CN114756805B

CN114756805B - Sea surface emissivity correction method and device

Info

Publication number: CN114756805B
Application number: CN202210664314.6A
Authority: CN
Inventors: 李艳艳; 董庆; 胡义强; 任永政; 孟德利; 边民; 赵文博
Original assignee: South China Sea Information Center Of State Oceanic Administration; Aerospace Information Research Institute of CAS
Current assignee: South China Sea Information Center Of State Oceanic Administration; Aerospace Information Research Institute of CAS
Priority date: 2022-06-14
Filing date: 2022-06-14
Publication date: 2022-10-28
Anticipated expiration: 2042-06-14
Also published as: CN114756805A

Abstract

The invention provides a method and a device for correcting sea surface emissivity, which relate to the technical field of marine data prediction, wherein the method comprises the following steps: determining a sea surface data set formed by first data and second data which meet a preset matching rule based on the preset matching rule; acquiring rough sea surface emissivity of each unit data in different polarization directions based on the sea surface data set; determining emissivity increment corresponding to each unit data in different polarization directions based on the corrected rough sea surface emissivity increment model; and determining a correction result of the rough sea surface emissivity based on the rough sea surface emissivity and the emissivity increment by taking the unit data as a unit. According to the method and the device, the constructed rough sea surface emissivity incremental model is used for analyzing the sea surface data sets from various data sources, so that the sea surface emissivity correction precision is improved, and the sea surface salinity precision obtained by inversion is further improved.

Description

Sea surface emissivity correction method and device

Technical Field

The invention relates to the technical field of marine data prediction, in particular to a method and a device for correcting sea surface emissivity.

Background

Sea Surface Salinity (SSS) plays a very important role in Sea-air interaction, ocean circulation and ocean processes as one of the important parameters describing the basic properties of the ocean. The Aquarius satellite is the first active and passive combined observation sea surface salinity satellite, is provided with a passive microwave radiometer and an active microwave scatterometer, and can observe the sea surface roughness. The SSS is not directly measured by a microwave radiometer on the Aquarius satellite, but the SSS is inverted by measuring the emissivity of 1-2 cm on the sea surface, and the measuring process is likely to be influenced by marine environmental factors, so that how to reduce the influence of the marine environmental factors and improve the sea surface emissivity correction precision becomes a technical problem to be solved urgently in the field.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a sea surface emissivity correction method and device.

In a first aspect, the present invention provides a method for sea surface emissivity correction, comprising:

determining a sea surface data set formed by first data and second data which meet a preset matching rule based on the preset matching rule;

acquiring rough sea surface emissivity of each unit data in different polarization directions based on the sea surface data set;

determining emissivity increment corresponding to each unit data in different polarization directions based on the corrected rough sea surface emissivity increment model;

determining a correction result of the rough sea surface emissivity based on the rough sea surface emissivity and the emissivity increment by taking the unit data as a unit;

the characteristic variables in each unit data of the sea surface data set comprise longitude, latitude, sampling time, sea surface emissivity, backscattering coefficient, antenna beam sight direction, wind speed, sea surface temperature, effective wave height, polarization direction, incidence angle and sea surface salinity; the preset matching rule comprises that the longitude difference of the first data and the second data is smaller than a first threshold; the latitude difference between the first data and the second data is smaller than a second threshold, and the difference between the sampling time of the first data and the sampling time of the second data is smaller than a third threshold; the first data comprises Aquarius L2 level data; the second data comprise wind direction data, sea surface temperature SST data, sea surface salinity SSS data and effective wave height SWH data; the corrected rough sea surface emissivity increment model is determined based on a geophysical function model of rough sea surface emissivity increment.

Optionally, the corrected rough sea surface emissivity increment model is determined based on a geophysical function model of rough sea surface emissivity increment, and the specific method includes:

determining Fourier series of the sea surface emissivity increment function relative to the wind direction, and taking the Fourier series as a geophysical function model of the rough sea surface emissivity increment;

determining a second rough sea surface emissivity incremental model based on the first ocean parameters in the sea surface data set after the decorrelation processing; the first marine parameters include wind speed, backscatter coefficient of a first VV polarization, backscatter coefficient of a first HH polarization, and effective wave height;

and constructing a corrected rough sea surface emissivity increment model based on the geophysical function model of the rough sea surface emissivity increment and the second rough sea surface emissivity increment model.

Optionally, the determining a second rough sea surface emissivity incremental model based on the decorrelated first sea parameter in the sea surface data set includes:

determining a first cost function based on a geophysical function model of a rough sea surface backscattering coefficient and a geophysical function model of the rough sea surface emissivity increment;

determining the optimal sea surface temperature corresponding to each unit data in the sea surface data set by taking the minimum value of the first cost function as a target;

acquiring the first ocean parameters to be decorrelated corresponding to the optimal sea surface temperature by taking each unit data in the sea surface data set as a unit;

and determining the first two characteristic variables with the contribution rates larger than a first threshold value in the unit data as a first characteristic variable and a second characteristic variable based on the PCA characteristic analysis result of the first ocean parameter.

Optionally, the method for constructing the geophysical function model of the rough sea backscattering coefficient includes:

determining a first expansion of a Fourier series of the sea surface backscattering coefficient function relative to the wind direction;

preprocessing the sea surface data set based on the constructed three-dimensional grid model;

determining expansion coefficients corresponding to the first expansion based on the preprocessed sea surface data set and the first expansion, wherein the expansion coefficients comprise a first harmonic coefficient, a second harmonic coefficient and a third harmonic coefficient;

using a first expansion comprising the determined expansion coefficients as a geophysical function model of the rough sea backscattering coefficients;

the three-dimensional grid model comprises a three-dimensional equal-interval grid formed by wind speed, sea surface temperature and relative wind direction.

