A method for optimizing the design of payloads for SAR measurement of sea surface current velocity

Abstract

This invention belongs to the field of ocean surface current velocity remote sensing measurement and satellite payload design technology. Specifically, it is a method for optimizing the design of SAR payloads for measuring sea surface current velocity, comprising the following steps: constructing a representative sea state set for the target sea area based on multi-year wind and wave reanalysis data; calculating wave-induced deviations under different observation geometries using a Doppler wave deviation model; searching for the optimal observation geometry and its distribution with the minimum time-averaged deviation for each spatial grid point; constructing task indicators such as deviation lower limit, coverage, and geometric consistency based on the optimal results; selecting typical observation corridors and comparing and evaluating the deviation improvement effects of the optimal geometry and the reference geometry; determining the SAR payload incident angle zone and line-of-sight pointing range that meet the task requirements based on the indicators and improvement effects, forming an optimized scheme and visualizing the output. This invention uses wave-induced deviation as a pre-design constraint, providing a quantitative basis for payload parameter selection, effectively reducing ocean current inversion deviations and improving observation accuracy.

Description

Technical Field

[0001] This invention belongs to the field of ocean surface current velocity remote sensing observation and satellite payload design technology, specifically a payload parameter optimization method for SAR sea surface current velocity observation based on wave-induced bias characteristics. Background Technology

[0002] Ocean surface currents (OSCs) are key parameters characterizing upper ocean dynamic processes and material transport patterns, and have significant application value in climate change research, ocean circulation analysis, pollutant drift forecasting, and ecological environment assessment. Synthetic Aperture Radar (SAR) offers advantages such as all-weather, all-time coverage and high spatial resolution. OSC inversion based on the Doppler centroid anomaly method and along-track interferometry has become an important means of obtaining high-resolution ocean current information. In actual observations, the line-of-sight Doppler velocity obtained by SAR simultaneously includes the actual ocean current drift component and additional components caused by the inherent motion of wave scattering units, long-wave hydrodynamics, and tilt modulation. The latter, collectively known as wave-induced bias (WB), is comparable in magnitude to radial currents and is the main source of bias limiting the accuracy of SAR ocean current inversion.

[0003] In existing technologies, most studies treat wave bias (WB) as an additional bias after observation, modeling and correcting it using DopRIM, IDopRIM, KaDOP, and various empirical or semi-empirical Doppler models. These methods, given the satellite incident angle and line-of-sight azimuth, estimate WB using wind and wave spectrum information and subtract it from the observed Doppler velocity, reducing residual bias to the level of tens of centimeters per second, thus improving the inversion results in coastal and western boundary current regions to some extent. However, these models generally assume that the observation geometry is pre-fixed by the platform, only correcting for bias after observation under the given geometry, lacking a systematic approach to reduce WB from the source through payload parameters and observation geometry design.

[0004] In high-energy western boundary current regions such as the Kuroshio Current, local wind waves coexist with distant swells, and their propagation directions are highly variable. The sensitivity of the WB (wave surface velocity) to the angle of incidence and line-of-sight varies significantly spatially and seasonally. Current satellite missions primarily determine their observation geometry based on engineering constraints such as swath width and coverage area, rarely utilizing multi-year wind and wave statistics to quantitatively assess the achievable lower limit of WB under different geometric configurations, the spatial coverage meeting given deviation thresholds, and the spatial coherence of optimal observation geometry. This lack of clear quantitative basis for payload parameter selection and pointing scheme design in operational ocean current observations further complicates the issue. Therefore, a key technical problem to be solved is how to explicitly incorporate the relationship between WB and observation geometry into the payload design process, considering long-term sea state statistical characteristics, and developing a method that can directly serve the demonstration and parameter optimization of SAR sea surface velocity measurement missions. Summary of the Invention

[0005] To address the problems in existing Doppler current inversion technologies, which generally treat wave-induced bias only as a post-hoc correction and lack quantitative methods for incorporating the relationship between waves and observation geometry into the design of satellite SAR payloads and observation modes, resulting in a lack of constraints such as lower bounds and spatial coverage for sea surface velocity observation geometry, this invention aims to provide a payload design optimization method for SAR measurement of sea surface velocity. Supported by long-term wind and wave reanalysis data, using a physical mechanism-driven KaDOP wave bias model, exhaustive calculations are performed on combinations of incident angle and line-of-sight azimuth under background current-free conditions to obtain the statistical characteristics of wave-induced bias variation with observation geometry in different seasons and sea areas, and to minimize the time... The mean wave-induced bias is used to search for the optimal incident angle and optimal line-of-sight azimuth at the grid scale. Furthermore, task indicators such as the lower limit of bias, spatial coverage under a given threshold, and spatial consistency of observation geometry are constructed to quantitatively evaluate and compare the advantages and disadvantages of different SAR payload incident angle zones and pointing sector configurations. At the same time, combined with typical observation corridors, the local optimal observation geometry is compared with the existing fixed geometry scheme, and the reduction magnitude and relative improvement rate of along-track bias are given. In this way, the impact of wave-induced bias on the accuracy of sea surface velocity inversion is minimized during the mission design stage, and a directly applicable technical solution is provided for the selection of payload parameters and the planning of observation modes for spaceborne SAR sea surface velocity operational observation.

[0006] The technical solution adopted by the present invention to achieve the above objectives is: a load design optimization method for SAR measurement of sea surface current velocity, comprising the following steps: Step S1: constructing a representative sea state set for the target sea area based on multi-year wind and wave reanalysis data;

[0007] Step S2: Under the condition of no background current, the KaDOP Doppler wave deviation model is used to perform traversal calculations on the representative sea state set mentioned in Step S1 within the preset incident angle and line of sight azimuth range to obtain wave-induced deviation results under different observation geometry conditions.

[0008] Step S3: For each spatial grid point, the time-averaged wave-induced deviation corresponding to different combinations of incident angle and line of sight azimuth in step S2 is statistically analyzed. The observation geometric parameters with the smallest time-averaged deviation are searched and determined to obtain the optimal observation geometric results of the spatial distribution of the optimal incident angle, optimal line of sight azimuth and corresponding minimum achievable deviation for each spatial grid point.

[0009] Step S4: Perform regional statistics on the optimal observation geometry results, calculate the lower limit of deviation under different seasons, the spatial coverage under a given deviation threshold, and the spatial consistency index of the observation geometry.

[0010] Step S5: Based on the mainstream area described in Step S1, select a typical observation corridor, and use the reference satellite observation geometry and the local optimal observation geometry determined in Step S3 respectively to calculate and compare the multi-year average wave-induced deviation in the observation corridor to obtain the absolute improvement amount and relative improvement rate of the deviation.

[0011] Step S6: Combining the task indicators obtained in Step S4 with the improvement rate obtained in Step S5, determine the SAR payload incident angle zone and line-of-sight pointing range that meet the target deviation level and observation coverage requirements, and form a payload design optimization scheme.

