CN110793649B - Method for correcting brightness and temperature of rough sea surface by using backscattering cross section - Google Patents

Method for correcting brightness and temperature of rough sea surface by using backscattering cross section Download PDF

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CN110793649B
CN110793649B CN201910975300.4A CN201910975300A CN110793649B CN 110793649 B CN110793649 B CN 110793649B CN 201910975300 A CN201910975300 A CN 201910975300A CN 110793649 B CN110793649 B CN 110793649B
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CN110793649A (en
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马文韬
于暘
刘桂红
杨晓峰
杜延磊
李紫薇
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Sanya Zhongke Remote Sensing Research Institute
Institute of Remote Sensing and Digital Earth of CAS
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Sanya Zhongke Remote Sensing Research Institute
Institute of Remote Sensing and Digital Earth of CAS
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    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
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Abstract

The invention discloses a method for correcting rough sea surface brightness temperature by using a backscattering cross section, which is applied to salinity remote sensing in ocean microwave remote sensing, is used for correcting rough sea surface brightness temperature, is suitable for an active and passive combined observation system of an Aquarius satellite, is different from the original method that sea surface wind speed is inverted or auxiliary wind speed is obtained by using a scatterometer, and sea surface roughness is corrected by using wind speed to influence brightness temperature.

Description

Method for correcting brightness and temperature of rough sea surface by using backscattering cross section
Technical Field
The invention relates to a sea surface salinity inversion method in the field of ocean microwave remote sensing, in particular to a method for correcting rough sea surface brightness temperature by using a backscattering section.
Background
The observation of sea surface salinity has important significance for researching and predicting global climate change, monitoring and forecasting of ocean circulation, ocean water mass and the like, and the distribution of the sea surface salinity is closely related to seawater evaporation and rainfall space-time change. Satellite salinity remote sensing is the most effective means for acquiring sea surface salinity data with global scope, long time sequence and wide coverage. The Aquarius satellite (i.e. the Baozhen satellite) is a satellite specially designed for salinity remote sensing, adopts an active and passive combined observation means, combines a scatterometer of 1.26GHz and a radiometer of 1.413GHz to observe the same position of the sea surface at the same angle, and plans and inverts to obtain the global sea surface salinity data with the resolution of 150km and the monthly average precision superior to 0.2psu (practical standard salinity unit).
The brightness temperature observed by the radiometer not only contains the influence of salinity change, but also comprises the influence of other factors, wherein the influence of sea surface roughness on the brightness temperature is the most main limiting factor for improving the salinity remote sensing precision. The conventional roughness correction method needs to firstly utilize a scatterometer to invert the sea surface wind speed or obtain other auxiliary wind speeds, and then utilizes the wind speed to correct the brightness temperature, so that the method has complex steps on one hand, and is easier to introduce model errors on the other hand. And the Aquarius satellite uses a radiometer and a scatterometer to observe the same position of the sea surface by using the same angle, so that backscattering cross section data (NRCS for short) observed by the scatterometer can be used for directly correcting the bright temperature without inverting the wind speed firstly.
Disclosure of Invention
In order to correct the sea surface roughness influence which is the most main influence on the sea surface brightness temperature during sea surface salinity inversion, the invention provides a method for correcting the rough sea surface brightness temperature by using a backscattering section. The method is based on an active and passive united observation system of an Aquarius satellite, utilizes a radiometer and a scatterometer which observe at the same incident angle and the same azimuth angle, utilizes backscattering cross section and auxiliary wind direction data observed by the scatterometer, and directly calculates and obtains rough sea surface bright temperature increment caused by wind by the method. Compared with the traditional correction algorithm, the method does not need to acquire auxiliary wind speed data and does not need to invert the sea surface wind speed of the intermediate variable firstly. The correction accuracy of the inventive method is better than that of the data using the auxiliary NCEP (national environmental prediction center).
