 1 (questionable but usable) are retained simultaneously, while cells with wse_qual > 1 (such as poor-quality data with wse_qual = 2) are removed. This strategy aims to solve the problems of retaining only wse_qual = 0 resulting in too little data and retaining wse_qual = 2 introducing too much noise, thereby achieving a balance between data integrity and signal-to-noise ratio.

[0043] In one feasible implementation, step S30 includes steps A21 to A25: Step A21: Read pixel-level quality control indicators; Understandably, step A21 reads the pixel-level quality control indicators corresponding to all water surface elevation pixel observations extracted in step A15, obtains the quality level value of each pixel, and provides input for subsequent comparison and judgment.

[0044] Step A22: Compare the values ​​of the pixel-level quality control indicators with the preset fully valid identifiers to obtain the first comparison result; It should be noted that the preset fully valid flag refers to a pre-set flag indicating the highest level of data quality. In this embodiment, the flag value is 0, indicating that the pixel observation values ​​are completely reliable, unaffected by significant interference, and mainly distributed in the open water area in the center of the lake. Furthermore, the first comparison result refers to the logical judgment result obtained by comparing the pixel-level quality control index with the preset fully valid flag, indicating whether the two values ​​are equal.

[0045] Understandably, step A22 compares the value of the pixel-level quality control index with the preset fully valid identifier to obtain a first comparison result indicating whether the two are equal.

[0046] Step A23: Compare the values ​​of the pixel-level quality control indicators with the preset suspicious usable indicators to obtain a second comparison result; It should be noted that the preset suspicious usable flag refers to a pre-defined level flag indicating that the data quality is moderate, with slight interference but containing valid information. In this embodiment, the flag value is 1, indicating that the pixel observation value is suspicious but potentially valid, and is mostly distributed in the lake transition zone or edge area. Furthermore, the second comparison result refers to the logical judgment result obtained by comparing the pixel-level quality control index with the preset suspicious usable flag, indicating whether the two values ​​are equal.

[0047] Understandably, step A23 compares the value of the pixel-level quality control index with a preset suspicious availability flag to obtain a second comparison result indicating whether the two are equal.

[0048] Step A24: When the value of the pixel-level quality control index in the first comparison result or the second comparison result is equal to the preset fully valid identifier or the preset suspicious usable identifier, the water surface elevation pixel observation value with the same pixel position as the pixel-level quality control index is retained as a candidate pixel. Understandably, in step A24, when the value of the pixel-level quality control index indicated by the first or second comparison result is equal to the preset fully valid identifier or the preset doubtful usable identifier (i.e., when the quality identifier is 0 or 1), the water surface elevation pixel observation with the same pixel position as the pixel-level quality control index is retained as a candidate pixel. This strategy solves the problems of insufficient data volume due to retaining only a quality identifier of 0 and excessive noise introduced by retaining a quality identifier of 2, thus achieving a balance between data integrity and signal-to-noise ratio.

[0049] Step A25: Construct a primary screening dataset based on all retained candidate pixels.

[0050] Understandably, step A25 summarizes all candidate pixels retained in step A24 to construct a primary screening dataset containing all pixels with a quality label of 0 or 1 and their water surface elevation values, which serve as input for subsequent median absolute deviation filtering.

[0051] Step S40: Based on the absolute deviation of the median, outliers are removed from each candidate pixel in the primary screening dataset to obtain the secondary screening dataset. It should be noted that median absolute deviation is a robust statistic based on the median. It measures the dispersion of data by calculating the median of the absolute deviations of each data point from the median. It is insensitive to outliers and is suitable for non-normally distributed data. It effectively avoids the problem of extreme values ​​skewing statistical characteristics in the traditional standard deviation method. Additionally, the secondary filtered dataset refers to the set of valid pixels retained after median absolute deviation filtering. Statistical outliers and isolated points have been removed, retaining only the valid data reflecting actual water level changes.

[0052] Understandably, step S40 removes outliers from each candidate pixel in the primary screening dataset based on the median absolute deviation. By constructing a robust standard deviation and an adaptive retention interval, it identifies and removes statistical outliers caused by multipath effects, land pollution, and thermal noise, while retaining effective signals that reflect real hydrological dynamics and avoiding the accidental deletion of normal seasonal water level fluctuations.

[0053] It should be understood that the application of the mean absolute deviation (MAD) method for secondary fine-tuning includes: Calculate the median of water surface elevation values ​​for all candidate pixels within the current observation date. The formula is:

[0054] in, For the first The water surface elevation value of each pixel The total number of candidate pixels; Calculate the water surface elevation value and median for each pixel. absolute deviation :

[0055] Calculate the median of all absolute deviations to obtain the median absolute deviation. :

[0056] Introducing a normal distribution consistency scaling factor Calculate the robust standard deviation of the data :

[0057] Construct an effective data retention interval ,in It is a multiple factor, with a value of 2; Determine whether each pixel falls within the reserved interval; if so... If it is an outlier, keep it; otherwise, mark it as an outlier and remove it.

[0058] Step S50: Aggregate the water surface elevation values ​​of each valid cell in the secondary screening dataset to determine the representative water level of the lake to be monitored at the observation time.

