CN116660106A - Aerosol parameter iterative inversion method for collaborative satellite-borne scalar and polarization observation data - Google Patents
Aerosol parameter iterative inversion method for collaborative satellite-borne scalar and polarization observation data Download PDFInfo
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Abstract
The invention provides an aerosol parameter iterative inversion method for collaborative satellite-borne scalar and polarization observation data. According to the method, based on aerosol scalar and polarization collaborative observation information, a fine particle polarization contribution quantization model is constructed, aerosol coarse and fine particle contributions in apparent reflectivity are decomposed and quantized, constraint is constructed, iteration optimization is conducted on the composition of coarse and fine particles in the aerosol model, and high-precision inversion of the total optical thickness and the fine particle ratio of the aerosol is achieved. The invention combines the two inversion methods, is beneficial to ground gas decoupling and weakening the preset deviation of the aerosol model, and improves the efficiency and the precision of parameter inversion.
Description
Technical Field
The invention relates to the technical field of aerosol measurement, in particular to an aerosol parameter iterative inversion method for collaborative satellite-borne scalar and polarization observation data.
Background
The atmospheric aerosol refers to a multiphase system formed by solid and liquid particles suspended in the atmosphere and a carrier thereof, and the multiphase system directly affects climate change and human production and life. The absorption and scattering effects of aerosols on solar radiation and ground long wave radiation affect radiation balance; and the optical characteristics and the maintenance time of the cloud are changed by interaction with the cloud, so that precipitation is indirectly influenced, and the climate is further influenced. In addition, acid rain formation, haze weather appearance and ozone layer destruction are closely related to the pollution harmful characteristics of the aerosol. The increase of the content of the aerosol in the atmosphere can lead to the problems of reduced atmospheric visibility, aggravated environmental pollution, infringement of human health and the like. However, aerosol sources are variable, components are complex, spatial and temporal distribution is uneven, and quantitative assessment of climate effect and environmental effect is extremely difficult. Therefore, based on the existing detection means, the aerosol is quantitatively and accurately monitored, and necessary basic research data can be provided for multiple fields such as climate, environment and the like.
The traditional ground station monitoring method is difficult to meet the requirements of various fields on large-scale aerosol real-time dynamic monitoring information. Passive satellite detection is considered currently the most effective method of acquiring global aerosol space-time variations. Considering the particle phase information and radiation characteristics contained in the polarization characteristics of atmospheric particle scattering, polarization observations are often introduced in current aerosol detectors to optimize the acquisition of aerosol information. The most representative of the internationally-based passive aerosol sensors is the POLDER series developed in france and the 3MI series developed in the european aerospace agency. Along with the increasing demand of aerosol information in multiple fields, a plurality of star carrier gas sol passive sensors in China are put into operation in recent years, including multi-angle polarization imaging instruments (DPC) carried on GF-5 series satellites, atmospheric environment monitoring satellites and carbon monitoring satellites, polarization scanning atmospheric correction instruments (PSAC) of HJ-2 series satellites and the like. The inversion aerosol method based on scalar observation information is mature, but ground gas decoupling is difficult because ground surface reflectivity is greatly influenced by ground surface in scalar detection and ground surface contribution is larger than atmosphere contribution. And scalar information has insufficient sensitivity to aerosol particle morphological characteristics, and model preset correctness influences aerosol inversion accuracy. The earth surface polarization signal contained in the polarization information is weak and has a small variation range, so that the problem of earth gas decoupling is solved; simultaneously, the sensitivity of the polarization signal to the morphology of the aerosol particles can also effectively improve the evaluation capability of the complex aerosol model. However, polarization information is less sensitive to coarse particles, and the contribution of coarse particles to polarization reflectivity is often ignored in practical inversion for the acquisition of aerosol fine particle optical thickness.
From the above, the introduction of the assumption conditions in the inversion process brings errors to the inversion accuracy of the aerosol optical thickness and the aerosol fine particle optical thickness, respectively, wherein the assumption of the aerosol model coarse and fine particles is an important factor affecting the model accuracy. The aerosol inversion method based on scalar information and polarization information inversion has advantages and emphasis, and can provide a new thought for optimizing the accuracy of an aerosol model and acquiring aerosol multi-element parameters. However, in the case of identifying the composition of coarse and fine particles by the constraint of scalar and polarization channel observation information, the effect between the coarse and fine particles needs to be considered because the contribution of mixed coarse and fine particles to apparent reflectance and the sum of the contributions when coarse and fine particles exist alone are greatly different.
