A method and device for retrieving water depth of polar surface lakes based on swot satellite
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN UNIV
- Filing Date
- 2026-04-13
- Publication Date
- 2026-05-29
Smart Images
Figure CN122116183A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of polar remote sensing monitoring and geodetic technology, specifically involving a method for extracting the water depth of polar ice lakes by using SWOT satellite L2-level high-resolution pixel cloud data through co-source dual-temporal observation and physical feature separation. Background Technology
[0002] During the melting season, numerous ice lakes easily form on the surface of polar ice sheets and ice shelves. Their volume and spatial distribution significantly influence ice sheet stability, ice shelf hydraulic fracturing processes, and sea-level changes. Accurately determining the depth of these ice lakes is crucial for calculating their volume and assessing meltwater runoff over the ice sheet surface.
[0003] Existing research on methods for probing the depth of polar ice lakes includes in-situ measurements, as well as meltwater depth estimation based on optical remote sensing imagery and lidar data. While in-situ measurements offer the highest reliability and accuracy, they are currently relatively rare due to cost and environmental constraints. Methods based on optical remote sensing imagery typically invert water depth using water spectral characteristics or optical attenuation models; however, these methods are susceptible to cloud cover, floating ice, and lighting conditions, limiting their applicability in polar regions. While methods based on lidar altimetry data offer high elevation accuracy, their low temporal resolution and discrete distribution along the orbital path make it difficult to achieve continuous spatial acquisition of the overall water depth of the ice lake.
[0004] The SWOT (Surface Water and Ocean Topography) satellite, equipped with a Ka-band interferometric radar (KaRIn), can acquire three-dimensional elevation information of water surfaces with high spatial resolution. Its L2-level high-resolution pixel cloud (PIXC) product provides spatial location and elevation information of water pixels in the form of irregular pixel sets, providing a new data foundation for extracting water depth in glacial lakes. However, SWOT pixel cloud data still faces many challenges in its application to polar glacial lakes. Although the SWOT satellite's Ka-band interferometric radar (KaRIn) provides two-dimensional elevation information, it suffers from inconsistent penetration depths and severe decorrelation noise on polar ice and snow surfaces, and the L2-level pixel cloud (PIXC) data is spatially irregularly distributed. Existing image super-resolution reconstruction methods are mostly based on visual texture generation, lacking geostatistical spatial interpretability, making it difficult to ensure the physical accuracy of elevation while repairing data gaps.
[0005] Currently, there is no method for retrieving water depth from SWOT pixel cloud data in polar ice lake scenarios, especially a technical solution that can simultaneously overcome interference from the ice sheet background field, repair observational voids, and eliminate self-reference errors. Therefore, there is an urgent need for a polar ice lake water depth retrieval method based on the SWOT satellite to fill the existing technological gap. Summary of the Invention
[0006] This invention provides a method for inverting the water depth of polar ice lakes based on SWOT satellites. It constructs a self-reference system using observation data from the same sensor at different hydrological phases (flood season / dry season), and separates the background field by combining ice sheet rheological characteristics, thereby achieving high-precision extraction of the water depth of polar ice lakes.
[0007] This invention provides a method for inverting the water depth of polar ice lakes based on the SWOT satellite. It constructs a self-reference system through dual-temporal observations from the same source, separates the background field using ice sheet rheological characteristics, and achieves the inversion of the water depth of ice lakes. The method includes the following steps: Based on optical remote sensing images, identify the water accumulation and dry periods of the target glacial lake and obtain SWOT satellite pixel cloud data for the corresponding time phases; Spatial masking is applied to the pixel cloud data based on the dynamic boundary of the frozen lake, dividing the lake surface pixel subset into a lake bottom pixel subset; After removing outliers from the subset of lake surface pixels, the mean elevation is calculated to obtain the instantaneous lake surface elevation, which is then used as the physical constraint threshold. Anomaly removal was performed on a subset of lake bottom pixels to separate the ice cover background trend surface and the lake basin micro-topography residual. A continuous lake bottom elevation grid was reconstructed using adaptive gridding and multi-level fault-tolerant interpolation. By performing grid difference analysis between the instantaneous lake surface elevation and the lake bottom elevation grid, the spatial distribution of lake water depth on the ice surface is obtained.