[0059] It should be noted that the representative water level refers to a single value obtained through aggregation calculation that can represent the water level height of the entire lake at the time of observation. It is usually expressed as an arithmetic mean, reflecting the overall water surface elevation level of the lake when the satellite passes over.

[0060] Understandably, step S50 aggregates and calculates the water surface elevation values ​​of each valid cell in the secondary screening dataset, determines the representative water level of the lake to be monitored at the observation time by using the arithmetic mean, and integrates the observation values ​​of multiple cells into a single lake water level index to generate a lake water level time series.

[0061] In one feasible implementation, step S50 includes steps A31 to A33: Step A31: Obtain the total number of valid pixels in the secondary filtered dataset and the water surface elevation value of each valid pixel; It should be noted that the total number of valid pixels refers to the number of valid pixels retained after filtering by median absolute deviation, i.e., the number of pixels contained in the secondary filtered dataset, and is used as the denominator when calculating the arithmetic mean.

[0062] Understandably, step A31 obtains the total number of valid pixels in the secondary filtering dataset, i.e., the total number of valid pixels, and at the same time obtains the water surface elevation value corresponding to each valid pixel, in order to prepare data for aggregation calculation.

[0063] Step A32: Sum the water surface elevation values ​​of each valid pixel to obtain the total elevation; It should be noted that the total elevation refers to the cumulative value of the water surface elevation of all valid pixels, that is, the algebraic sum of the elevation values ​​of each valid pixel in the secondary filtered dataset, which is used as the numerator when calculating the arithmetic mean.

[0064] Understandably, step A32 sums up the water surface elevation values ​​of each valid pixel obtained in step A31 to obtain the total elevation values ​​of all valid pixels, i.e., the total elevation.

[0065] Step A33: Divide the sum of elevations by the total number of valid pixels to obtain the representative water level of the lake to be monitored at the observation time.

[0066] Understandably, step A33 divides the sum of elevations obtained in step A32 by the total number of valid pixels acquired in step A31, calculates the arithmetic mean, and obtains the representative water level of the lake to be monitored at the observation time. This aggregated result is the lake water level observation value during this satellite transit, used to reflect the overall water level status of the lake.

[0067] Furthermore, after step S50, there are steps A41 to A43: Step A41: Obtain the lateral distance of the effective pixel relative to the satellite nadir point; It should be noted that the satellite nadir point refers to the vertical projection of the satellite's orbit onto the Earth's surface, i.e., the ground position directly below the satellite, and is the geometric center reference point for the satellite's observation swath. Additionally, the lateral distance refers to the horizontal distance between the pixel position and the satellite nadir point, used to characterize the pixel's relative position within the satellite's observation swath, reflecting whether the pixel is located at the center or edge of the orbit.

[0068] Understandably, step A41 obtains the lateral distance of each effective pixel relative to the satellite nadir point, which is retained in step S40, to provide spatial position parameters for analyzing the impact of orbital geometry on measurement accuracy.

[0069] Step A42: Based on the preset distance interval length, segment and statistically analyze the effective pixels to obtain the error distribution characteristics of each horizontal distance interval; It should be noted that the preset distance interval length refers to the pre-set distance interval used for segmented statistics. In this embodiment, this length is set to 5 kilometers to divide the observation swath into several intervals. Additionally, the error distribution characteristics refer to the statistical characteristics of data errors within different lateral distance intervals, including mean absolute error and standard deviation, reflecting the differences in data quality at different locations.

[0070] Understandably, step A42 groups the effective pixels according to the horizontal distance based on the preset distance interval length, for example, every 5 kilometers is an interval, calculates the error distribution characteristics of the data in each horizontal distance interval, and analyzes the data quality difference between the orbit center and the edge area.

[0071] Step A43: Based on the error distribution characteristics, assign weights or perform geometric filtering on the effective pixels located at the edge or center of the track.

[0072] Understandably, step A43 identifies high-error regions located at the edge or center of the track splicing based on the error distribution characteristics obtained in step A42, assigns lower weights to the effective pixels in these regions to reduce their impact on the final water level result, or further performs geometric filtering to improve monitoring accuracy.

[0073] It should be understood that the SWOT observation data is segmented and statistically analyzed according to the lateral distance from the satellite's nadir point (e.g., every 5km is an interval); the error distribution characteristics of the data after MAD filtering described in step S4 are analyzed in different lateral distance intervals; based on the analysis results, the high-error area data located at the edge of the orbit or the center of the splice are assigned a lower weight or further geometric filtering is performed.

[0074] In one possible implementation, after step S50, steps A51 to A56 are also included: Step A51: Obtain the raw altimetry data of the reference altimetry satellite for the lake to be monitored at the time of observation. The raw altimetry data includes the satellite orbital altitude and the altimeter observation distance. It should be noted that the reference altimeter satellite refers to other altimeter satellites used to verify the accuracy of the SWOT data. In this embodiment, the Sentinel-3A altimeter satellite is used, which employs synthetic aperture radar mode to provide one-dimensional profile data along its orbit. Furthermore, the raw altimeter data refers to the original observation data without geophysical error correction, including basic measurement parameters such as satellite orbital altitude and altimeter observation distance. Further, the satellite orbital altitude refers to the satellite's height relative to the reference ellipsoid, determined by precise orbit determination. Additionally, the altimeter observation distance refers to the measurement distance between the radar altimeter onboard the satellite and the water surface, i.e., the distance converted from the radar pulse round-trip time.

