CN112033547A - Sea temperature remote sensing multi-frequency point determination method for one-dimensional synthetic aperture microwave radiometer - Google Patents
Sea temperature remote sensing multi-frequency point determination method for one-dimensional synthetic aperture microwave radiometer Download PDFInfo
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Abstract
The invention discloses a sea temperature remote sensing multi-frequency point determination method for a one-dimensional synthetic aperture microwave radiometer in the technical field of remote sensing, and aims to solve the problem that the detection accuracy of the one-dimensional synthetic aperture microwave radiometer in the prior art is not high. The main process is as follows: constructing a data set; constructing a full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer, simulating the remote sensing sea surface brightness and inverting sea surface temperature of the satellite-borne one-dimensional synthetic aperture microwave radiometer, and establishing a genetic algorithm on the basis; the fitness of individuals with different frequency point combinations is obtained by utilizing an input environment sample, so that the advantages and disadvantages of the individuals with different frequency point combinations for inverting the sea surface temperature are judged, the individuals are eliminated according to the advantages and disadvantages, and an optimal frequency point combination scheme is obtained through T generation evolution and selection. The target characteristics can be extracted from a plurality of frequency points, so that the detection accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of remote sensing, and particularly relates to a sea temperature remote sensing multi-frequency point determination method for a one-dimensional synthetic aperture microwave radiometer.
Background
A real aperture microwave radiometer is one of the representative instruments of passive microwave remote sensing, which can provide a variety of detection products including sea surface temperature. However, the resolution of the real aperture microwave radiometer is difficult to improve because of the limitation of the size and quality of the antenna. The synthetic aperture microwave radiometer is a product combining coherent observation technology and a microwave radiometer, is equivalent to the observation effect of a large aperture antenna through small antenna array observation, and can obviously improve the spatial resolution of the satellite-borne microwave radiometer. The specific application of the one-dimensional synthetic aperture microwave radiometer as a microwave radiometer has the characteristics of simple structure and good spatial resolution, but has the problem of low detection accuracy.
Disclosure of Invention
The invention aims to provide a multi-frequency point determining method for sea temperature remote sensing of a one-dimensional synthetic aperture microwave radiometer, and aims to solve the problem that the detection accuracy of the one-dimensional synthetic aperture microwave radiometer in the prior art is not high.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: the sea temperature remote sensing multi-frequency point determining method of the one-dimensional synthetic aperture microwave radiometer comprises the following steps: a. constructing a data set A comprising sea surface temperature, sea water salinity, zenith angle, sea surface wind speed, sea surface relative wind direction, atmospheric water vapor content and cloud liquid water content; b. constructing a full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer; c. a genetic algorithm is constructed on the basis of a full-link simulation and inversion model of a one-dimensional synthetic aperture microwave radiometer, and the genetic algorithm comprises the following steps: setting the number of frequency points of each individual gene, taking the root mean square error between the sea surface temperature inversion value and the real sea surface temperature and the sensitivity of the inverted sea surface temperature to the real sea surface temperature as fitness parameters, and adding selection operation, variation operation and termination condition judgment; d. presetting a genetic algebra T, randomly selecting data in a data set A as an environment sample, inputting the data into the genetic algorithm established in the step c, obtaining the fitness of individuals with different frequency point combinations by using the input environment sample, judging the advantages and disadvantages of the individuals with different frequency point combinations for inverting the sea surface temperature, eliminating the individuals according to the fitness, adding cross operation and variation operation in the population evolution process to increase the diversity of the frequency point combinations in the individuals, and obtaining an optimal frequency point combination scheme through the evolution and selection of the T generation.
Further, the full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer comprises: the system comprises a sea surface multi-frequency multi-angle emissivity model, an atmospheric absorption emission model, a one-dimensional synthetic aperture microwave radiometer imaging simulation model and a sea surface temperature physical inversion model.
