Polar region multi-source remote sensing sea wave significant wave height optimization interpolation fusion method and device

Abstract

The invention provides a polar region multi-source remote sensing sea wave significant wave height optimization interpolation fusion method and device, and belongs to the field of remote sensing, and the method comprises the steps: carrying out the preprocessing of collected sea ice marginal region and open sea surface data; north-pole geometric adaptation correction is carried out, system deviation of multiple satellite sensors is eliminated, and consistency and accuracy of data are ensured. Real-time wave height forecast is used as a background field, a weighting function is constructed in combination with a satellite observation error covariance matrix, dynamic background field fusion is carried out, and the precision and reliability of a data sparse region are improved; according to different characteristics of a sea ice marginal region and an open sea surface, different weights are set, a transition weight function is introduced, partition fusion is carried out, boundary discontinuity is avoided, and therefore the final fusion wave height is obtained; the robustness of the fusion method is verified through K-fold cross validation, and meanwhile precision verification is conducted in combination with multi-dimensional data. According to the method, the problem of insufficient accuracy verification spatial representativeness caused by rare north pole in-situ observation quantity and concentrated distribution is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of remote sensing, and particularly relates to a polar multi-source remote sensing sea wave effective wave height optimization interpolation fusion method and device. BACKGROUND

[0002] Significant Wave Height (SWH) is a key indicator to measure the energy of ocean waves, which is defined as the average of the highest one-third of the wave heights. This parameter can directly reflect the condition of the marine dynamic environment, and is indispensable for the safety of the Arctic shipping route, the structural design of offshore platforms, the interaction between sea ice and waves, and the verification of climate models. Especially under the background of rapid warming in the Arctic region, the melting of sea ice makes the wave energy continue to increase, therefore, accurately obtaining the data of effective wave height has become a scientific basis for evaluating the risk of polar marine disasters and ecological evolution.

[0003] The emergence of satellite altimeter has brought a new breakthrough for the measurement of significant wave height. It measures the height of the sea surface by emitting radar pulses, thereby inverting the significant wave height. This technology effectively solves the problem of insufficient spatial coverage of traditional buoys in the Arctic ice area, and realizes observation in the global range. At present, a plurality of satellites including CryoSat-2, SARAL / AltiKa, etc. have been able to observe the Arctic region, and data fusion technology, such as the European CMEMS global wave product, has become an important means to improve the spatial and temporal resolution of observation. However, the special environmental conditions in the Arctic region bring many challenges to the existing technology, such as the altimeter signal being easily affected by the ice cover reflection, and the model grid adaptation problem, etc., which need to be optimized accordingly.

[0004] Existing significant wave height data fusion techniques suffer from several key issues. First, they simply exclude data from all sea ice areas, failing to distinguish between the sea ice edge and thick ice areas. However, field observations show that strong wave activity and distinct wave characteristics persist at the sea ice edge. Ignoring this data not only fails to reflect true sea conditions at the ice edge but also limits the coverage of Arctic significant wave height (SWH) products at high latitudes, disrupting data continuity and ultimately compromising their spatial integrity and scientific validity. Second, the fusion process often relies on simple weighted fusion, which fails to accurately reflect spatial distribution characteristics. For example, the inverse distance weighting method uses only distance weighting and fails to consider spatial correlation in the data. This method fails to capture the complex spatial variability of wave fields due to spatial discontinuities caused by sea ice in the Arctic, and its failure to incorporate background data limits interpolation accuracy in the Arctic, where observational data is sparse. Kriging assumes spatial stationary correlation, but Arctic sea ice renders the wave field nonstationary, rendering the variogram model ineffective. Furthermore, the method fails to integrate external model data, making interpolation results unreliable in data-sparse Arctic regions. The stepwise correction method does not explicitly consider the characteristics of background field errors and differences in observational data quality, and lacks the ability to assimilate multiple data. Finally, the verification reliability is insufficient. Due to the small number and concentrated distribution of buoys in the Arctic, the spatial representativeness of the accuracy verification is seriously insufficient. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method and device for optimizing interpolation and fusion of polar multi-source remote sensing ocean wave significant wave height.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method includes the following steps:

[0008] Step 1: Preprocess the collected sea ice edge area and open sea surface data;

[0009] Step 2: Perform Arctic geometry adaptation correction to eliminate the system bias of multiple satellite sensors and ensure data consistency and accuracy;

[0010] Step 3: Using the real-time wave height forecast as the background field, the weight function is constructed in combination with the satellite observation error covariance matrix to perform dynamic background field fusion to improve the accuracy and reliability in data-sparse areas.

