CN114266155B - Sea gas parameter inversion method and device - Google Patents
Sea gas parameter inversion method and device Download PDFInfo
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- CN114266155B CN114266155B CN202111576689.9A CN202111576689A CN114266155B CN 114266155 B CN114266155 B CN 114266155B CN 202111576689 A CN202111576689 A CN 202111576689A CN 114266155 B CN114266155 B CN 114266155B
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Abstract
The application provides a sea gas parameter inversion method and a sea gas parameter inversion device, wherein the sea gas parameter inversion method comprises the following steps: constructing an original regression inversion model; acquiring simulated observation brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions; determining a model coefficient of an original regression inversion model according to observed bright temperature sample data and high-precision remote sensing sea gas parameter data; substituting the model coefficient into the original regression inversion model to obtain a regression inversion model; calculating an ocean gas parameter inversion value according to the regression inversion model; evaluating the inversion accuracy of the regression inversion model through the high-accuracy remote sensing sea gas parameter data and the sea gas parameter inversion value to obtain an evaluation result; and determining model data of the regression inversion model corresponding to different sea gas application scenes according to the evaluation result. Therefore, by implementing the implementation mode, the problem of limitation brought to the sea gas parameter inversion by the traditional method can be solved, and the sea gas parameter inversion accuracy can be improved.
Description
Technical Field
The application relates to the field of satellite application, in particular to a method and a device for sea air parameter inversion.
Background
In the prior art, the brightness temperature and the sea gas parameter observed based on the scanning microwave radiometer have highly related backgrounds, a multiple linear regression algorithm is usually adopted for modeling, and the inversion and the application of the sea gas parameter of the multi-band scanning microwave radiometer are carried out according to the established inversion model. However, in practice, the current method cannot realize the same high-precision inversion under different sea parameter conditions, so that certain limitation is brought to sea parameter inversion; in addition, buoys and atmospheric sounding data or forecast reanalysis data are generally used in the current method, the data volume of the data is small, and the inversion accuracy performed based on the data is not high.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for inverting the sea gas parameters, which can solve the problem of limitation of the traditional method on sea gas parameter inversion and can improve the accuracy of sea gas parameter inversion.
A first aspect of an embodiment of the present application provides an ocean air parameter inversion method, including: constructing an original regression inversion model;
acquiring simulated observation brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions;
determining a model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea gas parameter data;
substituting the model coefficient into the original regression inversion model to obtain a regression inversion model;
calculating an ocean gas parameter inversion value according to the regression inversion model;
evaluating the inversion accuracy of the regression inversion model through the high-accuracy remote sensing sea gas parameter data and the sea gas parameter inversion value to obtain an evaluation result;
and determining model data of regression inversion models corresponding to different sea gas application scenes according to the evaluation result.
Therefore, by implementing the implementation mode, the sea parameter inversion can be carried out according to the observed brightness temperature sample data and the high-precision remote sensing sea parameter data under different sea parameter conditions, so that a more accurate sea parameter inversion result can be obtained based on the data; meanwhile, the method can evaluate the sea gas parameter inversion result, so that model data suitable for different sea gas application scenes are determined, and the general applicability and the practicability of sea gas inversion are improved.
Further, the step of determining the model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea gas parameter data comprises:
determining physical parameters needing inversion according to the observed bright temperature sample data;
determining a quality control mark of each datum in the high-precision remote sensing sea gas parameter data;
reading target remote sensing sea gas data from the high-precision remote sensing sea gas parameter data according to the physical parameters to be inverted and the quality control marks;
performing source data space-time matching on the target remote sensing sea air data and the observed bright temperature sample data to obtain matched data;
and fitting the model coefficient of the original regression inversion model according to the matching data.
Further, the step of calculating the sea parameter inversion value according to the regression inversion model includes:
acquiring multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer, which is different from the observed brightness temperature sample data;
and calculating the sea parameter inversion value according to the multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer and the regression inversion model.