Further, sea surface emissivity E in the sea surface multi-frequency multi-angle emissivity modelPExpressed as:
wherein E is0Denotes the emissivity of the calm sea surface, Δ EWIndicating the sea surface emissivity increase caused by wind speed,
indicating the sea surface emissivity increase caused by wind direction.
Further, the atmospheric transmittance in the atmospheric absorption emission model is fitted by the contents of atmospheric water vapor and cloud liquid water, and the formula is as follows:
τ＝a1+b1V+c1L+d1V2+e1VL (2)
wherein L is the atmospheric water vapor content, V is the cloud liquid water content, τ is the atmospheric transmission rate, a1＝0.9902，b1＝2.073e-5，c1＝-0.0105，d1＝-9.818e-7，e1＝-4.545e-6。
Further, the root mean square error between the sea surface temperature inversion value and the real sea surface temperature and the sensitivity of the inversion sea surface temperature to the real sea surface temperature are respectively obtained through the following formulas:
where SE is the sensitivity of the inverted sea surface temperature to the true sea surface temperature, RMS is the root mean square error between the inverted sea surface temperature and the true sea surface temperature, Tmod,iInverted sea surface temperature, T, for the ith sample points,iThe true sea surface temperature of the ith sample point, and N is the total number of sample points.
Further, in the step c, the values of the frequency points are randomly selected from 3Ghz to 50Ghz, and the minimum interval between the two frequency points is 10 MHz.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, a genetic algorithm is constructed on the basis of full-link simulation and inversion models of the one-dimensional synthetic aperture microwave radiometer, the fitness of individuals with different frequency point combinations is obtained by utilizing an input environment sample, an optimal frequency point combination scheme is obtained through T generation evolution and selection, and target characteristics can be extracted from multiple frequency points, so that the detection accuracy is improved.
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FIG. 1 shows a final output frequency point inversion sea surface temperature result of a genetic algorithm constructed in a multi-frequency point determination method for sea temperature remote sensing of a one-dimensional synthetic aperture microwave radiometer according to an embodiment of the present invention;
fig. 2 is a flow chart of an implementation of the sea temperature remote sensing multi-frequency point determining method of the one-dimensional synthetic aperture microwave radiometer according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 2, the method for determining sea temperature remote sensing multi-frequency points of a one-dimensional synthetic aperture microwave radiometer comprises the following steps: a. constructing a data set A comprising sea surface temperature, sea water salinity, zenith angle, sea surface wind speed, sea surface relative wind direction, atmospheric water vapor content and cloud liquid water content; b. constructing a full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer; c. a genetic algorithm is constructed on the basis of a full-link simulation and inversion model of a one-dimensional synthetic aperture microwave radiometer, and the genetic algorithm comprises the following steps: setting the frequency point number of each individual gene, taking the root mean square error between the sea surface temperature inversion value and the real sea surface temperature and the sensitivity of the inverted sea surface temperature to the real sea surface temperature as fitness parameters, and adding selection operation, variation operation and termination condition judgment; d. presetting a genetic algebra T, randomly selecting data in a data set A as an environment sample, inputting the data into the genetic algorithm established in the step c, obtaining the fitness of individuals with different frequency point combinations by using the input environment sample, judging the advantages and disadvantages of the individuals with different frequency point combinations for inverting the sea surface temperature, eliminating the individuals in the selection operation according to the fitness, adding cross operation and variation operation in the population evolution process to increase the diversity of the frequency point combinations in the individuals, and obtaining an optimal frequency point combination scheme through the evolution and selection of the T generation.
Acquisition and establishment of data set
Acquiring 1 degree x 1 degree sea plane mode data from a European middle-term weather forecast center (ECMWF) every 1 month and 1 day to 12 months and 31 days in 2018, wherein the sea plane mode data comprises factors such as sea surface wind speed, sea surface temperature, sea surface wind direction, cloud liquid water content, atmospheric water vapor content and the like. 200000 sets of data were selected as data set A.