[0075] Understandably, step A51 obtains the raw altimetry data of the Sentinel-3A satellite for the lake to be monitored at the same observation time, including the satellite orbital altitude and altimeter observation distance, as a reference data source for accuracy verification.

[0076] Step A52: Perform ionospheric delay correction, dry tropospheric delay correction, wet tropospheric delay correction, solid earth tide correction, and polar tide correction on the original altimetry data to obtain various correction values; It should be noted that ionospheric delay correction refers to calculating and correcting the impact of the ionosphere on radar signal propagation delay using dual-frequency radar observation data. Additionally, dry tropospheric delay correction refers to calculating and correcting the impact of dry atmosphere on signal delay using a dry tropospheric gas model. Furthermore, moist tropospheric delay correction refers to calculating and correcting the impact of water vapor on signal delay using measured water vapor content from a spaceborne microwave radiometer. Additionally, solid Earth tide correction refers to calculating and correcting the vertical crustal movement caused by solid Earth tides using a geophysical model. Finally, polar tide correction refers to calculating and correcting the vertical crustal movement caused by polar tides using a geophysical model.

[0077] Understandably, step A52 performs multiple geophysical error corrections on the raw altimetry data obtained in step A51, including ionospheric delay correction, dry tropospheric delay correction, wet tropospheric delay correction, solid earth tide correction, and polar tide correction, to obtain various correction values ​​and eliminate measurement errors caused by atmospheric and geophysical factors.

[0078] Step A53: Determine the reference water level based on the satellite orbital altitude, altimeter observation distance, and various correction values; It should be noted that the reference water level refers to the precise water level value obtained after various geophysical error corrections, which serves as the true benchmark for evaluating the accuracy of SWOT data.

[0079] Understandably, step A53 calculates the corrected reference water level by subtracting the sum of the various corrections obtained in step A52 from the satellite orbital altitude and altimeter observation distance obtained in step A51.

[0080] Step A54: Perform spatiotemporal matching between the representative water level and the reference water level to obtain matching data pairs; It should be noted that a matched data pair refers to the corresponding data combination formed by matching the representative SWOT water level with the reference water level in time and space, ensuring that the two correspond to the same lake and the observation at the same time.

[0081] Understandably, step A54 matches and aligns the representative water level determined in step S50 with the reference water level calculated in step A53 in time and space to ensure that the two correspond to the same lake and the observation at the same time, thus obtaining a matching data pair for accuracy assessment.

[0082] Step A55: Determine the deviation value, root mean square error value, and correlation coefficient based on the matching data; It should be noted that the deviation value refers to the average difference between the representative SWOT water level and the reference water level, reflecting the magnitude of the systematic error. Additionally, the root mean square error (RMSE) value is the square root of the average of the squared differences between the representative SWOT water level and the reference water level, reflecting the overall accuracy level. Furthermore, the correlation coefficient refers to the Pearson correlation coefficient between the SWOT water level time series and the reference water level time series, reflecting the consistency and linear correlation between the two time series.

[0083] Understandably, step A55 calculates the deviation, root mean square error, and correlation coefficient between the SWOT representative water level and the reference water level based on the matching data pairs obtained in step A54, thereby quantifying and evaluating the accuracy of the monitoring results.

[0084] Step A56: Evaluate the effectiveness of the representative water level based on the deviation value, root mean square error value, and correlation coefficient.

[0085] Understandably, step A56 comprehensively evaluates the effectiveness and reliability of the representative water level based on the deviation value, root mean square error value and correlation coefficient determined in step A55, verifies the accuracy level of the SWOT lake water level monitoring method based on the combined filtering strategy of quality label and median absolute deviation, and proves that the method can effectively remove outliers while preserving the spatial and temporal characteristics of the original data to the maximum extent.

[0086] It should be understood that accuracy verification using Sentinel-3A altimeter data includes the following error correction processing of the Sentinel-3A data: ionospheric delay correction using dual-frequency radar observations ( Dry tropospheric delay correction was performed using a dry tropospheric gas model. ); Moist tropospheric delay correction was performed using water vapor content measured by a spaceborne microwave radiometer. Using geophysical models to study solid Earth tides ( ) and extreme tides ( Correction for vertical crustal movement caused by ) ; the final reference water level The calculation formula is:

[0087] in, The satellite's orbital altitude, This refers to the distance observed by the altimeter.

[0088] In its implementation, this scheme collects high-resolution hydrological data of global inland water bodies from the SWOT altimeter satellite Level 2 product L2_HR_Raster_100m water surface elevation based on NASA Earthdata or CNES data centers. This embodiment takes Chaohu Lake, a typical lake in the Yangtze River Basin, as the study area and selects continuous observation data from January to December 2024 to ensure the temporal continuity and spatial integrity of the data.

