128 results about "Sea-surface height" patented technology

A method for inversing a depth of a subsurface chlorophyll-a maxima (SCM) of an oceanic water body based on remote sensing reflectance is provided. The method includes: band selection, R value calculation, data collection, model construction, and ZSCM calculation. This method can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentrations, sea surface temperature data and sea surface height data, and reduce the calculation resource consumption of running machine learning, but also effectively improve the inversion accuracy, and effectively reduce the calculation resource consumption and inversion difficulty of inversing the SCM.

The invention discloses a mesoscale eddy identification method based on sea surface height and a connected domain, which comprises the following steps of: acquiring sea surface height abnormal data, performing space-time filtering, searching an extreme point of the sea surface height abnormal data as an initial value, and expressing a relationship between sea surface height abnormal data values on surrounding grid points and the extreme point in a 0 / 1 matrix; wherein a point communicating with the extreme point is a point inside the vortex; iteratively searching points in the vortex by properly increasing or decreasing a sea surface height abnormal data threshold value, and when other extreme points communicated with the extreme point exist on the grid boundary, extracting a region contour communicated with the extreme point from the grid in the previous step as a mesoscale vortex boundary; calculating mesoscale vortex characteristics by using vortex internal points; and according to the sea surface temperature abnormal value in the vortex, eigenvalue calculation and recognition of the mesoscale vortex are completed, and preparation is made for subsequent further discussion of the influence of the mesoscale vortex on the atmosphere.

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention provides an in-ocean solitary wave amplitude inversion method, an electronic device and a readable storage medium, SWOT satellite observation data is combined with other remote sensing means observation data of close time to form a double-time-phase remote sensing image, and then the observed in-ocean solitary wave amplitude of a target sea area is obtained. According to the method, large-area two-dimensional sea surface height observation data with high resolution and high precision can be provided based on radar interference measurement, and under the condition that sea surface height abnormity caused by the marine internal solitary waves on the sea surface can be captured, the amplitude of the internal solitary waves is obtained in combination with other remote sensing observation data, so that the calculation process is simplified, and the calculation efficiency is improved. And the inversion precision is improved.

The invention discloses a marine environment forecasting method based on combination of machine learning and numerical forecasting, and particularly relates to the field of marine environment forecasting, which comprises the following steps: extracting marginal region features based on real-time observation data, constructing a forecasting field through a double-loss function, extracting extreme event features through space-time decoupling, and constructing a forecasting field through a double-loss function; and outputting a forecasting result of the target sea area. According to the marine environment forecasting method based on the combination of machine learning and numerical forecasting, the problem of initial field optimization failure caused by covariance matrix estimation deviation when marginal sea area data are sparse is relieved by embedding vorticity conservation equation residual calculation in a hidden layer; the dependence on the number of extreme event samples is reduced by realizing the targeted extraction of the extreme ocean event features; through a physical hard constraint layer technology of a residual error correction network, an initial field error of a data sparse region is corrected in combination with sea surface height and a flow velocity field constraint residual error feature map, and the problem of a large prediction error of an edge sea area is relieved.

The invention provides a high-resolution ocean thermocline depth rapid reconstruction method based on a random forest. The method is used for carrying out rapid reconstruction and quasi-real-time acquisition on the depth of an ocean thermocline by using sea surface data. A random forest model is established, sea surface data such as sea surface temperature, salinity and sea surface height abnormity are utilized, historical data of thermocline depth calculated based on actually measured temperature data are combined, and regression relation fitting is carried out on sea surface elements and thermocline depth, so that the sea surface elements and the thermocline depth are obtained. Therefore, the targets of multi-source ocean data fusion and high-resolution thermocline depth rapid reconstruction are achieved. In order to train the model, a large amount of measured data is used for sample fitting and optimization, so that the prediction accuracy is improved; the method has the advantages of being accurate, efficient and wide in applicability, the temporal-spatial resolution of ocean thermocline depth calculation can be improved, the ability to understand and predict an ocean temperature field is enhanced, and the method has important significance in the fields of ocean engineering, physical ocean and the like.

The invention provides a sea surface height abnormal data downscaling method based on deep learning, and relates to the technical field of data processing, and the method specifically comprises the following steps: preprocessing data including AVISO data, SWOT data and key auxiliary variables; an enhanced super-resolution generative adversarial network ESRGAN is constructed, a high-resolution feature map is generated, and the ESRGAN comprises a generative network and a discrimination network; using a plurality of loss function combinations to optimize the performance of the generative network and the discriminant network, including adversarial loss, perception loss and pixel loss; and evaluating the pixel-level error and the spatial detail consistency output by the ESRGAN by adopting a root-mean-square error and a structural similarity index respectively. According to the technical scheme, the problems that in the prior art, a data downscaling method is limited in applicability and generalization ability, and is difficult to adapt to fusion requirements of different regions and multi-source data are solved.