Optionally, the determining a fourier series of the sea surface emissivity increment function with respect to the wind direction as a geophysical function model of the rough sea surface emissivity increment includes:

determining a second expansion of the Fourier series of the sea surface emissivity increment function relative to the wind direction;

determining expansion coefficients corresponding to the second expansion based on the preprocessed sea surface data set and the second expansion, wherein the expansion coefficients comprise a fourth harmonic coefficient, a fifth harmonic coefficient and a sixth harmonic coefficient;

using a second expansion comprising the determined expansion coefficient as a geophysical function model of the rough sea surface emissivity increment;

Optionally, preprocessing the sea surface data set based on the constructed three-dimensional mesh model, including:

determining the three-dimensional grid model formed by three dimensions of wind speed, sea surface temperature and relative wind direction based on the wind speed, the sea surface temperature, the wind direction and the antenna beam apparent direction included in the sea surface data set, wherein the relative wind direction is an included angle between the wind direction and the antenna beam apparent direction;

determining a grid to which each unit data belongs in the three-dimensional grid model based on the value of the characteristic variable of each unit data in the sea surface data set;

and if the number of the unit data in the designated grid belonging to the three-dimensional grid model is larger than or equal to a preset threshold value, determining the average value of all the unit data in the designated grid as the three-dimensional characteristic value of the designated grid.

constructing a two-dimensional grid model corresponding to the second rough sea surface emissivity incremental model based on the first characteristic variable and the second characteristic variable;

determining a grid to which a first difference value of emissivity corresponding to each unit data in the sea surface data set belongs based on the constructed two-dimensional grid model;

determining an average value of all the first difference values in each grid of the two-dimensional grid model as a two-dimensional parameter value of the second rough sea surface emissivity incremental model;

the two-dimensional grid model comprises a two-dimensional equal-interval grid formed by the first characteristic variable and the second characteristic variable;

the first difference value is a difference value between a sea surface emissivity measurement value in each unit datum of the sea surface data set and a corresponding first incremental value obtained by a geophysical function model based on the rough sea surface emissivity increment.

In a second aspect, the present invention also provides a device for sea surface emissivity correction, comprising:

the data set module is used for determining a sea surface data set formed by first data and second data which meet a preset matching rule based on the preset matching rule;

the acquisition module is used for acquiring rough sea surface emissivity of each unit data in different polarization directions based on the sea surface data set;

the determining module is used for determining emissivity increment corresponding to each unit data in different polarization directions based on the corrected rough sea surface emissivity increment model;

the correction module is used for determining a correction result of the rough sea surface emissivity based on the rough sea surface emissivity and the emissivity increment by taking the unit data as a unit;

the characteristic variables in each unit data of the sea surface data set comprise longitude, latitude, sampling time, sea surface emissivity, backscattering coefficient, antenna beam sight direction, wind speed, sea surface temperature, effective wave height, polarization direction, incidence angle and sea surface salinity; the preset matching rule comprises that the longitude difference of the first data and the second data is smaller than a first threshold; the latitude difference between the first data and the second data is smaller than a second threshold, and the difference between the sampling time of the first data and the sampling time of the second data is smaller than a third threshold; the first data comprises Aquarius L2 level data; the second data comprise wind direction data, sea surface temperature SST data, sea surface salinity SSS data and effective wave height SWH data; and the corrected rough sea surface emissivity increment model is determined based on a geophysical function model of rough sea surface emissivity increment.

In a third aspect, the present invention also provides an electronic device, comprising a memory, a transceiver, a processor;

a memory for storing a computer program; a transceiver for transceiving data under the control of the processor; a processor for reading the computer program in the memory and implementing the method of surface emissivity correction as described above in relation to the first aspect.

In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of surface emissivity correction as described above in the first aspect.

In a fifth aspect, the present invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of surface emissivity correction as described above in relation to the first aspect.

According to the method and the device for correcting the sea surface emissivity, the constructed rough sea surface emissivity incremental model is used for analyzing the sea surface data set from various data sources, so that the correction precision of the sea surface emissivity is improved, and the precision of sea surface salinity obtained by inversion is further improved.

Drawings

In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a schematic flow chart of a method of surface emissivity correction provided by the present invention;

FIG. 2 is a schematic diagram of a method implementation flow of sea surface emissivity correction provided by the invention;

FIG. 3 is a schematic structural diagram of a sea surface emissivity correction device provided by the invention;

fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The method and apparatus for sea surface emissivity correction provided by the present invention are described with reference to fig. 1 to 4.

Fig. 1 is a schematic flow chart of a method for sea surface emissivity correction provided by the present invention, as shown in fig. 1, the method includes:

step 101, determining a sea surface data set formed by first data and second data which meet a preset matching rule based on the preset matching rule;

102, acquiring rough sea surface emissivity of each unit data in different polarization directions based on the sea surface data set;

103, determining emissivity increments corresponding to each unit data in different polarization directions based on the corrected rough sea surface emissivity increment model;

step 104, determining a correction result of the rough sea surface emissivity based on the rough sea surface emissivity and the emissivity increment by taking the unit data as a unit;

the characteristic variables in each unit data of the sea surface data set comprise longitude, latitude, sampling time, sea surface emissivity, backscattering coefficient, antenna beam sight direction, wind speed, sea surface temperature, effective wave height, polarization direction, incidence angle and sea surface salinity; the preset matching rule comprises that the longitude difference of the first data and the second data is smaller than a first threshold; the latitude difference between the first data and the second data is smaller than a second threshold, and the difference between the sampling moments of the first data and the second data is smaller than a third threshold; the first data comprises Aquarius L2 level data; the second data comprise wind direction data, sea surface temperature SST data, sea surface salinity SSS data and effective wave height SWH data; and the corrected rough sea surface emissivity increment model is determined based on a geophysical function model of rough sea surface emissivity increment.

Specifically, in order to improve the accuracy of the inversion result of the sea surface salinity, the active and passive joint observation characteristics of the Aquarius L2-level data are utilized while auxiliary sea surface temperature data are considered, and a sea surface data set is constructed, namely the corresponding sea surface data set is determined according to basic data (first data) and commonly used auxiliary data (second data); the first data comprise Aquarius L2 level data, namely Aquarius L2 level track product data; the second data comprises wind direction data, sea surface temperature SST data, sea surface salinity SSS data and effective wave height SWH data. Aquarius L2 grade track production data comprising: the method comprises the following steps that radiometer sea surface emissivity observation data, scatterometer HH polarization backscattering coefficient observation data, scatterometer VV polarization backscattering coefficient observation data and HHH wind speed data are obtained through inversion according to measured values of scatterometer HH polarization and radiometer H polarization; the assistance data comprises: wind direction data, sea Surface Temperature (SST) data, effective Wave Height (SWH) data, sea Surface Salinity (SSS) data, and single-point Argo buoy Sea Surface Temperature (SST) observation data.