[0008] Moreover, the co-source dual-temporal observation uses the same sensor to acquire data on both water accumulation and dry states, eliminating systematic elevation errors in heterogeneous data sources.
[0009] Furthermore, the interquartile range method was used to remove outlier values of lake surface pixels, and the mean elevation of the lake surface was determined after iterative optimization.
[0010] Furthermore, by using the instantaneous lake surface elevation as a constraint, abnormal pixels and local outliers in the lake bottom pixel subset are removed.
[0011] Furthermore, an ice sheet background trend surface reflecting the large-scale rheological characteristics of the ice sheet was constructed, and pixel elevation residuals were extracted as micro-geomorphic components of the lake basin.
[0012] Furthermore, the interpolation grid resolution is adaptively determined based on the surface area of the frozen lake, adapting to the non-uniform sampling characteristics of SWOT pixel clouds.
[0013] Moreover, a multi-level fault-tolerant mechanism is adopted to reconstruct the residual field, with local interpolation as the priority and global and inverse distance weighted interpolation as the auxiliary methods. After repairing the observation voids, the background trend surface is superimposed to obtain the continuous lake bottom elevation.
[0014] On the other hand, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described above.
[0015] On the other hand, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.
[0016] On the other hand, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described above.
[0017] This invention provides a polar ice lake depth inversion technique based on SWOT satellites. By effectively suppressing pixel cloud elevation noise and outliers, it achieves reliable acquisition of lake surface and bottom elevations, thereby obtaining spatial distribution information of polar ice lake depths. Compared with existing technologies, this invention has the following advantages: 1) Eliminate heterogeneous source errors: By using dual-temporal observations from the same source, system elevation deviations between different sensors are avoided; 2) Physical background separation: For the first time, the rheological background field of the ice sheet was separated from the micro-topography of the lake basin, which significantly improved the accuracy of lake bottom topography reconstruction; 3) Not dependent on external DEMs: Suitable for polar regions lacking high-precision terrain data; 4) Adaptive interpolation grid: Adapts to the non-uniform sampling characteristics of SWOT pixel clouds to repair observation holes. Attached Figure Description
[0018] Figure 1 This is a SWOT satellite pixel cloud data processing flowchart according to an embodiment of the present invention; Figure 2 This is a comparison image of the lake surface pixel elevation distribution before and after outlier removal in an embodiment of the present invention; Figure 3 This is a diagram showing the final spatial distribution inversion result of water depth according to an embodiment of the present invention. Detailed Implementation
[0019] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.
[0020] Example 1 This invention provides a method for inverting the water depth of polar ice lakes based on SWOT satellite, comprising the following steps: Step 1: Construct a dual-temporal observation dataset from the same source: Utilizing the revisit observation characteristics of the SWOT satellite, for the target ice lake, identify its two typical hydrological states, "water accumulation period" and "waterless period," based on external optical remote sensing images; acquire the corresponding SWOT satellite L2-level high-resolution pixel cloud (PIXC) data for the above two states respectively, and construct an observation set for the water accumulation state and the observation set for the waterless state acquired by the same sensor to eliminate systematic elevation errors between heterogeneous data sources; Step 2, Geospatial Constraints and Pixel Classification: Extract the dynamic geographic boundary of the target ice lake based on the external optical remote sensing image, and use this boundary to perform spatial masking filtering on the pixel cloud data; wherein, the pixel subset obtained under the observation phase of water accumulation is defined as the lake surface pixel subset, and the pixel subset obtained under the observation phase of dry state is defined as the lake bottom pixel subset. Step 3, Obtain the lake surface elevation benchmark: Process the subset of lake surface pixels to obtain the instantaneous lake surface elevation of the target ice lake; including: removing outliers based on the interquartile range method and calculating the average elevation of the remaining valid pixels as the physical constraint threshold for lake bottom reconstruction. Step 4, Lakebed Topography Reconstruction Based on Ice Sheet Background Field Separation: Process the lakebed pixel subset to obtain the continuous lakebed elevation distribution of the target ice-covered lake. Furthermore, step 4 includes the following sub-steps: Step 4.1, Anomaly Removal under Physical Constraints: Using the instantaneous lake surface elevation obtained in Step 3 as the physical constraint threshold, abnormal floating ice rafts or noise pixels in the ice surface lake basin topography pixel set that are higher than the threshold are removed to obtain an effective subset of lake bottom pixels. In step 4.1, the anomaly removal under physical constraints not only removes pixels that are higher than the lake surface elevation, but also performs adaptive denoising based on spatial neighborhood statistical features to reduce the impact of speckle noise generated by SWOT interferometric radar at the ice-water mixing interface on lake bottom inversion.
