CN114970214A - Aerosol optical thickness inversion method and device - Google Patents

Aerosol optical thickness inversion method and device Download PDF

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CN114970214A
CN114970214A CN202210894502.8A CN202210894502A CN114970214A CN 114970214 A CN114970214 A CN 114970214A CN 202210894502 A CN202210894502 A CN 202210894502A CN 114970214 A CN114970214 A CN 114970214A
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王诗圣
王宇翔
黄葵
陶进
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Nanjing Aerospace Hongtu Information Technology Co ltd
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Abstract

The application provides an aerosol optical thickness inversion method and device, relates to the technical field of satellite remote sensing images, and comprises the following steps: acquiring a remote sensing image of an FY4A AGRI sensor, and acquiring observation geometric parameters and blue light data of each pixel from the remote sensing image; obtaining the apparent reflectivity of a first blue light wave band based on the blue light data of each pixel; calculating the apparent reflectivity of a second blue light wave band corresponding to a plurality of simulated aerosol optical thicknesses of each pixel by using the observation geometric parameters of the pixels and a pre-established lookup table through a theoretical simulation method; fitting to obtain a linear equation of each pixel by utilizing the apparent reflectivity of the first blue light band of the pixel and the apparent reflectivity of the second blue light band corresponding to the optical thicknesses of the plurality of simulated aerosols; and calculating the optical thickness of the aerosol of each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation. The method and the device improve the inversion accuracy of the optical thickness of the aerosol.

Description

Aerosol optical thickness inversion method and device
Technical Field
The application relates to the technical field of satellite remote sensing images, in particular to an aerosol optical thickness inversion method and device.
Background
In recent years, the problem of atmospheric pollution becomes an important factor influencing the health, daily life and urban sustainable development of people, the optical thickness of the aerosol is accurately inverted by remote sensing, and the comprehensive monitoring of atmospheric pollution change has important demand value and scientific significance for environmental governance and urban planning. And preprocessing steps such as geometric correction and the like are carried out on the FY4A AGRI data, cross radiometric calibration is carried out on the FY4A AGRI Blue light wave band by using MODIS Blue light wave band data, an accurate DN value is obtained, and aerosol inversion is carried out by using a Deep Blue algorithm (Deep Blue, DB).
Current aerosol product data is obtained primarily by MODIS data or by inversion using, for example, Landsat satellite data. The FY4A is used as a second generation geostationary orbit meteorological satellite in China and is the first star of a new generation geostationary meteorological satellite in China. The interference type atmosphere vertical detector and the static track scanning imaging radiometer 'join in one' and realize three-dimensional atmosphere monitoring on the static track for the first time in the world. In addition, the 'wind cloud number four' can provide continuous monitoring data aiming at the land, water, lightning and space weather, can clearly distinguish different forms of the cloud, high-middle-layer water vapor and snow, and also has the capability of capturing aerosol and snow, but a technical scheme for inverting the optical thickness of the aerosol by using an AGRI sensor is not available at present.
Disclosure of Invention
In view of the above, the present application provides an aerosol optical thickness inversion method and apparatus to solve the above technical problems.
In a first aspect, an embodiment of the present application provides an aerosol optical thickness inversion method, including:
acquiring a remote sensing image of an FY4A AGRI sensor, and acquiring observation geometric parameters and blue light data of each pixel from the remote sensing image;
obtaining a first blue light band apparent reflectivity of each pixel based on the blue light data of each pixel by using a pre-established linear equation of the blue light data and the blue light band apparent reflectivity;
calculating the apparent reflectivity of a second blue light wave band corresponding to a plurality of simulated aerosol optical thicknesses of each pixel by using the observation geometric parameters of the pixels and a pre-established lookup table through a theoretical simulation method;
fitting to obtain a linear equation of each pixel by utilizing the first blue light band apparent reflectivity of the pixel and the second blue light band apparent reflectivity corresponding to a plurality of simulated aerosol optical thicknesses, wherein the linear equation takes the blue light band apparent reflectivity as an independent variable and the aerosol optical thickness as a dependent variable;
and calculating the optical thickness of the aerosol of each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation.
Further, the fitting step of the linear equation of the apparent reflectivity of the blue light data and the blue light wave band comprises the following steps:
acquiring time-space matched FY4A AGRI sensor data and MODIS product data;
acquiring a satellite zenith angle of MODIS product data corresponding to the satellite zenith angle in the FY4A AGRI sensor data;
acquiring a relative azimuth angle of MODIS product data corresponding to the relative azimuth angle in the FY4A AGRI sensor data;
obtaining the apparent reflectivity of a blue light wave band corresponding to a satellite zenith angle and a relative azimuth angle of MODIS product data;
fitting the blue light data and the coefficient of the linear equation of the apparent reflectivity of the blue light wave band
Figure P_220719102402106_106114001
And
Figure P_220719102402137_137369002
the linear equation is:
Figure P_220719102402168_168590001
wherein, the first and the second end of the pipe are connected with each other,
Figure P_220719102402184_184251001
in the case of blue-ray data,
Figure P_220719102402215_215496002
the apparent reflectivity of the blue light wave band is a dependent variable,
Figure P_220719102402231_231144003
and
Figure P_220719102402263_263301004
respectively representing the reflectivity gain amount and the reflectivity offset amount of the blue light band.