Determining observation longitude L, latitude B and time T of the Aquarius L2-level track product data, and reading observation longitude L0, latitude B0 and time T0 corresponding to the auxiliary data;

the screening satisfies the presettingThe method comprises the steps that first data and second data of a matching rule are matched, and the preset matching rule comprises the condition that the longitude difference of the first data and the second data is smaller than a first threshold value; the latitude difference between the first data and the second data is smaller than a second threshold, and the difference between the sampling time of the first data and the sampling time of the second data is smaller than a third threshold; for example

When the number of the second data satisfying the preset matching rule is greater than or equal to 1, the value of each characteristic variable in the unit data in the sea surface data set is set to be the average value of the same characteristic variables in the second data, for example, when the longitude in the first data is 40 ° of east longitude, 10 ° of latitude in north latitude, and the sampling time is 13 at 5 months and 8 days in 2021 year, two data satisfying the preset matching rule are included in the second data, and the corresponding records are 40.4 ° of east longitude, 9.8 ° of north latitude, 12 ° of sea surface temperature at the sampling time 2021 year 5 months and 8 days, and 12 ° of east longitude, and 39.7 ° of north latitude 10.3 ° of north latitude, 14 at the sampling time 2021 year 5 months and 8 days, and 10 ° of sea surface temperature is 10 °, then the corresponding sea surface data set unit data includes 40 ° of east longitude, 10 ° of north, and 13 ° of 2021 year 5 months and 8 days, and the sea surface temperature is 11 °. And when the number of the second data meeting the preset matching rule is less than 1, setting the corresponding unit data in the sea surface data set as invalid. And completing construction of the sea surface data set until all data in the second data are screened. The longitude, the latitude and the sampling time mainly represent the geographic position and the specific time of the data acquisition, and the data from different data sets are integrated by taking the three characteristic variables as main keys, so that each unit data of the finally obtained sea surface data set comprises a plurality of characteristic variables, such as the longitude, the latitude, the sampling time, the sea surface emissivity, the backscattering coefficient, the antenna beam visual direction, the wind speed, the sea surface temperature, the effective wave height, the polarization direction, the incident angle and the like, can reflect the characteristics of the sea surface with different longitudes and latitudes from multiple aspects, and the influence of the characteristics on the sea surface reflectivity can be conveniently evaluated. The Aquarius satellite comprises 3 antennas, each antenna corresponds to 1 fixed beam sight direction, and the included angle between the antenna beam sight direction and the north direction of the geocenter is the azimuth angle of the antennaThe angle between the antenna beam boresight and the ground normal is the angle of incidence, which is typically less than 90 °.

Based on the constructed sea surface data set, sea surface emissivity in different polarization directions can be directly obtained according to different longitudes and latitudes and sampling moments and is used as rough sea surface emissivity. The sea surface emissivity is an actual measurement result in Aquarius L2 level data, and may be influenced by marine environmental factors and is not accurate enough. Therefore, the corrected rough sea surface emissivity increment model provided by the application needs to be combined to determine the corresponding emissivity increment under the same longitude and latitude, and the rough sea surface emissivity is corrected to obtain more accurate sea surface emissivity. According to the corrected rough sea surface emissivity incremental model, the active and passive joint observation characteristics of Aquarius L2-level data are utilized, data acquired from a sea surface data set are analyzed and processed from multiple dimensions, the sea surface emissivity correction precision is improved, and the precision of sea surface salinity obtained by inversion can be further improved.

According to the sea surface emissivity correction method provided by the invention, the sea surface data sets from various data sources are analyzed through the constructed corrected rough sea surface emissivity incremental model, so that the sea surface emissivity correction precision is improved, and the precision of the sea surface salinity obtained by inversion is further improved.

Specifically, the corrected rough surface emissivity incremental model comprises two parts, the first part is determined according to a surface emissivity incremental function which can be expressed as

Determining the function relative to the wind direction

Performing Fourier series expansion of even harmonic function, and only retaining to corresponding second harmonic, wherein the corresponding Fourier series expansion is as follows:

wherein,

is composed of

The harmonic coefficients of the Fourier series expansion of (1), and the harmonic coefficients of k are numbered

，

Representing different directions of polarization, harmonic coefficients

Value and wind speed of

Sea surface temperature

Polarization mode

And both the incident angle.

The second part is a second rough sea surface emissivity incremental model, the sea surface data set is preprocessed in the model, characteristic variables which can reflect sea surface emissivity change most are screened, and the characteristic variables are used as first sea parameters after corresponding operation, wherein the first sea parameters specifically comprise wind speed, a backscattering coefficient of first VV polarization, a backscattering coefficient of first HH polarization and effective wave height; and performing decorrelation processing to reduce the correlation among the parameters, and constructing a second rough sea surface emissivity incremental model by the sea surface data set subjected to decorrelation processing.

According to the geophysical function model of the rough sea surface emissivity increment and the second rough sea surface emissivity increment model, a corrected rough sea surface emissivity increment model is constructed, and the corresponding mathematical expression is as follows:

. Wherein,

representing the sea surface emissivity increment determined by a geophysical function model of the rough sea surface emissivity increment;

representing the sea surface emissivity increment determined by the second rough sea surface emissivity increment model; p represents the different polarization directions.

And then determining emissivity increment corresponding to the rough sea surface emissivity through the corrected rough sea surface emissivity increment model for the rough sea surface emissivity obtained from the auxiliary data, further determining an accurate sea surface emissivity correction result, and taking the accurate sea surface emissivity correction result as a calm sea surface measurement conversion emissivity. On one hand, the corrected rough sea surface emissivity increment model is based on a geophysical function model of rough sea surface emissivity increment and is corrected for one time on the basis of the same longitude and latitude and wind direction, and on the other hand, unit data in an ocean data set are corrected for the rough sea surface emissivity again according to the wind speed, the backscattering coefficient of the first VV polarization, the backscattering coefficient of the first HH polarization and the effective wave height, so that the influence of the correlation of ocean dynamic parameters on the measurement result is reduced.

Optionally, determining a second rough sea surface emissivity incremental model based on the decorrelated first sea parameter in the sea surface data set, including:

determining a first cost function based on a geophysical function model of a back scattering coefficient of the rough sea surface and a geophysical function model of emissivity increment of the rough sea surface;

Specifically, before determining the second rough sea surface emissivity incremental model, it is necessary to determine an optimal sea surface temperature corresponding to each unit data in the sea surface data set, specifically by constructing a cost function (first cost function) of sea surface temperature inversion, where the first cost function is determined by a geophysical function model based on a rough sea surface backscattering coefficient and the geophysical function model of the rough sea surface emissivity incremental model, and a corresponding formula is represented as:

wherein,

a first cost function representing sea surface temperature dependence,

and

for scatterometer observation data, according to longitude and latitude, sampling time is obtained from sea surface data set,

representing the backscattering coefficients in different polarization directions determined by a geophysical function model of the backscattering coefficients of the rough sea surface;

indicating different polarization directions, VV indicating vertical polarization, HH indicating horizontal polarization;

and

respectively representing the backscattering coefficients in the vertical polarization direction and the backscattering coefficients in the horizontal polarization direction which are determined by a geophysical function model of the backscattering coefficients of the rough sea surface;

the observation data of the radiometer can be directly obtained from a sea surface data set,

indicating different polarization directions, V indicating vertical polarization, H indicating horizontal polarization;

，

for geophysical increase in emissivity at rough sea surfaceThe sea surface emissivity increment determined by the function model,

theoretical emissivity for a calm sea;

respectively representing the sea surface emissivity after being corrected by the geophysical function model of the rough sea surface emissivity increment in different polarization directions.