[0021] Step 4.2, Ice sheet background field and lake basin morphology separation: For the effective subset of lake bottom pixels, construct an ice sheet background trend surface that reflects the large-scale rheological characteristics of the ice sheet; calculate the elevation residual of each pixel relative to the background trend surface, and define the residual as the lake basin micro-topographic morphology component. In step 4.2, the separation of the ice sheet background field and the lake basin morphology is based on the physical difference that the polar ice sheet surface has smooth rheological properties on a large scale, while the ice surface lake basin has concave morphological properties on a local scale; the ice sheet background trend surface is used to characterize the overall slope and flow direction of the ice sheet surface, and the elevation residual is used to characterize the actual water storage volume morphology of the ice surface lake.
[0022] Step 4.3, Adaptive resolution grid construction: Based on the surface area of the target ice lake, the spatial resolution of the interpolation grid is adaptively determined to adapt to the non-uniform sampling density of the SWOT pixel cloud at the polar orbit intersection point; In one possible implementation, the calculation logic for adaptively determining the spatial interpolation grid resolution is as follows:
[0023] in For spatial interpolation grid resolution, The target is the area of the frozen lake. Adjustments are made based on the target ice lake area; when the ice lake area is less than 1 km². 2 hour Equal to 0.03, when the area of the frozen lake is greater than 1 km². 2 Less than 10km 2 hour Equal to 0.02, when the surface area of the frozen lake is greater than 10 km². 2 Less than 10km 2 hour It equals 0.01.
[0024] Step 4.4, Multi-level fault-tolerant spatial reconstruction: For the sparse lake basin micro-topographical components (i.e., residuals), a multi-level fault-tolerant mechanism is adopted to perform interpolation reconstruction, which prioritizes local spatial interpolation and is supplemented by global spatial interpolation and distance-inverse weighted interpolation, to generate a continuous residual field in order to repair the observation gaps in the SWOT point cloud. In step 4.4, the multi-level fault tolerance mechanism specifically refers to: firstly, attempting local kriging interpolation or spline function interpolation on the residuals; if the local sampling points are insufficient, causing the interpolation to fail, then triggering global polynomial interpolation or inverse distance weighted interpolation (IDW) to fill the void areas, thereby ensuring that a continuous lake bottom surface can still be obtained in extremely sparse areas of SWOT data.
[0025] Step 4.5, Topographic overlay and restoration: The reconstructed continuous residual field is overlaid with the ice sheet background trend surface to generate a continuous lake bottom elevation grid that reflects the real ice surface texture and lake basin shape. Step 5, Source Differential Water Depth Extraction: Perform grid-level differential calculations on the instantaneous lake surface elevation obtained in Step 3 and the continuous lake bottom elevation grid generated in Step 4.5 to extract the spatial distribution of water depth of the target ice surface lake.