[0011] Step 4: According to the different characteristics of the sea ice edge area and the open sea surface, different weights are set, and a transition weight function is introduced to perform partition fusion to obtain the final fused wave height;

[0012] Step 5: Use the K-fold cross-validation method to verify the robustness of the fusion, and combine multi-dimensional data for accuracy verification.

[0013] The application also provides a polar multi-source remote sensing sea wave significant wave height optimal interpolation fusion device, comprising the following modules:

[0014] The preprocessing module pre-processes the collected sea ice edge zone and open sea surface data.

[0015] The correction module performs Arctic geometric adaptation correction, eliminates the system deviation of the multi-satellite sensor, and ensures the consistency and accuracy of the data.

[0016] The fusion module constructs a weight function in combination with a satellite observation error covariance matrix based on a real-time wave height prediction background field, performs dynamic background field fusion, and improves the accuracy and reliability of the data sparse area.

[0017] The weight setting module sets different weights according to the different characteristics of the sea ice edge zone and the open sea surface, introduces a transition weight function, and performs zoned fusion to obtain the final fused wave height.

[0018] The verification module verifies the fusion robustness by using the K-fold cross-validation method, and verifies the accuracy by combining multi-dimensional data.

[0019] The application also provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned polar multi-source remote sensing sea wave significant wave height optimal interpolation fusion method when executing the program.

[0020] The application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the above-mentioned polar multi-source remote sensing sea wave significant wave height optimal interpolation fusion method.

[0021] Advantages:

[0022] 1. Data preprocessing and zoned processing: a strict data preprocessing procedure is formulated, the sea ice edge zone and open sea surface data are processed in zones, the data quality is improved, and the overall accuracy of the fusion result is improved. At the same time, the sea ice edge zone and the open sea surface are fused in zones, different weights are set, and a cosine smooth transition transition weight function is introduced to avoid discontinuity at the boundary, conform to the physical law, reduce errors, and make up for the blank of the significant wave height of the sea ice edge zone.

[0023] 2. Perfect the verification system: through K-fold cross-validation (single-star data is generated by removing each time), the robustness of the fusion method is verified, and the contribution difference of each satellite is quantitatively revealed to provide a basis for multi-source data selection. At the same time, combined with the in-situ observation data in the Arctic, the fusion results, WaveWatch III background field data and international similar data (such as CMEMS), multi-dimensional precision verification is carried out, which improves the problem of insufficient spatial representativeness of precision verification caused by the small number and concentrated distribution of in-situ observation data in the Arctic.

[0024] The application is not only suitable for areas where sea ice exists, such as the Arctic region, but also suitable for open sea areas, and at the same time suitable for the precision verification system of the polar region. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 A flowchart of a polar multi-source remote sensing sea wave effective wave height optimal interpolation fusion method of the application;

[0026] Figure 2 A schematic diagram of a polar multi-source remote sensing sea wave effective wave height optimal interpolation fusion device of the application;

[0027] Figure 3a , Figure 3b A precision verification diagram of the fusion result; wherein, Figure 3a The optimal interpolation fusion result proposed by the application; Figure 3b Global Ocean L4 effective wave height near real-time product (multi-source satellite fusion grid data provided by Copernicus Marine Service, belonging to Level 4). DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the application clearer and more apparent, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and not to limit the application. In addition, the technical features involved in each embodiment of the application described below can be combined with each other as long as they do not conflict with each other.