Further, the step of evaluating the inversion accuracy of the regression inversion model through the high-precision remote sensing sea parameter data and the sea parameter inversion value to obtain an evaluation result comprises:
calculating the root mean square error, the average absolute error and the standard deviation between inversion results in different months of all the data according to the high-precision remote sensing sea air parameter data and the sea air parameter inversion values;
and evaluating the inversion accuracy of the regression inversion model according to the root mean square error, the average absolute error and the standard deviation to obtain an evaluation result.
Further, the step of determining model data of the regression inversion models corresponding to different ocean gas application scenarios according to the evaluation result includes:
comparing the merits of regression inversion models with different orders according to the evaluation result and the model coefficient to obtain a merit comparison result;
and determining model data of regression inversion models corresponding to different sea gas application scenes according to the quality comparison result, wherein the model data comprises the order of a polynomial, a model coefficient and inversion precision.
Further, the original regression inversion model is:
T B ＝T BU +τ(e×SST+(1-e)×(T BD +τ×T BC ))；
wherein, T BU Indicating upward atmospheric radiation, T BD Representing descending atmospheric radiation, τ being the transmission rate of the total path from the surface to the top of the atmosphere, T BC Indicating cold air background radiation, T S And E represents sea surface temperature, E represents sea surface emissivity, wherein c represents the model coefficient, P represents the sea gas parameter inversion value to be inverted, tb represents observation brightness temperature, i represents different observation channels, and j represents the order of a polynomial.
Further, the root mean square error Q of all data is calculated RMSE The formula of (1) is:
calculating the mean absolute error Q MAE The formula of (1) is as follows:
calculating standard deviation Q between inversion results of different months STD The formula of (1) is:
wherein m represents the number of data, P i Representing the inverse of the sea parameter, P i，oberved And representing high-precision remote sensing sea gas parameter data.
A second aspect of an embodiment of the present application provides an ocean gas parameter inversion apparatus, including:
the construction unit is used for constructing an original regression inversion model;
the acquisition unit is used for acquiring simulated observation brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions;
the first determining unit is used for determining a model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea air parameter data;
a substitution unit for substituting the model coefficient into the original regression inversion model to obtain a regression inversion model;
the calculation unit is used for calculating the sea air parameter inversion value according to the regression inversion model;
the evaluation unit is used for evaluating the inversion accuracy of the regression inversion model through the high-precision remote sensing sea gas parameter data and the sea gas parameter inversion value to obtain an evaluation result;
and the second determining unit is used for determining model data of the regression inversion models corresponding to different ocean gas application scenes according to the evaluation result.
In the implementation process, the device can perform sea parameter inversion according to observed brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions, so that a more accurate sea parameter inversion result can be obtained based on the data; meanwhile, the method can evaluate the sea gas parameter inversion result, so that model data suitable for different sea gas application scenes can be determined, and the general applicability and the practicability of sea gas inversion can be improved.
A third aspect of embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the method for inverting ocean gas parameters according to any one of the first aspect of embodiments of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium storing computer program instructions, which when read and executed by a processor, perform the method for inverting sea water parameters according to any one of the first aspect of embodiments of the present application.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of an ocean gas parameter inversion method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an apparatus for inverting marine parameters according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for inverting sea water parameters according to an embodiment of the present disclosure. The sea gas parameter inversion method comprises the following steps:
s101, constructing an original regression inversion model.
As an alternative embodiment, the original regression inversion model is:
T B ＝T BU +τ(e×SST+(1-e)×(T BD +τ×T BC ))；
wherein, T BU Representing upward atmospheric radiation, T BD Representing descending atmospheric radiation, τ being the transmission rate of the total path from the surface to the top of the atmosphere, T BC Indicating cold air background radiation, T S And E is the sea surface emissivity, wherein c represents the model coefficient, P represents the sea air parameter inversion value to be inverted, tb represents the observed bright temperature, i represents different observation channels, and j represents the order of the polynomial.