[0089] After acquiring the raw data, the effective water surface elevation pixels within the lake area are extracted using a lake boundary vector mask. Specifically, a spatial mask is constructed by importing the precise boundary vector file of the lake, i.e., a Shapefile format file. The geographic coordinate system of the SWOT_L2_HR_Raster product is aligned with the mask to extract all water surface elevation pixels falling within the mask area and remove background data of land and non-lake water areas, thereby achieving accurate positioning of the study area.

[0090] After spatial cropping, the pixel-level quality control index wse_qual of the extracted water surface elevation pixels is read, and a preliminary screening logic based on quality label is executed. Pixels with wse_qual less than or equal to 1 are retained, that is, only data with a quality label of 0 (completely valid) and data with a quality label of 1 (suspicious but potentially valid) are retained. At the same time, poor-quality data with a quality label of 2 or above are removed. This step aims to remove obvious measurement errors caused by severe land cover or land pollution, while retaining valid observations in the lake edge area to prevent excessive data sparsity.

[0091] Based on the above initial screening results, in order to further improve the data quality, the median absolute deviation method is used to perform secondary fine filtering on the data after the initial screening to identify and remove statistical outliers. Compared with the interquartile range method (IQR method), this method can handle non-normally distributed data more effectively. The specific implementation process is as follows.

[0092] Calculating the median water surface elevation involves calculating the median of the WSE (Wide Frame Array) set of pixels retained after initial screening for a specific observation date (Pass). ,in Let be the water surface elevation value of the i-th pixel, and N be the total number of pixels, thus determining the center position of the data.

[0093] After determining the median, calculate the median for each cell observation. absolute deviation This refers to the degree of deviation of each data point from the center position.

[0094] Subsequently, the median of the aforementioned set of absolute deviations is calculated. To establish a robust measure of the degree of data dispersion.

[0095] Based on this discrete metric, a robust standard deviation is constructed. A normal distribution consistency scaling factor k equal to 1.4826 is introduced to transform the absolute deviation of the median under a non-normal distribution into a robust estimate consistent with the standard deviation. This ensures the comparability of statistical data.

[0096] The robust standard deviation is used to determine the effective data retention interval, and the threshold interval is constructed as follows: Subtract 2 times to Add 2 times Pixels falling outside this interval are marked as outliers and removed, while only valid pixels within the interval are retained, thus completing the outlier removal process.

[0097] The final lake level at that moment is obtained by aggregating the effective pixels retained after filtering. Specifically, the arithmetic mean of the effective pixel set after filtering by median absolute deviation is taken as the lake level observation value for this SWOT transit. , where M is the final number of valid pixels retained.

[0098] To verify the accuracy of the above processing results, Sentinel-3A altimeter data was used for validation. Sentinel-3A altimeter data passing through the region during the same period were collected and subjected to rigorous geophysical error correction to ensure comparison accuracy. The correction process included ionospheric delay correction (ΔRion) using dual-frequency radar observations and dry tropospheric delay correction using a dry gas model. Moist tropospheric delay correction is performed using water vapor content measured by a spaceborne microwave radiometer. And using geophysical models to perform solid Earth tidal corrections, i.e. And polar tide correction .

[0099] Based on the corrected satellite orbit altitude Altimeter observation distance Calculate reference water level with various correction values Then, the SWOT inversion water level was spatiotemporally matched with the corrected Sentinel-3A water level, and the root mean square error and deviation were calculated to verify the effectiveness of the median absolute deviation combination strategy.

[0100] Finally, the variation of the filtered data error with the cross-track distance was analyzed. Pixels were grouped according to their lateral distance from the satellite's nadir point, and the mean absolute error of each group was calculated to assess the reliability difference between data located at the orbital center and edge, providing a basis for subsequent data weighting processing.

[0101] This embodiment provides a water level monitoring method based on SWOT altimetry satellites. By combining pixel-level quality control indicators with a median absolute deviation filtering strategy, it effectively solves the problem that traditional standard deviation methods cannot effectively identify outliers when high-resolution water surface topography observation satellite data exhibits non-Gaussian distribution characteristics due to multipath effects, ground object interference, and thermal noise in inland lakes. This is because the mean is easily skewed by extreme values. In the initial screening stage, this method retains pixels marked as fully valid and those that are questionable but usable, avoiding data sparsity caused by strict filtering. In the secondary filtering stage, robust standard deviation and adaptive retention intervals are constructed using median absolute deviation. This achieves the goal of effectively removing outliers caused by instrument thermal noise, multipath effects, and algorithm inversion errors while maximizing the retention of effective signals reflecting the true hydrological dynamics. This avoids the loss of marginal data caused by relying solely on quality labels in traditional methods and the loss of true extreme water level information caused by traditional statistical removal.

[0102] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S30 includes steps S301 to S308: Step S301: Determine the first median of the water surface elevation values ​​for all candidate pixels in the primary screening dataset; It should be noted that the first median refers to the median value of the water surface elevation of all candidate pixels in the primary screening dataset. That is, the value in the middle position after arranging all candidate pixels in order of size. When the amount of data is even, the average of the two middle values ​​is taken. This statistic is not sensitive to extreme values ​​and can represent the central tendency of the data.