[0026] The method is based entirely on time-series data from SWOT satellite L2-level pixel cloud products (PIXC), without relying on an external high-precision digital elevation model (DEM) as a lakebed reference. It eliminates systematic errors by using the observation differences of the same sensor at different time phases, and is suitable for polar ice sheet regions lacking high-precision topographic data.
[0027] The above process addresses the problems of existing polar ice lake depth extraction methods, such as difficulty in data acquisition, insufficient spatial continuity, and limited applicability. It constructs a self-reference system using revisited observation data from the same sensor (SWOT) during both flood and dry periods: First, a dual-temporal observation dataset from the same source is built, using instantaneous lake surface elevation as the upper bound of physical constraints to eliminate anomalous noise from the lakebed. Second, based on the physical differences between the large-scale rheological characteristics of the ice sheet and the local micro-topography of the lake basin, an ice sheet background field separation technique is used to remove background trends, and an adaptive resolution grid and multi-level fault-tolerant interpolation mechanism are combined to reconstruct the continuous lakebed topography. Finally, water depth is inverted through raster-level difference inversion of the same-source data. This invention effectively eliminates systematic elevation errors in heterogeneous data, solves the problems of sparsity in SWOT point cloud data and pixel cloud elevation noise interference, and achieves reliable reconstruction of ice lake depth in polar environments.
[0028] Example 2 See Figure 1 This embodiment provides a method for inverting the water depth of polar ice lakes based on SWOT satellite, including the following steps: Step 1: Construct a dual-temporal observation dataset from the same source: For the target glacial lake (located in the eastern Greenland ice sheet) in this example, its hydrological dynamics were monitored using Sentinel-2 optical imagery. July 27, 2023, was identified as a typical "water accumulation period," and September 15, 2023, was identified as a "waterless period" after the lake had drained. SWOT satellite L2-level PIXC pixel cloud data corresponding to these two periods were acquired respectively. This co-source observation strategy ensures that the lake surface and lakebed data are under the same radar system parameters and geometric reference, effectively avoiding systematic bias from heterogeneous data sources.
[0029] Step 2, Geospatial Constraints and Pixel Classification: The maximum inundation area of the frozen lake is extracted based on optical imagery as a spatial mask. The PIXC data acquired on July 27th is labeled as the lake surface pixel subset, and the data acquired on September 15th is labeled as the lake bottom pixel subset.
[0030] Step 3, Obtain the lake surface elevation benchmark: Process the subset of lake surface pixels. Since the water surface should remain an equipotential surface under gravity, any point that deviates significantly from the mean is noise. Outliers are removed using the interquartile range (IQR) method:
[0031] in, and These are the first and third quartiles of the pixel elevation distribution on the lake surface, respectively. The interquartile range (IQR) is used to characterize the dispersion of the main distribution of elevation data. Any pixel elevation value exceeding the interval (1) is judged as outlier noise and removed. Subsequently, the RANSAC algorithm is used to iteratively sample and fit the candidate pixels. In each iteration, samples are randomly selected to construct a lake surface plane model, and the residuals of the elevations of the remaining pixels to the model are calculated. Pixels with residuals less than the preset threshold (0.5 m) are retained as inliers. After multiple iterations, the fitting result with the most inliers and the smallest overall residual is selected to achieve the removal of abnormal observations and the optimization screening of effective lake surface pixels.
[0032] like Figure 2 As shown in the figure, the distribution comparison of lake surface pixel elevations before and after outlier removal in the embodiment is presented. Before outlier removal, there were a certain number of discrete points in the lake surface pixel elevations that deviated from the main distribution range, which manifested as local elevation anomalies that were too high or too low. After outlier removal based on the interquartile range method and RANSAC plane constraints, the distribution of lake surface pixel elevations converged significantly, the number of discrete outliers decreased significantly, and the main elevation distribution became more concentrated.