[0012] Step S7: Visualize the spatial distribution, task indicators, and improvement effects obtained in steps S3 to S6.

[0013] Step S1 includes the following steps:

[0014] Step S11: Select the target sea area according to the needs of ocean current observation, determine the latitude and longitude range and spatial grid resolution of the target sea area, and divide the study area according to sub-sea areas and seasons;

[0015] Step S12: Extract hourly wind speed, wind direction, significant wave height, and wave propagation direction data covering the target sea area from the ERA5 wind and wave reanalysis dataset over many years;

[0016] Step S13: Classify and organize the elements obtained in step S12 by month to form a set of sea state samples representing different seasons, and establish a continuous time series for each grid point.

[0017] Step S2 specifically includes:

[0018] Step S21: Set the radial current velocity term to zero in the Doppler wave deviation model to obtain the line-of-sight Doppler velocity expression used to calculate wave-induced deviation;

[0019] Step S22: Set the incident angle from 20° to 60° with a step size of 2°, and the line of sight azimuth from 0° to 360° with a step size of 2°, and construct a set of observation geometric parameters composed of discrete incident angles and line of sight azimuth;

[0020] Step S23: For each sea state sample and spatial grid point obtained in step S1, traverse the set of observed geometric parameters, calculate the corresponding wave-induced deviation using the line-of-sight Doppler velocity expression from step S21, and store it in a database.

[0021] Step S3 includes the following steps:

[0022] Step S31: For each spatial grid point and the month it represents, the wave-induced deviation obtained in step S2 under the combination of each incident angle and line of sight is statistically averaged over time to obtain the time-averaged wave-induced deviation dataset.

[0023] Step S32: For each spatial grid point, within the preset range of incident angle and line of sight azimuth, the combination of incident angle and line of sight with the smallest average deviation in search time is taken as the optimal observation geometric parameters for that grid point.

[0024] Step S33: Spatially summarize the optimal incident angle, optimal line of sight azimuth and corresponding minimum reachable deviation of each spatial grid point to generate the optimal incident angle field, optimal line of sight azimuth field and minimum reachable wave-induced deviation field.

[0025] Step S32 specifically includes:

[0026] a. For a given grid point, calculate the Doppler velocity of VV polarization along the line-of-sight direction using the Doppler model:

[0027] ;

[0028] in, Let be the line-of-sight Doppler velocity at time t. Angle of incidence This indicates the radar's line-of-sight orientation. Contribution to the Prague scale For the curvature spectrum of the Bragg wave direction, Contributes to the long-wave modulation term;

[0029] b. Set the ocean current drift term to zero when optimizing the geometry, so that Directly representing wave-induced deviation, its instantaneous value is calculated. for:

[0030] ;

[0031] Among them, the angle of incidence of the grid point is obtained. Azimuth Instantaneous wave-induced deviation;

[0032] c. For all T valid ERA5 samples of the same grid point within a representative month, calculate the time-averaged deviation:

[0033] ;

[0034] The monthly mean wave-induced deviation corresponding to this combination of incident angle and azimuth angle is obtained. ;

[0035] d. In the discrete set of incident angles and azimuth set Perform a traversal search on the top to find the solution.

[0036] ;

[0037] And define the minimum reachable deviation:

[0038] ;

[0039] Thus, the optimal incident angle of this lattice point is obtained. Optimal line-of-sight azimuth and the corresponding minimum wave-induced deviation .

[0040] Step S4 specifically includes:

[0041] Step S41: Perform regional and seasonal statistics on the minimum reachable wave-induced deviation and its corresponding optimal observation geometry obtained in step S3 to obtain the typical deviation level and lower limit of deviation for each sub-sea area and the whole area under different seasons;

[0042] Step S42: Under the preset deviation threshold, the proportion of spatial area in each season that meets the minimum achievable deviation not exceeding the threshold is statistically analyzed to form the relationship between the deviation threshold and spatial coverage.

[0043] Step S43: Perform spatial consistency analysis on the optimal incident angle and optimal line-of-sight orientation of each spatial grid point, and calculate the task indicators of observation geometry in terms of directional distribution and spatial connectivity.

[0044] Step S43 specifically includes:

[0045] Step S431: For each grid point within the region Latitude Assign area weights:

[0046] ;

[0047] The two-dimensional weights are obtained by normalization on the effective grid points:

[0048] ;

[0049] A lattice field representing a certain physical quantity For example, its area-weighted average over the entire region is defined as:

[0050] ;

[0051] in, To minimize the wave-induced deviation, the optimal incident angle, or the optimal line-of-sight azimuth angle;

[0052] Step S432: To characterize the available coverage under a given deviation tolerance T, the area-weighted coverage function is defined as follows:

[0053] ;

[0054] Here, 1(⋅) is an indicator function that takes the value 1 when the condition inside the parentheses is met, and 0 otherwise. This represents the area proportion within the region where the minimum reachable deviation does not exceed the threshold T;

[0055] Step S433: Define the weighted cosine sum and sine sum to characterize the optimal line-of-sight orientation field. Directional concentration:

[0056]

[0057] And thus calculate the length of the composite vector: ;

[0058] Step S434: Calculate the circular average line-of-sight azimuth angle using C and S obtained in step S433: ;

[0059] And mapped to the range of 0° to 360°, the circular standard deviation is: ;

[0060] Among them, the larger R is, The smaller the value, the more concentrated the optimal line-of-sight orientation is within the area, and the more consistent the observation geometry is in space.

[0061] Step S5 specifically includes:

[0062] Step S51: Select a typical observation corridor that crosses the main target area. Based on the actual incident angle and line of sight of the reference satellite, as well as the local optimal observation geometry determined in step S3, calculate the wave-induced deviation in the observation corridor under two different conditions.

[0063] Step S52: Compare the wave-induced deviations calculated under the two cases, and calculate the absolute improvement amount and relative improvement rate of the deviation.

[0064] Step S521: Select several representative location points (x, y) along the observation corridor within the reanalysis data coverage area, and calculate the hourly wave-induced bias using the KaDOP Doppler model under the actual observation geometry of the reference satellite. Let N be the total number of hourly samples in a certain month over several consecutive years. Then, the multi-year monthly average reference satellite observation geometric bias is defined as:

[0065]

[0066] Step S522: Under the same hourly forcing, for each time t, call the Doppler model on the discrete incident angle and line-of-sight azimuth grid to obtain the deviation under different geometries. The local geometric optimal deviation for that hour is defined as:

[0067] ;

[0068] Step S523: When continuously using the local optimal observation geometry, the lower geometrical limit deviation that can be achieved at location (x,y) in a certain month over many years is defined as:

[0069]

[0070] Step S524: Construct two diagnostic quantities. The first diagnostic quantity is the absolute improvement of wave-induced bias, i.e.:

[0071]

[0072] The second is the relative improvement rate, that is:

[0073]

[0074] By observing along the observation corridor and Spatial distribution and statistical analysis were used to quantitatively evaluate the bias reduction effect of observational geometric optimization.