Establishment of full-link simulation and inversion model of two-dimensional and one-dimensional synthetic aperture microwave radiometers
The method comprises the following steps of constructing a full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer, simulating and simulating the whole process of detecting sea surface brightness and inverting sea surface temperature of the one-dimensional synthetic aperture microwave radiometer, wherein the model comprises the following steps: the device comprises a sea surface multi-frequency multi-angle emissivity model, an atmospheric absorption emission model, a one-dimensional synthetic aperture microwave radiometer imaging simulation model and a sea surface temperature physical inversion model.
The sea surface multi-frequency multi-angle emissivity model is used for simulating the sea surface emission bright temperature by using the sea surface temperature and other data in the data set A; sea surface emissivity E in sea surface multi-frequency multi-angle emissivity modelPExpressed as:
wherein E is0Denotes the emissivity of the calm sea surface, Δ EWIndicating the sea surface emissivity increase caused by wind speed,
indicating the sea surface emissivity increase caused by wind direction.
The sea surface emission brightness temperature can be calculated by utilizing the sea surface emissivity and the sea surface temperature, and the formula is as follows:
TB,sea＝Ep·Ts (1-1)
wherein, TB,seaRough sea level with bright and warm texture, EpEmissivity of sea surface, TsIs the sea surface temperature.
The atmosphere absorption emission model is used for simulating the brightness temperature ejected out of the atmosphere after the sea surface brightness temperature is transmitted through the atmosphere; the atmospheric transmittance in the atmospheric absorption emission model is fitted by atmospheric water vapor and cloud liquid water content, and the formula is as follows:
τ＝a1+b1V+c1L+d1V2+e1VL (2)
wherein L is the atmospheric water vapor content, V is the cloud liquid water content, and τ is the atmospheric transmittance. a is1＝0.9902，b1＝2.073e-5，c1＝-0.0105，d1＝-9.818e-7，e1＝-4.545e-6。
The upward radiation brightness temperature T of the atmosphereBUAtmospheric downlink radiation bright temperature TBDThe parameterization of (a) is as follows:
TBU＝TU(1-τ) (2-1)
TBD＝TD(1-τ) (2-2)
in the formula, TUIndicating the effective upward radiation brightness temperature of the atmosphere, TDRepresenting the effective radiation bright temperature of the descending atmosphere;
wherein, TDAnd TUCan be respectively expressed as:
TD＝a2+b2V+c2V2+d2V3+e2Ts (2-3)
TU＝TD+a3+b3V (2-4)
wherein, a2＝162.5，b2＝1.505，c2＝-0.0277，d2＝1.695e-4,e2＝0.2898,a3＝-0.0906,b3＝-0.0011。
The radiant bright temperature reaching the top of the atmosphere is thus obtained:
TB,p＝TBU+τ·Ep·Ts+τ·TBΩ (2-5)
TBΩ＝Rp·[TBD+τ·Tcold]+TB,scat,p (2-6)
in the formula, TB,pThe brightness temperature T received by the satellite-borne one-dimensional synthetic aperture microwave radiometer at the atmospheric topcoldIndicating a bright background in the universe due to TcoldThe influence in microwave remote sensing is small, and a fixed value of 2.7K is generally assumed. T isB,scat,pRepresents the scattering effect of the non-calm sea surface on the atmospheric downlink radiation bright temperature, RpThe total reflectivity of the sea surface is shown, and according to the blackbody radiation law, the reflectivity is as follows: rp＝1-EpSubscript p is polarization, p ═ v, h, v denotes vertical polarization, h denotes horizontal polarization, TBΩRepresenting the remaining forms of upstream radiation bright temperature.
The imaging simulation model of the one-dimensional synthetic aperture microwave radiometer is used for simulating the process that the one-dimensional synthetic aperture microwave radiometer receives the brightness temperature at the top of the atmosphere and detects the result of the brightness temperature.
And the sea surface temperature physical inversion model is used for inverting the sea surface temperature by detecting the obtained brightness temperature by using the radiometer.