[0033] Finally, the mean value of the remaining valid lake surface pixel elevations is calculated to obtain the lake surface elevation. H surface The aforementioned H surface It not only characterizes the lake surface elevation at the time of lake observation in the example, but also serves as a physical constraint threshold for subsequent lake bottom topography reconstruction, that is, the lake bottom elevation should not be physically higher than the lake surface elevation.
[0034] Step 4, Lakebed Topography Reconstruction Based on Ice Sheet Background Field Separation: This step aims to separate the true lake basin morphology from the complex ice surface topography, and specifically includes the following process: 4.1 Physical Constraint Removal: A subset of pixels on the ice surface and lake bottom (data from the dry season) is examined. Pixels with an elevation higher than the lake surface elevation are identified as false signals generated by floating ice rafts or radar multipath effects during the observation period and are directly removed. Simultaneously, K-nearest neighbor statistical filtering is used to remove local outliers at three times the standard deviation. Here, k is the neighborhood parameter of the K-nearest neighbor statistical filtering, representing the number of nearest neighbor pixels participating in the local elevation consistency check. In this embodiment, k is preferably set to 12 to balance outlier suppression and preservation of local terrain features.
[0035] 4.2 Ice Sheet Background Field Separation: Considering that the ice sheet surface is generally controlled by ice flow in the local area where the lake basin is located, and usually exhibits continuous, smooth and approximately linear large-scale slope characteristics, a linear polynomial model is used to fit the ice sheet background trend surface (i.e., the ice sheet background trend surface):
[0036] Compared to quadratic models, linear models have fewer parameters and a more robust fit. They can characterize large-scale background undulations of the ice sheet while avoiding excessive absorption of local lake basin depressions into the trend term, thus better highlighting the anomalous depression features of the lake basin relative to the background ice surface. Among these, , The coordinates are projected plane coordinates, which in this embodiment are obtained by converting pixel latitude and longitude provided by PIXC data; For the ice sheet background trend surface, coefficient , b , c The lakebed pixel data was determined using a least-squares linear regression method. Based on this trend surface, the residual of each pixel relative to the model was further calculated:
[0037] in, The observed elevation value for each pixel, and the residual. This refers to the pure lake basin micro-geomorphic morphology component after the influence of the ice sheet topography has been removed.
[0038] 4.3 Adaptive Grid Construction: To take into account the spatial characteristics of lakes at different scales, this embodiment adaptively determines the residual reconstruction resolution based on the lake area. The specific calculation formula is as follows:
[0039] in Grid resolution (unit: meters). The area of the frozen lake in the example (unit: m²) 2 To address the differences in effective sample size, spatial heterogeneity, and residual field stability among glacial lakes of different area sizes, a proportionality coefficient was used. Dynamic adjustments based on segmented lake areas: When the area is <1 km², =0.03; When 1 km² ≤ area < 10 km², =0.02; When the area is ≥ 10 km², =0.01.
[0040] Compared with using a fixed grid resolution or a single scaling method, this invention introduces a piecewise adaptive parameter mechanism based on lake area levels, which realizes dynamic matching of reconstruction resolution to lake scale, avoids the problems of overfitting for small lakes and underfitting for large lakes, and improves the stability and accuracy of residual reconstruction results.
[0041] In this embodiment, the area of the ice lake is 8.8 km², and the grid resolution is 59.330 meters when substituted into the formula.
[0042] 4.4 Multi-level fault-tolerant space reconstruction: for residuals Reconstruction is performed. When the number of valid points within the search radius is sufficient (e.g., more than 8 valid points within a search radius of 500 m), ordinary Kriging interpolation is used to improve the recovery of local spatial details. In areas with data voids, it automatically switches to inverse distance weighted interpolation (IDW) or global polynomial interpolation to ensure a continuous and seamless residual field and improve reconstruction stability. Compared with schemes using a single interpolation method, this invention dynamically selects the interpolation model based on local sample support conditions, achieving a synergistic balance between spatial statistical accuracy and continuous reconstruction capability in void areas.
