33 results about "Satellite altimeter" patented technology

The invention relates to the field of ocean internal solitary wave automatic detection, and discloses an internal solitary wave detection method based on sentinel No.3 satellite altimeter data, which comprises the following steps: acquiring sentinel No.3 satellite altimeter data of a sea area to be detected, and preprocessing the data; sequentially extracting a posterior scattering coefficient of each coordinate point of the Ku wave band and the C wave band, and response characteristic parameters SSHA and SWH; sequentially calculating the difference mean square slopes of the posterior scattering coefficients of the Ku wave band and the C wave band; the response characteristic parameters are smoothed twice, and variables # imgabs0 # and # imgabs1 # are obtained; and respectively calculating a differential mean square slope and a local standard deviation of variables # imgabs2 # and # imgabs3 #, and extracting data fluctuation characteristics by setting a threshold value so as to realize internal solitary wave detection. The method disclosed by the invention has higher accuracy and lower false detection rate, and still has a good detection effect under the condition that the propagation direction of the inner solitary wave is greatly different from the running direction of the satellite.

The invention provides an ocean internal wave data acquisition method, medium and system, and belongs to the technical field of ocean hydrological observation.The ocean internal wave data acquisition method comprises the steps that an acoustic Doppler flow velocity profiler is used for acquiring water body movement data, a temperature-salinity-depth profiler is used for acquiring seawater parameters, and meanwhile a satellite altimeter and drifting buoy data are combined. Performing time-frequency analysis on the water body motion data, and obtaining internal wave candidate data by using band-pass filtering; and constructing an internal wave mathematical equation set comprising a hydromechanics basic equation, buoyancy frequency, a frequency dispersion relation and the like, and verifying and screening out effective data. And inputting the effective data into a pre-trained classification model to determine an internal wave type, finally extracting a key waveform segment through a preset extraction equation set, and generating and outputting internal wave characteristic waveform data. According to the method, multi-source observation data and a mathematical physical model are combined, identification, classification and feature extraction of the ocean internal waves are realized, and the problem that a data acquisition method capable of effectively extracting effective signals of the ocean internal waves and accurately identifying the features of the internal waves is lacked at present is solved.

The invention belongs to the technical field of wave observation, and proposes an adaptive window fusion method for capturing wave height in a large wave process, the method comprising: given a target area, collecting effective wave height data of multi-source satellite altimeters in the target area; determining the initial spatial resolution and initial time resolution of the wave height field in the target area; based on the time-space inverse distance weighted fusion method, generating a wave height sequence of target grid points in the target area at the initial time resolution; performing large wave process identification on the wave height sequence, capturing peak information in the large wave process; refining the fusion process of the preset time period threshold before and after the peak moment, adaptively adjusting the time resolution, and recording the dynamic change of the wave height sequence and the corresponding peak information in the large wave process at the adjusted time resolution. The method realizes the fusion of multi-source satellite data under different sea conditions, takes into account the fusion efficiency and fusion accuracy, and autonomously judges and identifies the large wave process.

The present application provides a vertical line deviation adaptive fusion system and method based on multi-source satellite altimeter data, wherein the system classifies and pre-processes the multi-source altimeter data to obtain high-precision sea surface height data; performs differential processing to calculate the sea surface height slope information of the multi-source satellite altimeter; calculates the initial value of the multi-source fusion weighting factor; respectively solves the vertical line deviation component of the sea surface height slope data of the single-satellite altimeter; calculates the evaluation accuracy of the vertical line deviation grid data of each type of satellite altimeter; dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative update; calculates the multi-source slope fusion weight, solves the vertical line deviation component of the sea surface height slope data of the multi-source satellite altimeter, and obtains high-resolution ocean vertical line deviation grid data. The advantages of the present application are: fully tapping the value of different types of satellite altimeter data, and flexibly and efficiently using multi-source altimeter data to improve the spatial resolution of ocean vertical line deviation.