[0075] Step S6 specifically includes:

[0076] Step S61: Based on the deviation lower limit, spatial coverage and observation geometric consistency index obtained in step S4, and in combination with the operational requirements for accuracy and coverage, determine the allowable deviation threshold and minimum coverage performance constraints.

[0077] Step S62: Under the performance constraints determined in step S61, comprehensively compare the candidate configurations of different incident angle zones and line-of-sight sectors, and select the configuration that achieves a balance between deviation level, spatial coverage and engineering feasibility as the preferred load design scheme and recommended set of observation geometric parameters.

[0078] Step S7 specifically includes:

[0079] S71: Based on the minimum reachable wave-induced deviation field obtained in step S3, generate two-dimensional spatial distribution maps for different seasons to characterize the magnitude of the deviation, and overlay the coastline, sub-sea area boundary and typical observation corridor locations.

[0080] S72: Based on the optimal incident angle field and optimal line-of-sight azimuth field obtained in step S3, generate spatial distribution maps respectively;

[0081] S73: Based on the task indicators and corridor evaluation results of steps S4 and S5, generate statistical comparison charts;

[0082] S74: Compile the lower limit of deviation, spatial coverage, consistency index of observation geometry, and absolute and relative improvement results on the observation corridor for each region and season, and form a comprehensive evaluation report;

[0083] S75: Integrate the spatial distribution map, statistical charts, and comprehensive evaluation report into a standardized visualization product output.

[0084] The present invention has the following beneficial effects and advantages:

[0085] 1. This invention elevates the relationship between wave-induced deviation and SAR observation geometry from a post-observation correction to a pre-constraint for load and observation mode design, and provides a lower limit for deviation under different combinations of incident angles and line-of-sight azimuths, thus providing a quantitative optimization basis for sea surface current observation geometry.

[0086] 2. This invention utilizes multi-year ERA5 wind and wave reanalysis data to drive the KaDOP wave deviation model, systematically sampling and statistically analyzing actual sea conditions under background current-free conditions. This avoids the limitations of relying on single-event parameter tuning and makes the optimization results more climatologically representative and robust.

[0087] 3. This invention obtains the optimal observation geometry and corresponding minimum reachable deviation spatial distribution by traversing the incident angle and line of sight at the grid scale. It can reveal the preferred observation corridors and geometrically sensitive areas of high-energy regions such as the Kuroshio Current in different seasons, providing refined guidance for orbit coverage and pointing strategies.

[0088] 4. This invention constructs task indicators such as the lower limit of deviation, spatial coverage under a given threshold, and observation geometric spatial consistency, which can be used for lateral comparison and trade-off of different load incident angle zones and line-of-sight sectors, and is conducive to achieving quantifiable engineering trade-offs between accuracy, coverage and geometric coherence.

[0089] 5. This invention compares the local optimal observation geometry with the existing fixed left-looking geometry on a typical observation corridor, showing that the wave-induced bias along the Kuroshio Corridor can be reduced by about one-third, providing an intuitive quantitative basis for assessing the actual benefits that can be brought about by adjusting the observation geometry in existing or candidate satellite missions.

[0090] 6. The optimization framework proposed in this invention is based on publicly available reanalysis data and existing Doppler bias models. It is easy to embed into satellite mission demonstration and ground processing systems, supports the generation of various visualization products and scenario analysis results, and is easy for engineers and business users to understand and apply. It has good scalability and engineering application prospects. Attached Figure Description

[0091] Figure 1 This is a flowchart of a load design optimization method for SAR measurement of sea surface current velocity provided by an embodiment of the present invention;

[0092] Figure 2 This is a schematic diagram of the five-year monthly average distribution of the wind field at 10 m above the sea surface, obtained based on five-year ERA5 data, provided by an embodiment of the present invention.

[0093] Figure 3 This is a five-year monthly average distribution map of the ERA5 surge field in different months provided by an embodiment of the present invention;

[0094] Figure 4 This is a five-year monthly average distribution map of the ERA5 wind and wave field in different months, provided by an embodiment of the present invention.

[0095] Figure 5 This is a spatial distribution map of the optimal line-of-sight azimuth angle for a representative month provided in an embodiment of the present invention;

[0096] Figure 6 This is a spatial distribution map of the optimal incident angle for a representative month provided by an embodiment of the present invention;

[0097] Figure 7 This is a spatial distribution map of the minimum achievable wave-induced deviation amplitude for a representative month, provided by an embodiment of the present invention.

[0098] Figure 8 This is a task diagnostic graph based on area-weighted statistics provided in an embodiment of the present invention;

[0099] Figure 9 This is an illustration of the application effect of the optimized observation geometry provided by the embodiments of the present invention in the actual upstream Kuroshio observation corridor of Luojia-2. Detailed Implementation

[0100] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0101] Synthetic Aperture Radar (SAR)-based remote sensing of ocean surface currents plays a crucial role in the study of small- and medium-scale dynamic processes and air-sea interactions. However, observational results commonly exhibit wave-induced bias caused by the inherent motion of wave scattering units and long-wave modulation. This bias is highly dependent on sea state and sensitive to the angle of incidence and radar line-of-sight azimuth, making it difficult to meet the high-precision application requirements of Doppler current inversion under fixed observation geometry. Most existing methods treat wave-induced bias as an additional bias after observation and correct it using models such as DopRIM and KaDOP under given geometry. There is a lack of technical approaches to systematically optimize the observation geometry using multi-year sea state statistics during the mission and payload design phase. To address this, this invention proposes a payload design optimization method for SAR measurement of sea surface current velocity. It utilizes multi-year ERA5 wind and wave reanalysis data to construct a seasonal sea state set for the Kuroshio Current region. Under background current-free conditions, the KaDOP wave deviation model is invoked to perform ergonomic calculations on combinations of incident angles from 20° to 60° and line-of-sight azimuths from 0° to 360°, obtaining the statistical response of wave-induced deviation to the observation geometry. The method then searches for the incident angle and line-of-sight azimuth with the minimum time-averaged deviation at a grid scale, forming the optimal observation geometry and the spatial distribution of the corresponding minimum achievable deviation. Furthermore, it constructs task indicators such as the lower limit of deviation, spatial coverage under given threshold conditions, and consistency of observation geometry direction, which are used to compare and evaluate different payload incident angle zones and pointing sector schemes. Application of this optimization framework to the geometric configuration analysis of the Kuroshio Current observation corridor upstream of the Luojia-2 satellite shows that, compared to the existing fixed left-looking geometry, using the locally optimal observation geometry can significantly reduce the multi-year July average wave-induced deviation, providing a quantifiable design basis for operational SAR sea surface current observation in terms of observation geometry and payload parameter selection.