[0043] 4.5 Terrain Overlay: Add the reconstructed residual field back to the background trend surface:
[0044] in The residual after reconstruction in step 4.4 , A continuous lakebed elevation grid was created to preserve the true texture of the lakebed.
[0045] Step 5, Differential depth extraction: Calculate the difference between the lake surface reference and the lake bottom grid cell grid cell grid by grid cell.
[0046] The final spatial distribution of water depth in the embodiment is obtained. Figure 3 As shown in the figure, the final spatial distribution inversion results of the water depth according to the embodiment of the present invention are presented. The results show that the lake water depth is spatially continuously distributed, with relatively deeper central areas and relatively shallower peripheral areas, which generally conforms to the spatial variation law of the ice-covered lake basin topography. Furthermore, for local areas with sparse data, the present invention can still achieve continuous and seamless reconstruction, indicating that the method has good stability, integrity, and physical rationality. This result eliminates the influence of ice sheet topographic slope on water depth calculation, and because it uses data from the same source, complex reference surface correction is not required.
[0047] To facilitate understanding of the technical effects of this invention, during the polar ice surface lake depth inversion process based on SWOT analysis using the method of this invention, the errors affecting the inversion accuracy mainly include elevation observation errors of the SWOT PIXC product, lake surface pixel identification errors, outlier removal residuals, ice sheet background trend surface fitting errors, and lake basin residual reconstruction errors. Among these, SWOT elevation observation errors directly affect the lake surface elevation benchmark and lake depth calculation results; lake surface pixel identification and outlier removal errors mainly affect the stability and reliability of lake surface elevation estimation; ice sheet background trend surface fitting errors and lake basin residual reconstruction errors further affect the accuracy of lakebed topography restoration and the final lake depth inversion results. Therefore, the technical effectiveness of the present invention can be mainly evaluated through the accuracy of lake surface elevation extraction and lake depth inversion.
[0048] To verify the technical effectiveness of the present invention, five typical polar ice lakes were selected as experimental test objects to quantitatively evaluate the lake surface elevation and depth inversion results obtained by the present invention. One-dimensional high-precision lake surface elevation and water depth data obtained from ICESat-2 observations were used as a reference for comparative analysis of the calculation results of the present invention. Following the implementation method described above, the lake surface elevation deviation and the maximum water depth, average water depth, and error evaluation indicators (including root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MedAE) of the lake surface elevation inversion results for each embodiment were statistically analyzed. The relevant statistical results are shown in Tables 1 and 2.
[0049] Table 1. Statistical comparison between lake surface elevation obtained by the present invention and ICESat-2 observation values.
[0050] Table 2. Statistical results of lake depth inversion obtained by the present invention and ICESat-2 reference depth.
[0051]
[0052] As shown in Table 1, under the data processing conditions adopted in this invention, the lake surface elevation inversion results show high consistency with the ICESat-2 reference data, with an average error of -0.018 m and a root mean square error of only 0.091 m. This indicates that the present invention can accurately extract the lake surface elevation of icy lakes and effectively suppress the influence of abnormal observations and local noise on the determination of the lake surface elevation benchmark. These results demonstrate that the present invention has good stability and reliability in lake surface pixel identification, outlier removal, and lake surface elevation estimation.
[0053] As shown in Table 2, the mean RMSE of the final water depth inversion results is 0.388 m, the mean MAE is 0.327 m, and the mean MedAE is 0.307 m. This indicates that the lake depth results obtained by this invention generally match the reference water depth data well, demonstrating high inversion accuracy. The low median absolute error indicates that the inversion error for most sample points is controlled within a small range, further reflecting the good overall stability and resistance to abnormal disturbances of the method. Combining the maximum water depth, average water depth, and error statistics of the lakes in each embodiment, it can be seen that this invention can effectively recover the water depth information of ice-covered lakes at different scales.