[0029] As shown in Figure 1 The application proposes a polar multi-source remote sensing sea wave effective wave height optimal interpolation fusion method, which comprises the following steps:

[0030] Step 1, data preprocessing: develop a data preprocessing procedure with clear zoning, reasonable threshold and noise removal function, process the observation data in the sea ice edge area and open sea area respectively to improve the quality of the observation data; develop a data preprocessing procedure, process the sea ice edge area and open sea data in different zones to improve the data quality and thus improve the overall precision of the fusion result;

[0031] Step 2: North Pole geometric adaptation and correction: In the orthogonal projection coordinate system, a time-space matching algorithm is used to obtain the regression relationship between different sensor data to correct the systematic bias of multi-sensor data; North Pole geometric adaptation and correction is performed. In the orthogonal projection coordinate system, a time-space matching algorithm is used to obtain the regression relationship between different sensor data to eliminate the systematic bias of multi-satellite sensors;

[0032] Step 3: Dynamic background field fusion to obtain dynamic background fusion results. Using WaveWatch III (WW3), a global third-generation wave model developed and publicly released by the National Oceanic and Atmospheric Administration (NOAA), its real-time wave height forecast data is used as the background field. A fusion weight function is constructed in combination with the satellite observation error covariance matrix to achieve high-precision fusion in data-sparse areas. Dynamic background field fusion is performed, using the real-time wave height forecast of the WaveWatch III model as the background field and the satellite observation error covariance matrix to construct a weight function. This method demonstrates higher accuracy and reliability in data-sparse areas compared to other methods.

[0033] Step 4: Perform partition fusion based on the dynamic background fusion results, set different weights for observations in the sea ice edge area and the open sea, and introduce a transition weight function based on cosine smooth transition to avoid boundary discontinuity;

[0034] Step 5: Improve the verification system and verify the robustness of the fusion method through K-fold cross-validation (successively eliminating single-star data to generate a fusion field). At the same time, combine Arctic in-situ observation data, fusion results, background field data, and international similar data for multi-dimensional accuracy verification.

[0035] Specifically, the step 1 includes:

[0036] (1) Data collection: Collect data from satellites with Arctic observation capabilities, including CFOSAT, CryoSat-2, HY-2B, Jason-3, Sentinel-3A, Sentinel-3B, Sentinel-6MF, ALTIKA / SARAL, and SWOT sub-satellite radar altimeter observation data. Each observation data includes longitude, latitude, time, significant wave height, and root mean square error of significant wave height ( ) and a high-quality significant wave indicator. Also available are real-time wave height forecasts from WaveWatch III, near-real-time significant wave height products from Copernicus Global Ocean L4, and CMEMS wave height analysis data. These data also include longitude, latitude, time, and significant wave height information.

[0037] (2) Data preprocessing: There are outliers in the collected foot-point radar altimeter significant wave height data. First, threshold method and flag method are used to filter outliers. The threshold of the root mean square error of significant wave height is calculated as follows:

[0038] The foot-point radar altimeter measured significant wave height value SWH (0m to 12m) is binned with 0.5m width and 0.05m step. The mean and standard deviation of in each bin are calculated, and only bins with data amount greater than 100 are kept. The upper threshold of each SWH bin is calculated as:

[0039]

[0040] where is the mean, is the standard deviation, is the exponential function. The threshold of

[0041] is fitted with a second order polynomial, and is extrapolated to 12m. The threshold lookup table is constructed as:

[0042]

[0043] where is the fitted second order polynomial function, is the value of the polynomial at SWH=12m. This lookup table is used to control the threshold of at different SWH, and remove data exceeding the threshold. This method can be used to eliminate false significant wave height measurement data caused by land interference, sudden increase of radar backscatter coefficient sigma0 and strong rainfall attenuation. To protect the observation data in the sea ice edge area, only the above-mentioned processing is performed on the area with sea ice concentration equal to 0, and not on the area with sea ice concentration greater than 0.