[0102] like Figure 1 The diagram shown is a flowchart of a SAR load design optimization method for measuring sea surface velocity according to an embodiment of the present invention. The SAR load design optimization method for measuring sea surface velocity according to the present invention includes the following steps:

[0103] Step S1: Construct a representative set of sea states for the Kuroshio Current region based on reanalysis wind and wave data to provide input data for subsequent observational geometry optimization;

[0104] Specifically, constructing a representative set of sea states for the Kuroshio Current region includes the following steps:

[0105] Step S11: Select the Kuroshio Current system in the Northwest Pacific as the target sea area, and divide the study area into three sub-regions: the Kuroshio Current Main Current (KMS) along the shelf slope east of the first sea area and adjacent sea areas, roughly ranging from 18°N to 31°N and 121°E to 131°E; the Kuroshio Southern Current (KSJ) south of the second sea area, roughly ranging from 21°N to 34°N and 131°E to 142°E; and the Kuroshio Extension Zone (KE) extending eastward from the third sea area, roughly ranging from 28°N to 37°N and 142°E to 154°E. All three are considered as the observation target areas simultaneously, and subsequent statistics and calculations are performed on a unified latitude and longitude grid.

[0106] Step S12: Extract 5-year hourly 10 m wind field data covering the above area from ERA5 reanalysis data, select four representative months: January, April, July and October, summarize all hourly wind fields for the corresponding months for five consecutive years, and perform time averaging on the east-west and north-south wind components at each grid point to obtain the vector average wind direction and its corresponding average wind speed for that month, and construct seasonal wind field background accordingly.

[0107] Step S13: Extract wave parameters from the ERA5 wave reanalysis product for the same time period and region as in Step S12. Under a uniform spatial resolution of 0.5° and a temporal resolution of 1 h, obtain the dominant swell significant wave height and propagation direction, local wind wave significant wave height, and other elements for each grid point. Organize these wind wave elements into sea state sample sets according to representative months. The sea state at each moment is characterized by wind speed, wind direction, swell, and wind wave characteristic parameters, and is used as the input sea state set for subsequent KaDOP wave induced bias calculation and observation geometry optimization.

[0108] Step S2: Calculate wave-induced deviations under different observation geometries based on the KaDOP Doppler velocity model to provide physical constraints for subsequent observation geometry optimization;

[0109] Specifically, calculating wave-induced bias under different observation geometries includes the following steps:

[0110] Step S21: Establish the Doppler velocity expression based on the KaDOP model and separate the wave-induced term. The KaDOP model expresses the line-of-sight Doppler velocity of the spaceborne SAR as:

[0111]

[0112] in, The velocity of the sea surface current. Let ϕ be the radar incident angle. dr v is the azimuth angle of the radar line of sight relative to the drift direction. sc β represents the intrinsic velocity of the scattering unit. nH is the weight of the third spectral moment of the nth wave system. s,n and ω p,n These are the effective wave height and peak angular frequency of the wave system, respectively. This depends on the incident angle, the angle between the radar and the wave system, and the 10 m wind speed U. 10 The complex modulation transfer function, The geometric factor that projects the long-wave orbital velocity onto the radar line-of-sight direction. Let be the azimuth angle between the radar line of sight and the main propagation direction of the nth wave system, g be the gravitational acceleration, and Re(⋅) denote the real part. According to this decomposition, the first term in the Doppler velocity is contributed by ocean current drift, and the remaining terms together constitute the wave-induced deviation caused by Bragg-scale scattering and long-wave modulation.

[0113] Step S22: Under the condition of no background current, input the hourly sea state samples obtained in step S1 into the KaDOP wave deviation model, and calculate the wave-induced deviation value under the given combination of incident angle and line of sight for each sea state.

[0114] Step S3: For each latitude and longitude grid point, each representative month, and each observation geometric combination in Step S2 The line-of-sight Doppler velocity given by the KaDOP model Based on this, the wave-induced deviation amplitude for this hour is defined as:

[0115]

[0116] in, This is a time index of all valid hourly ERA5 samples within this month for five consecutive years. The radar incident angle, This is the radar line-of-sight azimuth angle. This indicates the magnitude of the wave-induced deviation for that hour under given sea conditions and observation geometry;

[0117] Step S32: Within the same grid point and the same representative month, perform time averaging of the deviation amplitudes of all valid hourly samples to obtain the monthly average wave-induced deviation corresponding to this observational geometric combination:

[0118]

[0119] Where T is the total number of valid ERA5 samples, Characterizing the angle of incidence in a long-term statistical sense Line of sight The average level of deviation achievable by this combination of observational geometry;

[0120] Step S33: At each latitude and longitude grid point, perform a traversal search on the pre-defined discrete set of incident angle range 20°~60° and line-of-sight azimuth range 0°~360° to solve for the observation geometric parameter pair that minimizes the monthly average deviation. , The minimum value is This yields the optimal incident angle field, optimal line-of-sight azimuth field, and corresponding minimum achievable wave-induced deviation field at the regional scale, providing fundamental inputs for subsequent mission performance calculations and load design optimization.

[0121] Step S4: Perform regional statistics on the optimal observation geometry results obtained in Step S3 to construct task evaluation indicators;

[0122] Specifically, the task evaluation metrics are constructed by following these steps:

[0123] Step S41: For each grid point within the minimum achievable wave-induced deviation and its corresponding observation geometry obtained in step S3. Latitude Assign area weights:

[0124]

[0125] The two-dimensional weights are obtained by normalization on the effective grid points:

[0126]

[0127] A lattice field representing a certain physical quantity For example, its area-weighted average over the entire region is defined as:

[0128]

[0129] in This can be set as the minimum achievable wave-induced deviation, the optimal incident angle, or the optimal line-of-sight azimuth.

[0130] Step S42: Define an area-weighted coverage function to characterize the available coverage under a given deviation tolerance T, expressed as:

[0131]

[0132] Where 1(⋅) is an indicator function, which takes the value 1 when the condition in the parentheses is met, and 0 otherwise. C(T) represents the area proportion in the region where the minimum reachable deviation does not exceed the threshold T.

[0133] Step S43: Define the weighted cosine sum and sine sum to characterize the optimal line-of-sight orientation field using a theorem. The directional concentration is expressed as:

[0134]

[0135] And thus calculate the length of the composite vector:

[0136]

[0137] Step S44: Calculate the circular average line-of-sight azimuth angle using C and S obtained in step S43:

[0138]

[0139] And mapped to the range of 0° to 360°, the circular standard deviation is:

[0140]

[0141] Where R is larger, The smaller the value, the more concentrated the optimal line-of-sight orientation is within the area and the more consistent the observation geometry is in space, thus providing a quantitative indicator for assessing the spatial consistency of the observation geometry and the feasibility of the project.

[0142] Step S5: Select a typical observation corridor that traverses the main target area, and calculate the multi-year average wave-induced deviation in the corridor using the observation geometry of Luojia-2 satellite and the local optimal observation geometry determined in step S3 of this invention, respectively. Compare the two results to obtain the deviation difference and relative improvement rate.