[0054] In summary, this invention enables high-precision, high-stability polar ice lake depth inversion with good physical plausibility. Through steps such as joint removal of lake surface outliers, fitting of ice sheet background trend surfaces, and adaptive reconstruction of lake basin residuals, this invention effectively improves the accuracy of lake surface elevation extraction and lake depth inversion. It also boasts advantages such as a clear processing flow, wide applicability, strong adaptability to locally missing data, and good spatial continuity of results.
[0055] In specific implementation, the method proposed in the technical solution of this invention can be automatically executed by those skilled in the art using computer software technology. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of this invention and computer equipment including the computer program running the corresponding computer program, should also be within the protection scope of this invention.
[0056] The following describes the electronic device for inverting polar ice lake depth based on SWOT satellite provided by the present invention. The electronic device for inverting polar ice lake depth based on SWOT satellite described below can be referred to in correspondence with the polar ice lake depth inversion method based on SWOT satellite described above.
[0057] The electronic device may include a processor, a communications interface, memory, and a communication bus. The processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory to execute the SWOT satellite-based polar ice lake depth inversion method, which mainly includes the software processing portion described above.
[0058] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0059] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the software processing part of the polar ice surface lake water depth inversion method based on SWOT satellite provided by the above methods.
[0060] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the software processing portion of the polar ice surface lake water depth inversion method based on SWOT satellite provided by the above methods.
[0061] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0062] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for inverting the water depth of polar ice lakes based on SWOT satellite, characterized in that, By constructing a self-reference system through dual-temporal observations from the same source and separating the background field by combining ice sheet rheological characteristics, the lake water depth on the ice surface is inverted, including the following processes: Based on optical remote sensing images, identify the water accumulation and dry periods of the target glacial lake and obtain SWOT satellite pixel cloud data for the corresponding time phases; Spatial masking is applied to the pixel cloud data based on the dynamic boundary of the frozen lake, dividing the lake surface pixel subset into a lake bottom pixel subset; After removing outliers from the subset of lake surface pixels, the mean elevation is calculated to obtain the instantaneous lake surface elevation, which is then used as the physical constraint threshold. Anomaly removal was performed on a subset of lake bottom pixels to separate the ice cover background trend surface and the lake basin micro-topography residual. A continuous lake bottom elevation grid was reconstructed using adaptive gridding and multi-level fault-tolerant interpolation. By performing grid difference analysis between the instantaneous lake surface elevation and the lake bottom elevation grid, the spatial distribution of lake water depth on the ice surface is obtained.
2. The polar ice lake depth inversion method based on SWOT satellite according to claim 1, characterized in that: The co-source dual-temporal observation uses the same sensor to acquire data on both water accumulation and dry states, eliminating systematic elevation errors in heterogeneous data sources.
3. The polar ice lake depth inversion method based on SWOT satellite according to claim 1, characterized in that: Outlier values of lake surface pixels were removed using the interquartile range method, and the mean elevation of the lake surface was determined after iterative optimization.
4. The polar ice lake depth inversion method based on SWOT satellite according to claim 1, characterized in that: Using the instantaneous lake surface elevation as a constraint, abnormal pixels and local outliers in the lake bottom pixel subset are removed.
5. The polar ice lake depth inversion method based on SWOT satellite according to claim 1, characterized in that: An ice sheet background trend surface reflecting the large-scale rheological characteristics of the ice sheet was constructed, and pixel elevation residuals were extracted as micro-geomorphic components of the lake basin.
6. The polar ice lake depth inversion method based on SWOT satellite according to claim 1, characterized in that: The interpolation grid resolution is adaptively determined based on the surface area of the frozen lake, adapting to the non-uniform sampling characteristics of SWOT pixel clouds.
7. The polar ice lake depth inversion method based on SWOT satellite according to claim 1, characterized in that: A multi-level fault-tolerant mechanism, prioritizing local interpolation and supplementing with global and inverse distance weighted interpolation, was adopted to reconstruct the residual field. After repairing the observation voids, the background trend surface was superimposed to obtain the continuous lake bottom elevation.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the method as described in any one of claims 1 to 7.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.