Further, the data items of the lookup table include: hemispherical reflectivity, atmospheric transmittance, equivalent reflectivity of atmospheric radiation along an atmospheric transmission path, a solar zenith angle, a satellite zenith angle, a relative azimuth angle and aerosol optical thickness; wherein, 9 values of the solar zenith angle include: 0, 10, 20, 30, 40, 50, 60, 70 or 80; the 9 values of the satellite zenith angle include: 0, 10, 20, 30, 40, 50, 60, 70 or 80; the 19 values of the relative azimuth include: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170 and 180.
Further, observing geometric parameters includes: a solar zenith angle, a satellite zenith angle and a relative azimuth angle; calculating the apparent reflectivity of a second blue light wave band corresponding to a plurality of simulated aerosol optical thicknesses of each pixel by using the observation geometric parameters of the pixels and a pre-established lookup table through a theoretical simulation method; the method comprises the following steps:
obtaining the surface reflectivity of the pixel by using the observation geometric parameters of the pixel and a pre-established surface reflectivity linear equation; the independent variable of the surface reflectivity linear equation is the surface reflectivity of the MODIS sensor, and the dependent variable is the surface reflectivity of the FY4A AGRI sensor;
by utilizing the observation geometric parameters of the pixel, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to the optical thicknesses of a plurality of simulated aerosols are obtained by inquiring in a pre-established lookup table;
and calculating to obtain the apparent reflectivity of a plurality of simulated aerosol optical thicknesses corresponding to the second blue light wave band by utilizing the earth surface reflectivity, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path of the pixel.
Further, the fitting step of the surface reflectivity linear equation comprises:
respectively calculating the earth surface reflectivity observed by the FY4A AGRI sensor and the MODIS sensor in the blue light wave band
Figure P_220719102402279_279439001
And
Figure P_220719102402310_310693002
：
Figure P_220719102402341_341942001
Figure P_220719102402373_373198001
wherein the content of the first and second substances,
Figure P_220719102402404_404454001
and
Figure P_220719102402420_420135002
FY4A AGRI sensor and MODIS sensor respectively
Figure P_220719102402451_451354003
Spectral response at wavelength;
Figure P_220719102402484_484028004
and
Figure P_220719102402515_515993005
wavelength intervals used for spectral calibration of the two sensors are respectively adopted;
Figure P_220719102402546_546534006
is as follows
Figure P_220719102402577_577796007
The ground features are planted in
Figure P_220719102402593_593421008
The feature comprising: grasslands, floor tiles, bodies of water, cement, and asphalt;
using a plurality of
Figure P_220719102402624_624665001
And
Figure P_220719102402655_655921002
fitting a surface reflectivity linear formula:
Figure P_220719102402673_673475001
wherein the content of the first and second substances,
Figure P_220719102402705_705259001
and
Figure P_220719102402736_736479002
are fitting coefficients.
Further, the observation geometric parameters of the pixel and a pre-established surface reflectivity linear equation are utilized to obtain the surface reflectivity of the pixel; the method comprises the following steps:
acquiring the solar zenith angle of MODIS product data corresponding to the solar zenith angle of the pixel;
acquiring a satellite zenith angle of MODIS product data corresponding to the satellite zenith angle of the pixel;
obtaining a relative azimuth angle of MODIS product data corresponding to the relative azimuth angle of the pixel;
acquiring the earth surface reflectivity of the MODIS product data corresponding to the solar zenith angle, the satellite zenith angle and the relative azimuth angle of the MODIS product data;
and substituting the earth surface reflectivity of the MODIS product data as an independent variable into an earth surface reflectivity linear equation to obtain the earth surface reflectivity of the pixel.
Further, by using the observation geometric parameters of the pixel, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to the optical thicknesses of the plurality of aerosols are obtained by inquiring in a pre-established lookup table, and the method comprises the following steps:
for each aerosol optical thickness in the look-up table, performing the following steps:
acquiring two adjacent solar zenith angles of the pixel; acquiring two adjacent satellite zenith angles of the pixels in a lookup table; acquiring two adjacent relative azimuth angles of the relative azimuth angle of the pixel in a lookup table;
combining the two solar zenith angles, the two satellite zenith angles and the two relative azimuth angles to obtain 8 groups of combinations of the solar zenith angles, the satellite zenith angles and the relative azimuth angles;
8 hemispherical reflectivities corresponding to 8 combinations, 8 atmospheric transmittance and 8 equivalent reflectivities of atmospheric radiation along an atmospheric transmission path are obtained through a lookup table;
calculating the average value of the 8 hemispherical reflectances as the hemispherical reflectivity of the pixel element;
calculating the average value of the 8 atmospheric transmittance as the atmospheric transmittance of the pixel;
calculating the equivalent reflectivity of 8 atmospheric radiations along an atmospheric transmission path, and taking the equivalent reflectivity as the equivalent reflectivity of the atmospheric radiation of the pixel along the atmospheric transmission path;
thus obtaining the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to a plurality of aerosol optical thicknesses.
Further, fitting to obtain a linear equation of each pixel by utilizing the apparent reflectivity of the first blue light band of the pixel and the apparent reflectivity of the second blue light band corresponding to the optical thicknesses of the plurality of simulated aerosols; the method comprises the following steps:
calculating the absolute value of the difference between the apparent reflectivity of the first blue light band of the pixel and the apparent reflectivity of each second blue light band, and acquiring the two second blue light band apparent reflectivities corresponding to the minimum and the second smallest in the calculation result and the corresponding optical thickness of the aerosol;
and fitting the coefficient of the linear equation of each pixel by using the apparent reflectivity of the two second blue light bands of each pixel and the corresponding optical thickness of the aerosol.