[0143] Specifically, the quantification of the improvement rate between the two observation results includes the following steps:

[0144] S51: Within the reanalysis data coverage area, select several representative location points along the observation corridor. For grid points falling within the corridor, directly extract their reanalysis grid data. For points not at the grid center, select the ocean grid point closest to the corridor center as the location (x, y). Under the actual observation geometry of the Luojia-2 satellite, use the KaDOP model to calculate the hourly wave-induced deviation. Let N be the total number of hourly samples over five consecutive July months. Then, the geometric bias of the multi-year July average observations from the Luojia-2 satellite is defined as:

[0145]

[0146] S52: Under the same hourly forcing, for each time t, in the line-of-sight orientation From 0° to 360° (in 2° increments) and angle of incidence The KaDOP model is invoked on a discrete grid ranging from 2° to 60° (in 2° increments) to obtain the geometry. Deviation below The local geometric optimal deviation for that hour is defined as:

[0147]

[0148] S53: When continuously employing the local optimal observation geometry, the lower geometrical limit deviation achievable at location (x,y) in July over multiple years is defined as:

[0149]

[0150] S54: Construct two diagnostic quantities from steps a and c. The first is the absolute improvement of wave-induced bias:

[0151]

[0152] The first is used to quantify the reduction in the average deviation over 7 months over multiple years if the radar adopts the locally optimal geometry of this invention instead of the existing Luojia-2 satellite observation geometry; the second is the relative improvement rate.

[0153]

[0154] By observing along the observation corridor and Spatial distribution and statistical analysis were used to quantitatively evaluate the bias reduction effect of observational geometric optimization.

[0155] Step S6: Based on the task indicators and deviation improvement results obtained in Steps S4 and S5, determine the SAR payload incident angle zone and line-of-sight pointing range that meet the target deviation level and observation coverage requirements, and form a payload design optimization scheme for the sea surface current velocity measurement task.

[0156] Specifically, the optimization scheme for the payload design of the sea surface current velocity measurement mission includes the following steps:

[0157] S61: Based on the task indicators such as the lower limit of deviation, spatial coverage and observation geometric consistency obtained in step S4, and in combination with the operational requirements for accuracy and coverage, determine the allowable deviation threshold and minimum coverage and other performance constraints.

[0158] S62: Under the performance constraints determined in step S61, comprehensively compare the candidate configurations of different incident angle zones and line-of-sight sectors, and select the configuration that achieves a balance between deviation level, spatial coverage and engineering feasibility as the preferred payload design scheme and recommended set of observation geometric parameters for the SAR sea surface current measurement mission.

[0159] Step S7: Visualize the optimal observation geometry distribution, mission indicators, and the improvement results of the observation corridor deviation of Luojia-2 satellite obtained from steps S3 to S6, so as to provide technical support for the scheme design and engineering application of SAR sea surface current observation mission.

[0160] Step S71: Based on the minimum reachable wave-induced deviation field obtained in step S3, generate a two-dimensional spatial distribution map of different seasons, use color filling to represent the deviation magnitude, and overlay the coastline, sub-sea area boundary and typical observation corridor location to show the deviation level of each region after geometric optimization.

[0161] Step S72: Based on the optimal incident angle field and optimal line-of-sight azimuth field obtained in step S3, generate spatial distribution maps respectively. The incident angle is displayed using hierarchical color codes, and the line-of-sight azimuth is displayed using color coding or arrow overlay. The preferred corridor area where the observation geometry is spatially continuous and oriented in the same direction is marked.

[0162] Step S73: Based on the task indicators and corridor assessment results of Step S4 and Step S5, generate statistical comparison charts, including: (1) box plots or bar charts of minimum reachable deviations in different seasons or different sub-sea areas; (2) curves showing the relationship between deviation threshold and area coverage, used to display the observable proportion under different deviation constraints; (3) a profile of the multi-year average deviation and improvement of the actual geometry and local optimal geometry of the Luojia-2 satellite along the existing observation corridor.

[0163] Step S74: Compile the lower limit of deviation, spatial coverage, consistency index of observation geometry direction, and absolute and relative improvement results on the observation corridor for each region and season, and form a comprehensive evaluation report. Provide a quantitative comparison of different geometric configurations in terms of accuracy, coverage and consistency, and provide a decision-making basis for the selection of load design parameters.

[0164] Step S75: Integrate the spatial distribution map, statistical charts, and comprehensive evaluation report into a standardized visualization product, and output it in a format that supports GIS platform loading and mission demonstration document integration, so as to facilitate browsing, comparison, and archiving during satellite mission design and engineering application.

[0165] Example 1:

[0166] like Figure 1 As shown in the figure, an embodiment of the present invention provides a payload design optimization method for SAR measurement of sea surface current velocity, comprising the following steps:

[0167] S1: The Kuroshio Current and its extension in the Northwest Pacific Ocean were selected as the target sea area. The study area was divided into three sub-sea areas: the main Kuroshio Current area, the Japanese section of the Kuroshio Current, and the extension area of ​​the Kuroshio Current. Hourly 10 m wind field and wave data for four representative months (January, April, July, and October) were extracted based on ERA5 reanalysis data from five consecutive years on a unified latitude and longitude grid. Time averaging of the wind vectors yielded the seasonal wind field distribution (corresponding to...). Figure 2 As shown, Figure 2This illustration shows the five-year monthly average distribution of wind fields at 10 m above the sea surface in January, April, July, and October, obtained from five-year ERA5 data, according to an embodiment of the present invention. Simultaneously, the swell and local wind wave components are separated to obtain the seasonal spatial distribution of the swell and wind wave fields (corresponding to...). Figure 3 and Figure 4 The images show the five-year monthly average distribution of the ERA5 swell field in different months, as provided in this embodiment of the invention. The colors represent the significant wave height of the swell, and the arrows indicate the average propagation direction of the swell. The images also show the five-year monthly average distribution of the ERA5 wind and wave field in different months, where the colors represent the significant wave height of the wind and waves. This allows for the construction of a multi-year representative sea state sample set for different seasons, providing input for subsequent wave-induced deviation calculations.

[0168] S2: Input the hourly sea state samples obtained in step S1 into the KaDOP Doppler wave deviation model. Under the condition of no background current, set the current drift term to zero, and retain only the intrinsic motion of the scattering cells and the long-wave modulation contribution to obtain the results at the grid. Time t, angle of incidence and line of sight The line-of-sight Doppler velocity is used as the wave-induced deviation. The deviation response and minimum deviation trajectory under different geometries are calculated by traversing a discrete grid with an incident angle of 20° to 60° and a line-of-sight azimuth of 0° to 360°, thus forming a diagnostic result of the observation geometry's sensitivity to deviation.