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

The invention relates to the technical field of data processing, and discloses a data fusion method and system based on multi-source satellite altimeter data. The method comprises the following steps: performing multi-satellite intersection analysis and orbit error data correction on preprocessed multi-source satellite altimeter data to obtain satellite orbit error correction data; carrying out height subtraction processing on the earth water surface height and the average sea surface height on the sea surface height measured by the correction satellite, obtaining along-orbit sea surface height data, carrying out abnormal height data screening, obtaining coastal height abnormal data, carrying out space-time interpolation processing on the coastal height abnormal data, and obtaining a coastal height data set; the method comprises the steps of obtaining interpolation coastal height abnormal data, performing multi-source fusion to obtain gridded sea surface height abnormal fusion data, and performing abnormal reason analysis on the gridded sea surface height abnormal fusion data to obtain a predicted abnormal reason. According to the invention, the accuracy of data fusion based on the multi-source satellite altimeter data is improved.

The invention provides an ocean mesoscale vortex identification method and device, and the method comprises the steps: obtaining the input data of an ocean environment numerical forecasting product, and the input data at least comprise the sea surface height abnormality, the horizontal flow velocity field, the temperature and the salinity; preprocessing the input data to obtain processed marine environment data; recognizing vortexes in the processed marine environment data according to a plurality of preset vortex recognition algorithms to obtain a plurality of vortexes; wherein the preset multiple vortex recognition algorithms at least comprise an Okubo-Wei ss parameter method, a vector geometry method, a rotation invariance method and an SSH isoline method; performing clustering analysis according to the vortex centers of the plurality of vortexes to obtain a target vortex corresponding to the input data; performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex; and the feature parameters are displayed according to a preset display mode, so that the recognition accuracy and comprehensiveness are improved.

The invention relates to the technical field of satellite altimetry, in particular to a height measurement method based on a feature-enhanced multi-base integrated re-tracking model, which is characterized in that the feature-enhanced multi-base integrated re-tracking model FMERM is constructed to optimize the position of a reflection waveform re-tracking point, and the FMERM model executes the following processing processes: 1, extracting input variable features by using PCA, and extracting the input variable features by using PCA; combining with an original input variable to form an enhanced feature set; secondly, optimizing the hyper-parameters of the XGBoost model by using a grid search method, so that the hyper-parameters of the XGBoost model are optimal; thirdly, utilizing the trained model to accurately calculate a re-tracking point normalization power value of the reflected signal waveform; fourthly, the sea surface height is inverted through the geometrical relationship and direct reflection signal time delay and atmospheric correction; an effective means is provided for solving the problem that a reflection waveform re-tracking method is inaccurate, and powerful support is provided for high-precision satellite-borne GNSS-R sea surface height measurement.

The invention relates to the technical field of satellite height measurement, and discloses a GNSS-R ocean plumb line deviation calculation method, device and equipment and a storage medium. The method comprises the following steps: acquiring a height measurement result, a geoid height and an average dynamic terrain of each GNSS-R mirror reflection point in an ocean area, and calculating a residual geoid height; dividing a global geographic area according to a longitude and latitude target interval to generate a target grid; for any lattice point in the target grid, screening a plurality of GNSS-R mirror reflection points around the lattice point as target points; and determining the weight of the residual geoid height of each target point based on the GNSS-R height measurement precision table, calculating the residual geoid height of the network point through weighted average, and further calculating the corresponding ocean plumb line deviation. By adopting the method, the global ocean plumb line deviation can be quickly calculated based on the GNSS-R sea surface height measurement result, and the accuracy of ocean plumb line deviation calculation is improved.

The invention relates to an internal solitary wave identification and amplitude inversion method in mode data based on machine learning, and belongs to the technical field of ocean observation. In combination with a Mask-RCNN and a variational auto-encoder, firstly, sea surface height data is preprocessed and imaged, texture features are extracted through a local binary pattern algorithm, a labeled data set is constructed, model training is performed, and internal solitary wave stripes and positions thereof are identified from the labeled data set; through extraction and processing of temperature field data, the temperature field at the water depth is inversed by using a variational auto-encoder; and extracting the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. According to the method, large-scale and refined ocean mode data can be efficiently and accurately processed, and the efficiency and accuracy of data processing are remarkably improved. The method has a wide application prospect, and especially in marine disaster early warning, marine scientific research and internal wave mechanics research, the automatic identification and amplitude extraction capability of internal waves can be greatly improved.