In a second aspect, an embodiment of the present application provides an aerosol optical thickness inversion apparatus, including:
the acquisition unit is used for acquiring a remote sensing image of the FY4A AGRI sensor and acquiring observation geometric parameters and blue light data of each pixel from the remote sensing image;
the first calculation unit is used for obtaining the first blue light band apparent reflectivity of each pixel based on the blue light data of each pixel by using the pre-established linear equation of the blue light data and the blue light band apparent reflectivity;
the second calculation unit is used for calculating the apparent reflectivity of a second blue light wave band corresponding to the optical thicknesses of the simulated aerosols of each pixel by a theoretical simulation method by using the observation geometric parameters of the pixels and a pre-established lookup table;
the fitting unit is used for fitting to obtain a linear equation of each pixel by utilizing the first blue light band apparent reflectivity of the pixel and the second blue light band apparent reflectivity corresponding to the plurality of simulated aerosol optical thicknesses, wherein the linear equation takes the blue light band apparent reflectivity as an independent variable and the aerosol optical thickness as a dependent variable;
and the third calculating unit is used for calculating the optical thickness of the aerosol of each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing an aerosol optical thickness inversion method as an embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement an aerosol optical thickness inversion method according to embodiments of the present application.
The method and the device improve the inversion accuracy of the optical thickness of the aerosol.
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In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an aerosol optical thickness inversion method provided in an embodiment of the present application;
fig. 2 is a functional block diagram of an aerosol optical thickness inversion apparatus provided in an embodiment of the present application;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
First, the design idea of the embodiment of the present application is briefly introduced.
With the wide application of remote sensing data quantification, the requirement on the radiometric calibration precision of the satellite sensor is higher and higher. The radiation response of the sensor is attenuated along with the increase of the service time, and the data received by the sensor is affected by factors such as solar radiation and the like, so that the radiation response change of the remote sensing detector in the operation period needs to be accurately detected, and the radiation calibration parameters need to be updated in time to ensure the accuracy of quantitative research of the remote sensing data. And performing cross radiometric calibration on the blue light waveband of the FY4A AGRI sensor by adopting MODIS data, calculating the apparent reflectivity of the FY4A AGRI blue light waveband by a theoretical simulation method on the basis of correction of differences of solar zenith angles, satellite zenith angles, relative azimuth angles and channel spectral response of different sensors, performing position matching on the apparent reflectivity and a DN value output by the sensor, and further calculating the radiometric calibration coefficient of the blue light waveband of the FY4A AGRI sensor. The method has very important significance for high-precision quantitative application of the sensor.
According to the method, the AOD is inverted by using the FY4A AGRI sensor, cross radiometric calibration is carried out by using the AGRI and the MODIS blue light wave band, a more accurate calibration coefficient of the blue light wave band is obtained, and a more accurate data base is provided for the inversion of the AOD; by using the deep blue algorithm suitable for inverting the AOD of the blue light wave band, the inversion accuracy is more accurate compared with other principle methods.
After introducing the application scenario and the design concept of the embodiment of the present application, the following describes a technical solution provided by the embodiment of the present application.
As shown in fig. 1, an aerosol optical thickness inversion method is provided in an embodiment of the present application, including the following steps:
step 101: acquiring a remote sensing image of an FY4A AGRI sensor, and acquiring observation geometric parameters and blue light data of each pixel from the remote sensing image;
step 102: obtaining a first blue light band apparent reflectivity of each pixel based on the blue light data of each pixel by using a pre-established linear equation of the blue light data and the blue light band apparent reflectivity;
in order to obtain a linear equation of blue light data and blue light band apparent reflectivity, historical data is required to be used for fitting the data, but since the blue light band apparent reflectivity of the FY4A AGRI sensor cannot be directly obtained, the blue light band apparent reflectivity can be directly obtained from MODIS product data, if the blue light band apparent reflectivity of the MODIS product data is used for the blue light band apparent reflectivity of the FY4A AGRI sensor, the MODIS product data and the FY4A AGRI data need to be accurately registered. Meanwhile, the influence of angles such as a satellite zenith angle, a relative azimuth angle and the like at the imaging moment and the difference of spectral response between two channels need to be fully considered.
Due to the change of the satellite zenith angle, the detection path is increased due to the fact that the detection path is close to the edge of the scanning line, so that the atmospheric attenuation is serious, and the image is displayed darker; meanwhile, the zenith angle of the satellite at the same position is obviously changed on different orbits, different time phases and different satellite images. Based on the above effects, correction of zenith angle data of different satellites is required. The correction model is as follows (1):
Figure P_220719102402752_752168001
（1）
in the formula (I), the compound is shown in the specification,
Figure P_220719102402783_783339001
the satellite zenith angles of the images at the time of imaging for the MODIS,
Figure P_220719102402814_814608002
is the satellite zenith angle corresponding to the imaging time of the FY4A AGRI satellite,
Figure P_220719102402845_845867003
the satellite relative azimuth (sec represents secant function) for each pel position at the imaging time of MODIS.