[0169] S3: For each latitude and longitude grid point and representing month, the result obtained in step S2... By performing time averaging, the monthly average wave-induced deviation corresponding to each combination of incident angle and line-of-sight azimuth is obtained:

[0170]

[0171] Where T is the number of valid hourly samples within the month, then the combination that minimizes the above expression is searched through the preset discrete sets of incident angles and line of sight, denoted as the optimal incident angle and optimal line of sight orientation, and the corresponding minimum reachability deviation is recorded, finally obtaining:

[0172] like Figure 5 As shown, the line-of-sight observation direction that minimizes the average time value of wave-induced deviation is displayed on a 0.5°×0.5° grid in the Kuroshio region, thereby obtaining the optimal line-of-sight azimuth field;

[0173] like Figure 6 As shown, the range of local incident angles required to minimize wave-induced deviation in each sub-region of the Kuroshio Current is given, and the optimal incident angle field is obtained.

[0174] like Figure 7 As shown, this illustrates the lower bound of the geometric deviation of each grid point after simultaneously optimizing the line-of-sight azimuth and incident angle, thereby obtaining the spatial distribution of the minimum achievable wave-induced deviation.

[0175] S4: Based on the minimum deviation, optimal incident angle, and optimal line-of-sight obtained in step S3, latitudinal cosine is used as the area weight to perform weighted statistics on each sub-sea area and the overall region. The weighted average of the minimum deviation is taken as the lower limit of deviation for this season. Under a given series of deviation thresholds, the proportion of grid areas that satisfy the condition that the minimum deviation does not exceed the threshold is statistically analyzed to obtain the relationship between the deviation threshold and spatial coverage. At the same time, circular statistics are performed on the optimal line-of-sight, and the length of the composite vector and the standard deviation of the circle are calculated to characterize the spatial directional concentration and coherence of the optimal line-of-sight orientation, thereby forming task indicators such as the lower limit of deviation, coverage, and geometric consistency. The above results are as follows: Figure 8 As shown, Figure 8 This invention provides a task diagnostic map based on area-weighted statistics, which comprehensively provides indicators such as the lower limit of deviation and its distribution range in different seasons, spatial coverage under a given deviation threshold, statistics of required incident angles, and concentration and spatial coherence of line-of-sight orientation. It is displayed in a multi-panel format. Figure 8 (a) Figure 8 (b) Figure 8 Table (e) provides the lower limit of regional deviation and the deviation threshold-coverage relationship for different seasons. Figure 8 (c) Figure 8 (g) shows the seasonal distribution and quantile range of the incident angle required to achieve minimum deviation. Figure 8 (d) Figure 8 (f) Figure 8 The middle (h) reflects the directional stability and spatial consistency of the optimal line-of-sight orientation, providing an intuitive task diagnosis for comparing the advantages and disadvantages of different observation geometry configurations.

[0176] S5: Select a typical observation corridor of approximately 213 km × 6 km that traverses the main axis of the Kuroshio Current. Within the reanalysis data coverage area, select several representative location points (x, y) along the corridor centerline. For points falling at the grid center, directly use the sea state of that grid point. For points located between grid points, select the ocean grid data closest to the corridor center. Under the fixed observation geometry of the Luojia-2 satellite, calculate the average deviation for July over many years using KaDOP. And under the same sea state, the corresponding average deviation is obtained using the local optimal observation geometry given in step S3. ,by Define the absolute improvement along the corridor and calculate the relative reduction percentage accordingly to obtain the deviation distribution and improvement effect of fixed geometry and local optimal geometry on a typical observation corridor (corresponding to...). Figure 9 ).

[0177] S6: Combining the regional deviation lower limit, spatial coverage, and observation geometry consistency index obtained in step S4, with the absolute improvement and relative reduction rate of typical observation corridors given in step S5, and under the premise of meeting the operational requirements for accuracy and coverage, a comparative analysis is conducted on the configuration of working zones and line-of-sight sectors with different incident angles. The optimal observation geometry scheme that achieves a reasonable balance between deviation level, spatial coverage, and geometric coherence is selected, forming a payload design optimization suggestion for SAR sea surface current observation missions.

[0178] Based on the above calculations and analysis results, S7 generates seasonal distribution maps including wind and wave fields. Figures 2-4 ), Optimal observation geometry and minimum reachable deviation distribution map ( Figures 5-7 ), Task Diagnostic Chart ( Figure 8 Visualization products, including observation corridor geometry optimization renderings, such as... Figure 9 As shown, Figure 9 The image shows the application effect of the optimized observation geometry provided in this embodiment of the invention in the Kuroshio Current observation corridor upstream of Luojia-2, illustrating the absolute reduction and relative reduction percentage of wave-induced deviation relative to the fixed left-looking geometry within a corridor of approximately 213 km × 6 km. Finally, a comprehensive evaluation report including deviation lower limit, spatial coverage, geometric consistency, and corridor improvement effects is compiled and output in standardized map and document formats for easy reference and archiving in the design and engineering applications of satellite SAR sea surface current observation missions.

[0179] Example 2:

[0180] Based on the steps of Example 1, taking the demonstration of a future SAR sea surface velocity observation mission in the Kuroshio region as an example, the application process of the payload design optimization method of this invention in satellite platform and observation mode planning is explained. First, based on the multi-year representative sea state set constructed in Example 1, typical wind field and wave data for the target sea area in January, April, July, and October are obtained, such as... Figures 2-4 As shown, this is used to characterize the relative strength and direction of wind waves and swells in different seasons. Based on this, the KaDOP model is used to calculate the response of wave-induced deviations as a function of incident angle and line-of-sight azimuth under background current-free conditions, providing physical constraints for the candidate range of subsequent load incident angle zones and pointing sectors.

[0181] Based on the given sea state samples and geometric scan results, the search strategy in step S3 of Example 1 is adopted to perform a point-by-point traversal of the combination of incident angle and line-of-sight azimuth on a 0.5°×0.5° latitude and longitude grid. This yields the observational geometric parameters that minimize the time-averaged wave-induced deviation for each grid point in each representative month, thereby obtaining the spatial distribution of the optimal line-of-sight azimuth field, the optimal incident angle field, and the minimum achievable deviation (e.g., ...). Figures 5-7 (As shown). These results clearly demonstrate the preferred observation corridors for the main Kuroshio Current, the Japanese section, and its extensions in different seasons. For example, they show which areas are more suitable for using a direction close to or against the current, and which areas need to deviate from the main current direction to reduce the bias caused by swells. This provides an intuitive basis for designing feasible observation modes.

[0182] After obtaining the optimal observation geometry at the grid scale, according to step S4 in Example 1, area-weighted statistics are performed on the minimum reachability deviation, optimal incident angle, and optimal line-of-sight orientation to construct task diagnostic indicators. Figure 8 This paper comprehensively presents the lower limit of deviation and its distribution range under different seasons, the spatial coverage curve that can meet the accuracy requirements under multiple sets of deviation threshold constraints, and the information on the incident angle range and line-of-sight direction concentration required to achieve geometric optimization. By comparing the task indicators of different incident angle working zones (e.g., 20°~30°, 30°~40°, etc.) and different pointing sectors (e.g., near-forward, near-backward, and near-tangential), the trade-off between deviation level, effective coverage area, and observation geometric coherence of each candidate payload configuration can be quantified, providing a quantitative decision-making basis for selecting a limited number of working modes in engineering.