The invention relates to a method for correcting brightness and temperature of a rough sea surface by using a backscattering section, which is characterized by comprising the following steps of:
the method comprises the following steps: reading and preprocessing scatterometry data;
extracting the backscattering cross section data of the horizontal emission level receiving scatterometer from the L2 level data of the Aquarius satellite, and recording the backscattering cross section data as scat _ HH _ toa; then, reducing the scat _ HH _ toa by adopting a logarithmic reduction model to obtain reduced horizontal emission level receiving backscattering cross section data R sigma _ HH;
extracting scatterometer vertical transmitting and vertical receiving backscattering section data from Aquarius satellite L2 level data and recording the backscattering section data as scat _ VV _ toa; then, reducing the scat _ VV _ toa by adopting a logarithmic reduction model to obtain reduced vertical transmitting and vertical receiving backscattering cross section data R sigma _ VV;
step two: acquiring wind direction data and adjusting a wind direction angle;
sea surface wind direction data are extracted from the Aquarius satellite L2 level data and recorded as anc _ wind _ dir;
extracting an instrument azimuth angle marked as celphi from L2 level data of the Aquarius satellite;
then using the wind direction correction model
Figure GDA0002607714690000021
Calculating to obtain the wind direction angle relative to the satellite observation
Figure GDA0002607714690000022
If it is
Figure GDA0002607714690000023
Less than 0 deg., by adding 360 deg., make
Figure GDA0002607714690000024
Adjusting to 0-360 degrees;
if it is
Figure GDA0002607714690000025
Greater than 360 deg., by subtracting 360 deg., make
Figure GDA0002607714690000026
Adjusting to 0-360 degrees;
step three: calculating the sea surface emissivity increment by using a scattering emissivity model;
calculating the sea surface emissivity increment by using a scattering emissivity model as follows:
Figure GDA0002607714690000031
cosine coefficient function AnThe calculation of (beam, p, R σ) is:
Figure GDA0002607714690000032
step four: obtaining sea surface temperature data;
extracting sea surface temperature from L2 grade data of the Aquarius satellite, and marking the sea surface temperature as anc _ surface _ temp;
step five: performing sea surface brightness temperature correction;
the horizontally polarized sea surface brightness temperature for removing the influence of the rough sea surface is as follows:
TBflat,H＝rad_TbH-ewNRCS×anc_surface_temp
TBflat,Hcalculating the horizontal polarization calm sea surface brightness temperature;
rad _ TbH is horizontal polarization sea surface brightness temperature data;
the vertical polarization sea surface brightness temperature for removing the influence of the rough sea surface is as follows:
TBflat,V＝rad_TbV-ewNRCS×anc_surface_temp
TBflat,Vcalculating the vertical polarization calm sea surface brightness temperature;
rad-TbV is vertical polarization sea surface brightness temperature data;
in the present invention, the log reduction model is:
Figure GDA0002607714690000033
in the invention, the rough sea surface brightness temperature is directly corrected by using the backscattering section without inverting the wind speed firstly.
In the present invention, the scatterometer and radiometer observe the sea surface using the same angle of incidence and the same azimuth.
Compared with the traditional method, the method for correcting the rough sea surface brightness temperature by using the backscattering cross section has the advantages that:
the method directly establishes a relation model of the emissivity of the rough sea surface and the backscattering cross section of the scatterometer, reduces model errors caused by inverting the wind speed and then calculating the emissivity, and is simpler in model and higher in calculation speed.
The method can directly exert the advantages of joint observation of the radiometer and the scatterometer, and accurately correct the part of the radiometer affected by the roughness by utilizing the characteristic that the scatterometer is only sensitive to the roughness of the sea surface and has higher sensitivity.
The influence of the roughness on the sea surface brightness temperature is corrected by using the emissivity increment obtained by calculating the backscattering cross section and the sea surface temperature, and the corrected calm sea surface brightness temperature can be directly used for sea surface salinity inversion, so that the salinity inversion error can be obviously reduced.