Figure P_220719102402877_877151004
，
Figure P_220719102402892_892737005
，
Figure P_220719102402923_923982006
，
Figure P_220719102402955_955252007
Are known coefficients.
The correction of the relative azimuth angle is mainly processed by a regression analysis method. And converting the data corresponding to the relative azimuth angle of the MODIS into the data corresponding to the relative azimuth angle of 0 degree. And then the relative azimuth of the FY4A AGRI satellite is converted into the corresponding apparent reflectivity.
And after the MODIS apparent reflectivity is subjected to angle difference correction and spectral response difference correction, obtaining the theoretical FY4A satellite blue light waveband apparent reflectivity, and performing linear correlation analysis on the MODIS apparent reflectivity and the FY4A AGRI blue light waveband original DN value at the same position to obtain a linear relation between the MODIS apparent reflectivity and the FY4A AGRI blue light waveband apparent reflectivity.
Specifically, the fitting step of the linear equation of the apparent reflectivity of the blue light data and the blue light wave band comprises the following steps:
acquiring time-space matched FY4A AGRI sensor data and MODIS product data;
acquiring a satellite zenith angle of MODIS product data corresponding to the satellite zenith angle in the FY4A AGRI sensor data;
acquiring a relative azimuth angle of MODIS product data corresponding to the relative azimuth angle in the FY4A AGRI sensor data;
obtaining the apparent reflectivity of a blue light wave band corresponding to a satellite zenith angle and a relative azimuth angle of MODIS product data;
fitting coefficients of linear equations of blue light data and blue light band apparent reflectivity by using blue light data in FY4A AGRI sensor data and blue light band apparent reflectivity of corresponding MODIS product data
Figure P_220719102402986_986535001
And
Figure P_220719102403002_002168002
the linear equation is:
Figure P_220719102403033_033373001
（2）
wherein the content of the first and second substances,
Figure P_220719102403068_068498001
in the case of blue-ray data,
Figure P_220719102403084_084608002
the apparent reflectivity of the blue light wave band is a dependent variable,
Figure P_220719102403115_115870003
and
Figure P_220719102403147_147147004
respectively representing the reflectivity gain amount and the reflectivity offset amount of the blue light band.
Step 103: calculating the apparent reflectivity of a second blue light wave band corresponding to a plurality of simulated aerosol optical thicknesses of each pixel by using the observation geometric parameters of the pixels and a pre-established lookup table through a theoretical simulation method;
the atmospheric radiation transmission model of the present embodiment is as follows:
at present, most remote sensing inversion means invert the optical thickness value of the aerosol based on an atmospheric radiation transmission equation. Apparent reflectivity of upper atmosphere received by satellite sensor
Figure P_220719102403162_162753001
The calculation formula is as follows:
Figure P_220719102403194_194027001
（3）
in the formula (I), the compound is shown in the specification,
Figure P_220719102403225_225261001
for the apparent brightness of the radiation received by the sensor,
Figure P_220719102403256_256507002
is the distance between the day and the earth (astronomical units),
Figure P_220719102403289_289696003
is the solar radiation flux of the upper air boundary,
Figure P_220719102403414_414730004
the cosine of the zenith angle of the sun.
Under the sunny and cloudy weather condition, the total radiance received by the satellite for the underlying surface of the uniform Lambert surface is obtained under the assumption that the atmosphere is in a horizontal and uniform state without considering the gas absorption condition
Figure P_220719102403445_445937001
Can be expressed as follows:
Figure P_220719102403480_480120001
（4）
in the formula (I), the compound is shown in the specification,
Figure P_220719102403526_526990001
is the cosine of the solar observation angle,
Figure P_220719102403558_558268002
is the relative azimuth angle of the sensor.
Figure P_220719102403589_589523003
For the purpose of observing the path radiation in the direction,
Figure P_220719102403620_620767004
is the solar radiation flux density at the top of the atmospheric layer,
Figure P_220719102403652_652006005
is the surface reflectivity of a lambertian body,Srepresenting the hemispherical reflectivity in the downward direction of the atmosphere,
Figure P_220719102403671_671503006
which represents the transmittance in the downward direction of the atmosphere,
Figure P_220719102403718_718914007
the transmittance in the atmosphere direction.
According to the above formula (4), the solar emissivity incident in the vertical direction is utilized
Figure M_220719102403749_749665001
After normalizing the reflection value, the apparent reflectivity of the upper air bound received by the satellite sensor can be obtained, namely the following relation exists between the apparent reflectivity and the surface two-way reflectivity:
Figure P_220719102403796_796578001
（5）
in the formula (I), the compound is shown in the specification,
Figure P_220719102403828_828295001
in order for the apparent reflectivity received by the satellite,
Figure P_220719102403859_859553002
for equivalent reflectivity of atmospheric radiation along the atmospheric transmission path,
Figure P_220719102403896_896177003
is the surface reflectivity. Further conversion of the form of formula (5):
Figure P_220719102403927_927408001
（6）
wherein the content of the first and second substances,
Figure P_220719102403974_974283001
，S、
Figure P_220719102404005_005546002
and
Figure P_220719102404052_052394003
three parameters are used to represent atmospheric conditions. In the actual inversion process, the correlation between the aerosol optical thickness and the 3 atmospheric parameters is simulated and calculated through a radiation transmission model under different atmospheric conditions and observation geometric paths (including a solar zenith angle, a solar azimuth angle, a satellite zenith angle and a satellite azimuth angle), a lookup table corresponding to inversion requirements is established, earth surface contributions are removed through earth decoupling, and finally an inversion result AOD is obtained.