The invention discloses a tidal flat terrain inversion method and system, and the method comprises the following steps: 1, obtaining a multi-source remote sensing image and ICESat-2 satellite altimeter data under the condition of less clouds or no clouds, and carrying out the preprocessing of the multi-source remote sensing image and ICESat-2 satellite altimeter data; 2, classifying the preprocessed multi-source remote sensing images by using a support vector machine algorithm, and extracting submerging frequency information of the tidal flat; 3, analyzing photon topographic features of ICESat-2 satellite altimeter data, and accurately extracting prior elevation information of the tidal flat by adopting a multi-stage denoising fusion method; step 4, enabling the tidal flat submerging frequency to be in one-to-one correspondence with the prior elevation information, constructing a tidal flat terrain inversion universal model, and mapping the submerging frequency into the tidal flat terrain; and 5, performing precision evaluation on the inverted tidal flat terrain by using the high-precision airborne LiDAR data. The relation between the tidal flat elevation and the submerging frequency is established, the method can be used for inverting the interannual tidal flat terrain, and high-frequency monitoring of the tidal flat terrain is achieved.

The invention discloses a survey method and system for ocean subsurface vortex and a sub-mesoscale process thereof, and the method comprises the steps: obtaining three-dimensional temperature-salinity-depth flow data of a designated sea area, and drawing a vertical distribution diagram of temperature, salinity, density and a flow field; screening an equal-density surface according to the vertical density abnormal structure and the maximum flow velocity occurrence position; identifying the secondary surface vortex based on the determined potential height contour map and the vertical flow field map of the equal-density surface; acquiring satellite altimeter data of a specified sea area and drawing satellite orbit distribution diagrams of different dates; according to the identified subsurface vortex and the satellite orbit distribution map, carrying out route design and drawing a route; and drawing a site distribution map for hydrological survey according to the route distribution map. The invention aims to provide a scheme for identifying and positioning the subsurface layer vortex, investigating and analyzing the subsurface layer vortex sub-mesoscale process and designing an observation route aiming at the ocean subsurface layer vortex so as to solve the problem that the ocean subsurface layer vortex and the sub-mesoscale process thereof are difficult to position and observe.

The invention belongs to the technical field of ocean remote sensing measurement, and particularly relates to a double-GNSS buoy wide-swath altimeter wave number spectrum slope calibration method and system. In combination with a wave number spectrum-frequency spectrum model, frequency spectrum data obtained by a buoy are processed and subjected to numerical analysis, and the wave number spectrum slope of the double-GNSS buoy wide-swath altimeter is calibrated. Therefore, quantitative evaluation is carried out on the wide-swath satellite altimeter wave number spectrum slope. According to the technical scheme, while the defects of traditional single-point measurement are effectively overcome, the evaluation cost and the implementation difficulty are reduced, and reliable verification of the spatial domain two-dimensional sea surface height wave number spectrum characteristics of the wide-swath altimeter is realized.

The present invention belongs to the field of satellite altimeter data deviation correction, and specifically relates to a method for correcting the deviation of a domestic satellite altimeter fusion product based on deep learning. The present invention includes the following steps: S1, obtaining satellite remote sensing data as input and label data, and integrating the obtained input and label data sets into a certain organizational structure and dividing them into subsets for model training, verification and testing; S2, building a deep learning network A2A; S3, using the data set obtained in step S1 to alternately train and verify the deep learning network A2A obtained in step S2; S4, using the trained deep learning network A2A to correct the domestic satellite altimeter fusion product, that is, to obtain domestic satellite altimeter data with higher accuracy. The present invention builds a model based on a deep learning framework and uses public data sets for model training and prediction. It is more convenient to operate and has stronger generalization ability than traditional methods, and provides a new and effective way to improve the accuracy of domestic satellite altimeter fusion products.