[0103] Understandably, step S301 calculates the median of the water surface elevation values ​​of all candidate pixels in the primary screening dataset to obtain the first median, which serves as the benchmark value for subsequent calculation of the absolute deviation.

[0104] Step S302: Determine the absolute value of the difference between the water surface elevation value and the first median for each candidate pixel to obtain the first absolute deviation for each candidate pixel; It should be noted that the first absolute deviation refers to the absolute value of the difference between the water surface elevation value of each candidate pixel and the first median. It is used to measure the degree of deviation of a single data point from the location of the data center. This value is non-negative and reflects the magnitude of the deviation of the individual observation from the central trend. Additionally, the difference refers to the algebraic difference obtained by subtracting the first median from the water surface elevation value of the candidate pixel. It can be positive or negative, indicating the relationship between the candidate pixel and the median.

[0105] Understandably, step S302 calculates the absolute value of the difference between the water surface elevation value and the first median for each candidate pixel, and obtains the first absolute deviation corresponding to each candidate pixel, reflecting the dispersion of each observation value relative to the median.

[0106] Step S303: Determine the median absolute deviation of the first absolute deviation for all candidate pixels; Understandably, step S303 determines the median of the first absolute deviation corresponding to all candidate pixels, and obtains the median absolute deviation, which serves as the basis for constructing the robust standard deviation.

[0107] Step S304: Multiply the absolute deviation of the median by a preset scaling factor to obtain the robust standard deviation; It should be noted that the preset scaling factor is a coefficient used to convert the absolute deviation of the median into a robust standard deviation comparable to the standard deviation. In this embodiment, the factor is set to 1.4826. Under the assumption of normal distribution, this value ensures that the absolute deviation of the median and the standard deviation are consistent, guaranteeing the comparability of the statistics. Furthermore, the robust standard deviation refers to the robust standard deviation estimate calculated based on the absolute deviation of the median and the preset scaling factor. It is used to construct an effective data retention interval, is unaffected by extreme outliers, and is suitable for non-normally distributed data.

[0108] Understandably, step S304 multiplies the absolute deviation of the median by a preset scaling factor to obtain the robust standard deviation, thus establishing a measure of dispersion that adapts to non-normally distributed data.

[0109] Step S305: Calculate the lower limit and upper limit of the effective data retention interval based on the first median and the robust standard deviation; It should be noted that the lower limit of the interval refers to the minimum boundary value of the valid data retention interval. It is calculated by subtracting the product of a preset multiplier factor and the robust standard deviation from the first median. It is used to define the lower limit of valid data; pixels below this value will be considered outliers and removed. Conversely, the upper limit of the interval refers to the maximum boundary value of the valid data retention interval. It is calculated by adding the product of the preset multiplier factor and the robust standard deviation to the first median. It is used to define the upper limit of valid data; pixels above this value will be considered outliers and removed. Furthermore, the preset multiplier factor is a multiplier coefficient used to determine the width of the retention interval. In this embodiment, the factor is set to two, representing a range of plus or minus two robust standard deviations.

[0110] Understandably, step S305 calculates the lower and upper limits of the valid data retention interval based on the first median and robust standard deviation, thus determining the boundary range of the valid data.

[0111] Step S306: Construct a valid data retention interval based on the lower limit and upper limit of the interval; Understandably, step S306 constructs an effective data retention interval based on the lower limit and upper limit of the interval, forming a numerical range interval for filtering effective pixels. This interval is centered on the first median and extends to both sides by a width of a preset multiple factor and a robust standard deviation.

[0112] Step S307: Determine whether the water surface elevation value of each candidate pixel is within the valid data retention range to obtain the location determination result; It should be noted that the location determination result refers to the logical conclusion obtained by judging whether the water surface elevation value of each candidate pixel is within the valid data retention interval. It indicates whether the pixel belongs to the retention range or the rejection range, and serves as the basis for subsequent diversion processing. Furthermore, being within the valid data retention interval means that the water surface elevation value of the candidate pixel is greater than or equal to the lower limit of the interval and less than or equal to the upper limit of the interval; values ​​within this closed interval are considered normal observation values.

[0113] Understandably, step S307 determines whether the water surface elevation value of each candidate pixel is within the valid data retention range, obtains the position determination result, and determines the retention status of each pixel.

[0114] Step S308: Based on the location determination result, candidate pixels located within the valid data retention interval are retained as valid pixels, and candidate pixels located outside the valid data retention interval are removed to obtain the secondary filtered dataset.

[0115] It should be noted that valid pixels refer to the pixels retained after median absolute deviation filtering, i.e., candidate pixels whose water surface elevation values ​​fall within the valid data retention interval. These pixels are considered reliable observations reflecting true water level changes and are not significantly affected by multipath effects or land pollution. Additionally, the secondary screening dataset refers to the data set consisting of all valid pixels, in which statistical outliers and isolated points have been removed, retaining only reliable data that meets robust statistical criteria for subsequent water level aggregation calculations.

[0116] Understandably, step S308 retains candidate pixels within the valid data retention range as valid pixels based on the location determination result, and removes candidate pixels outside the valid data retention range to obtain the secondary screening dataset, thus completing the outlier removal process.