The data items of the lookup table of the present embodiment include: hemispherical reflectivity, atmospheric transmittance, equivalent reflectivity of atmospheric radiation along an atmospheric transmission path, a solar zenith angle, a satellite zenith angle, a relative azimuth angle and aerosol optical thickness; wherein, 9 values of the solar zenith angle include: 0, 10, 20, 30, 40, 50, 60, 70 or 80; the 9 values of the satellite zenith angle include: 0, 10, 20, 30, 40, 50, 60, 70 or 80; the 19 values of the relative azimuth include: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170 and 180. As shown in table 1:
TABLE 1
Figure P_220719102404086_086116001
According to the atmospheric radiation transmission equation (5), if the path radiation reflectivity under the corresponding optical thickness is obtained
Figure P_220719102404148_148575001
The values of the atmospheric transmittance T and the hemispherical reflectance S can be used to calculate the apparent reflectance from the surface reflectance or to calculate the surface reflectance from the apparent reflectance. And 6S radiation transmission model can be used for calculating the optical thicknesses of the aerosol under different optical thicknesses by assuming corresponding atmospheric mode and aerosol model
Figure P_220719102404179_179836002
T, S parameter values. Thus, by simulating different gasesAnd (5) carrying out radiation transmission under the optical thickness value of the sol, operating a 6S program to sequentially extract corresponding parameter results, and constructing a lookup table.
In this embodiment, observing the geometric parameters includes: a solar zenith angle, a satellite zenith angle and a relative azimuth angle; the method specifically comprises the following steps:
obtaining the surface reflectivity of the pixel by using the observation geometric parameters of the pixel and a pre-established surface reflectivity linear equation; the independent variable of the surface reflectivity linear equation is the surface reflectivity of the MODIS sensor, and the dependent variable is the surface reflectivity of the FY4A AGRI sensor;
by utilizing the observation geometric parameters of the pixel, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to the optical thicknesses of a plurality of simulated aerosols are obtained by inquiring in a pre-established lookup table;
and calculating to obtain the apparent reflectivity of a plurality of simulated aerosol optical thicknesses corresponding to the second blue light wave band by utilizing the earth surface reflectivity, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path of the pixel.
The fitting step of the surface reflectivity linear equation comprises the following steps:
the method comprises the steps of carrying out AOD inversion by using a blue light wave band of FY4A AGRI sensor data, constructing earth surface reflectivity data by using MODIS earth surface reflectivity product MOD09A1 data, fitting an earth surface reflectivity linear formula, synthesizing the earth surface reflectivity product by using 8 days, aiming at eliminating the influence of cloud coverage and the influence of instantaneous change of the earth surface reflectivity, and ensuring that the remote sensing image period is short and the earth surface reflectivity at the same position in a short time can be almost seen as constant.
Due to the difference of the wave band response functions of the FY4A AGRI sensor and the MODIS in the blue light wave band, the following surface reflectivity linear formula is needed to be adopted for channel spectral response difference correction:
Figure P_220719102404211_211085001
（7）
wherein the content of the first and second substances,
Figure P_220719102404242_242358001
and
Figure P_220719102404257_257976002
the earth surface reflectivity is respectively observed by blue light wave bands of an FY4A AGRI sensor and an MODIS sensor; coefficient of performance
Figure P_220719102404293_293595003
And
Figure P_220719102404324_324873004
and fitting by combining the ASD measured data with the channel spectral responses of the two sensors. In the coefficient
Figure M_220719102404356_356120001
And
Figure M_220719102404387_387345002
and substituting the MODIS 8-day synthetic surface reflectivity to obtain the surface reflectivity corresponding to the blue light band of the FY4A AGRI sensor.
Figure P_220719102404402_402992001
（8）
Figure P_220719102404434_434228001
（9）
In the above two formulas,
Figure P_220719102404483_483086001
and
Figure P_220719102404498_498707002
FY4A AGRI sensor and MODIS sensor respectively
Figure P_220719102404529_529922003
Spectral response at wavelength;
Figure P_220719102404561_561181004
and
Figure P_220719102404592_592428005
wavelength intervals used for spectral calibration of the two sensors are respectively adopted;
Figure P_220719102404623_623693006
is a first
Figure P_220719102404654_654940007
The ground features are planted in
Figure P_220719102404672_672044008
The feature comprising: grasslands, floor tiles, bodies of water, cement, and asphalt; the ASD measurement is obtained by utilizing a portable surface feature spectrometer.
After the FY4A AGRI and MODIS blue light wave band reflectivity corresponding to different ground objects are fitted by using the formulas (8) and (9), the correction coefficient of the channel spectral response difference can be obtained by regression by using the formula (7)
Figure P_220719102404703_703767001
And
Figure P_220719102404735_735015002
。
in the embodiment, the observation geometric parameters of the pixel and a pre-established surface reflectivity linear equation are utilized to obtain the surface reflectivity of the pixel; the method comprises the following steps:
acquiring the solar zenith angle of MODIS product data corresponding to the solar zenith angle of the pixel;
acquiring a satellite zenith angle of MODIS product data corresponding to the satellite zenith angle of the pixel;
obtaining a relative azimuth angle of MODIS product data corresponding to the relative azimuth angle of the pixel;
acquiring the earth surface reflectivity of the MODIS product data corresponding to the solar zenith angle, the satellite zenith angle and the relative azimuth angle of the MODIS product data;
and substituting the earth surface reflectivity of the MODIS product data as an independent variable into an earth surface reflectivity linear equation to obtain the earth surface reflectivity of the pixel.