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FIG. 1 is a flow chart of the present invention using backscattering cross-sections to correct for the effect of sea surface roughness on light temperature.
FIG. 2A is a graph of emissivity increase for vertical polarization as a function of R σ for vertical transmission and vertical reception and wind direction.
Fig. 2B is a graph of the emissivity increase for vertical polarization as a function of horizontal transmit-horizontal receive R σ and wind direction.
FIG. 2C is a graph of horizontally polarized emissivity gain versus vertical received R σ and wind direction for vertical transmission.
FIG. 2D is a graph of horizontally polarized emissivity increase as a function of R σ and wind direction received at the horizontal transmission level.
FIG. 3 is a plot of the emissivity gain calculated by the model as a function of wind direction and NRCS (beam3, V polarization emissivity gain calculated by the HH polarized NRCS).
FIG. 4A is a diagram of the variation of the root mean square error of the increment of the vertical polarization emissivity obtained by model calculation and the actually measured increment of the vertical polarization emissivity with the wind direction and the R sigma of vertical transmission and vertical reception.
FIG. 4B is a diagram of the variation of the RMS error of the increment of the vertical polarization emissivity calculated by the model and the actually measured increment of the vertical polarization emissivity with the wind direction and the R sigma received horizontally by the horizontal transmission.
FIG. 4C is a diagram of the variation of the RMS error of the incremental horizontal polarization emissivity calculated by the model and the incremental measured horizontal polarization emissivity with the wind direction and the R σ received vertically by the vertical transmission.
FIG. 4D is a diagram of the RMS error of the model calculated increase in horizontal polarization emissivity versus the measured increase in horizontal polarization emissivity as a function of wind direction and horizontal transmit level received R σ.
Fig. 5A is a graph comparing the corrected beam1 vertical polarization calm sea surface light temperature to a theoretical estimate of the light temperature.
Fig. 5B is a graph comparing the corrected beam2 vertical polarization calm sea light temperature to a theoretical estimate of the light temperature.
Fig. 5C is a graph comparing the corrected beam3 vertical polarization calm surface light temperature to a theoretical estimate of the light temperature.
Fig. 5D is a graph comparing the corrected beam1 horizontal polarization calm sea light temperature with the theoretical estimate of light temperature.
Fig. 5E is a graph comparing the corrected beam2 horizontal polarization calm sea light temperature to the theoretical estimate of light temperature.
Fig. 5F is a graph comparing the corrected beam3 horizontal polarization calm sea light temperature to the theoretical estimate of light temperature.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention aims at a system that a radiometer and a scatterometer are adopted to observe the same position at the same incidence angle and azimuth angle, backscattering cross section data (NRCS for short) obtained by the scatterometer is used for correcting sea surface roughness influence in bright temperature obtained by the radiometer, and at present, a satellite using the system is mainly an Aquarius satellite.
Description of the data
The radiometer model is an Aquarius satellite L-band microwave radiometer, and the scatterometer model is an Aquarius satellite L-band microwave scatterometer. The same position of the sea surface was observed using the Aquarius satellite, which observed the sea surface using three angles of incidence, a first angle of incidence designated beam1, a second angle of incidence designated beam2, and a third angle of incidence designated beam3, which were 28.7 degrees, 37.8 degrees, and 45.6 degrees, respectively. The radiometer used in the present invention includes a horizontal polarization state H and a vertical polarization state V, and the radiometer used includes a horizontal transmission and horizontal reception state HH and a vertical transmission and vertical reception state VV. The data used by the invention is Aquarius satellite L2 level data, which specifically comprises the following data: recording the horizontal polarization sea surface brightness temperature data as rad _ TbH, and recording the vertical polarization sea surface brightness temperature data as rad _ TbV; the horizontal emission horizontal receiving backscattering section data of the scatterometer is recorded as scat _ HH _ toa, and the vertical emission vertical receiving backscattering section data of the scatterometer is recorded as scat _ VV _ toa; the sea surface wind direction data is recorded as anc _ wind _ dir, the sea surface temperature is recorded as anc _ surface _ temp, the instrument azimuth angle is recorded as celphi, the sea surface wind speed data is recorded as anc _ wind _ speed, the calm sea surface horizontal polarization brightness temperature data is recorded as rad _ exp _ TbH0, and the calm sea surface vertical polarization brightness temperature data is recorded as rad _ exp _ TbV 0.