[0044] ​​​​​The data is then filtered using Empirical Mode Decomposition (EMD). EMD filtering is an adaptive time-frequency analysis method primarily used to process nonlinear and nonstationary signals. EMD gradually extracts the signal's intrinsic mode function (IMF) and decomposes the complex signal into several IMF components and a residual component, thereby achieving signal noise reduction, filtering, and trend separation. Unlike traditional Fourier transforms or wavelet transforms, EMD does not require a pre-defined basis function and decomposes the signal entirely based on its own characteristics. The specific steps are:

[0045] Step (1) Obtain the original significant wave height sequence obtained by inversion of satellite radar altimeter ,in It is the time variable corresponding to the significant wave height data, which can be obtained at the same time as the significant wave height data;

[0046] Step (2) in the original effective wave height sequence Search for all local maximum and local minimum points in the , and record their corresponding time positions respectively and , and the corresponding significant wave height values and ;

[0047] Step (3) Use cubic spline interpolation to connect all local maximum points and local minimum points to construct the upper envelope of the sequence and lower envelope :

[0048] ;

[0049] Step (4) Calculate the mean of the upper and lower envelopes : ;

[0050] Step (5) calculates the difference sequence: ;

[0051] judge Whether the conditions of the intrinsic mode function are met, that is, The absolute value of the difference between the number of zero crossing points and the number of extreme points does not exceed 1, and the integral value over the entire sequence interval is close to zero, that is: ;

[0052] If the conditions are met, Determined as the first IMF, denoted as If not satisfied, Repeat steps (2) to (5) above as a new sequence until the IMF conditions are met;

[0053] Step (6) extracts the first IMF (I1) ) and updates the remaining sequence : When , ;

[0054] The above steps (2) to (6) are repeated until the remaining sequence cannot be further decomposed or the energy is lower than a predetermined threshold, and finally, a plurality of IMF components , , , and a residual trend sequence are obtained.

[0055] Subsequently, according to the frequency characteristics of each IMF component, the first high-frequency IMF (the experience recommends a value of 2) is removed, and the low-frequency information and the trend item are retained, and the filtered effective wave height sequence is obtained:

[0056] ;

[0057] wherein, is the total number of IMF, is the starting IMF order of filtering, is the first IMF component, is the residual trend sequence.

[0058] Specifically, the step 2 comprises:

[0059] Due to the system error of multiple satellite sensors, the consistency of data needs to be improved. Generally, cross-correction uses buoy data to compare each satellite altimeter. However, in the Arctic region, in-situ observation data is scarce and concentrated, and the spatial representativeness is poor, so it is more suitable to use one strictly calibrated satellite data as a reference to correct other satellite data. At present, the latest reference satellite used by CMEMS is Sentinel-6MF.

[0060] However, the subsatellite point measurement effective wave height of the satellite radar altimeter is discrete data along the track, and with the increase of latitude, the grid density in the longitude direction increases. In order to facilitate cross-track matching and correction, the orthogonal projection method is used to convert the data to the plane coordinate system to improve the matching accuracy.

[0061] ​​​During the matching process, a time window of 1 hour and a spatial window of 50 km radius were set. Data within this window were considered matched data, but the time window often contains multiple measurements. To ensure data quality, the standard deviation of the data for each satellite within the window was first calculated, and outliers exceeding three standard deviations were removed. The average of the remaining data was then calculated as the significant wave height value for that satellite at the matching point. By obtaining multiple matching points and fitting the data using the least squares method, a correction relationship was established. Based on this relationship, the satellite data to be corrected were systematically corrected to improve consistency between different satellite data.

[0062] Specifically, step 3 includes:

[0063] Optimal interpolation optimizes the estimation results by combining the background field (model calculation) and observation data. The core idea is to use the observation data to correct the model background field to obtain the analysis field:

[0064] (1)

[0065] in, Points to be interpolated The significant wave height value at (analysis field); is the background field value corresponding to the point to be interpolated; is the first Observation point values; is the first The background field value corresponding to each observation point; Points to be interpolated The surrounding The weight of each observation point, Points to be interpolated The total number of observation points selected around. The weight solution is obtained by minimizing the variance of the analysis error:

[0066] (2)

[0067] in, is the covariance matrix of the background error; is the covariance matrix of the observation errors; , and are the variance of the observation error and the variance of the background error, respectively. is the point to be interpolated The weights of the surrounding observation points, is the point to be interpolated The covariance of the background error between each observation point.