In this embodiment, in the ocean microwave remote sensing, the radiation brightness temperature observed by the scanning microwave radiometer is mainly affected by the sea surface temperature, the sea surface salinity, the sea surface wind speed, the atmospheric water vapor and the atmospheric cloud liquid water, and the observation energy of the microwave radiometer is usually expressed as the brightness temperature T B The form of (A) is as follows:
T B ＝T BU +τ(e×SST+(1-e)×(T BD +τ×T BC ))
here T BU Expressed as upward atmospheric radiation, T BD Is descending atmospheric radiation, tau is the transmittance of the total path from the surface to the top of the atmosphere, and is influenced by the combined action of oxygen, atmospheric water vapor and cloud liquid water content, wherein the influence of the oxygen is very fixed, the influence of the atmospheric water vapor and the atmospheric cloud liquid water is greatly changed, and the change of the atmospheric water vapor and the atmospheric cloud liquid water brings the change of tau and further brings the change of observed brightness temperature. T is BC Indicating cold air background radiation, is substantially a constant, T S And E is the emissivity of the sea surface, wherein E is mainly influenced by the sea surface temperature, the sea surface wind speed and the salinity of the sea water, the influence caused by the salinity of the sea water is small, and the change of the sea surface temperature and the sea surface wind speed mainly causes large change to the brightness temperature. In summary, the observation bright temperature T of the microwave radiometer is mainly determined by four sea air parameters including sea surface temperature, sea surface wind speed, atmospheric water vapor and atmospheric cloud liquid water B Bring variations, so the inversion is to use different channel brightness temperatures T B The sea surface temperature, the sea surface wind speed, the atmospheric water vapor and the atmospheric cloud liquid water can be solved, and the four sea surface parameters are also physical quantities to be inverted.
In this embodiment, the method adopts a multivariate nonlinear regression algorithm, newly establishes an inversion model based on light temperature and sea air parameters, can effectively simulate the relationship between the light temperature and the sea air parameters under different sea air parameter conditions, and realizes accurate inversion under all sea air conditions, and the algorithm is as follows:
where c represents a model coefficient, P represents an ocean parameter to be inverted, tb represents an observation brightness temperature, and i represents different observation channels, in a specific implementation, a value of i is determined according to the number of channels of the multi-frequency scanning microwave radiometer, generally speaking, main observation frequencies of the multi-frequency scanning microwave radiometer for inverting the ocean parameter are 6.9GHz, 10.8GHz, 18.7GHz, 23.8GHz, 37GHz, and 89GHz, different frequencies are different in sensitivity to ocean and atmospheric parameters, generally, lower frequencies are less sensitive to the atmosphere and more sensitive to the ocean, each frequency is divided into horizontal and vertical polarizations, the horizontal polarizations are more sensitive to the atmosphere, the vertical polarizations are more sensitive to the ocean, and a combination of different frequencies and polarizations is called an observation channel of the scanning microwave radiometer. j represents the order of the polynomial and the j power of the polynomial, so that a preliminary nonlinear regression model is established through the high-order polynomial, the relationship between the brightness and the inversion sea gas parameters of different channels can be better expressed, and more accurate inversion accuracy can be obtained in different intervals. In practical application, different orders can be selected according to the fitting result, and the optimal order is tried after inversion evaluation.
S102, obtaining simulated observation brightness temperature sample data and high-precision remote sensing sea gas parameter data under different sea gas parameter conditions.
S103, determining a model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea gas parameter data.
As an alternative implementation, step S103 includes:
determining physical parameters needing inversion according to observed bright temperature sample data;
determining a quality control mark of each datum in the high-precision remote sensing sea gas parameter data;
reading target remote sensing sea gas data from the high-precision remote sensing sea gas parameter data according to the physical parameters and the quality control marks which need to be inverted;
performing source data space-time matching on the target remote sensing sea air data and the observed brightness temperature sample data to obtain matched data;
and fitting the model coefficient of the original regression inversion model according to the matching data.