The invention relates to an ocean near surface layer flow field reconstruction method and system, and the method comprises the steps: calculating the linear similarity between a sea surface height data sequence of a target grid and a sea surface height data sequence of benchmarking grids, and calculating a first distance corresponding to the benchmarking grid closest to the target grid according to the linear similarity; the average value of the first distances corresponding to all the target grids is calculated, the weft-wise average value of the grids serves as a weft-wise space decorrelation scale, and the warp-wise average value of the grids serves as a warp-wise space decorrelation scale; a window is formed by taking a grid where a position point is located as a central point and taking a rectangle constructed by a latitudinal space decorrelation scale and a longitudinal space decorrelation scale as a physical constraint to constrain the space range of each reconstruction field, so that compared with a deep learning model driven by pure data, the actual physical conditions of the sea surface are considered, and the real-time performance of the deep learning model is improved. The generalization ability of the reconstructed model can be improved, and the precision of reconstructing the ocean near-surface flow field can be improved.

The invention provides a curly wave sea surface modeling method, device and equipment based on modified Lie transformation, and relates to the technical field of computer modeling. The method comprises the steps that when a curly wave sea surface model is constructed, sea spectrum information of a to-be-modeled target sea surface can be firstly acquired; based on the sea spectrum information, constructing a linear sea surface height model of a frequency domain dimension corresponding to the target sea surface; transforming the linear sea surface height model of the frequency domain dimension to obtain a linear sea surface height model of the time domain dimension; under a linear sea surface height model of a time domain dimension, correcting Lie transformation based on a wind condition of a target sea surface; and then based on the corrected Lie transformation, carrying out superposition on the linear sea surface height model of the frequency domain dimension so as to construct a curled wave sea surface model of the target sea surface. In this way, the Lie transformation is corrected in combination with the wind condition of the sea surface, so that the corrected Lie transformation can better simulate the curl wave under the high sea condition, and the modeling effect of the constructed curl wave sea surface model is effectively improved.

The invention relates to the technical field of satellite altimetry, in particular to an altimetry method based on a residual network multi-mode deep learning model, which can remarkably improve the robustness of SSH inversion under sea condition noise interference, and is characterized in that a fusion self-attention mechanism and a residual network multi-mode deep learning model ViTResNetMDL is established, and the model ViTResNetMDL is applied to improve the robustness of the SSH inversion under sea condition noise interference. A satellite-borne GNSS-R sea surface height inversion geometric physical model is established, a ViTResNetMDL model is composed of ResNet50, ViT and MHL-NN, the first part adopts a ResNet50 convolution structure to extract effective features from an original DDM, the second part adopts a ViT layer which captures global features in an effective scattering area DDM based on a self-attention mechanism in a Transform module, the third part is composed of linear layers, and the fourth part is composed of linear layers. And using the MHL-NN to retrieve the sea surface height.

The invention relates to the technical field of electronic signal processing and satellite height measurement, in particular to a GNSS-R (Global Navigation Satellite System-Receiver) sea surface height measurement method based on a novel stacked beam method, which can obviously improve the measurement precision, and is characterized in that a plurality of GNSS-R beam signals corresponding to a plurality of GNSS satellites are synthesized through the novel stacked beam method; the method is used for improving the signal-to-noise ratio of reflected signals received by GNSS-R satellites, and comprises the following steps: step 1, receiving and processing the reflected signals from a plurality of satellites; 2, synthesizing and superposing the received signals by using a beam forming technology; and step 3, analyzing the superposed signal to obtain sea surface height information.

The invention relates to the technical field of marine surveying and mapping, and discloses a sea level change trend accurate calculation method based on multi-source data fusion, which comprises the following steps: acquiring multi-source satellite height measurement data, performing data grouping, establishing a plurality of average sea level models, establishing a time-varying average sea level model through the average sea level models, and calculating the sea level change trend according to the time-varying average sea level model. And performing linear regression fitting on the average sea surface height time sequence at each grid point, constructing a grid average sea level change model, and calculating a change trend at each grid point. The method can improve the separation capability of signals with different time scales in sea level changes, is not affected by extreme climate events, natural environment changes and physical environment correction, and can greatly improve the prediction accuracy of the sea level trend.