In the embodiment, by using the observation geometric parameters of the pixel, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to a plurality of simulated aerosol optical thicknesses are obtained by inquiring in a pre-established lookup table; the method comprises the following steps:
for each aerosol optical thickness in the look-up table, performing the following steps:
acquiring two adjacent solar zenith angles of the pixel; acquiring two satellite zenith angles of the satellite of the pixel, which are adjacent in a lookup table; acquiring two adjacent relative azimuth angles of the relative azimuth angle of the pixel in a lookup table;
combining the two solar zenith angles, the two satellite zenith angles and the two relative azimuth angles to obtain 8 groups of combinations of the solar zenith angles, the satellite zenith angles and the relative azimuth angles;
8 hemispherical reflectivities corresponding to 8 combinations, 8 atmospheric transmittance and 8 equivalent reflectivities of atmospheric radiation along an atmospheric transmission path are obtained through a lookup table;
calculating the average value of the 8 hemispherical reflectances as the hemispherical reflectivity of the pixel element;
calculating the average value of the 8 atmospheric transmittance as the atmospheric transmittance of the pixel;
calculating the equivalent reflectivity of 8 atmospheric radiations along the atmospheric transmission path, and taking the equivalent reflectivity as the equivalent reflectivity of the atmospheric radiations of the pixel along the atmospheric transmission path;
thus obtaining the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to a plurality of aerosol optical thicknesses.
Step 104: fitting to obtain a linear equation of each pixel by utilizing the first blue light band apparent reflectivity of the pixel and the second blue light band apparent reflectivity corresponding to a plurality of simulated aerosol optical thicknesses, wherein the linear equation takes the blue light band apparent reflectivity as an independent variable and the aerosol optical thickness as a dependent variable;
specifically, the absolute value of the difference between the apparent reflectivity of the first blue light band of the pixel and the apparent reflectivity of each second blue light band is calculated, and the two second blue light band apparent reflectivities corresponding to the smallest sum and the second smallest in the calculation result and the corresponding optical thickness of the aerosol are obtained; and fitting the coefficient of the linear equation of each pixel by using the apparent reflectivity of the two second blue light bands of each pixel and the corresponding optical thickness of the aerosol.
Step 105: and calculating the optical thickness of the aerosol of each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation.
And substituting the apparent reflectivity of the first blue light band of each pixel into the linear equation in the step 104 to obtain the optical thickness of the aerosol of each pixel.
Based on the foregoing embodiments, an aerosol optical thickness inversion apparatus is provided in the embodiments of the present application, and referring to fig. 2, an aerosol optical thickness inversion apparatus 200 provided in the embodiments of the present application at least includes:
the obtaining unit 201 is configured to obtain a remote sensing image of an FY4A AGRI sensor, and obtain an observation geometric parameter and blue light data of each pixel from the remote sensing image;
the first calculating unit 202 is configured to obtain a first blue light band apparent reflectivity of each pixel based on the blue light data of each pixel by using a linear equation of the blue light data and the blue light band apparent reflectivity, which are established in advance;
the second calculating unit 203 is configured to calculate, by using the observation geometric parameters of the pixels and a pre-established lookup table, the apparent reflectivity of a second blue light band corresponding to the optical thicknesses of the plurality of simulated aerosols of each pixel by a theoretical simulation method;
the fitting unit 204 is configured to obtain a linear equation of each pixel by fitting the first blue light band apparent reflectivity of the pixel and the second blue light band apparent reflectivity corresponding to the plurality of simulated aerosol optical thicknesses, where the linear equation uses the blue light band apparent reflectivity as an independent variable and the aerosol optical thickness as a dependent variable;
and the third calculating unit 205 is configured to calculate the optical thickness of the aerosol for each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation.
It should be noted that the principle of the aerosol optical thickness inversion apparatus 200 provided in the embodiment of the present application for solving the technical problem is similar to that of the aerosol optical thickness inversion method provided in the embodiment of the present application, and therefore, the implementation of the aerosol optical thickness inversion apparatus 200 provided in the embodiment of the present application can refer to the implementation of the aerosol optical thickness inversion method provided in the embodiment of the present application, and repeated details are not repeated.
As shown in fig. 3, an electronic device 300 provided in the embodiment of the present application at least includes: a processor 301, a memory 302 and a computer program stored on the memory 302 and executable on the processor 301, the processor 301 implementing the aerosol optical thickness inversion method provided by the embodiments of the present application when executing the computer program.
The electronic device 300 provided by the embodiment of the present application may further include a bus 303 connecting different components (including the processor 301 and the memory 302). Bus 303 represents one or more of any of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.
The Memory 302 may include readable media in the form of volatile Memory, such as Random Access Memory (RAM) 3021 and/or cache Memory 3022, and may further include Read Only Memory (ROM) 3023.