In this embodiment, after c is obtained by regression fitting in the above steps, the method can invert P, and the key of model fitting is whether there is a representative sample enough, and a large number of high-precision data sources are needed.
In the embodiment, the method can adopt high-precision remote sensing data as input data, and sample data accumulation only needs about one month, so that the requirement on inversion precision can be met, and the requirement on large sample data can be met. The specific method comprises the following steps:
the first step, read each passageway observed quantity brightness temperature data of multifrequency scanning satellite radiometer, according to the physical parameter of needs retrieval, read high accuracy sea gas parameter data, include: infrared sea temperature remote sensing data (such as MODIS), microwave imager (such as GMI), scatterometer wind field data (such as MetOp), etc.; when data is read, selecting the data with good quality to read according to the quality control mark of the data source;
secondly, performing multisource data space-time matching on the read data, selecting a matching rule, and selecting data with interval within two hours and geographical interval within fifty kilometers for the sea surface temperature, wherein the change of the sea surface temperature along with time is slow, and the spatial distribution is uniform; for the atmospheric water vapor content and the wind speed, the spatial distribution is uniform, but the change is rapid, so that data within half an hour of time interval and within one hundred kilometers of geography interval are selected; the liquid water content changes rapidly and is distributed unevenly, the time-space matching rule is within half an hour, and the geographical interval is twenty-five kilometers;
and thirdly, performing parameter fitting by using a least square method, wherein a known nonlinear regression inversion model is as follows:
the fitted residual σ of the kth set of data samples k Expressed as:
the coefficients of the regression model are determined such that
And minimum.
After the nonlinear regression model coefficients are obtained, the Goodness of Fit (Goodness of Fit) is calculated and refers to how well the regression curve fits to the observed values. The goodness of fit is the Coefficient of Determination (coeffient of Determination) R 2 The larger the better, the following is expressed:
and S104, substituting the model coefficient into the original regression inversion model to obtain the regression inversion model.
And S105, calculating the sea air parameter inversion value according to the regression inversion model.
As an alternative implementation, step S105 includes:
and acquiring multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer, which is different from the observed brightness temperature sample data.
And calculating the sea parameter inversion value according to the multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer and the regression inversion model.
In this embodiment, after obtaining the coefficients of the nonlinear regression model, the method may bring the coefficients into the nonlinear regression model
And respectively calculating to obtain data of sea surface temperature, sea surface wind speed, atmospheric water vapor and atmospheric liquid water.
By implementing the embodiment, when the multi-channel brightness temperature of the multi-frequency scanning microwave radiometer is input, data different from a sample can be selected, so that the inversion speed and the inversion stability are improved. In practical engineering application, the method is used for inverting the single-track satellite data, only a few minutes or even shorter time is needed, and due to the fact that a large number of samples are used, inversion accuracy and stability are usually excellent.
And S106, evaluating the inversion accuracy of the regression inversion model through the high-precision remote sensing sea gas parameter data and the sea gas parameter inversion value to obtain an evaluation result.
As an alternative implementation, step S106 includes:
calculating the root mean square error, the average absolute error and the standard deviation between inversion results of different months of all the data according to the high-precision remote sensing sea parameter data and the sea parameter inversion values;
and evaluating the inversion accuracy of the regression inversion model according to the root mean square error, the average absolute error and the standard deviation to obtain an evaluation result.
In this embodiment, after obtaining the ocean gas parameters, the accuracy and stability of inversion need to be evaluated to obtain the inversion effect, the ocean gas field observation data is adopted to calculate the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of all data, the smaller the RMSE and the MAE is, the better the inversion accuracy is, and the Standard Deviation (Standard development) between the inversion results in different months is calculated.