For the pre-acquired sea surface temperature SST data,

、

、

、

、

the estimated variance of each variable.

Wherein is given

And estimating a variance value, and assigning an initial value to the sea surface temperature to be inverted, wherein the specific initial value can be set as Argo buoy SST observation data.

The theoretical emissivity of calm sea surface is involved

The specific determination method comprises the following steps:

(1) The emissivity of a calm sea surface can be known according to kirchhoff's law

Can be determined by the following formula:

wherein,

in order to be the reflectivity of the fresnel,

(2) Fresnel reflectivity for sea-air interface

This can be found by the following equation:

wherein,

as angle of incidence, fresnel reflectivity

Lower foot mark

Indicating that different polarization directions V represent vertical polarization, H horizontal polarization,

is a complex dielectric constant, also known as complex relative permittivity,

is a complex refractive index of the light beam,

is composed of

The real part of (a). Therefore, the key to determine the fresnel reflectivity is to solve the complex dielectric constant of seawater.

(3) The complex permittivity of seawater can be solved by the equation of double Debye (Debye), as follows:

in the formula,

is the complex dielectric constant (dimensionless) of seawater,

is sea surface temperature, is pre-acquired sea surface temperature SST data,

sea salinity is sea salinity SSS data which is obtained in advance,

frequency of electromagnetic waves

，

Is the angular frequency of electromagnetic waves

，

Is a relative permittivity of the medium frequency,

in order to have a static relative permittivity,

is the permittivity in a vacuum, and,

in order to achieve a wireless high-frequency relative permittivity,

is the ionic conductivity of the water-soluble polymer,

and

first and second debye recovery frequencies, respectively.

The double debye equation, also known as the Meissner-Wentz model, is proposed by Meissner and Wentz to satisfy the need for a larger frequency range (up to 1 THz), noting that the internal physical mechanisms of the double debye equation are not well understood and can be simply understood as the necessary parameters to provide a more accurate fit of the dielectric constant over a larger frequency range than the single debye equation while maintaining the analytical properties in the complex plane necessary for discrete relationships.

For seawater the Meissner-Wentz model is suitable for a larger frequency range, at least up to 90GHz, and for pure water the Meissner-Wentz model is suitable for a larger temperature range, at least down to-20 ℃.

Ionic conductivities proposed by Meissner and Wentz

The calculation formula of (a) is as follows:

static relative permittivity

Relative permittivity of medium frequency

Radio frequency relative permittivity

And first and second Debye recovery frequencies

And

can be expressed as pure water condition

The various coefficients and the temperature and salinity of the seawater are expressed as follows:

wherein,

is an empirical constant, given in the table below, under pure water

The various coefficient expressions of (a) are:

wherein,

also empirical constants, are given in table 1.

TABLE 1 empirical constants in the double Debye equation for Meissner and Wentz models

(4) And determining the complex dielectric constant of the seawater, so that the Fresnel reflectivity can be obtained, and further the emissivity of the calm sea surface can be obtained through calculation.

On the basis of the embodiment, the method for constructing the geophysical function model of the backscattering coefficient of the rough sea surface comprises the following steps:

In particular, a rough sea surface backscattering coefficient is constructed

The geophysical Function Model (GMF) Model comprises the following steps:

(1) Preprocessing sea surface datasets

In the sea surface data set, determining whether the corresponding unit data are acquired under the rainfall condition, and if so, deleting the backscattering coefficient and the corresponding wind speed, wind direction and sea surface temperature data under the rainfall condition, namely deleting the unit data acquired under the rainfall condition;

(2) Constructing three-dimensional mesh models

Determining the three-dimensional concrete of the three-dimensional grid model by using the sea surface data set is as follows: wind speed, sea surface temperature and relative wind direction, which is the angle between the characteristic variables "wind direction" and "antenna beam look direction" in each unit of data. The first dimension is wind speed which is HHH wind speed data from Aquarius, the range is selected to be 0m/s to 27m/s, and the interval is 0.3m/s; the second dimension is sea surface temperature, which is pre-acquired sea surface temperature SST data, the range is selected from 0 ℃ to 32 ℃, the interval is 0.3 ℃, the third dimension is relative wind direction (the included angle between the wind direction and the antenna beam visual direction), which is pre-acquired wind direction data and Aquarius product data, the range is selected from 0 degrees to 360 degrees, the interval is 5 degrees, and therefore 90 x 107 x 72 three-dimensional equispaced grids are formed;

(3) Classifying sea surface datasets into corresponding grids

Storing the backscattering coefficient corresponding to each unit data in the sea surface data set into a corresponding grid according to the wind speed, the relative wind direction and the sea surface temperature; determining whether the number of the unit data in each grid exceeds a preset threshold, wherein the threshold can be dynamically set according to the number of the characteristic variables needing to be judged, for example, in one unit data, the number of the characteristic variables needing to be judged is 3, the value is usually greater than or equal to 20, and if the number of the characteristic variables needing to be judged is 4, the value is usually greater than or equal to 30; if the number of the unit data in the grid is smaller than the preset threshold value, the grid is not required to be further processed; if the number of the unit data in the grid is determined to be larger than or equal to a preset threshold value, averaging corresponding characteristic variables 'backscattering coefficients' to be used as values of the backscattering coefficients corresponding to the grid;

(4) Construction of rough sea surface backscattering coefficient

GMF model of (1)

Function of sea surface backscattering coefficient

To the relative wind direction

Performing Fourier series expansion of even harmonic function, wherein only second order is reserved as a first expansion:

wherein the harmonic coefficients

According to different values of k, the first harmonic coefficients are respectively corresponded

Second harmonic coefficient

And third harmonic coefficient

。

Is the wind speed,

Is sea surface temperature, P is polarization mode, specifically comprises

Four, and the backscattering coefficient in Aquarius L2 data

The cross polarization VH and HV are identical, so only the HV polarization is retained here. Corresponding rough sea surface backscattering coefficients can be respectively constructed according to different incidence angles

And typically only three angles of incidence, and is fixed, so the corresponding rough sea surface backscatter coefficient

There will also be three GMF models. Harmonic coefficient

Is dependent on the wind speed

Sea surface temperature

Polarization mode

And an angle of incidence.