Referring to fig. 1, a method for correcting rough sea surface brightness temperature using backscattering cross section according to the present invention comprises the following steps:
the method comprises the following steps: reading and preprocessing scatterometry data;
extracting the backscattering cross section data of the horizontal emission level receiving scatterometer from the L2 level data of the Aquarius satellite, and recording the backscattering cross section data as scat _ HH _ toa; then, reducing the scat _ HH _ toa by adopting a logarithmic reduction model to obtain reduced horizontal emission level receiving backscattering cross section data R sigma _ HH;
extracting scatterometer vertical transmitting and vertical receiving backscattering section data from Aquarius satellite L2 level data and recording the backscattering section data as scat _ VV _ toa; then, reducing the scat _ VV _ toa by adopting a logarithmic reduction model to obtain R sigma _ VV of the reduced vertical transmitting and vertical receiving back scattering cross section data;
in the invention, as scat _ HH _ toa and scat _ VV _ toa exist in a decibel form, a logarithmic reduction model is adopted for reduction to obtain an observed quantity R sigma of backscattering section data;
in the present invention, scat _ HH _ toa and scat _ VV _ toa are expressed in a collective form as σ ═ { scat _ HH _ toa, scat _ VV _ toa }.
In the present invention, the log reduction model is:
Figure GDA0002607714690000061
r σ represents the reduced backscatter cross-sectional data (i.e., observed quantity), which can be denoted as R σ _ HH for scat _ HH _ toa and R σ _ VV _ toa for scat _ VV _ toa; σ represents backscattering cross-sectional data; "Λ" represents the sign of the power.
And reducing the scat _ HH _ toa by adopting a logarithmic reduction model to obtain reduced horizontal emission level receiving backscattering cross section data R sigma _ HH.
And reducing the scat _ VV _ toa by adopting a logarithmic reduction model to obtain reduced vertical transmitting and receiving backscattering cross section data R sigma _ VV.
Step two: acquiring wind direction data and adjusting a wind direction angle;
extracting sea surface wind direction data from L2 level data of the Aquarius satellite, and recording the sea surface wind direction data as anc _ wind _ dir and instrument azimuth as celphi; then, the wind direction angle observed relative to the satellite is calculated by utilizing a wind direction correction model
Figure GDA0002607714690000071
The wind direction correction model is as follows:
Figure GDA0002607714690000072
in the present invention, if
Figure GDA0002607714690000073
Less than 0 deg., by adding 360 deg., make
Figure GDA0002607714690000074
Is adjusted to be within the range of 0-360 degrees.
In the inventionIn, if
Figure GDA0002607714690000075
Greater than 360 deg., by subtracting 360 deg., make
Figure GDA0002607714690000076
Is adjusted to be within the range of 0-360 degrees.
In the invention, the acquisition anc _ wind _ dir and celphi refers to the corrected brightness temperature data at the same time and at the same longitude and latitude.
Step three: calculating the sea surface emissivity increment by using a scattering emissivity model;
according to the invention, according to the load working characteristics of the Aquarius satellite, aiming at the advantage that active and passive parameters can be simultaneously obtained, by utilizing the characteristics that a scatterometer is sensitive only to the roughness of the sea surface and has higher sensitivity, the R sigma actively obtained by the scatterometer and a scattering emissivity model are used for calculating to obtain the sea surface emissivity increment ewNRCS. In the traditional seawater salinity inversion algorithm, a wind speed inversion algorithm is required to be introduced for obtaining the sea surface emissivity increment, the wind speed is obtained by inversion, and then the sea surface emissivity increment is obtained by calculation, so that the error caused by a wind speed inversion model is introduced, and the calculation time of seawater salinity inversion is increased. Therefore, the method has strong business application significance for directly calculating the sea surface emissivity increment by using the parameters obtained by satellite observation.