As a further alternative embodiment, the root mean square error Q of all data is calculated RMSE The formula of (1) is as follows:
calculating the mean absolute error Q MAE The formula of (1) is:
calculating standard deviation Q between inversion results of different months STD The formula of (1) is:
wherein m represents the number of data, P i Representing the inverse of the sea parameter, P i，oberved And representing high-precision remote sensing sea gas parameter data.
And S107, determining model data of the regression inversion models corresponding to different sea gas application scenes according to the evaluation result.
As an alternative implementation, step S107 includes:
comparing the merits of the regression inversion models with different orders according to the evaluation result and the model coefficient to obtain a merit comparison result;
and determining model data of the regression inversion model corresponding to different sea gas application scenes according to the quality comparison result, wherein the model data comprises the order of the polynomial, the model coefficient and the inversion precision.
In this embodiment, after the inversion accuracy and stability are evaluated, the method compares the merits of models with different orders by using the fitting decision coefficient, the inversion root mean square error, the inversion absolute error, and the inversion standard deviation index, and finally determines the order of the polynomial, the corresponding fitting coefficient, and the corresponding inversion accuracy respectively adopted by each type of ocean gas product.
In the embodiment of the present application, the execution subject of the method may be a computing device such as a computer and a server, and is not limited in this embodiment.
It can be seen that, by implementing the sea parameter inversion method described in this embodiment, a non-linear regression inversion model based on the light temperature and sea parameter can be established based on the multi-frequency scanning microwave radiometer light temperature observation principle, and under the condition of different sea parameters, the relationship between the light temperature and sea parameter can be effectively simulated, so that accurate inversion under all sea conditions is realized, and the application effect is greatly improved. Meanwhile, the method can also establish a method for confirming the order of the model aiming at the inversion of different sea gas parameters, thereby improving the applicability and the practicability of the method. In addition, the method adopts mass high-precision remote sensing data, and can obtain an effective fitting coefficient in a short time, so that satellite data can be put into use in a short time, good inversion precision is obtained, and the application efficiency and effect are greatly improved. Finally, the method can adopt a targeted space-time matching rule aiming at the characteristics of space-time distribution without sea air parameters, so that a large number of samples can be generated by using less data, and the application efficiency is further improved.
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of an apparatus for inverting sea water parameters according to an embodiment of the present disclosure. As shown in fig. 2, the sea parameter inversion apparatus includes:
a constructing unit 210, configured to construct an original regression inversion model;
the acquisition unit 220 is used for acquiring simulated observation brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions;
the first determining unit 230 is configured to determine a model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea parameter data;
a substituting unit 240, configured to substitute the model coefficients into the original regression and inversion model to obtain a regression and inversion model;
a calculating unit 250, configured to calculate an ocean gas parameter inversion value according to the regression inversion model;
the evaluation unit 260 is used for evaluating the inversion accuracy of the regression inversion model through the high-accuracy remote sensing sea air parameter data and the sea air parameter inversion value to obtain an evaluation result;
and a second determining unit 270, configured to determine, according to the evaluation result, model data of the regression inversion model corresponding to different ocean gas application scenarios.
As an alternative embodiment, the first determining unit 230 includes:
the first determining subunit 231 is configured to determine, according to the observed brightness temperature sample data, a physical parameter that needs to be inverted;
the first determining subunit 231 is further configured to determine a quality control label of each piece of data in the high-precision remote sensing sea gas parameter data;
the first reading subunit 232 is configured to read target remote sensing sea air data from the high-precision remote sensing sea air parameter data according to the physical parameters and the quality control marks that need to be inverted;
a first matching subunit 233, configured to perform source data space-time matching on the target remote sensing sea air data and the observed light temperature sample data to obtain matching data;
a first fitting subunit 234, configured to fit model coefficients of the original regression inversion model according to the matching data.
As an alternative embodiment, the calculation unit 250 includes:
the second obtaining subunit 251 is configured to obtain multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer, which is different from the observed brightness temperature sample data;
and the second calculating subunit 252 is configured to calculate an inversion value of the ocean parameter according to the multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer and the regression inversion model.