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 provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention belongs to the technical field of physical ocean and remote sensing application, and particularly relates to a sub-mesoscale signal extraction method based on Ku / Ka dual-frequency SAR (Synthetic Aperture Radar) height measurement, which comprises the following steps of: acquiring a Ku / Ka dual-frequency sea surface height observation original data set and IMU (Inertial Measurement Unit) attitude and trajectory data; registering the average sea surface and tide height to a space-time sampling position of Ku / Ka synchronous observation and deducting from the original height to obtain an initial sea surface height anomaly sequence of the two channels, and processing the initial sea surface height anomaly data of the two channels to obtain a fused height anomaly sequence; performing attitude coupling mode extraction on the fused height anomaly sequence to obtain a height anomaly sequence after platform error elimination; meanwhile, smoothing processing is carried out to obtain a smoothed sequence; and interpolating the smoothed sequence into a two-dimensional regular grid, performing small-scale secondary smoothing denoising on grid data, interpolating a mesoscale background field at the same time into the same grid, and deducting the mesoscale background field to obtain a sea surface height anomaly data system with sub-mesoscale as a main part.

The invention provides a novel GNSS-IR elevation angle deviation correction method and system, and relates to the technical field of data processing, and the method comprises the steps: building a novel elevation angle deviation correction method taking electron density as a reference through a refraction angle relation; generating an SNR file, wherein the SNR file contains pseudo-random noise codes, azimuth angles, elevation angles and S1C observation value data of about 15000 epochs in one day; acquiring a satellite zenith distance and a position of an ionosphere puncture point by using a Krounberg model, and inputting the satellite zenith distance and the position of the ionosphere puncture point into a Nequick-G model to calculate and obtain electron density; the electron density and the satellite zenith distance are used as input parameters to be input into the novel elevation angle deviation correction method to obtain angle errors, the elevation angle is corrected, and the sea surface height is calculated according to the corrected elevation angle. The precision and reliability of sea surface height measurement are improved.

The invention discloses a high-precision two-dimensional flow field measurement method based on multiple parameters of an ocean surface. In interference measurement, an along-track baseline can provide along-track interference for SAR imaging, and along-track interference SAR complex images can invert the radial velocity of a sea surface flow field; a cross-rail baseline can provide cross-rail interference for SAR and altimeter imaging, a cross-rail interference complex image can generate sea surface elevation information, the height difference between an ocean current area and other sea areas is measured to assist in judging the flow direction and position of ocean current, and an ocean earth transfer flow field is inverted; the mixed baseline can provide multi-angle observation freedom degrees of along-track and cross-track baselines for system imaging, the system can obtain multi-angle scattering characteristics of the sea surface, image saturation can be avoided under complex sea conditions, a two-dimensional sea surface wind field and sea waves can be inverted through multi-angle interference complex images, and the system has a wide application prospect. When the sea surface two-dimensional flow field is inverted, the sea background field (sea surface wind, sea wave and sea surface height) is removed, so that the measurement precision of the two-dimensional flow field can be further improved.

The invention discloses an ocean vortex three-dimensional flow field reconstruction method based on physical constraints, and belongs to the technical field of physical oceanography and data modeling, and the method comprises the steps: calculating sea surface height abnormal data according to climate state data and sea surface observation data, recognizing vortex features, and dividing a vortex into grid points; according to the climate state data and the vortex vertical mode characteristic equation, constructing a vortex vertical mode function, and obtaining a vortex horizontal flow velocity vertical structure function; by taking the sea surface flow velocity abnormal condition and the deep layer flow velocity abnormal condition as constraints, determining a vortex vertical modal coefficient according to a vortex horizontal flow velocity vertical structure function, and reconstructing a vortex horizontal flow velocity vertical structure at each grid point; and superposing the vortex horizontal flow velocity vertical structure and the climate state horizontal flow velocity vertical structure to obtain a seawater horizontal flow velocity vertical structure to construct a vortex three-dimensional flow field structure. The problems that the prior art is limited to the sea surface, and the vertical structure of the vortex is difficult to accurately describe are solved.

The invention relates to the technical field of crossing of satellite altimetry, electronic signaling and the like, in particular to a CYGNSS satellite intermediate frequency signal processing and sea surface altimetry method capable of improving altimetry precision, which comprises the following steps of: processing direct intermediate frequency signals, calculating specular reflection points, calculating bistatic delay, establishing an open-loop tracking model to obtain reflected signal parameters; the reflected intermediate frequency is processed, and direct intermediate frequency signal processing is achieved through open source GNSS receiver software so as to achieve estimation of the position, the speed and the time; according to the satellite-borne GNSS-R reflected intermediate frequency signal processing method, the SGRISP one-dimensional time delay correlation result is compared with the processing result provided by the Spanish Space Science Institute ICE-CSIC, and it is indicated that the SGRISP has high reliability in processing the satellite-borne GNSS-R reflected intermediate frequency signal.