The invention discloses an ocean mesoscale vortex intelligent identification method and system based on flow field geometric features and a satellite altimeter, and the method comprises the steps: obtaining the synchronous SSHA data of at least three satellites, and carrying out the calibration according to the actual measurement data of a buoy; on the basis of the calibrated SSHA data, utilizing a ground-to-balance equation to calculate weft-direction and warp-direction ground-to-balance flow velocity components; generating a flow field geometric feature VG vortex center criterion; dynamically adjusting VG vortex core criterion parameters according to the ocean dynamic environment; converting the SSHA and geostationary flow field data into a grayscale image, marking vortex polarity and a bounding box, and adding a salt and pepper noise expansion sample through random rotation; resNet-50 is adopted as a backbone network, and a bidirectional feature pyramid network BiFPN is added to optimize multi-scale feature fusion; and inputting the VG preliminary identification result into the trained ResNet-BiFPN model, and outputting the vortex position, radius and polarity. The technical defects of a traditional method in weak vortex recognition, background noise interference and calculation timeliness are overcome.

The invention discloses a satellite height measurement data optimal interpolation fusion method based on cross calibration and spectrum analysis, and the method comprises the steps: carrying out the sea surface height anomaly calculation and power spectrum analysis, and calculating the sea surface height anomaly of a satellite altimeter according to the precise orbit determination coordinates of a satellite and the distance measurement value of the altimeter; the related scale of adjacent observation points of the to-be-fused satellite altimeter is obtained through the power spectrum analysis of the sea surface height anomaly of the to-be-fused satellite altimeter; cross calibration: performing cross calibration on sea surface height anomaly of the fusion satellite altimeters by using the sea surface height anomaly of the reference satellite altimeter to obtain system deviation in the to-be-fused satellite altimeters; performing self-crossing calibration by using sea surface height anomaly of the satellite altimeter to be fused to obtain a diagonal element of an observation error covariance matrix; constructing a covariance matrix, wherein a non-diagonal element of the observation error covariance matrix is a product of a standard deviation at the two measurement points and a correlation function of the two measurement points; and fusing the fused satellite altimeter and the background field data by using an optimal interpolation method.

The present invention discloses a method for constructing a composite vortex identification model based on Helmholtz decomposition and deep learning, which comprises the following steps: obtaining the east-west flow velocity U and the north-south flow velocity V data of seawater to construct a velocity vector field image; performing Helmholtz decomposition on the velocity vector field image to obtain a potential field F d Image and rotation field F c Image; According to the rotation field F c The potential function and UV data in the image are used to mark single vortex cyclonic vortex, single vortex anticyclonic vortex, compound vortex cyclonic vortex and compound vortex anticyclonic vortex, and the vortex field images before and after the marking are used as deep learning data sets; the deep learning data sets are trained using a neural network to obtain a compound vortex recognition model. The present invention uses the Helmholtz decomposition method to process the derived data of the satellite altimeter to improve the recognition accuracy of the compound vortex, and uses a deep learning method to improve the recognition efficiency of the compound vortex, and finally realizes the application of the model in the entire sea area.

This invention discloses a method and system for investigating subsurface eddies and their submesoscale processes in the ocean. The method includes acquiring three-dimensional temperature, salinity, depth, and current data for a designated sea area, and plotting vertical distribution maps of temperature, salinity, density, and flow field; screening isodense surfaces based on vertical density anomalies and the location of maximum flow velocity; identifying subsurface eddies based on the geopotential height contour maps and vertical flow field maps of the identified isodense surfaces; acquiring satellite altimeter data for the designated sea area and plotting satellite orbit distribution maps for different dates; designing and plotting flight routes for the identified subsurface eddies and satellite orbit distribution maps; and plotting station distribution maps based on the flight route distribution maps for hydrological surveys. This invention aims to provide a solution for identifying and locating subsurface eddies, investigating and analyzing submesoscale processes of subsurface eddies, and designing observation flight routes, thereby solving the problems of difficulty in locating and observing subsurface eddies and their submesoscale processes in the ocean.