The memory 302 may also include a program tool 3024 having a set (at least one) of program modules 3025, the program modules 3025 including, but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboard, remote control, etc.), with one or more devices that enable a user to interact with the electronic device 300 (e.g., cell phone, computer, etc.), and/or with any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., router, modem, etc.). Such communication may be through an Input/Output (I/O) interface 305. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network, such as the internet) via the Network adapter 306. As shown in FIG. 3, the network adapter 306 communicates with the other modules of the electronic device 300 via the bus 303. It should be understood that although not shown in FIG. 3, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, Redundant processors, external disk drive Arrays, disk array (RAID) subsystems, tape drives, and data backup storage subsystems, to name a few.
It should be noted that the electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
Embodiments of the present application also provide a computer-readable storage medium storing computer instructions, which when executed by a processor, implement the aerosol optical thickness inversion method provided by embodiments of the present application.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. An aerosol optical thickness inversion method, comprising:
acquiring a remote sensing image of an FY4A AGRI sensor, and acquiring observation geometric parameters and blue light data of each pixel from the remote sensing image;
obtaining a first blue light band apparent reflectivity of each pixel based on the blue light data of each pixel by using a pre-established linear equation of the blue light data and the blue light band apparent reflectivity;
calculating the apparent reflectivity of a second blue light wave band corresponding to a plurality of simulated aerosol optical thicknesses of each pixel by using the observation geometric parameters of the pixels and a pre-established lookup table through a theoretical simulation method;
fitting to obtain a linear equation of each pixel by utilizing the first blue light band apparent reflectivity of the pixel and the second blue light band apparent reflectivity corresponding to a plurality of simulated aerosol optical thicknesses, wherein the linear equation takes the blue light band apparent reflectivity as an independent variable and the aerosol optical thickness as a dependent variable;
and calculating the optical thickness of the aerosol of each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation.
2. The aerosol optical thickness inversion method of claim 1, wherein the fitting step of the linear equation of the blue light data and the apparent reflectance of the blue light band comprises:
acquiring time-space matched FY4A AGRI sensor data and MODIS product data;
acquiring a satellite zenith angle of MODIS product data corresponding to the satellite zenith angle in the FY4A AGRI sensor data;
acquiring a relative azimuth angle of MODIS product data corresponding to the relative azimuth angle in the FY4A AGRI sensor data;
obtaining the apparent reflectivity of a blue light wave band corresponding to a satellite zenith angle and a relative azimuth angle of MODIS product data;
fitting coefficients of linear equations of blue light data and blue light band apparent reflectivity by using blue light data in FY4A AGRI sensor data and blue light band apparent reflectivity of corresponding MODIS product data
Figure P_220719102358118_118823001
And
Figure P_220719102358150_150042002
the linear equation is:
Figure P_220719102358181_181290001
wherein the content of the first and second substances,
Figure P_220719102358212_212545001
in the case of blue-ray data,
Figure P_220719102358243_243794002
the apparent reflectivity of the blue light wave band is a dependent variable,
Figure P_220719102358259_259439003
and
Figure P_220719102358279_279416004
respectively representing the reflectivity gain amount and the reflectivity offset amount of the blue light band.
3. The aerosol optical thickness inversion method of claim 1, wherein the data entries of the lookup table comprise: hemispherical reflectivity, atmospheric transmittance, equivalent reflectivity of atmospheric radiation along an atmospheric transmission path, a solar zenith angle, a satellite zenith angle, a relative azimuth angle and aerosol optical thickness; wherein, 9 values of the solar zenith angle include: 0, 10, 20, 30, 40, 50, 60, 70 and 80; the 9 values of the satellite zenith angle include: 0, 10, 20, 30, 40, 50, 60, 70 and 80; the 19 values of the relative azimuth include: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170 and 180.
4. The aerosol optical thickness inversion method of claim 3, wherein observing geometric parameters comprises: a solar zenith angle, a satellite zenith angle and a relative azimuth angle; calculating the apparent reflectivity of a second blue light wave band corresponding to a plurality of simulated aerosol optical thicknesses of each pixel by using the observation geometric parameters of the pixels and a pre-established lookup table through a theoretical simulation method; the method comprises the following steps:
obtaining the surface reflectivity of the pixel by using the observation geometric parameters of the pixel and a pre-established surface reflectivity linear equation; the independent variable of the surface reflectivity linear equation is the surface reflectivity of the MODIS sensor, and the dependent variable is the surface reflectivity of the FY4A AGRI sensor;
by utilizing the observation geometric parameters of the pixel, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to the optical thicknesses of a plurality of simulated aerosols are obtained by inquiring in a pre-established lookup table;
and calculating to obtain the apparent reflectivity of a plurality of simulated aerosol optical thicknesses corresponding to the second blue light wave band by utilizing the earth surface reflectivity, the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path of the pixel.
5. The aerosol optical thickness inversion method of claim 4, wherein the fitting step of the surface reflectance linear equation comprises:
respectively calculating the earth surface reflectivity observed by the FY4A AGRI sensor and the MODIS sensor in the blue light wave band
Figure P_220719102358311_311183001
And
Figure P_220719102358342_342439002
：
Figure P_220719102358358_358056001
Figure P_220719102358389_389296001
wherein the content of the first and second substances,
Figure P_220719102358420_420557001
and
Figure P_220719102358451_451804002
FY4A AGRI sensor and MODIS sensor respectively
Figure P_220719102358469_469842003
Spectral response at wavelength;
Figure P_220719102358485_485984004
and
Figure P_220719102358517_517246005
wavelength intervals used for spectral calibration of the two sensors are respectively adopted;
Figure P_220719102358532_532848006
is as follows
Figure P_220719102358564_564115007
The ground features are planted in
Figure P_220719102358579_579747008
The feature comprising: grasslands, floor tiles, water bodies, cement and asphalt;
using a plurality of
Figure P_220719102358611_611033001
And
Figure P_220719102358642_642290002
fitting a surface reflectivity linear formula:
Figure P_220719102358657_657881001
wherein the content of the first and second substances,
Figure P_220719102358694_694032001
and
Figure P_220719102358725_725271002
are fitting coefficients.