[0183] Based on the mission evaluation, to verify the feasibility of the geometric optimization results under actual orbital constraints, this embodiment further verifies the geometric optimization scheme using the Kuroshio Current observation corridor upstream of the Luojia-2 satellite as an example. A typical observation corridor of approximately 213 km × 6 km traversing the main axis of the Kuroshio Current is selected. Representative sampling points are extracted along the corridor centerline within the reanalysis grid using ERA5 sea state. Under fixed Luojia-2 geometry conditions, the average wave-induced deviation for July over many years is calculated. Simultaneously, under the same sea state drive, the lower limit of the achievable deviation is calculated using the locally optimal observation geometry, and based on this, the absolute improvement and relative reduction percentage along the corridor are obtained. Figure 9 The deviation distribution and differences between the fixed geometry and the local optimal geometry of the Luojia-2 satellite are presented. The results show that after adopting the geometry optimization scheme in this typical corridor, the multi-year average wave-induced deviation can be reduced by about one-third, and the improvement is most significant at stations with strong Kuroshio Current velocity and wave energy, proving the effectiveness and operability of the method of the present invention in actual orbital scenarios.

[0184] Based on the above analysis, this embodiment will Figures 5-7 The optimal observation geometry reflected at the grid scale Figure 8 The reflected task deviation and coverage indicators and Figure 9The actual improvement effects of the observation corridors were combined, and several representative payload configuration schemes (such as different incident angle working zones and different directional sector combinations) were compared. Finally, a recommended scheme that achieves a reasonable balance between deviation level, spatial coverage and observation geometric coherence was selected, which can be directly used as a parameter reference for the design stage of the new generation SAR sea surface current observation mission.

[0185] In summary, Example 2 demonstrates that the payload design optimization method proposed in this invention can fully utilize years of wind and wave statistics and physical mechanism models during the mission demonstration phase, providing quantitative constraints on the lower limit of observation geometry and spatial coverage. Compared with the traditional approach of relying on experience to select the incident angle and direction, this method is more systematic and interpretable, and can significantly reduce the impact of wave-induced bias on ocean current observations. It provides an engineering-feasible technical path for payload scheme design, orbit planning, and observation mode optimization for future operational satellite SAR sea surface current velocity observations.

[0186] This invention addresses the long-standing challenges of large wave-induced bias and a lack of quantitative optimization basis for observation geometry in spaceborne SAR sea surface current remote sensing measurements. It proposes an engineering solution that can directly support payload and observation mode design. Unlike traditional methods that only perform post-hoc bias correction under given geometry, this invention, supported by physical mechanism models and years of reanalysis wind and wave data, systematically quantifies the comprehensive impact of incident angle and line-of-sight azimuth on the lower limit of wave-induced bias, spatial coverage, and geometric consistency. It provides a task index system for optimizing observation geometry under target accuracy constraints, thereby significantly improving the designability and reliability of SAR sea surface current observation schemes and providing clear quantitative basis for future mission demonstration and parameter selection.

[0187] It should be understood that the above description, in conjunction with the accompanying drawings, represents preferred embodiments of the present invention and serves to illustrate the technical concept and implementation path of the invention. The present invention can be appropriately combined, split, or replaced according to specific application scenarios, such as changing the wind and wave reanalysis data source, adjusting the spatial resolution, or selecting the representative month. Even if such combinations or replacements are not listed individually in this invention, they should not be construed as an abandonment or limitation of the scope of protection of the present invention. As long as it does not deviate from the core idea of ​​the present invention, namely, using a wave deviation model driven by physical mechanisms and a statistical optimization framework to perform forward constraints and optimization of SAR observation geometry, it should be considered a reasonable variation of the technical solution of the present invention.

[0188] Although the present invention has been described in detail with reference to embodiments, those skilled in the art, based on their understanding of the basic concept of the invention, can still make various equivalent modifications and improvements to its form and details, such as applying it to SAR missions in different frequency bands or with different orbital configurations, extending it to other sea areas, or introducing more operational constraints. All modifications, substitutions, and improvements falling within the scope of the present invention and its equivalents should be considered as protected by the present invention.

Claims

1. A method for optimizing a load design for measuring sea surface current velocity by SAR, characterized in that, The method comprises the following steps: Step S1: constructing a representative sea state set of the target sea area based on multi-year wind wave reanalysis data; comprising the following steps: Step S11: selecting a target sea area according to the requirements of current observation, determining the latitude and longitude range of the target sea area, the spatial grid resolution, and dividing the research area according to sub-sea areas and seasons; Step S12: extracting multi-year hourly wind speed, wind direction, significant wave height and wave propagation direction elements covering the target sea area from the ERA5 wind wave reanalysis data set; Step S13: classifying and organizing the elements obtained in step S12 by month to form a sea state sample set representing different seasons, and establishing a continuous time series for each grid point; Step S2: under the condition of no background current, using the KaDOP Doppler wave bias model to calculate the wave-induced bias under different observation geometries by traversing the representative sea state set obtained in step S1 at a preset incident angle and line-of-sight range; Step S3: for each spatial grid point, respectively, the time-averaged wave-induced bias corresponding to different incident angles and line-of-sight combinations in step S2 is statistically calculated, and the observation geometry parameters with the minimum time-averaged bias are searched and determined to obtain the optimal incident angle, the optimal line-of-sight direction and the spatial distribution of the optimal observation geometry corresponding to the minimum reachable bias of each spatial grid point; Step S4: regional statistics of the optimal observation geometry result, calculation of the bias lower limit under different seasons, spatial coverage under the given bias threshold condition and observation geometry spatial consistency index; Step S5: based on the main flow area in step S1, selecting a typical observation corridor, and calculating and comparing the multi-year average wave-induced bias in the observation corridor by using the reference satellite observation geometry and the locally optimal observation geometry determined in step S3, to obtain the absolute improvement amount and the relative improvement rate of the bias; Step S6: combining the task index obtained in step S4 with the improvement rate obtained in step S5 to determine the SAR load incident angle band and line-of-sight pointing range that meet the target bias level and observation coverage requirements, and forming a load design optimization scheme; Step S7: visualizing the spatial distribution, task index and improvement effect obtained in steps S3 to S6.

2. The method of claim 1, wherein, The step S2 specifically comprises: Step S21: setting the radial current velocity term to zero in the Doppler wave bias model to obtain the line-of-sight Doppler velocity expression for calculating the wave-induced bias; Step S22: setting the incident angle from 20° to 60° with a step size of 2°, and the line-of-sight direction from 0° to 360° with a step size of 2°, to construct an observation geometry parameter set composed of discrete incident angles and line-of-sight directions; Step S23: for each sea state sample and spatial grid point obtained in step S1, traverse the observation geometry parameter set, calculate the corresponding wave-induced bias using the line-of-sight Doppler velocity expression in step S21, and store it as a database.