[0068] If the observation errors are uncorrelated, I is an identity matrix , equation (2) can be written as:

[0069] (3)

[0070] where, is a distance-dependent function, as equation (4):

[0071] (4)

[0072] where, is the distance between the analysis point and the observation point, is the background field error correlation scale.

[0073] Solving equation (3) can obtain the weight of the observation point value around the interpolation point , and substituting equation (1) can obtain the significant wave height with the interpolation point.

[0074] Specifically, the step 4 comprises:

[0075] For the optimization of the significant wave height in the sea ice edge area, a partition optimization idea is used, and the following strategy is constructed:

[0076] (1) In the open sea area, based on experience, it is considered that the observation error is relatively small, a smaller observation error variance is set, so that the weight of the observation data is relatively larger; considering that the sea wave propagates a long distance in the open sea, a larger correlation scale is set based on experience;

[0077] (2) In the area with low sea ice density, based on experience, it is considered that the observation error is relatively large, a larger observation error variance is set, so that the weight of the background field data is larger; considering that the sea wave propagation in the sea ice edge area is affected by the sea ice and the propagation distance is shorter, a smaller correlation scale is set based on experience;

[0078] (3) When the sea ice density exceeds a certain threshold (empirical threshold), the observation data is considered unreliable, and only the model data is used;

[0079] (4) When the sea ice density further exceeds another empirical threshold, the model data is considered unreliable, and the data is set to be empty.

[0080] Through such partition weight, the fusion partition optimization is realized. In order to ensure the continuity and rationality of the wave height field in the sea ice edge area between different physical regions, the fusion area is divided into four segments according to the sea ice density (unit: %), and the final fusion wave height is defined in the form of a segmented function:

[0081] ;

[0082] where, , , denote the effective wave height estimation results in open water, ice edge zone and background model field, respectively, is the first stage fusion result, which is defined as follows:

[0083] ;

[0084] cosine weight function and are defined as follows:

[0085] ;

[0086] ;

[0087] When the sea ice density is 0%, the open water interpolation result is directly used; when the sea ice density is 0-15%, the cosine smoothing weight between and is used; when the sea ice density is 15-30%, the fusion result of the previous stage is quickly transitioned with the background model field ; when the sea ice density is 30-50%, the background model field result is directly used; when the sea ice density exceeds 50%, it is set to empty. This method takes into account the physical rationality and spatial continuity, and can effectively improve the wave height estimation quality in the ice area.

[0088] Specifically, the step 5 comprises:

[0089] In the Arctic region, although in-situ observation data is relatively scarce and spatially concentrated, it can still be used for accuracy evaluation. To comprehensively verify the accuracy of the fusion data, a multi-dimensional verification method is used, combining the Arctic in-situ observation data, fusion results, background field data and international similar data for comprehensive evaluation. Specifically, the fusion results, background field data and international similar data are compared and analyzed with the in-situ observation data to quantify the error characteristics of different data sets.

[0090] The accuracy evaluation uses the following statistical indicators:

[0091] Bias: measures the systematic error between the fusion results and the reference data (in-situ observation data or satellite data not involved in the fusion), and the calculation formula is:

[0092] ;

[0093] where, is the fusion data or background field data, For in-situ observation data or satellite data not involved in fusion, For matching data points, j represents the index value.

[0094] Root Mean Square Error (RMSE): measures the overall error level of the fused data compared to the reference data, calculated as:

[0095] ;

[0096] Scatter Index (SI): measures the proportion of error relative to the observed value, defined as:

[0097] ;

[0098] where, is the mean of the reference data (in-situ observation or satellite data not involved in fusion).

[0099] Pearson Correlation Coefficient (R): measures the correlation between the fused data and the reference data, calculated as:

[0100] ;

[0101] where, and are the means of the reference data and the fused data, respectively.