[0117] This embodiment provides a water level monitoring method based on SWOT altimetry satellites. By combining pixel-level quality control indicators with a median absolute deviation filtering strategy, it effectively solves the problem that traditional standard deviation methods cannot effectively identify outliers when high-resolution water surface topography observation satellite data exhibits non-Gaussian distribution characteristics due to multipath effects, ground object interference, and thermal noise in inland lakes. This is because the mean is easily skewed by extreme values. In the initial screening stage, this method retains pixels marked as fully valid and those that are questionable but usable, avoiding data sparsity caused by strict filtering. In the secondary filtering stage, robust standard deviation and adaptive retention intervals are constructed using median absolute deviation. This achieves the goal of effectively removing outliers caused by instrument thermal noise, multipath effects, and algorithm inversion errors while maximizing the retention of effective signals reflecting the true hydrological dynamics. This avoids the loss of marginal data caused by relying solely on quality labels in traditional methods and the loss of true extreme water level information caused by traditional statistical removal.

[0118] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the water level monitoring method based on SWOT altimeter satellites. Any simple modifications based on this technical concept are within the protection scope of this application.

[0119] This application also provides a water level monitoring device based on a SWOT altimeter satellite; please refer to [reference needed]. Figure 4 The water level monitoring device based on SWOT altimeter satellites includes: Data acquisition module 10 is used to acquire secondary high-resolution raster data of water surface topography observation satellite and boundary vector data of the lake to be monitored; based on the boundary vector data, it extracts all water surface elevation pixel observation values ​​and corresponding pixel-level quality control indicators within the water area of ​​the lake to be monitored from the raster data. The primary screening module 20 is used to judge pixel-level quality control indicators based on preset quality thresholds to obtain the primary screening dataset; The secondary filtering module 30 is used to remove outliers from each candidate pixel in the primary filtering dataset based on the absolute deviation of the median, so as to obtain the secondary filtering dataset. The water level calculation module 40 is used to aggregate and calculate the water surface elevation values ​​of each effective cell in the secondary screening dataset to determine the representative water level of the lake to be monitored at the observation time.

[0120] The water level monitoring device based on SWOT altimeter satellite provided in this application employs the water level monitoring method based on SWOT altimeter satellite in the above embodiments. It can accurately identify and eliminate abnormal observations when SWOT wide-swath interferometric radar data exhibits high data dispersion and a non-normal distribution due to multipath effects, ground object interference, and thermal noise. Compared with the prior art, the beneficial effects of the water level monitoring device based on SWOT altimeter satellite provided in this application are the same as those of the water level monitoring method based on SWOT altimeter satellite provided in the above embodiments. Furthermore, other technical features of the water level monitoring device based on SWOT altimeter satellite are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0121] In one embodiment, the primary screening module 20 is further configured to compare the value of the pixel-level quality control index with a preset fully valid identifier to obtain a first comparison result; The values ​​of pixel-level quality control indicators are compared with preset suspicious and usable identifiers to obtain a second comparison result; When the value of the pixel-level quality control index is equal to the preset fully valid identifier or the preset suspicious usable identifier in the first comparison result or the second comparison result, the water surface elevation pixel observation value with the same pixel position as the pixel-level quality control index will be retained as a candidate pixel. A primary screening dataset is constructed based on all retained candidate pixels.

[0122] In one embodiment, the secondary filtering module 30 is further configured to determine the first median of the water surface elevation values ​​of all candidate pixels in the primary filtering dataset; Determine the absolute value of the difference between the water surface elevation value of each candidate pixel and the first median to obtain the first absolute deviation for each candidate pixel; Determine the median absolute deviation of the first absolute deviation for all candidate pixels; Multiply the absolute deviation of the median by the preset scaling factor to obtain the robust standard deviation; Calculate the lower and upper limits of the effective data retention interval based on the first median and the robust standard deviation; Construct a valid data retention interval based on the lower and upper limits of the interval; Determine whether the water surface elevation value of each candidate pixel is within the valid data retention range to obtain the location determination result; Based on the location determination results, candidate pixels located within the valid data retention interval are retained as valid pixels, while candidate pixels located outside the valid data retention interval are removed, resulting in a secondary filtered dataset.

[0123] In one embodiment, the water level calculation module 40 is also used to obtain the total number of valid pixels in the secondary filtering dataset and the water surface elevation value of each valid pixel; The water surface elevation values ​​of each effective pixel are summed to obtain the total elevation. Dividing the sum of elevations by the total number of effective pixels yields the representative water level of the lake at the observation time.

[0124] In one embodiment, the water level calculation module 40 is also used to obtain the lateral distance of the effective pixel relative to the satellite nadir point; Based on the preset distance interval length, the effective pixels are segmented and statistically analyzed to obtain the error distribution characteristics of each horizontal distance interval; Based on the error distribution characteristics, effective pixels located at the edge or center of the track are assigned weights or subjected to geometric filtering.