The simulation process of the full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer is as follows: inputting the sea surface temperature into a sea surface multi-frequency multi-angle emissivity model to obtain multi-angle sea surface emission bright temperature; the multi-angle sea surface emitting bright temperature is transmitted to and received from the transmitting and receiving model through the atmosphere, and the simulation sea surface emitting bright temperature is transmitted to the bright temperature of the top of the atmosphere layer through the atmosphere; inputting the bright temperature reaching the top of the atmospheric layer into a one-dimensional synthetic aperture microwave radiometer imaging simulation model to obtain the detected bright temperature of the radiometer; and finally, obtaining the detected sea surface temperature by using a sea surface temperature physical inversion model.
Thirdly, constructing a genetic algorithm on the basis of a full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer
(1) Initialization: setting an evolution algebra calculator T to be 0, setting a maximum evolution algebra T to be 250, setting genes of each individual to be 5 frequency points (the number of the frequency points is not necessarily 5, the number of the frequency points can be determined according to the fact, if the sea temperature is measured by 5 frequency points through the past sea surface temperature detection experience, the frequency points are more suitable), randomly selecting the numerical values of the frequency points from 3GHz to 50GHz, and meeting the requirement that the minimum interval between the two frequency points is 10MHz (the frequency interval is not necessarily 10MHz, the frequency points can be determined according to the actual requirement, if the interval between the frequency points of different detected physical quantities is different, and the excessively small frequency point interval is not easily realized on hardware), and randomly selecting 10000 individuals as an initial population P (0);
(2) individual evaluation: establishing two indexes of root mean square error between an inversion value of sea surface temperature and real sea surface temperature and sensitivity of the inversion sea surface temperature to the real sea surface temperature as individual fitness, wherein the formula is as follows:
where SE is the sensitivity of the inverted sea surface temperature to the true sea surface temperature, RMS is the root mean square error between the inverted sea surface temperature and the true sea surface temperature, Tmod,iInverted sea surface temperature, T, for the ith sample points,iThe true sea surface temperature of the ith sample point is obtained, and N is the total number of the sample points;
(3) selecting and operating: establishing selection operation for each individual based on fitness evaluation, wherein the selection aims to directly transmit the optimized individual (namely a better frequency point combination) to the next generation or generate a new individual through pairing and crossing and then transmit the new individual to the next generation;
(4) and (3) cross operation: the crossover operator acts on the group, so that a new frequency point combination is generated and is inherited to the next generation;
(5) and (3) mutation operation: acting a mutation operator on the population, namely changing the individual genes in the population (in the embodiment, randomly selecting frequency points from 3-50 Ghz and randomly selecting the frequency points of the individuals for replacement); wherein the probability of occurrence of mutation is set to 5%;
(6) and (4) judging termination conditions: and if T is equal to T, outputting the optimal fitness individual obtained in the evolution process as an optimal solution, and stopping operation to obtain the optimal 5-frequency point combination for the one-dimensional synthetic aperture microwave radiometer to detect the sea surface temperature.
Solving multi-frequency point problem of genetic algorithm
Presetting a genetic algebra T, randomly selecting data in a data set A as an environment sample, inputting the data into the genetic algorithm established in the step, and obtaining the fitness of individuals with different frequency point combinations by using the input environment sample so as to judge the advantages and disadvantages of the individuals with different frequency point combinations for inverting the sea surface temperature and eliminate the individuals according to the advantages and disadvantages. And cross operation and mutation operation are added in the population evolution process to increase the diversity of the frequency point combinations in the individuals. And obtaining an optimal frequency point combination scheme through evolution and selection of the T generation. As shown in fig. 1, the final output frequency point inversion sea surface temperature result of the genetic algorithm constructed in this embodiment is shown.