3. The method of claim 1, wherein the method is characterized by: The step S3 comprises the following steps: Step S31: for each spatial grid point and representative month, statistically average the wave-induced bias under each incident angle and line-of-sight combination obtained in step S2 by time to obtain a time-averaged wave-induced bias dataset; Step S32: For each spatial grid point, search the combination of incidence angle and line-of-sight direction with the minimum time-averaged deviation within the preset incidence angle and line-of-sight direction range, as the optimal observation geometry parameter of the grid point; Step S33: Spatially aggregate the optimal incidence angle, optimal line-of-sight direction and corresponding minimum achievable deviation of each spatial grid point to generate the optimal incidence angle field, optimal line-of-sight direction field and minimum achievable wave-induced deviation field.

4. The method of claim 1, wherein, The step S32 is specifically: a. For a given grid point, the Doppler model is used to calculate the Doppler velocity of the VV polarization along the line-of-sight direction: ； wherein, is the line-of-sight Doppler velocity at time t, is the angle of incidence, is the radar line-of-sight azimuth, is the Bragg scale contribution, is the Bragg wave direction curvature spectrum, is the long-wave modulation term contribution; b. Set the current drift term to zero when optimizing the geometry, so that The instantaneous value of the wave-induced bias is calculated directly is: ； where the instantaneous wave-induced deviation of the grid point at the incidence angle , azimuth angle is obtained. c. For all T effective ERA5 samples of a given grid point in a representative month, the time-averaged deviation is calculated: ； obtaining a monthly mean wave-induced bias corresponding to the incident angle and azimuth angle combination ; d. A set of discrete incidence angles and azimuth angles is traversed, solving for ； And define the minimum achievable deviation: ； Thus the optimal incidence angle of the grid point is obtained , the optimal line-of-sight azimuth and the corresponding minimum wave-induced bias .

5. The method of claim 1, wherein, The step S4 is specifically: Step S41: Regionally and seasonally statistically analyze the minimum achievable wave-induced deviation and its corresponding optimal observation geometry obtained in step S3 to obtain the typical deviation level and deviation lower limit of each sub-sea area and the overall region in different seasons; Step S42: Under the condition of a preset deviation threshold, statistically analyze the spatial area ratio that meets the condition that the minimum achievable deviation does not exceed the threshold in each season to form a deviation threshold and spatial coverage relationship; Step S43: Spatially analyze the consistency of the optimal incidence angle and the optimal line-of-sight direction of each spatial grid point to calculate the task index of the observation geometry in terms of direction distribution and spatial connectivity.

6. The method of claim 1, wherein, The step S43 is specifically: Step S431: For each grid point within the region the latitude at which it is located Assigning area weight: ； And normalize to obtain a two-dimensional weight on the effective grid point: ； a lattice field representing a certain physical quantity for example, is defined as an area-weighted average over the entire region: ； wherein, is the minimum attainable wave-induced bias, the optimal angle of incidence or the optimal line-of-sight azimuth angle; Step S432: To depict the available coverage under a given deviation tolerance T, define the area-weighted coverage function as: ； wherein 1(·) is an indicator function that takes the value 1 when the condition in the parentheses is satisfied and 0 otherwise, represents the area ratio of the region where the minimum attainable bias does not exceed the threshold value T; Step S433: Define weighted cosine sum and sine sum to characterize the optimal boresight field of direction concentration: ； and from this the length of the resultant vector is calculated: ; Step S434: The C and S obtained from step S433 are further calculated to obtain the circular mean line-of-sight azimuth: ; and mapped to the range 0° ~ 360°, with a circular standard deviation of: ; where R is larger, the smaller, indicating that the optimal line-of-sight directions are more concentrated and the observation geometry is more consistent in space.

7. The method of claim 1, wherein, The step S5 is specifically: Step S51: Select a typical observation corridor that passes through the target main flow area, and statistically analyze the wave-induced deviation in the observation corridor under two conditions according to the actual incidence angle and line-of-sight direction of the reference satellite and the local optimal observation geometry determined in step S3; Step S52: Compare the wave-induced deviations calculated under the two conditions to calculate the absolute improvement amount and the relative improvement rate of the deviation; Step S521: Select several representative position points (x, y) along the observation corridor within the reanalysis data coverage, and calculate the wave-induced bias every hour under the actual observation geometry of the reference satellite using the KaDOP Doppler model Let N be the total number of all hourly samples in a certain month in consecutive years, and the monthly average reference satellite observation geometry bias is defined as: ； Step S522: Under the same hourly forcing, for each time t, the Doppler model is called on a discrete grid of incidence angles and line-of-sight azimuths to obtain the bias for different geometries The local geometry-optimized bias is defined for this hour as: ； Step S523: When the local optimal observation geometry is continuously used, the geometric lower limit deviation that can be achieved at position (x, y) in a certain month over multiple years is defined as: ； Step S524: Two diagnostic quantities are constructed, the first diagnostic quantity is the absolute improvement amount of the wave-induced deviation, that is: ； The second is the relative improvement rate, that is: ； The bias reduction effect of observation geometry optimization is quantitatively evaluated by analyzing the spatial distribution and statistics of the observation geometry along the observation corridor. and The bias reduction effect of observation geometry optimization is quantitatively evaluated by analyzing the spatial distribution and statistics of the observation geometry along the observation corridor.

8. The method of claim 1, wherein, The step S6 is specifically: Step S61: According to the deviation lower limit, spatial coverage and observation geometry consistency index obtained in step S4, combined with the requirements of business for precision and coverage range, determine the allowed deviation threshold and the lowest coverage rate performance constraint condition; Step S62: Under the performance constraint condition determined in step S61, comprehensively compare the candidate configurations of different incidence angle bands and line-of-sight direction sectors, select the configuration that balances the deviation level, spatial coverage and engineering realizability as the preferred payload design scheme and recommended observation geometry parameter set.

9. The method of claim 1, wherein, The step S7 is specifically: S71: Based on the minimum wave-induced bias field obtained in step S3, generate a two-dimensional spatial distribution map for different seasons, representing the bias size, and superimpose the coastline, sub-sea area boundary and typical observation corridor position; S72: Based on the optimal incidence angle field and the optimal line-of-sight azimuth field obtained in step S3, generate spatial distribution maps respectively; S73: Based on the task indicators and corridor evaluation results of steps S4 and S5, generate statistical comparison charts; S74: Organize the bias lower limit, spatial coverage, observation geometric direction consistency index and absolute and relative improvement results on the observation corridor of each region and each season to form a comprehensive evaluation report; S75: Integrate the spatial distribution map, statistical chart and comprehensive evaluation report into a standardized visualized product output.

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