(5) Determining harmonic coefficients corresponding to geophysical function model of rough sea surface backscattering coefficient

Based on the three-dimensional grid already established, will

The measurement is based on even harmonic basis functions

Regression is carried out to obtain harmonic coefficient in each grid

To relate to

And

the two-dimensional look-up table of (2). The classified sea surface data set in the three-dimensional grid model is brought into a first expansion formula, and first harmonic coefficients are respectively determined

Second harmonic coefficient

And the third harmonic coefficient

About

And

the two-dimensional look-up table of (2).

Determining the harmonic coefficients

Thereafter, it may be based on roughnessDetermining the backscattering coefficient of the corresponding GMF model by the Fourier series expansion corresponding to the geophysical function model of the sea surface backscattering coefficient

。

On the basis of the above embodiment, the determining a fourier series of the sea surface emissivity increment function relative to the wind direction as a geophysical function model of the rough sea surface emissivity increment includes:

Specifically, the method for determining the geophysical function model of the rough sea backscattering coefficient can adopt a similar method to determine the geophysical function model of the rough sea emissivity increment, and comprises the following specific steps:

(1) Preprocessing a sea surface dataset

In the sea surface data set, determining whether the corresponding unit data are acquired under the rainfall condition, and if so, deleting the backscattering coefficient under the rainfall condition and the corresponding wind speed, wind direction and sea surface temperature data, namely deleting the unit data acquired under the rainfall condition;

(2) Constructing three-dimensional mesh models

Determining the three dimensions of the three-dimensional grid model by using the sea surface data set specifically comprises the following steps: wind speed, sea surface temperature and relative wind direction, which is the included angle between the characteristic variables "wind direction" and "antenna beam look direction" in each unit of data. The first dimension is wind speed which is HHH wind speed data from Aquarius, the range is selected to be 0m/s to 27m/s, and the interval is 0.3m/s; the second dimension is sea surface temperature, which is pre-acquired sea surface temperature SST data, the range is selected from 0 ℃ to 32 ℃, the interval is 0.3 ℃, the third dimension is relative wind direction (the included angle between the wind direction and the antenna beam visual direction), which is pre-acquired wind direction data and Aquarius product data, the range is selected from 0 degrees to 360 degrees, the interval is 5 degrees, and therefore 90 x 107 x 72 three-dimensional equispaced grids are formed;

(3) Classifying sea surface datasets into corresponding grids

According to the wind speed, the relative wind direction and the sea surface temperature, the sea surface emissivity increment corresponding to each unit data in the sea surface data set is respectively carried out

Storing the data in the corresponding grids; determining whether the number of the unit data in each grid exceeds a preset threshold, wherein the threshold can be dynamically set according to the number of the characteristic variables needing to be judged, for example, in one unit data, the number of the characteristic variables needing to be judged is 3, the value is usually greater than or equal to 20, and if the number of the characteristic variables needing to be judged is 4, the value is usually greater than or equal to 30; if the number of the unit data in the grid is smaller than the preset threshold value, the grid is not required to be further processed; if the number of the unit data in the grid is determined to be larger than or equal to the preset threshold value, averaging the corresponding characteristic variable 'backscattering coefficient' to be used as the sea surface emissivity increment corresponding to the grid

A value of (d);

(4) Constructing rough sea surface emissivity increments

GMF model of

Incremental function of rough sea surface emissivity

To the relative wind direction

Performing Fourier series expansion of even harmonic function, wherein only second order is reserved as a second expansion:

wherein the harmonic coefficient

According to different values of k, the coefficients respectively correspond to the fourth harmonic coefficient

Fifth harmonic coefficient

And sixth harmonic coefficient

。

Is the wind speed,

Is sea surface temperature, P is polarization mode, specifically comprises

Two kinds. And corresponding rough sea surface emissivity increment can be respectively constructed according to different incidence angles

And typically only three angles of incidence, and is fixed, so the corresponding rough surface emissivity increasesQuantity of

There will also be three GMF models of (c). Harmonic coefficient

Is dependent on the wind speed

Sea surface temperature

Polarization mode

And an angle of incidence.

(5) Harmonic coefficients of GMF model for determining rough sea surface emissivity increment

Based on the three-dimensional grid already established, will

The measurement is based on even harmonic basis functions

Performing regression to obtain harmonic coefficient in each grid

To relate to

And

the two-dimensional look-up table of (2). The sea surface data sets classified into the three-dimensional grid model are brought into a second expansion formula, and fourth harmonic coefficients are respectively determined

Fifth harmonic coefficient

And the sixth harmonic coefficient

About

And

the two-dimensional look-up table of (2).

Determining the harmonic coefficients

Then, the rough sea surface emissivity increment of the corresponding GMF model can be determined according to the Fourier series expansion corresponding to the geophysical function model of the rough sea surface emissivity increment

。

After the geophysical function model of the rough sea surface backscattering coefficient and the geophysical function model of the rough sea surface emissivity increment are determined, the value of a first cost function is determined, and the optimal sea surface temperature corresponding to each unit data in the sea surface data set is determined in an iterative mode by taking the minimum value of the first cost function as a target. Corresponding to the determination of the wind speed W, wind direction

Relatively fixed, corresponding to the optimal sea surface temperature. In the application, the initial value of the sea surface temperature is set as Argo buoy SST observation data, namely corresponding wind speed W and wind direction

Under the condition of respectively giving

An initial sea surfaceTemperature data.

Determining each wind speed W and each wind direction in an iterative mode

And obtaining the sea surface temperature value when the corresponding cost function obtains the minimum value, namely obtaining the sea surface temperature value obtained by inversion.

If the iteration condition satisfies that the difference between the values of the cost function of the two times before and after the iteration condition is less than a first threshold, or the iteration frequency is more than or equal to a second threshold, or the descending gradient is less than any one of a third threshold, determining each wind speed W and each wind direction

And the corresponding sea surface temperature value when the cost function is minimum.

Iterative solution of first cost function

And obtaining the numerical solution of the sea surface temperature value T when the minimum value is obtained. The iteration termination condition is that the descending gradient is less than the threshold value T1, or two times

The difference in values is less than a threshold T2, or the number of iterations reaches a threshold T3. If the iteration times reach a threshold value T3 when the iteration is terminated, the function is not converged, an optimal solution does not exist, and the corresponding optimal sea surface temperature value is an invalid value; otherwise, the function is explained to obtain an optimal solution, and the sea surface temperature value T at the moment is recorded, namely the sea surface temperature value obtained by inversion. Wherein the falling gradient may be expressed as a derivative or partial derivative of the first cost function.