Calculating the sea surface emissivity increment by using a scattering emissivity model as follows:
Figure GDA0002607714690000081
ewNRCSindicating the emissivity increase.
beam represents the angle of incidence; in the present invention, the first incident angle may be denoted as beam1, the second incident angle may be denoted as beam2, and the third incident angle may be denoted as beam 3.
p represents the polarization mode of the radiometer; in the present invention, it may be horizontally polarized (denoted as H) or vertically polarized (denoted as V).
R σ represents the reduced backscatter cross-section data;
Figure GDA0002607714690000083
representing the wind direction angle relative to the satellite observation;
An(beam, p, R sigma) represents a cosine coefficient function, and the lower subscript n represents a coefficient identification number (in the present invention, the value of n is 0, 1, 2, 4. in the present invention, A is0(beam, p, R σ) represents a cosine coefficient function of order 0; a. the1(beam, p, R σ) represents a first order cosine coefficient function; a. the2(beam, p, R σ) represents a second order cosine coefficient function; a. the4(beam, p, R σ) represents a fourth order cosine coefficient function.
Because A isnThe calculation of (beam, p, R σ) is:
Figure GDA0002607714690000082
an,iand the coefficient of a cosine emissivity model is represented, the lower subscript n represents a coefficient identification number, and the lower subscript i represents an order identification number. In the present invention, through an,iThe training data are fitted and tables about the angle of incidence beam and the polarization mode are made for lookup, i.e., table 1, table 2 and table 3.
Step four: obtaining sea surface temperature data;
the sea surface temperature was extracted from the Aquarius satellite level L2 data and reported as anc _ surface _ temp. The sea surface temperature is bright temperature data obtained by correcting observation of the radiometer.
In the invention, the anc _ surface _ temp is cited to construct the relation between the sea surface emissivity increment and the sea surface bright temperature increment.
Step five: performing sea surface brightness temperature correction;
in the invention, in order to obtain the calm sea surface brightness temperature which can be directly used for salinity inversion, sea surface brightness temperature increment is obtained by calculating the sea surface emissivity increment and the sea surface temperature data in the third step and the fourth step, and then the sea surface brightness temperature increment part caused by rough sea surface is removed from the total sea surface brightness temperature data obtained by the Aquarius satellite. When the factor influencing the inversion accuracy of the seawater salinity, namely the brightness temperature increment of the rough sea surface, is eliminated, the characteristic that the Aquarius satellite is used for simultaneously obtaining the active and passive parameters is used, and the model error caused by sea surface wind speed inversion performed by the traditional method is reduced, so that the precision of the brightness temperature of the calm sea surface obtained by calculation is higher, and the inversion accuracy of the seawater salinity is improved.
The horizontally polarized sea surface brightness temperature for removing the influence of the rough sea surface is as follows:
TBflat,H＝rad_TbH-ewNRCS×anc_surface_temp
TBflat,Hand (4) calculating the horizontal polarization calm sea surface brightness temperature.
The vertical polarization sea surface brightness temperature for removing the influence of the rough sea surface is as follows:
TBflat,V＝rad_TbV-ewNRCS×anc_surface_temp
TBflat,Vand calculating the obtained vertical polarization calm sea surface brightness temperature.
Example 1
And (3) establishing and verifying a model by using data from 9 to 3 months 2015, wherein the data from 9 to 12 months 2015 are used for method establishment, and the data from 1 to 3 months 2016 are used for method verification. Data affected by rainfall, land, sea ice, etc. in the data are eliminated. The data for the method build is greater than 120 ten thousand pairs per angle of incidence and the data for the method validation is greater than 90 ten thousand pairs per angle of incidence.