The application discloses a cross-dimension multi-source satellite altimeter sea level anomaly joint estimation method and system, and relates to the field of ocean remote sensing.The joint estimation method comprises the following steps: obtaining multi-source observation data of a target sea area about a target period, forming a corrected joint observation data set through cross-dimension data processing and reconstruction operation and bias correction operation, and mapping the corrected joint observation data set to a corresponding regular space-time grid of the target sea area; based on a preset space-time search radius, constructing an observation operator, a background covariance matrix and an observation error covariance matrix corresponding to each grid point in the regular space-time grid, and generating a regular gridded sea level anomaly product of the target sea area about the target period through an optimal interpolation method without performing explicit system error correction.The application can consider large-scale background consistency and small and medium-scale structure resolution capability on multiple spatial scales, and improves the spatial continuity, statistical consistency and overall stability of the sea level anomaly estimation result.

The application belongs to the technical field of circulation mixing coefficient estimation, and discloses a global estimation method, system, device and terminal of a wave-induced turbulent mixing model, the global estimation method of the wave-induced turbulent mixing model comprising: estimating the global three-dimensional structure of the internal tide-induced turbulent mixing coefficient based on the wave-induced turbulent mixing coefficient, the internal tide information extracted by the satellite altimeter and the WOA13 temperature and salinity data; determining the structure of the global internal wave spectrum; and estimating the distribution characteristics of the global annual average internal wave-induced turbulent mixing coefficient BVIW. The internal tide information extraction comprises: obtaining, processing and filtering satellite along-track sea surface height anomaly data, performing tidal harmonic analysis on the filtered data, and obtaining the internal tide harmonic constant of the global ocean tidal frequency M2. According to the derived mixing coefficient, new mixing is not created, but a mixing process that is not available under the existing framework and is rich in physical connotation is created, and the important significance of wave mixing to the circulation is given.

The invention discloses a sea surface height continuous expression and reconstruction method and system for sparse satellite orbit observation, and the method comprises the steps: forming an input-output data pair according to three-dimensional time-space coordinates of an observation point of a satellite altimeter and an original along-orbit sea surface height observation value of the satellite altimeter; the three-dimensional time-space coordinates are converted into meshless continuous input coordinates; constructing a space-time continuous expression model based on implicit neural representation on the basis of a network structure of a sine cycle activation function, introducing gradient field consistency constraint and total variation regularization, and constructing comprehensive loss; and according to global sea surface height estimation output by the model, reconstructing a regular grid, calculating a sea surface height gradient, gradient change, local curvature and power agent quantity, completing continuous expression and reconstruction of the sea surface height, and realizing synchronous and high-quality reconstruction of a fine structure and an overall scene of a sea surface height field. And the defects of non-uniform sampling, high-gradient recovery and micro-analysis capability in the prior art can be effectively overcome.

The present application belongs to the field of marine surveying and geodetic surveying, and discloses a far-sea GNSS buoy height reference transmission method combined with satellite altimeter, aiming to solve the technical problems of difficult transmission of 1985 national height reference in far-sea area and large height measurement error of GNSS buoy antenna, the method establishes height reference conversion relationship through shore-based leveling joint measurement, carries out precise positioning and calculation on far-sea GNSS buoy observation data, and retains effective sea surface information through low-pass filtering processing; combines satellite altimeter data to carry out space-time matching, inverses and dynamically corrects the height of buoy antenna, converts the ellipsoidal height of buoy sea surface into 1985 national height through geodetic datum difference correction, and finally outputs the results through quality evaluation. The method realizes accurate transmission of far-sea height reference and dynamic correction of the height of buoy antenna, guarantees the reliability of far-sea sea surface height calculation, and provides accurate height data support for far-sea marine observation, marine engineering and other fields.

The invention discloses an absolute sea surface elevation measurement system based on an offshore mobile platform, which relates to the field of marine surveying and mapping and satellite remote sensing calibration and comprises an offshore mobile platform part, a shore-based static GNSS (Global Navigation Satellite System) base station and a data processing module, the offshore mobile platform part is integrated with a GNSS antenna and receiver, an attitude sensor, a radar tide gauge and a data acquisition and storage module; the shore-based static GNSS base station provides static coordinates and GNSS observation data, and the data processing module comprises a shore-based base station resolving module for resolving to obtain three-dimensional coordinates; the offshore platform GNSS dynamic resolving module is used for resolving a dynamic position; and the absolute sea surface height calculation module is used for calculating the initial sea surface height and outputting a final result through low-pass or median filtering. The device does not need to distribute a buoy, can be directly carried on a ship, a floating platform and other mobile platforms, realizes high-precision, continuous and safe absolute sea surface elevation measurement, and is suitable for satellite altimeter calibration and other application scenes.