On the basis of the foregoing embodiment, determining a second rough surface emissivity incremental model based on the decorrelated first ocean parameters in the surface dataset includes:

the first difference value is a difference value between a sea surface emissivity measurement value in each unit of data of the sea surface data set and a corresponding first incremental value obtained based on a geophysical function model of the rough sea surface emissivity increment.

Specifically, with the minimum value of the first cost function as a target, after determining the optimal sea surface temperature corresponding to each unit data in the sea surface data set, the influence of the marine power parameters in the sea surface data set on the sea surface emissivity increment needs to be considered, specifically, decorrelation processing is performed on the unit data in the sea surface data set, and during the decorrelation processing, based on related experience and statistical data analysis, four characteristic variables with the maximum influence are screened

And

w is wind speed, from Aquarius L2 track level product data, and WH is the effective wave height, which is the pre-acquired effective wave height SWH data.

The backscattering coefficient of the first VV polarization is expressed as:

wherein,

for scatterometry observations, i.e.Can be concentrated on the sea surface according to the corresponding wind speed

Sea surface temperature

Wind direction

The corresponding value is determined and the corresponding value is determined,

respectively corresponding to a second harmonic coefficient and a third harmonic coefficient of the GMF model of the rough sea surface backscattering coefficient, and searching the harmonic coefficients in the VV polarization direction

To relate to

And

the two-dimensional lookup table of (2) obtains a corresponding value, and it is noted that the temperature T here is the optimal sea surface temperature determined by the first cost function.

The backscattering coefficient and effective wave height of the first HH polarization are expressed as:

wherein,

for scatterometer observation data, it can be concentrated in sea surface data according to corresponding wind speed

Sea surface temperature

Wind direction

respectively corresponding to a second harmonic coefficient and a third harmonic coefficient of the GMF model of the rough sea surface backscattering coefficient, and searching the harmonic coefficient in the HH polarization direction

To relate to

And

Original variable sets are constructed

By performing Principal Component Analysis (PCA), a corresponding transformation matrix can be obtained

；

Using formulas

By finding new variables after decorrelation

The four variables are not related to each other, and the contribution rate to the result is decreased in sequence. And transforming the characteristic variable and the new variable corresponding to each column in the matrix A

And correspond to each other.

Screening out variables in which the contribution rate is greater than a threshold value, and retaining new variables in the application

And further correcting the geophysical function model of the rough sea surface emissivity increment as the first characteristic variable and the second characteristic variable, namely a second rough sea surface emissivity increment model.

Specifically, the method for constructing the corrected rough sea surface emissivity incremental model comprises the following steps:

(1) And constructing a corrected rough sea surface emissivity increment model based on the geophysical function model of the rough sea surface emissivity increment and the second rough sea surface emissivity increment model, wherein a corresponding formula is expressed as follows:

here, ,

the method is an incremental GMF model of rough sea surface emissivity, the influence on the result of the sea surface emissivity is the maximum, the temperature T is the optimal sea surface temperature determined by a first cost function, and a second term

The effect on the sea surface emissivity results is small.

(2) Determining a first characteristic variable and a second characteristic variable, corresponding two-dimensional grid model

Determining the value range of the first characteristic variable and the corresponding unit interval;

determining the value range of the second characteristic variable and the corresponding unit interval;

and determining a two-dimensional grid model corresponding to the second rough sea surface emissivity incremental model according to the value range and the corresponding unit interval.

(3) Classifying the processed unit data in the sea surface data set into corresponding two-dimensional grids

Obtaining a sea surface emissivity measured value in each unit data of a sea surface data set, and obtaining a corresponding sea surface emissivity increment based on a geophysical function model of the rough sea surface emissivity increment

As the first increment value, the difference between the two is determined. Determining which grid the difference value should belong to according to the two-dimensional grids, averaging data in each grid to serve as a second rough sea surface emissivity increment corresponding to the grid until data in all grids are processed, and determining a second rough sea surface emissivity increment in a second rough sea surface emissivity increment model

The two-dimensional look-up table of (2).

Subsequently, according to the first characteristic variable and the second characteristic variable, a corresponding two-dimensional lookup table is searched, namely, the emissivity increment of the second rough sea surface can be obtained

The value of (c).

Finally, obtaining the rough sea surface emissivity incremental model after correction

。

By using the sea surface emissivity correction method provided by the application, the sea surface emissivity correction method can be used for acquiring different wind speeds according to the sea surface data set

Sea surface temperature

Emissivity of rough sea surface in polarization mode P

。

Calculating to obtain the rough sea surface emissivity increment according to the corrected rough sea surface emissivity increment model

；

Finally, determining

And

the difference value of the measured data is used as the corrected rough sea surface emissivity, and the measured conversion emissivity of the calm sea surface can be obtained.

Fig. 2 is a schematic diagram of an implementation flow of the method for correcting sea surface emissivity provided by the invention, as shown in fig. 2, specifically including:

step 201, constructing a sea surface data set

(1) The basic data used is Aquarius L2 track level product data, including: the method comprises the following steps that radiometers are used for observing sea surface emissivity data, scatterometer HH polarization backscattering coefficient observation data, scatterometer VV polarization backscattering coefficient observation data and HHH wind speed data, and the HHH wind speed is obtained through inversion according to measured values of scatterometer HH polarization and radiometer H polarization;

(2) The assistance data comprises: wind direction data, sea surface temperature SST data, effective wave height SWH data, sea surface salinity SSS data and single-point Argo buoy SST observation data from different mechanisms or organizations;

(3) Acquiring observation longitude L, latitude B and time T of Aquarius L2 track level data;

(4) Reading auxiliary data in a cycle: observation longitudes L0, latitude B0, and time T0 of wind direction data, SST data, SWH data, SSS data, and Argo SST data;

(5) Search for satisfication

When the number of eligible assistance data is greater than or equal to 1, setting the assistance data value as the average value of all eligible assistance data; when the quantity of the auxiliary data meeting the condition is less than 1, setting the auxiliary data value as an invalid value;

(6) And circularly searching until all auxiliary data are completely matched, and constructing a time-space matched sea surface data set.

Step 202, quiet sea surface theoretical emissivity

(1) The complex permittivity of the seawater is determined by using a double Debye (Debye) equation corresponding to a Meissner-Wentz model:

in the formula,

is the complex dielectric constant (dimensionless) of seawater,

sea surface temperature, pre-acquired sea surface temperature SST data,

obtaining sea surface salinity SSS data for sea surface salinity in advance,

frequency of electromagnetic wave

，

Is the angular frequency of electromagnetic waves

，

And

static and infinite high frequency relative permittivity (dimensionless) respectively,

is an empirical constant (dimensionless),

is the ionic conductivity.