Fig. 2A, 2B, 2C and 2D show that the mean value of the emissivity increment of beam3 on the rough sea surface calculated by using the training data varies with R σ and wind direction, and it can be seen that the emissivity increment of the rough sea surface has high correlation with R σ and wind direction, the emissivity increment of the rough sea surface increases with the increase of R σ and varies in a cosine manner with wind direction, so the model proposed in step 3 of the method of the present invention is very reasonable, and beam3, an incidence angle of 45.6 °, a wind direction interval of 10 °, and an R σ interval of 0.001 are used for averaging in the acquisition. In embodiment 1, the cosine emissivity model coefficient an,iTable 1, table 2 and table 3 were fitted from the training data using the least squares method.
TABLE 1A of Beam1n,iCoefficient table (2)
Figure GDA0002607714690000101
Note: v VV is a model coefficient for calculating the increment of the vertical polarization emissivity for the backscattering cross section data vertically transmitted and received, V HH is a model coefficient for calculating the increment of the vertical polarization emissivity for the backscattering cross section data horizontally transmitted and received, H VV is a model coefficient for calculating the increment of the horizontal polarization emissivity for the backscattering cross section data vertically transmitted and received, and HHH is a model coefficient for calculating the increment of the horizontal polarization emissivity for the backscattering cross section data horizontally transmitted and received.
TABLE 2A of Beam2n,iCoefficient table (2)
Figure GDA0002607714690000111
TABLE 3A of Beam3n,iCoefficient table (2)
Figure GDA0002607714690000121
The sea surface emissivity obtained by simulation is increased by using the model coefficients and the scattering emissivity model in table 3, please refer to fig. 3. According to the comparison between the graph in fig. 3 and the graph in fig. 2B, it can be seen that the scattering emissivity model proposed by the method of the invention has a high fitting degree to data, and can very accurately display the change of the sea surface emissivity increment with the NRCS and the wind direction. Referring to fig. 4A, 4B, 4C, and 4D, it can be seen that the simulation accuracy of the method of the present invention is higher in most cases, and can be better than 0.3K.
According to the data information given in the embodiment 1 and by combining the method of the present invention, the quiet sea surface brightness temperature is calculated and compared with the quiet sea surface brightness temperature obtained by sea surface salinity simulation, and the comparison results are shown in fig. 5A, fig. 5B, fig. 5C, fig. 5D, fig. 5E and fig. 5F, it can be seen that the quiet sea surface brightness temperature corrected by using the method of the present invention has a high fitting degree with the simulated brightness temperature at each incident angle and polarization mode.
Following the data information given in example 1 in conjunction with the method of the present invention, light temperature calculations were also performed using the model in Aquarius V5.0 for light temperature gain calculation using wind speed and the auxiliary NCEP wind speed, and the results are shown in Table 4.
TABLE 4 comparison of model results
Figure GDA0002607714690000131
Note: v is vertical polarization and H is horizontal polarization.
It can be seen from table 4 that the flat surface light temperature deviation and root mean square error corrected using the method of the present invention are minimal.