According to the invention, a genetic algorithm is constructed on the basis of full-link simulation and inversion models of the one-dimensional synthetic aperture microwave radiometer, the fitness of individuals with different frequency point combinations is obtained by utilizing an input environment sample, an optimal frequency point combination scheme is obtained through T generation evolution and selection, and target characteristics can be extracted from multiple frequency points, so that the detection accuracy is improved.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. The sea temperature remote sensing multi-frequency point determining method of the one-dimensional synthetic aperture microwave radiometer is characterized by comprising the following steps:
a. constructing a data set A comprising sea surface temperature, sea water salinity, zenith angle, sea surface wind speed, sea surface relative wind direction, atmospheric water vapor content and cloud liquid water content;
b. constructing a full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer;
c. a genetic algorithm is constructed on the basis of a full-link simulation and inversion model of a one-dimensional synthetic aperture microwave radiometer, and the genetic algorithm comprises the following steps: setting the number of frequency points of each individual gene, taking the root mean square error between the sea surface temperature inversion value and the real sea surface temperature and the sensitivity of the inverted sea surface temperature to the real sea surface temperature as fitness parameters, and adding selection operation, variation operation and termination condition judgment;
d. presetting a genetic algebra T, randomly selecting data in a data set A as an environment sample, inputting the data into the genetic algorithm established in the step c, obtaining the fitness of individuals with different frequency point combinations by using the input environment sample, judging the advantages and disadvantages of the individuals with different frequency point combinations for inverting the sea surface temperature, eliminating the individuals according to the fitness, adding cross operation and variation operation in the population evolution process to increase the diversity of the frequency point combinations in the individuals, and obtaining an optimal frequency point combination scheme through the evolution and selection of the T generation.
2. The method for determining the multi-frequency point of the one-dimensional synthetic aperture microwave radiometer by the sea temperature remote sensing method as recited in claim 1, wherein the full-link simulation and inversion model of the one-dimensional synthetic aperture microwave radiometer comprises: the system comprises a sea surface multi-frequency multi-angle emissivity model, an atmospheric absorption emission model, a one-dimensional synthetic aperture microwave radiometer imaging simulation model and a sea surface temperature physical inversion model.
3. The method for determining the multi-frequency points of the ocean temperature remote sensing of the one-dimensional synthetic aperture microwave radiometer according to claim 2, wherein the sea surface emissivity E in the sea surface multi-frequency multi-angle emissivity modelPExpressed as:
wherein E is0Denotes the emissivity of the calm sea surface, Δ EWIndicating the sea surface emissivity increase caused by wind speed,
indicating the sea surface emissivity increase caused by wind direction.
4. The method for determining the multi-frequency points of the ocean temperature remote sensing of the one-dimensional synthetic aperture microwave radiometer according to claim 2, wherein the atmospheric transmittance in the atmospheric absorption emission model is fitted by atmospheric water vapor and cloud liquid water content, and the formula is as follows:
τ＝a1+b1V+c1L+d1V2+e1VL (2)
wherein L is the atmospheric water vapor content, V is the cloud liquid water content, τ is the atmospheric transmission rate, a1＝0.9902，b1＝2.073e-5，c1＝-0.0105，d1＝-9.818e-7，e1＝-4.545e-6。
5. The method for determining the multi-frequency point of the one-dimensional synthetic aperture microwave radiometer by the sea temperature remote sensing of claim 1, wherein the root mean square error between the sea surface temperature inversion value and the true sea surface temperature and the sensitivity of the inversion sea surface temperature to the true sea surface temperature are respectively obtained by the following formulas:
where SE is the sensitivity of the inverted sea surface temperature to the true sea surface temperature, RMS is the root mean square error between the inverted sea surface temperature and the true sea surface temperature, Tmod,iInverted sea surface temperature, T, for the ith sample points,iThe true sea surface temperature of the ith sample point, and N is the total number of sample points.
6. The method for determining the multi-frequency points of the one-dimensional synthetic aperture microwave radiometer by remote sensing of sea temperature according to claim 1, wherein in the step c, the numerical values of the frequency points are randomly selected from 3Ghz to 50Ghz, and the minimum interval between the two frequency points is 10 MHz.
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