[0102] By comparing the accuracy evaluation results of the fusion results with the background field data, the role of the fused satellite observation data in improving the effective wave height expression capability of the background field can be verified. By comparing the accuracy evaluation results of the fusion results with international similar data, the overall accuracy level of the fused data can be further evaluated.

[0103] However, due to the limited in-situ observation data in the Arctic region and the limited spatial representativeness, to enhance the reliability of precision verification, the K-fold cross-validation method is used to evaluate the robustness of the fusion algorithm and analyze the contribution of single satellite data to the fusion results. Specifically, in each round of cross-validation, single satellite data is sequentially excluded, a new fusion field is generated based on the remaining data, and the excluded satellite data is used for independent verification. This method can evaluate the influence of single satellite data on the fusion results and ensure the stability of the fusion method.

[0104] In addition, due to the wide coverage and uniform spatial distribution of satellite data, the idea of K-fold cross-validation is used to replace the original in-situ observation data with satellite data not involved in the fusion to compensate for the lack of spatial representativeness of the in-situ observation data, so as to more comprehensively evaluate the precision of the fused data.

[0105] Preferably, all altimeter data does not need to be obtained during data collection, and if the satellite data is less, the method of reducing the spatio-temporal resolution can be used to increase the amount of observation data.

[0106] Preferably, in the data preprocessing, other satellites can be used instead of Sentinel-6MF satellites as reference satellites for multi-task cross-correction.

[0107] Preferably, in the data preprocessing, other suitable projections for the polar region can be used instead of orthogonal projection.

[0108] Preferably, the optimal interpolation does not necessarily use WaveWatch III as the background field, and can also use WAM and other numerical wave model data.

[0109] As shown in Figure 2 The application also provides a polar multi-source remote sensing sea wave significant wave height optimal interpolation fusion device, comprising the following modules:

[0110] The preprocessing module pre-processes the collected sea ice edge zone and open sea data;

[0111] The correction module performs Arctic geometric adaptation correction to eliminate the system deviation of the multi-satellite sensor and ensure the consistency and accuracy of the data;

[0112] The fusion module uses real-time wave height prediction as the background field, combines the satellite observation error covariance matrix to construct a weight function, performs dynamic background field fusion, and improves the precision and reliability of the data sparse area;

[0113] The weight setting module sets different weights according to the different characteristics of the sea ice edge zone and the open sea, introduces a transition weight function, performs zoned fusion, avoids boundary discontinuity, and thus obtains the final fused wave height;

[0114] The verification module verifies the robustness of the fusion method by K-fold cross-validation, and verifies the precision by combining multi-dimensional data.

[0115] The application also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned polar multi-source remote sensing sea wave significant wave height optimal interpolation fusion method.

[0116] The application further provides a non-transitory computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the polar multi-source remote sensing sea wave significant wave height optimization interpolation fusion method.

[0117] As shown in Figure 3a , Figure 3b The fusion result precision verification diagram is shown in the figure, and the result is compared and verified with ERA5 (global atmosphere and ocean reanalysis data set provided by the European Medium-term Weather Forecasting Center), wherein, Figure 3a is the optimal interpolation fusion result of the application, Figure 3b is a Global Ocean L4 significant wave height near real-time product (multi-source satellite fusion grid data provided by Copernicus Marine Services, belonging to Level 4). The inversion result obtained by the method of the application has good consistency with the observation data. A total of N=40164 sample points are evaluated, the root mean square error RMSE is 0.336 m, the system deviation Bias is 0.111 m, and the correlation coefficient R reaches 0.979, indicating that the method has high precision and stability in the fusion of significant wave height.

[0118] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code. The solutions in the embodiments of the application can be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0119] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0120] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0121] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0122] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those of skill in the art once they have the benefit of the present disclosure. Therefore, the appended claims are intended to encompass within their scope all possible variations and modifications of the preferred embodiments. 1

[0123] It is apparent that a person skilled in the art can make various changes and modifications to the application without departing from the spirit and scope thereof. Thus, if these modifications and changes fall within the scope of the claims and their equivalents, it is intended to include them in the application.