The present invention provides a method, medium and system for collecting ocean internal wave data, which belongs to the field of ocean hydrological observation technology. The present invention collects water body movement data through an acoustic Doppler current profiler and seawater parameters through a temperature-salinity-depth profiler, and combines satellite altimeter and drifting buoy data. Time-frequency analysis is performed on the water body movement data and bandpass filtering is used to obtain internal wave candidate data. A set of internal wave mathematical equations including basic fluid mechanics equations, buoyancy frequency, dispersion relationship, etc. is constructed to verify and filter out valid data. The valid data is input into a pre-trained classification model to determine the internal wave type, and finally the key waveform segment is extracted through a preset extraction equation set to generate and output internal wave characteristic waveform data. The present invention combines multi-source observation data and mathematical and physical models to realize the identification, classification and feature extraction of ocean internal waves, solving the current problem of the lack of a data acquisition method that can effectively extract effective ocean internal wave signals and accurately identify internal wave characteristics.

The invention discloses a ConvLSTM sea level height change prediction method based on reduced gravity hypothesis, and the method is suitable for the prediction of global sea level height anomaly (SLA). The method comprises the following steps: S1, acquiring and preprocessing global satellite altimeter data; s2, based on a ConvLSTM model, extracting spatio-temporal characteristics of sea level height anomaly; s3, introducing wind stress data, and combining physical constraints of a reduced gravity model to enhance the prediction capability of the model for wind-driven Rogobby waves; and S4, outputting a high-precision sea level height prediction result through error evaluation and optimization. According to the method, global satellite altimeter data in 1993-2014 are used for training, experiments in a black tide extension sea area show that the prediction correlation coefficient reaches 0.98, the effective prediction time efficiency can reach 365 days, the performance is more excellent in a low-frequency change area, and the accuracy, stability and timeliness of sea level prediction are remarkably improved.

The invention discloses a self-adaptive statistical modeling method and system for mesoscale vortex spatio-temporal evolution characteristic parameters. The method comprises the following steps: cooperatively acquiring initial parameters of a target sea area through a satellite altimeter and an Argo buoy array; calculating a vortex characteristic scale Rd based on a Ross-Be deformation radius formula; defining a state vector, and establishing a nonlinear state equation to construct a spatio-temporal evolution statistical model; dynamically correcting a prediction error by adopting variational Bayes-square root cubature Kalman filter (VB-SRCKF); obtaining vortex kinetic energy spatial distribution EKE, and processing a flow velocity abnormal component by adopting a discretization grid integral method; an incidence matrix Mlink is generated, and the element Mlink (i, j) of the incidence matrix Mlink is formed by linear combination of EKE and SLA; dimensionality reduction is carried out on the Mlink based on t-SNE manifold learning, and nonlinear features of vortex intensity-radius-life cycle are extracted; the method has the advantages of noise adaptive correction, nonlinear feature recognition enhancement, VB-SRCKF parallelization acceleration, manifold learning acceleration and the like.

The invention provides a global dynamic ocean front identification method based on remote sensing data. The global dynamic ocean front identification method based on remote sensing data comprises the following steps: S1, acquiring and preprocessing infrared satellite remote sensing data and satellite altimeter data; s2, based on a double-threshold method of a Bayes decision model, identifying an ocean peak region; s3, identifying the ocean frontal line based on a kinetic method of a geostationary flow speediness mechanism; s4, performing space-time distribution tracking analysis on the identified ocean frontal line based on a tracking algorithm of connected region centroid matching; and S5, carrying out feature analysis on the tracked ocean frontal line. The global dynamic ocean front identification method based on remote sensing data provided by the invention has the advantages that the ocean front can be comprehensively identified by using the temperature gradient and the earth turning flow velocity, so that the accuracy and continuity of ocean front identification are improved.