(2) According to Fresnel reflectivity

Determining Fresnel reflectivity in corresponding vertical polarization direction according to a relation formula of complex dielectric constants of seawater in different polarization directions

And Fresnel reflectivity in the horizontal polarization direction

。

(3) The emissivity of a calm sea surface can be known according to kirchhoff's law

Can be expressed as

In the formula,

for fresnel reflectivity, P = H, and V is polarization mode. And calculating to obtain the emissivity of the calm sea surface according to the Fresnel reflectivity in different polarization directions.

Step 203, establishing a rough sea surface back scattering coefficient geophysical function model (GMF)

(1) Preprocessing sea surface datasets

Preprocessing the sea surface data set obtained in the step 201, and excluding a backscattering coefficient under a rainfall condition;

(2) Constructing three-dimensional mesh models

Constructing a three-dimensional equal-interval grid, wherein the first dimension is wind speed, data come from an Aquarius HHH wind speed product, the range is selected to be 0 m/s-27 m/s, and the interval is 0.3m/s; the second dimension is the sea surface temperature, the data comes from the auxiliary data product, the range is selected from 0 ℃ to 32 ℃, the interval is 0.3 ℃, the third dimension is the relative wind direction (the included angle between the wind direction and the antenna beam visual direction), the data comes from the auxiliary data product, the range is selected from 0 ℃ to 360 ℃, the interval is 5 degrees, and therefore 90X 107X 72 grids are formed;

(3) The preprocessed sea surface data set (the (1) point in the step 201) is processed by the corresponding sea surface backscattering coefficient according to the wind speed, the relative wind direction and the sea surface temperature

Respectively storing the cell data into corresponding grids, and if the number of the cell data in the grids exceeds 20, averaging the backscattering coefficients in all the stored cell data to be used as the backscattering coefficients corresponding to the grids; if the number of the unit data in the grid is less than 20, rejecting the grid;

(4) Construction of rough sea surface backscattering coefficient

GMF model of

Function of sea surface backscattering coefficient

To the relative wind direction

wherein the harmonic coefficients

Second harmonic coefficient

And third harmonic coefficient

。

Is the wind speed,

Is sea surface temperature, P is polarization mode, specifically comprises

Four, and the backscattering coefficient in Aquarius L2 data

The cross polarization VH and HV are identical, so only the HV polarization is retained here. And corresponding rough sea surface backscattering coefficients can be respectively constructed according to different incidence angles

There will also be three GMF models of (c). Harmonic coefficient

Is dependent on wind speed

Sea surface temperature

Polarization mode

And an angle of incidence.

(5) Based on the three-dimensional grid that has been established, will

The measurement is based on even harmonic basis functions

Performing regression to obtain harmonic coefficient in each grid

To relate to

And

the two-dimensional look-up table of (2).

Step 204, rough sea surface emissivity increment geophysical function model

(1) Preprocessing the sea surface data set and constructing a three-dimensional grid model, which is the same as the step 203;

(2) Classifying sea surface datasets into corresponding grids

Storing the data in the corresponding grids; determining whether the number of unit data in each grid exceeds 20, if so, averaging corresponding characteristic variables 'backscattering coefficients' to serve as sea surface emissivity increment corresponding to the grid

A value of (d); if not, no further processing is required for the grid;

(3) Constructing rough sea surface emissivity increments

GMF model of

Incremental function of rough sea surface emissivity

To the relative wind direction

Performing Fourier series expansion of even harmonic function, wherein only second order is reserved, and using the second expansion formula as follows:

wherein the harmonic coefficient

Fifth harmonic coefficient

And sixth harmonic coefficient

。

Is the wind speed,

Is sea surface temperature, P is polarization mode, specifically comprises

And typically only three angles of incidence, and is fixed, so the corresponding rough surface emissivity increase

There will also be three GMF models of (c). Harmonic coefficient

Is dependent on wind speed

Sea surface temperature

Polarization mode

And an angle of incidence.

(4) Determining harmonic coefficients of a geophysical function model of the emissivity increase of the rough sea surface

Based on the three-dimensional grid that has been established, will

The measured values are based on even harmonic basis functions

Regression is carried out to obtain harmonic coefficient in each grid

To relate to

And

Fifth harmonic coefficient

And the sixth harmonic coefficient

About

And

the two-dimensional look-up table of (2).

Determining the harmonic coefficients

Then, the corresponding Fourier series expansion can be determined according to the Fourier series expansion corresponding to the geophysical function model of the rough sea surface emissivity incrementGMF model of rough sea surface emissivity delta

。

Step 205, constructing a cost function of sea surface temperature inversion

And iteratively solving the sea surface temperature value when the surrogate cost function obtains the minimum value, namely obtaining the sea surface temperature value by inversion.

Step 206, sea parameter decorrelation processing

Screening feature variables in each unit data in a sea surface dataset

And

as a marine parameter;

determining corresponding transformation matrix by PCA mode for the ocean parameters, and determining new variables with commonality based on the product of the source ocean parameter variable set and the transformation matrix

The four variables are not related to each other, and the contribution rate to the result is decreased in sequence;

preserving variables having a contribution rate greater than a first threshold

And carrying out subsequent complete sea surface emissivity incremental modeling by using the two variables.

Step 207, determining a second rough sea surface emissivity incremental model

(1) Determining a second rough surface emissivity delta model

As the first increment value, the difference between the two is determined. Determining which grid the difference value should belong to according to the two-dimensional grids, averaging data in each grid to serve as a second rough sea surface emissivity increment corresponding to the grid until data in all grids are processed, and determining the second rough sea surface emissivity increment in a second rough sea surface emissivity increment model

Of the two-dimensional look-up table.

The value of (c).

(2) Constructing a corrected rough sea surface emissivity increment model

Wherein,

corresponding to the rough sea surface emissivity incremental GMF model,

corresponding to a second rough surface emissivity incremental model;

finally, obtaining the rough sea surface emissivity increment model after correction

。

Step 208, determining the measurement conversion emissivity of the calm sea surface according to the corrected rough sea surface emissivity increment model

(1) Acquiring wind speeds at different depths from a sea surface data set

Sea surface temperature

Emissivity of rough sea surface under polarization mode P

。

(2) Calculating to obtain the rough sea surface emissivity increment according to the corrected rough sea surface emissivity increment model

；

(3) Determining

And

the difference value of the measured sea surface emissivity is used as the corrected rough sea surface emissivity, and the measured conversion emissivity of the calm sea surface can be obtained.