Claims (5)

1. A method for correcting rough sea surface brightness temperature by using backscattering cross section is characterized by comprising the following steps:
the method comprises the following steps: reading and preprocessing scatterometry data;
extracting the backscattering cross section data of the horizontal emission level receiving scatterometer from the L2 level data of the Aquarius satellite, and recording the backscattering cross section data as scat _ HH _ toa; then, reducing the scat _ HH _ toa by adopting a logarithmic reduction model to obtain reduced horizontal emission level receiving backscattering cross section data R sigma _ HH;
extracting scatterometer vertical transmitting and vertical receiving backscattering section data from Aquarius satellite L2 level data and recording the backscattering section data as scat _ VV _ toa; then, reducing the scat _ VV _ toa by adopting a logarithmic reduction model to obtain R sigma _ VV of the reduced vertical transmitting and vertical receiving back scattering cross section data;
step two: acquiring wind direction data and adjusting a wind direction angle;
sea surface wind direction data are extracted from the Aquarius satellite L2 level data and recorded as anc _ wind _ dir;
extracting an instrument azimuth angle marked as celphi from L2 level data of the Aquarius satellite;
then using the wind direction correction model
Figure FDA0002607714680000011
Calculating to obtain the wind direction angle relative to the satellite observation
Figure FDA0002607714680000012
If it is
Figure FDA0002607714680000013
Less than 0 deg., by adding 360 deg., make
Figure FDA0002607714680000014
Adjusting to 0-360 degrees;
if it is
Figure FDA0002607714680000015
Greater than 360 deg., by subtracting 360 deg., make
Figure FDA0002607714680000016
Adjusting to 0-360 degrees;
step three: calculating the sea surface emissivity increment by using a scattering emissivity model;
calculating the sea surface emissivity increment by using a scattering emissivity model as follows:
Figure FDA0002607714680000017
ewNRCSindicating an emissivity increase;
beam represents the angle of incidence;
p represents the polarization mode of the radiometer;
r σ represents the reduced backscatter cross-section data;
Figure FDA0002607714680000021
representing the wind direction angle relative to the satellite observation;
An(beam, p, R σ) represents cosineA coefficient function, wherein the lower subscript n represents a coefficient identification number, and the value of n is 0, 1, 2 and 4; wherein A is0(beam, p, R σ) represents a cosine coefficient function of order 0; a. the1(beam, p, R σ) represents a first order cosine coefficient function; a. the2(beam, p, R σ) represents a second order cosine coefficient function; a. the4(beam, p, R σ) represents a fourth order cosine coefficient function;
a is describednThe calculation of (beam, p, R σ) is:
Figure FDA0002607714680000022
an,iexpressing a cosine emissivity model coefficient, wherein a lower subscript n expresses a coefficient identification number, and a lower subscript i expresses an order identification number;
step four: obtaining sea surface temperature data;
extracting sea surface temperature from L2 grade data of the Aquarius satellite, and marking the sea surface temperature as anc _ surface _ temp;
step five: performing sea surface brightness temperature correction;
the horizontally polarized sea surface brightness temperature for removing the influence of the rough sea surface is as follows:
TBflat,H＝rad_TbH-ewNRCS×anc_surface_temp
TBflat,Hcalculating the horizontal polarization calm sea surface brightness temperature;
rad _ TbH is horizontal polarization sea surface brightness temperature data;
the vertical polarization sea surface brightness temperature for removing the influence of the rough sea surface is as follows:
TBflat,V＝rad_TbV-ewNRCS×anc_surface_temp
TBflat,Vcalculating the vertical polarization calm sea surface brightness temperature;
rad _ TbV is vertical polarization sea surface light temperature data.
2. The method of using backscattering cross-sections to correct rough sea surface light temperature according to claim 1, wherein: the log reduction model is:
Figure FDA0002607714680000031
r σ represents the reduced backscatter cross-section data; σ represents backscattering cross-sectional data; "Λ" represents the sign of the power.
3. The method of claim 1, wherein the method further comprises the step of correcting the rough sea surface brightness temperature using a backscattering cross section: the rough sea surface brightness temperature is directly corrected by using the backscattering section without inverting the wind speed firstly.
4. The method of claim 1, wherein the method further comprises the step of correcting the rough sea surface brightness temperature using a backscattering cross section: the scatterometer and radiometer observe the sea surface using the same angle of incidence and the same azimuth.
5. The method of claim 1, wherein the method further comprises the step of correcting the rough sea surface brightness temperature using a backscattering cross section: the radiometer model is an Aquarius satellite L-band microwave radiometer, and the scatterometer model is an Aquarius satellite L-band microwave scatterometer.
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