Claims

1. A polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method, characterized by: The following steps are involved: Step 1: Preprocess the collected sea ice edge area and open sea surface data; Step 2: Perform Arctic geometry adaptation correction to eliminate the system bias of multiple satellite sensors and ensure data consistency and accuracy; Step 3: Using the real-time wave height forecast as the background field, the weight function is constructed in combination with the satellite observation error covariance matrix to perform dynamic background field fusion to improve the accuracy and reliability in data-sparse areas. Step 4: According to the different characteristics of the sea ice edge area and the open sea surface, different weights are set, and a transition weight function is introduced to perform partition fusion to obtain the final fused wave height; Step 5: Use the K-fold cross-validation method to verify the robustness of the fusion, and combine multi-dimensional data for accuracy verification.

2. The polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to claim 1 is characterized in that: In step 1, data preprocessing includes: Use the threshold method and the sign method to filter outliers and construct the effective wave height root mean square error The threshold lookup table is used to calculate the root mean square error of the effective wave height at the effective wave height value SWH measured by the radar altimeter at different sub-satellite points. Perform threshold control to remove data exceeding the threshold; Empirical mode decomposition filtering is used on the data. By gradually extracting the intrinsic mode function of the signal, the complex signal is decomposed into multiple intrinsic mode functions and a residual component, thereby achieving signal noise reduction, filtering and trend separation.

3. The polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to claim 1 is characterized in that: In step 2, the North Pole geometry adaptation correction includes: The data are transformed into a plane coordinate system using the orthogonal projection method; Set the radius of the time window and space window, and perform data matching within the range of the time window and space window; The data are fitted by the least square method, a correction relationship is established, and the satellite data to be corrected are systematically corrected.

4. The polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to claim 1 is characterized in that: In step 3, the dynamic background field fusion includes: The weight solution is obtained by minimizing the variance of the analysis error, and then substituted into the interpolation formula to obtain the effective wave height value of the interpolation point.

5. The polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to claim 1 is characterized in that: In step 4, according to the sea ice density , the unit is %, the fusion area is divided into four segments, and the final fusion wave height is defined using a piecewise function form.

6. The polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to claim 5, characterized in that: When the sea ice concentration is 0%, the open water interpolation result is directly used; when the sea ice concentration is 0–15%, the and When the sea ice density is between 15% and 30%, the fusion results of the previous stage are Background field with pattern Perform fast transition fusion; when the sea ice density is between 30–50%, directly use the background model field results; when the sea ice density exceeds 50%, set it to empty; 、 、 They represent the significant wave height estimation results in open water, ice edge area, and background model field, respectively. This is the fusion result of the first stage.

7. The polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to claim 1 is characterized in that: In step 5, the verification method includes: K-fold cross validation was used to successively eliminate single-star data to generate the fusion field; Multi-dimensional accuracy verification is carried out by combining Arctic in-situ observation data, fusion results, background field data and international similar data, and statistical indicators are used for quantitative analysis.

8. A polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion device, characterized by: Includes the following modules: Preprocessing module, which preprocesses the collected sea ice edge area and open sea surface data; The correction module performs Arctic geometry adaptation correction to eliminate the system deviation of multiple satellite sensors and ensure the consistency and accuracy of data; The fusion module uses the real-time wave height forecast as the background field and combines it with the satellite observation error covariance matrix to construct a weight function to perform dynamic background field fusion, thereby improving the accuracy and reliability in data-sparse areas; The weight setting module sets different weights according to the different characteristics of the sea ice edge area and the open sea surface, and introduces a transition weight function to perform partition fusion to obtain the final fused wave height; The verification module uses the K-fold cross-validation method to verify the fusion robustness and combines multi-dimensional data for accuracy verification.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the polar multi-source remote sensing ocean wave significant wave height optimization interpolation fusion method according to any one of claims 1 to 7 are implemented.

10. 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, the steps of the method for optimizing interpolation and fusion of polar multi-source remote sensing ocean wave significant wave heights according to any one of claims 1 to 7 are realized.

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