As an alternative embodiment, the evaluation unit 260 includes:
the third calculation subunit 261 is configured to calculate, according to the high-precision remote sensing sea parameter data and the sea parameter inversion values, root mean square errors, average absolute errors, and standard deviations between inversion results in different months for all data;
and a third evaluation subunit 262, configured to evaluate the inversion accuracy of the regression inversion model according to the root mean square error, the average absolute error, and the standard deviation, so as to obtain an evaluation result.
As an alternative embodiment, the second determining unit 270 includes:
the fourth comparison subunit 271 is configured to compare the merits of the regression inversion models with different orders according to the evaluation result and the model coefficient to obtain a result of comparing the merits;
and a fourth determining subunit 272, configured to determine, according to the quality comparison result, model data of the regression inversion model corresponding to different ocean gas application scenarios, where the model data includes an order of the polynomial, a model coefficient, and an inversion accuracy.
As an alternative embodiment, the original regression inversion model is:
T B ＝T BU +τ(e×SST+(1-e)×(T BD +τ×T BC ))；
wherein, T BU Representing upward atmospheric radiation, T BD Representing descending atmospheric radiation, τ being the transmission rate of the total path from the surface to the top of the atmosphere, T BC Indicating cold air background radiation, T S And E is the sea surface emissivity, wherein c represents the model coefficient, P represents the sea air parameter inversion value to be inverted, tb represents the observed bright temperature, i represents different observation channels, and j represents the order of the polynomial.
As an alternative embodiment, the root mean square error Q of all data is calculated RMSE The formula of (1) is:
calculating the mean absolute error Q MAE The formula of (1) is:
calculating standard deviation Q between inversion results of different months STD The formula of (1) is:
wherein m represents the number of data, P i Representing the inverse of the sea parameter, P i，oberved And representing high-precision remote sensing sea gas parameter data.
In the embodiment of the present application, for the explanation of the sea parameter inversion apparatus, reference may be made to the description in embodiment 1, and details are not repeated in this embodiment.
As can be seen, by implementing the sea parameter inversion device described in this embodiment, sea parameter inversion can be performed according to observed bright temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions, so that a more accurate sea parameter inversion result can be obtained based on the data; meanwhile, the method can evaluate the sea gas parameter inversion result, so that model data suitable for different sea gas application scenes can be determined, and the general applicability and the practicability of sea gas inversion can be improved.
An embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the sea parameter inversion method in embodiment 1 of the present application.
An embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method for inverting sea parameters in embodiment 1 of the present application is performed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
Claims (9)
1. An ocean gas parameter inversion method is characterized by comprising the following steps:
constructing an original regression inversion model;
acquiring simulated observation brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions;
determining a model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea gas parameter data;
substituting the model coefficient into the original regression inversion model to obtain a regression inversion model;
calculating an ocean gas parameter inversion value according to the regression inversion model;
evaluating the inversion accuracy of the regression inversion model through the high-accuracy remote sensing sea gas parameter data and the sea gas parameter inversion value to obtain an evaluation result;
determining model data of regression inversion models corresponding to different sea gas application scenes according to the evaluation result;
wherein the original regression inversion model is:
T B ＝T BU +τ(e×SST+(1-e)×(T BD +τ×T BC ))；
wherein, T BU Representing upward atmospheric radiation, T BD Representing descending atmospheric radiation, τ being the transmission rate of the total path from the surface to the top of the atmosphere, T BC Indicating cold air background radiation, T S And E represents sea surface temperature, E represents sea surface emissivity, wherein c represents the model coefficient, P represents the sea gas parameter inversion value to be inverted, tb represents observation brightness temperature, i represents different observation channels, and j represents the order of a polynomial.