The present application provides a geoid height adaptive fusion system and method based on multi-source satellite altimeter data, wherein the system pre-processes multi-source altimeter data and unifies the data reference benchmark; removes the height value of the average dynamic terrain model to obtain the line geoid height data, performs intersection adjustment, and calculates the line geoid height precision of each type of satellite altimeter; calculates the initial value of the multi-source fusion weighting factor; grids the geoid height to calculate the assessment accuracy of the ocean geoid height of each type of satellite altimeter; dynamically adjusts the multi-source fusion weighting factor for iterative update; calculates the fusion weights required for the fusion of multi-source geoid heights, and generates high-resolution geoid heights through gridding. The advantages of the present application are: fully tapping the value of satellite altimeter data in different observation modes and different observation tasks, and flexibly and efficiently using multi-source altimeter data to improve the spatial resolution of the ocean geoid height.

The invention discloses a mesoscale vortex three-dimensional temperature and salt field reconstruction method and system based on an ROMS mode and data assimilation. The method comprises the steps that sea surface temperature data and vertical salinity profile data are acquired; initializing an ROMS mode control equation set; optimizing the vertical resolution of the ROMS mode control equation set by adopting Sigma coordinate transformation; constructing an ensemble Kalman filtering assimilation kernel function; adopting a nonlinear covariance localization function to suppress sampling noise of the assimilation core of the ensemble Kalman filter; generating a bit vortex conservation constraint function based on satellite altimeter data, and converting sea surface height anomaly into three-dimensional density disturbance; assimilation iteration is executed, and a plurality of set members are generated to update the temperature and salt field; embedding a temperature-salt constraint module; based on the output vorticity field, obtaining a vorticity field optimized vortex structure; and outputting the three-dimensional warm salt field. According to the method, the parameter precision is improved, the calculation efficiency is optimized, and the application value is expanded.

The invention provides a data processing method based on ocean tide quasi-harmonic analysis, and relates to the technical field of physical oceanography. The data processing method based on ocean power flow quasi-harmonic analysis comprises the following steps: S1, based on satellite altimeter surface flow velocity data, coastal radar horizontal flow field data and buoy vertical profile data, aligning a space-time reference by adopting a space-time registration algorithm, dynamically fusing three types of data sources through Bayesian weighted Kalman filtering, eliminating instrument system errors, and obtaining a time-space reference; and generating a three-dimensional power flow reference data set. By fusing multi-source ocean observation data and applying space-time registration and dynamic filtering technologies, high-precision three-dimensional power flow reference data is constructed, the data coverage range and reliability are remarkably expanded, in a partial tide parameter analysis stage, a least square fitting and complex vector superposition method is adopted, elliptic motion elements of a key partial tide are accurately solved, and the maximum power flow is obtained. And the integrity of tide motion rule description is ensured.

The invention belongs to the field of satellite altimeter data deviation correction, and particularly relates to a domestic satellite altimeter fusion product deviation correction method based on deep learning. The method comprises the following steps: S1, acquiring satellite remote sensing data as input and label data, integrating the acquired input and label data into a certain organization structure, and dividing the organization structure into subsets for model training, verification and testing; s2, establishing a deep learning network A2A; s3, alternately training and verifying the deep learning network A2A obtained in the step S2 by using the data set obtained in the step S1; and S4, correcting a domestic satellite altimeter fusion product by using the trained deep learning network A2A to obtain domestic satellite altimeter data with higher precision. According to the method, the model is built based on the deep learning framework, the public data set is used for model training and prediction, compared with a traditional method, operation is more convenient, the generalization ability is higher, and a new effective way is provided for improving the precision of a domestic satellite altimeter fusion product.