The invention belongs to the technical field of ocean measurement, and discloses a method and a system for measuring the sea surface height of deep and far sea based on Beidou PPP-B2b. The method comprises the following steps: calculating an antenna instantaneous geodetic height of a four-antenna GNSS receiver based on a WGS84 coordinate system; correcting the attitude of the buoy by using a buoy attitude correction algorithm to obtain the height of the buoy in the vertical direction; calculating the buoy draft according to a built-in instantaneous average water depth algorithm; the sea surface instantaneous height is calculated through a sea surface instantaneous height algorithm; carrying out noise reduction processing on the calculated instantaneous height of the sea surface through a filtering calculation unit; and the final result is sent to a monitoring service platform through a data transmission module. The method not only supports ship route planning and navigation safety, helps to avoid risks, but also provides long-term, stable and reliable data support for global climate change monitoring, marine environment protection and deep sea scientific research.

The invention relates to the technical field of ocean dynamic environment intelligent forecasting, and provides a three-dimensional ocean dynamic environment intelligent forecasting method and device and electronic equipment, and the forecasting method comprises the steps: S1000, preparing 1 / 30-degree high-resolution ocean dynamic environment element data (three-dimensional temperature and salt flow and two-dimensional water level), and carrying out the normalization; s2000, selecting a corresponding model input sequence from the training set according to a forecasting strategy of the model; s3000, training a hierarchical feature projection network; and S4000: performing reverse normalization and generating future prediction data: performing reverse normalization on the feature-enhanced output data according to the maximum value and the minimum value of the ocean data, and finally generating a day-by-day prediction product of the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity with 1 / 30-degree high resolution in the future day, namely, the product is obtained by predicting the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity.

The invention discloses a real-time sea clutter simulation method based on a physical neural network, belongs to the field of sea clutter simulation, and particularly relates to a real-time sea clutter simulation method. The invention aims to solve the problems of poor real-time performance and long time consumption of the existing sea clutter simulation method. The method comprises the following steps: S1, initializing parameters and simulation; s2, numerical simulation of sea surface dynamics is realized according to a shallow water equation, and the obtained sea surface height field changing along with time, the flow velocity of the sea surface height field in the axial direction and the flow velocity of the sea surface height field in the axial direction are periodically stored; s3, constructing a physical neural network; s4, obtaining a trained physical neural network; s5, inputting the space coordinate and the time coordinate of the real-time sea clutter to be measured into the trained physical neural network, and outputting the sum; generating a real-time file in an STL format; and S6, obtaining sea clutter data by using the open source library.

The invention provides a double-antenna sea surface height monitoring method based on GNSS-IR, and belongs to the technical field of GNSS-IR sea surface height monitoring. The method comprises the following steps: establishing a double-antenna GNSS-IR monitoring device; respectively extracting direct and interference signal observation values; performing direct and reflected signal separation on the signal-to-noise ratio data under the low elevation angle and the high elevation angle respectively; reflecting surface height characteristic values in the low-elevation-angle reflection component and the high-elevation-angle reflection component are calculated respectively; and error elimination is carried out on the height of the inverted reflecting surface, and the inversion precision is calculated by comparing with tide station data. According to the method, the limitation of a traditional GNSS-IR sea surface height monitoring method on the elevation angle is made up, the mode that only low elevation angle data can be used in an original method is expanded to the mode that high and low elevation angle data can be used for inversion, and the sampling rate of sea surface height inversion is greatly increased in the mode that observation data are segmented according to the elevation angle; the method has the characteristics of high observation time resolution, low cost and the like.

The invention discloses a physical constraint-based ocean vortex three-dimensional temperature-salinity density reconstruction method and system, and the method comprises the steps: constructing a vertical distribution model of vortex density through integrating climate state temperature-salinity data, sea surface height, temperature, salinity observation data and other multi-source information, and employing a physical constraint condition and a vertical modal structure of vortex, thereby achieving the reconstruction of the ocean vortex three-dimensional temperature-salinity density. And further calculating the temperature-salt vertical structure of the vortex, and correcting a reconstruction result in combination with sea surface observation data, thereby realizing reconstruction of the vortex three-dimensional temperature-salt density structure. According to the method, vortex feature recognition and vortex high-precision three-dimensional temperature-salinity density reconstruction can be quickly realized by only observing sea surface height, temperature and salinity data in combination with historical data.