[0125] In one embodiment, the water level calculation module 40 is also used to acquire the original altimetry data of the reference altimetry satellite for the lake to be monitored at the observation time, wherein the original altimetry data includes the satellite orbital altitude and the altimetry observation distance; Ionospheric delay correction, dry tropospheric delay correction, wet tropospheric delay correction, solid earth tide correction and polar tide correction were performed on the raw altimetry data to obtain various correction values; The reference water level is determined based on the satellite orbital altitude, altimeter observation distance, and various correction values. The representative water level is spatiotemporally matched with the reference water level to obtain a matching data pair; Based on the matching data, determine the deviation value, root mean square error value, and correlation coefficient; The effectiveness of representative water levels is assessed based on deviation values, root mean square error values, and correlation coefficients.

[0126] In one embodiment, the data acquisition module 10 is further configured to acquire secondary high-resolution raster data products from a water surface topography observation satellite, wherein the raster data products include water surface elevation raster data and quality control identifier raster data. Construct a spatial mask based on the boundary vector file of the lake to be monitored; Align the geographic coordinate system of the spatial mask with the geographic coordinate system of the water surface elevation raster data to obtain the aligned spatial mask. The water surface elevation raster data is cropped based on the aligned spatial mask, and all water surface elevation pixel observations within the aligned spatial mask range are extracted. Pixel-level quality control indicators are extracted from the quality control identifier raster data to correspond to the observed values ​​of each water surface elevation pixel.

[0127] This application provides a water level monitoring device based on SWOT altimetry satellites. The water level monitoring device based on SWOT altimetry satellites includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the water level monitoring method based on SWOT altimetry satellites in the above embodiment 1.

[0128] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a SWOT altimeter-based water level monitoring device suitable for implementing embodiments of this application. The SWOT altimeter-based water level monitoring device in this application can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The water level monitoring device based on SWOT altimeter satellite shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0129] like Figure 5As shown, the SWOT altimeter-based water level monitoring device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the SWOT altimeter-based water level monitoring device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the SWOT altimeter-based water level monitoring equipment to exchange data wirelessly or via wired communication with other devices. Although the figure shows a SWOT altimeter-based water level monitoring equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0130] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0131] The water level monitoring device based on SWOT altimeter satellite provided in this application, employing the water level monitoring method based on SWOT altimeter satellite in the above embodiments, can solve the technical problem of accurately identifying and eliminating abnormal observations when SWOT wide-swath interferometric radar data has high data dispersion and non-normal distribution due to multipath effects, ground object interference, and thermal noise. Compared with the prior art, the beneficial effects of the water level monitoring device based on SWOT altimeter satellite provided in this application are the same as those of the water level monitoring method based on SWOT altimeter satellite provided in the above embodiments, and other technical features of the water level monitoring device based on SWOT altimeter satellite are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0132] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0133] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0134] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the water level monitoring method based on SWOT altimeter satellite in the above embodiments.

[0135] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0136] The aforementioned computer-readable storage medium may be included in a SWOT altimeter-based water level monitoring device; or it may exist independently and not be assembled into a SWOT altimeter-based water level monitoring device.

[0137] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a SWOT altimeter-based water level monitoring device, the device performs the following actions: acquires secondary high-resolution raster data from a water surface topography observation satellite and boundary vector data of the lake to be monitored; extracts all water surface elevation pixel observations and corresponding pixel-level quality control indicators from the raster data based on the boundary vector data; judges the pixel-level quality control indicators based on a preset quality threshold to obtain a primary screening dataset; removes outliers from each candidate pixel in the primary screening dataset based on the median absolute deviation to obtain a secondary screening dataset; and aggregates and calculates the water surface elevation values ​​of each valid pixel in the secondary screening dataset to determine the representative water level of the lake at the observation time.

[0138] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0140] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0141] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned water level monitoring method based on SWOT altimeter satellites. This solves the technical problem of accurately identifying and eliminating abnormal observations when SWOT wide-swath interferometric radar data exhibits high data dispersion and a non-normal distribution due to multipath effects, ground object interference, and thermal noise. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the water level monitoring method based on SWOT altimeter satellites provided in the above embodiments, and will not be repeated here.

[0142] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the water level monitoring method based on SWOT altimeter satellite as described above.

[0143] The computer program product provided in this application can solve the technical problem of accurately identifying and eliminating abnormal observations when SWOT wide-swath interferometric radar data is subject to high dispersion and non-normal distribution due to multipath effects, ground object interference, and thermal noise. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the water level monitoring method based on SWOT altimetry satellite provided in the above embodiments, and will not be repeated here.

[0144] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A water level monitoring method based on SWOT altimeter satellites, characterized in that, The method includes: Acquire high-resolution secondary raster data from water surface topography observation satellites and boundary vector data of the lakes to be monitored; Based on the boundary vector data, extract all water surface elevation pixel observations and corresponding pixel-level quality control indicators located within the monitored lake area from the raster data; The pixel-level quality control indicators are judged based on preset quality thresholds to obtain a preliminary screening dataset; Outlier removal is performed on each candidate cell in the primary screening dataset based on the median absolute deviation to obtain the secondary screening dataset. The water surface elevation values ​​of each valid cell in the secondary screening dataset are aggregated and calculated to determine the representative water level of the lake to be monitored at the observation time.