2. The sea water parameter inversion method according to claim 1, wherein the step of determining model coefficients of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea water parameter data comprises:
determining physical parameters needing inversion according to the observed bright temperature sample data;
determining a quality control mark of each datum in the high-precision remote sensing sea gas parameter data;
reading target remote sensing sea gas data from the high-precision remote sensing sea gas parameter data according to the physical parameters to be inverted and the quality control marks;
performing source data space-time matching on the target remote sensing sea air data and the observed bright temperature sample data to obtain matched data;
and fitting the model coefficient of the original regression inversion model according to the matching data.
3. The sea water parameter inversion method according to claim 1, wherein the step of calculating the sea water parameter inversion value according to the regression inversion model comprises:
acquiring multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer, which is different from the observed brightness temperature sample data;
and calculating the sea parameter inversion value according to the multi-channel brightness temperature data of the multi-frequency scanning microwave radiometer and the regression inversion model.
4. The sea parameter inversion method according to claim 1, wherein the step of evaluating the inversion accuracy of the regression inversion model by the high-accuracy remote sensing sea parameter data and the sea parameter inversion value to obtain an evaluation result comprises:
calculating the root mean square error, the average absolute error and the standard deviation between inversion results in different months of all the data according to the high-precision remote sensing sea air parameter data and the sea air parameter inversion values;
and evaluating the inversion accuracy of the regression inversion model according to the root mean square error, the average absolute error and the standard deviation to obtain an evaluation result.
5. The sea parameter inversion method according to claim 4, wherein the step of determining model data of the regression inversion model corresponding to different sea application scenarios according to the evaluation result comprises:
comparing the advantages and the disadvantages of the regression inversion models with different orders according to the evaluation result and the model coefficient to obtain an advantage and disadvantage comparison result;
and determining model data of regression inversion models corresponding to different sea gas application scenes according to the quality comparison result, wherein the model data comprises the order of a polynomial, a model coefficient and inversion precision.
6. Sea gas parametric inversion method according to claim 4,
calculating the root mean square error Q of all the data RMSE The formula of (1) is:
calculating the mean absolute error Q MAE The formula of (1) is:
calculating the standard deviation Q between the inversion results of different months STD The formula of (1) is as follows:
wherein m represents the number of data, P i Representing the inverse of the sea parameter, P i，oberved And representing high-precision remote sensing sea gas parameter data.
7. An ocean gas parameter inversion device, characterized in that the ocean gas parameter inversion device comprises:
the construction unit is used for constructing an original regression inversion model;
the acquisition unit is used for acquiring simulated observation brightness temperature sample data and high-precision remote sensing sea parameter data under different sea parameter conditions;
the first determining unit is used for determining a model coefficient of the original regression inversion model according to the observed brightness temperature sample data and the high-precision remote sensing sea gas parameter data;
a substitution unit for substituting the model coefficient into the original regression inversion model to obtain a regression inversion model;
the calculation unit is used for calculating the sea air parameter inversion value according to the regression inversion model;
the evaluation unit is used for evaluating the inversion accuracy of the regression inversion model through the high-precision remote sensing sea air parameter data and the sea air parameter inversion value to obtain an evaluation result;
the second determining unit is used for determining model data of the regression inversion models corresponding to different ocean gas application scenes according to the evaluation result;
wherein the original regression inversion model is:
T B ＝T BU +τ(e×SST+(1-e)×(T BD +τ×T BC ))；
wherein, T BU Representing upward atmospheric radiation, T BD Representing descending atmospheric radiation, τ being the transmission of the total path from the surface to the top of the atmosphere, T BC Indicating cold air background radiation, T S And E is the sea surface emissivity, wherein c represents the model coefficient, P represents the sea air parameter inversion value to be inverted, tb represents the observed bright temperature, i represents different observation channels, and j represents the order of the polynomial.
8. An electronic device, characterized in that the electronic device comprises a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to perform the sea parameter inversion method of any one of claims 1 to 6.
9. A readable storage medium having stored thereon computer program instructions which, when read and executed by a processor, perform the method of marine parameter inversion of any of claims 1 to 6.
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