The present application relates to the technical field of ocean observation and remote sensing data processing, in particular to a kind of ocean three-dimensional detection method and system based on unmanned aerial vehicle fusion microwave and laser, comprising: target sea area identification and task planning;Based on satellite altimeter sea surface height anomaly data, determine target area and unmanned aerial vehicle route;Multi-sensor synchronous acquisition synchronous detection data;Synchronous detection data driven ocean structure joint inversion: microwave and laser synchronous detection result co-registration fusion and ocean quasi-three-dimensional structure construction.The present application takes microwave and laser synchronous detection result as data basis, after sea surface height anomaly calculation, internal thermal structure inversion, chlorophyll anomaly profile inversion, realizes the mutual corresponding ocean quasi-three-dimensional detection between sea surface dynamic signal, upper ocean internal thermal structure and water vertical parameter.

The application discloses a method for correcting wave height data of a sea wave model by using satellite data, and comprises the following steps: traversing the satellite data, and matching the satellite data with corresponding sea wave model data; establishing a preset full connection neural network with the sea wave integral parameter of the wave height of the sea wave component as input and the corrected component wave height as output; establishing a preset full connection neural network with the component residual wave height as input and the corrected component residual wave height as output; connecting the plurality of preset component wave height correction networks and the preset residual wave height correction network in parallel, and taking the square root of the sum of the output items of each network as the total output, namely the corrected overall significant wave height; training the preset parallel network by minimizing the mean square error of the overall significant wave height output by the parallel network and the matched satellite altimeter significant wave height, so as to obtain a neural network model for correcting the significant wave height data of the sea wave model; and applying the model to the output result of the sea wave model which needs to be corrected, so as to improve the accuracy of the sea wave model data.

The invention discloses a water level monitoring method and equipment of a double-satellite altimeter and a medium, and belongs to the technical field of hydrological remote sensing. Performing spatial resolution matching and preprocessing on the ICESat-2 and GEDI data, and extracting surface photon data; the method comprises the following steps: for a land surface region, extracting land surface cross observation points of two satellites, which are overlapped in space, by adopting a graph intersection method, screening effective paired data by combining an abnormal value elimination method, and constructing a land surface elevation correction model based on weighted least square regression; for a water body area, extracting observation photons falling in a water body range, respectively estimating average water levels measured by ICESat-2 and GEDI, and establishing a water level calibration model between the ICESat-2 and GEDI; and fusing the land elevation correction model and the water level calibration model to form a weighted correction function, correcting the GEDI observation water level, and finally obtaining a water level monitoring result of the research area. High-coverage and high-precision remote sensing monitoring of the water level of a water body in a research area is achieved.

The present application provides a system and method for constructing an ocean gravity field model by adaptively fusing multi-source altimetry data. The system removes time-varying noise on the sea surface, removes the reference geoid height and the average dynamic topography of the ocean after preprocessing the multi-source altimetry data; calculates the multi-source fusion weighting factor of each type of satellite altimeter data, and dynamically adjusts the multi-source fusion weighting factor of each type of satellite altimeter data for iterative updating in combination with the evaluation accuracy of the ocean gravity field; calculates the multi-source fusion weights required for the fusion of multi-source altimetry data, and calculates the geoid height grid data or the vertical deviation grid data through gridding processing; inverts the ocean gravity field, and restores the reference gravity field model to generate an ocean gravity field model. The advantages of the present application are: it can fully tap the value of satellite altimetry data of different observation modes and different observation tasks, and fully, flexibly and efficiently utilize multi-source altimetry data to improve the spatial resolution of the ocean gravity field.

The present application belongs to the field of marine remote sensing measurement technology, and specifically relates to a dual GNSS buoy wide-swath altimeter wavenumber spectrum slope calibration method and system. The present application combines the wavenumber spectrum-frequency spectrum model, processes and numerically analyzes the frequency spectrum data obtained by the buoy, and thus quantitatively evaluates the wavenumber spectrum slope of the wide-swath satellite altimeter. This technical solution effectively compensates for the shortcomings of traditional single-point measurement, reduces the evaluation cost and implementation difficulty, and realizes reliable verification of the wavenumber spectrum characteristics of the two-dimensional sea surface height in the spatial domain of the wide-swath altimeter.