6. The aerosol optical thickness inversion method of claim 5, wherein the surface reflectivity of the pixel is obtained by using the observation geometric parameters of the pixel and a pre-established surface reflectivity linear equation; the method comprises the following steps:
acquiring the solar zenith angle of MODIS product data corresponding to the solar zenith angle of the pixel;
acquiring a satellite zenith angle of MODIS product data corresponding to the satellite zenith angle of the pixel;
obtaining a relative azimuth angle of MODIS product data corresponding to the relative azimuth angle of the pixel;
acquiring the earth surface reflectivity of the MODIS product data corresponding to the solar zenith angle, the satellite zenith angle and the relative azimuth angle of the MODIS product data;
and substituting the earth surface reflectivity of the MODIS product data as an independent variable into an earth surface reflectivity linear equation to obtain the earth surface reflectivity of the pixel.
7. The aerosol optical thickness inversion method of claim 5, wherein the obtaining of the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to the plurality of aerosol optical thicknesses by querying in a pre-established lookup table using the observation geometric parameters of the pixel comprises:
for each aerosol optical thickness in the look-up table, performing the following steps:
acquiring two adjacent solar zenith angles of the pixel; acquiring two satellite zenith angles of the satellite of the pixel, which are adjacent in a lookup table; acquiring two adjacent relative azimuth angles of the relative azimuth angle of the pixel in a lookup table;
combining the two solar zenith angles, the two satellite zenith angles and the two relative azimuth angles to obtain 8 groups of combinations of the solar zenith angles, the satellite zenith angles and the relative azimuth angles;
8 hemispherical reflectivities corresponding to 8 combinations, 8 atmospheric transmittance and 8 equivalent reflectivities of atmospheric radiation along an atmospheric transmission path are obtained through a lookup table;
calculating the average value of the 8 hemispherical reflectances as the hemispherical reflectivity of the pixel element;
calculating the average value of the 8 atmospheric transmittance as the atmospheric transmittance of the pixel;
calculating the equivalent reflectivity of 8 atmospheric radiations along an atmospheric transmission path, and taking the equivalent reflectivity as the equivalent reflectivity of the atmospheric radiation of the pixel along the atmospheric transmission path;
thus obtaining the hemispherical reflectivity, the atmospheric transmittance and the equivalent reflectivity of atmospheric radiation along the atmospheric transmission path corresponding to a plurality of aerosol optical thicknesses.
8. The aerosol optical thickness inversion method according to claim 7, wherein a linear equation of each pixel is obtained by fitting a first blue light band apparent reflectivity of the pixel and a second blue light band apparent reflectivity corresponding to a plurality of simulated aerosol optical thicknesses; the method comprises the following steps:
calculating the absolute value of the difference between the apparent reflectivity of the first blue light band of the pixel and the apparent reflectivity of each second blue light band, and acquiring the two second blue light band apparent reflectivities corresponding to the minimum and the second smallest in the calculation result and the corresponding optical thickness of the aerosol;
and fitting the coefficient of the linear equation of each pixel by using the apparent reflectivity of the two second blue light bands of each pixel and the corresponding optical thickness of the aerosol.
9. An aerosol optical thickness inversion apparatus, comprising:
the acquisition unit is used for acquiring a remote sensing image of the FY4A AGRI sensor and acquiring observation geometric parameters and blue light data of each pixel from the remote sensing image;
the first calculation unit is used for obtaining the first blue light band apparent reflectivity of each pixel based on the blue light data of each pixel by using the pre-established linear equation of the blue light data and the blue light band apparent reflectivity;
the second calculation unit is used for calculating the apparent reflectivity of a second blue light wave band corresponding to the optical thicknesses of the simulated aerosols of each pixel by a theoretical simulation method by using the observation geometric parameters of the pixels and a pre-established lookup table;
the fitting unit is used for fitting to obtain a linear equation of each pixel by utilizing the first blue light band apparent reflectivity of the pixel and the second blue light band apparent reflectivity corresponding to the plurality of simulated aerosol optical thicknesses, wherein the linear equation takes the blue light band apparent reflectivity as an independent variable and the aerosol optical thickness as a dependent variable;
and the third calculating unit is used for calculating the optical thickness of the aerosol of each pixel based on the apparent reflectivity of the first blue light band of each pixel and a corresponding linear equation.
10. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the aerosol optical thickness inversion method of any one of claims 1-8.
CN202210894502.8A 2022-07-28 2022-07-28 Aerosol optical thickness inversion method and device Pending CN114970214A (en)

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CN117436286A (en) * 2023-12-20 2024-01-23 中国科学院合肥物质科学研究院 Aerosol and water vapor inversion and aerial remote sensing image correction method, device and equipment

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