2. The method as described in claim 1, characterized in that, The step of judging the pixel-level quality control indicators based on a preset quality threshold to obtain a preliminary screening dataset includes: Read pixel-level quality control indicators; The value of the pixel-level quality control index is compared with the preset fully valid identifier to obtain the first comparison result; The value of the pixel-level quality control index is compared with a preset suspicious usable identifier to obtain a second comparison result; When the value of the pixel-level quality control index is equal to the preset fully valid identifier or the preset suspicious usable identifier in the first comparison result or the second comparison result, the water surface elevation pixel observation value with the same pixel position as the pixel-level quality control index is retained as a candidate pixel. A primary screening dataset is constructed based on all the retained candidate pixels.

3. The method as described in claim 1, characterized in that, The step of removing outliers from each candidate pixel in the primary screening dataset based on the median absolute deviation to obtain the secondary screening dataset includes: Determine the first median of the water surface elevation values ​​for all candidate pixels in the primary screening dataset; Determine the absolute value of the difference between the water surface elevation value of each candidate pixel and the first median to obtain the first absolute deviation corresponding to each candidate pixel; Determine the median absolute deviation of the first absolute deviation for all candidate pixels; Multiply the absolute deviation of the median by a preset scaling factor to obtain the robust standard deviation; Calculate the lower limit and upper limit of the effective data retention interval based on the first median and the robust standard deviation; Construct an effective data retention interval based on the lower limit and the upper limit of the interval; Determine whether the water surface elevation value of each candidate pixel is within the valid data retention interval to obtain the location determination result; Based on the location determination result, candidate pixels located within the effective data retention interval are retained as effective pixels, and candidate pixels located outside the effective data retention interval are removed to obtain a secondary filtered dataset.

4. The method as described in claim 1, characterized in that, The step of aggregating and calculating the water surface elevation values ​​of each valid cell in the secondary screening dataset to determine the representative water level of the lake to be monitored at the observation time includes: Obtain the total number of valid pixels and the water surface elevation value of each valid pixel in the secondary filtered dataset; The water surface elevation values ​​of each effective pixel are summed to obtain the total elevation. Dividing the sum of the elevations by the total number of effective pixels yields the representative water level of the lake to be monitored at the observation time.

5. The method as described in claim 1, characterized in that, The method further includes: Obtain the lateral distance of the effective pixel relative to the satellite nadir point; Based on the preset distance interval length, the effective pixels are segmented and statistically analyzed to obtain the error distribution characteristics of each horizontal distance interval; Based on the error distribution characteristics, effective pixels located at the edge or center of the track are assigned weights or subjected to geometric filtering.

6. The method as described in claim 1, characterized in that, The method further includes: Obtain raw altimetry data of the reference altimeter satellite for the lake to be monitored at the observation time, wherein the raw altimetry data includes the satellite orbital altitude and the altimeter observation distance; The original altimetry data is subjected to ionospheric delay correction, dry tropospheric delay correction, wet tropospheric delay correction, solid earth tide correction and polar tide correction to obtain various correction values; The reference water level is determined based on the satellite orbital altitude, the altimeter observation distance, and the various correction values. The representative water level is spatiotemporally matched with the reference water level to obtain a matching data pair; Based on the matching data, determine the deviation value, root mean square error value, and correlation coefficient; The effectiveness of the representative water level is evaluated based on the deviation value, the root mean square error value, and the correlation coefficient.

7. The method as described in claim 1, characterized in that, The step of extracting all water surface elevation pixel observations and corresponding pixel-level quality control indicators within the monitored lake area from the raster data based on the boundary vector data includes: Acquire secondary high-resolution raster data products from water surface topography observation satellites, wherein the raster data products include water surface elevation raster data and quality control identifier raster data; Construct a spatial mask based on the boundary vector file of the lake to be monitored; Align the geographic coordinate system of the spatial mask with the geographic coordinate system of the water surface elevation raster data to obtain the aligned spatial mask; Based on the aligned spatial mask, the water surface elevation raster data is cropped, and all water surface elevation pixel observations within the range of the aligned spatial mask are extracted. Pixel-level quality control indicators are extracted from the quality control identifier raster data corresponding to each water surface elevation pixel observation value.

8. A water level monitoring device based on SWOT altimeter satellites, characterized in that, The device includes: The data acquisition module is used to acquire secondary high-resolution raster data from water surface topography observation satellites and boundary vector data of the lake to be monitored; based on the boundary vector data, it extracts all water surface elevation pixel observations and corresponding pixel-level quality control indicators located within the water area of ​​the lake to be monitored from the raster data. The primary screening module is used to judge the pixel-level quality control indicators based on a preset quality threshold to obtain a primary screening dataset. The secondary filtering module is used to remove outliers from each candidate cell in the primary filtered dataset based on the median absolute deviation, thereby obtaining the secondary filtered dataset. The water level calculation module is used to aggregate and calculate the water surface elevation values ​​of each valid cell in the secondary screening dataset to determine the representative water level of the lake to be monitored at the observation time.

9. A water level monitoring device based on SWOT altimeter satellites, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the water level monitoring method based on SWOT altimeter satellite as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the water level monitoring method based on SWOT altimeter satellite as described in any one of claims 1 to 7.

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