Fig. 3 is a schematic structural diagram of the sea surface emissivity correction device provided by the invention, and as shown in fig. 3, the device comprises:

the data set module 301 is configured to determine, based on a preset matching rule, a sea surface data set formed by first data and second data that satisfy the preset matching rule;

an obtaining module 302, configured to obtain a rough sea surface emissivity of each unit data in different polarization directions based on the sea surface data set;

a determining module 303, configured to determine, based on the corrected rough sea surface emissivity increment model, emissivity increments corresponding to each of the unit data in different polarization directions;

a correction module 304, configured to determine, in units of the unit data, a correction result of the rough sea surface emissivity based on the rough sea surface emissivity and the emissivity increment;

Optionally, the determining module 303 is further configured to:

determining Fourier series of the sea surface emissivity increment function relative to the wind direction, and using the Fourier series as a geophysical function model of the rough sea surface emissivity increment;

determining a second rough sea surface emissivity incremental model based on the first ocean parameters in the sea surface data set after the decorrelation processing; the first marine parameter comprises wind speed, backscatter coefficient of a first VV polarization, backscatter coefficient of a first HH polarization, and effective wave height;

Optionally, the determining module 303 is further configured to determine a second rough sea surface emissivity incremental model based on the decorrelated first sea parameters in the sea surface data set, and before:

Optionally, the apparatus further comprises a building module 305 for:

Optionally, the building module 305 is configured to:

using a second expansion comprising the determined expansion coefficients as a geophysical function model of the emissivity increase of the rough sea surface;

Optionally, the determining module 303 is further configured to:

based on the constructed three-dimensional grid model, preprocessing the sea surface data set, including:

determining the three-dimensional grid model formed by three dimensions of wind speed, sea surface temperature and relative wind direction based on the wind speed, the sea surface temperature, the wind direction and the antenna beam apparent direction in the sea surface data set, wherein the relative wind direction is an included angle between the wind direction and the antenna beam apparent direction;

if the number of the unit data in the designated grid belonging to the three-dimensional grid model is larger than or equal to a preset threshold value, determining the average value of all the unit data in the designated grid as the three-dimensional characteristic value of the designated grid.

Optionally, the determining module 303 is further configured to, in the process of determining a second rough sea surface emissivity incremental model based on the decorrelated first sea parameter in the sea surface data set:

Specifically, the sea surface emissivity correction device provided by the present invention can implement all the method steps implemented by the method embodiment, and can achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not described herein.

FIG. 4 is a schematic structural diagram of an electronic device according to the present invention; as shown in fig. 4, the electronic device includes a memory 420, a transceiver 410, and a processor 400; wherein the processor 400 and the memory 420 may also be physically separated.

A memory 420 for storing a computer program; a transceiver 410 for transceiving data under the control of the processor 400.

In particular, the transceiver 410 is used to receive and transmit data under the control of the processor 400.

Where in fig. 4, the bus architecture may include any number of interconnected buses and bridges, with various circuits of one or more processors, represented by processor 400, and memory, represented by memory 420, being linked together. The bus architecture may also link various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 410 may be a number of elements including a transmitter and receiver that provide a means for communicating with various other apparatus over a transmission medium including wireless channels, wired channels, fiber optic cables, and the like.

The processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 may store data used by the processor 400 in performing operations.

The processor 400 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and may also have a multi-core architecture.

The processor 400, by calling the computer program stored in the memory 420, is adapted to execute any of the methods provided by the present invention according to the obtained executable instructions, such as:

It should be noted that, the electronic device provided by the present invention can implement all the method steps implemented by the method embodiments and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as the method embodiments in this embodiment are omitted here.

In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform the method for sea surface emissivity correction provided by the above embodiments.

In another aspect, the present invention further provides a processor-readable storage medium, which stores a computer program, where the computer program is configured to cause the processor to execute the method for sea surface emissivity correction provided by the foregoing embodiments.

The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid State Disks (SSDs)), etc.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A method of sea surface emissivity correction, comprising:

determining emissivity increments corresponding to each unit data in different polarization directions based on the corrected rough sea surface emissivity increment model;

the characteristic variables in each unit data of the sea surface data set comprise longitude, latitude, sampling time, sea surface emissivity, backscattering coefficient, antenna beam sight direction, wind speed, sea surface temperature, effective wave height, polarization direction, incidence angle and sea surface salinity; the preset matching rule comprises that the longitude difference of the first data and the second data is smaller than a first threshold; the latitude difference between the first data and the second data is smaller than a second threshold, and the difference between the sampling moments of the first data and the second data is smaller than a third threshold; the first data comprises Aquarius L2 level data; the second data comprise wind direction data, sea surface temperature SST data, sea surface salinity SSS data and effective wave height SWH data; the corrected rough sea surface emissivity increment model is determined based on a geophysical function model of rough sea surface emissivity increment.

2. The method of sea surface emissivity correction according to claim 1, wherein the corrected rough sea surface emissivity incremental model is determined based on a geophysical function model of rough sea surface emissivity increment, the method comprising:

3. The method of surface emissivity correction according to claim 2, wherein prior to determining a second rough surface emissivity incremental model based on the decorrelated first sea parameter in the surface data set, comprising:

4. The method of surface emissivity correction of claim 3, wherein the method of constructing the geophysical function model of rough surface backscattering coefficients comprises:

using the first expansion comprising the determined expansion coefficients as a geophysical function model of the rough sea backscattering coefficients;

5. The method of surface emissivity correction of claim 2, wherein said determining a fourier series of surface emissivity increment functions versus wind direction as a geophysical function model of rough surface emissivity increments comprises:

using the second expansion comprising the determined expansion coefficient as a geophysical function model of the rough sea surface emissivity increment;

6. The method of sea surface emissivity correction according to claim 4 or 5, wherein the preprocessing the sea surface dataset based on the constructed three-dimensional mesh model comprises:

7. The method of surface emissivity correction according to claim 3, wherein determining a second rough surface emissivity incremental model based on the decorrelated first sea parameters in the surface dataset comprises:

8. An apparatus for sea surface emissivity correction, the apparatus comprising:

9. An electronic device comprising a memory, a transceiver, a processor;

a memory for storing a computer program; a transceiver for transceiving data under the control of the processor; a processor for reading the computer program in the memory and performing the following:

10. A computer-readable storage medium, characterized in that it stores a computer program for causing a computer to perform the method of surface emissivity correction of any one of claims 1 to 7.