In summary, the inversion process in the prior art has the following drawbacks:
1. the contribution of aerosol coarse particles is directly ignored in polarization inversion, and a larger error is brought to fine particle optical thickness inversion. To obtain a high precision aerosol fine particle optical thickness product must address the coarse mode aerosol particle contribution problem.
2. The contribution of the mixed coarse and fine particles to the apparent reflectivity/apparent polarized reflectivity is greatly different from the sum of the contributions of the coarse and fine particles alone. To obtain a high precision fine particle ratio, a precise zone of coarse-fine particle contribution is required.
3. The scalar inversion and polarization inversion aerosol parameter methods in the prior art have advantages and emphasis, generally utilize aerosol information corresponding to independent scalar channels and polarization channels, and do not consider mutual constraint of scalar and polarization signals.
Disclosure of Invention
In order to solve the technical problems, the invention provides an aerosol parameter iterative inversion method for collaborative satellite-borne scalar and polarization observation data, which is an inversion method for constructing constraints to iteratively optimize the composition of coarse and fine particles in an aerosol model based on aerosol scalar and polarization collaborative observation information and by deeply mining apparent reflectivity/apparent polarization reflectivity contribution difference of aerosol coarse and fine particles to form aerosol fine particle ratio and total optical thickness of collaborative scalar and polarization observation information.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an aerosol parameter iterative inversion method for collaborative satellite-borne scalar and polarization observation data comprises the following steps:
s1: calculating the earth surface contribution in the satellite-borne scalar observation data, and inverting to obtain the total optical thickness of the aerosol by combining an aerosol model according to local information assumption based on a scalar lookup table for aerosol parameter inversion of scalar observation;
s2: separating earth surface contribution in satellite-borne polarization observation data to obtain the polarization reflectivity of the atmosphere, inverting the optical thickness of aerosol fine particles based on a polarization lookup table for aerosol parameter inversion of polarization observation;
s3: calculating aerosol fine particle ratio by adopting total aerosol optical thickness and aerosol fine particle optical thickness, optimizing an aerosol model, and correcting atmospheric polarization reflectivity of the fine particle aerosol based on a constructed fine particle polarization contribution quantization model;
s4: repeating the steps S1-S3 until the difference between the aerosol fine particle ratio calculated in the step S3 and the aerosol fine particle ratio of the aerosol model in the step S1 reaches a threshold value, namely obtaining the optimal total optical thickness of the aerosol and the aerosol fine particle ratio information, thereby realizing the aerosol multi-parameter inversion model construction of scalar and polarization collaborative observation information.
Further, in the process of establishing the scalar lookup table in the step S1 and the polarization lookup table in the step S2, the aerosol model comprises a combination of particles with different thicknesses, the observation geometric parameters comprise solar zenith angles, observation zenith angles and relative azimuth angles, and the channels comprise 490nm, 670nm and 865nm wave bands.
Further, the step S1 adopts a scalar inversion method, specifically: solving the surface scalar contribution based on an empirical model method, and searching the atmospheric scalar contribution of different aerosol optical thicknesses of different aerosol models under the observation geometric condition of the remote sensing image by utilizing a scalar lookup table so as to obtain the simulated apparent reflectivity; comparing the simulated values of the apparent reflectances of the different aerosol optical thicknesses with the true values to obtain the total aerosol optical thickness closest to the true values;
the S2 adopts a polarization inversion method, and specifically comprises the following steps: solving earth surface polarization contribution based on an empirical model method, and searching atmospheric polarization contribution of different aerosol optical thicknesses of aerosol models of different fine particles under the observation geometric condition of a remote sensing image by utilizing a polarization lookup table, so as to obtain simulated apparent polarization reflectivity; and comparing the simulation value of apparent polarization reflectivity of different aerosol optical thicknesses with a true value to obtain the aerosol fine particle optical thickness closest to the true value.
Further, the specific method for constructing the fine particle polarization contribution quantization model in S3 is as follows:
and acquiring the atmospheric polarization contribution duty ratio of the aerosol fine particles with different combinations under different observation conditions by using a 6SV model, thereby realizing the correction of the aerosol fine particle polarization contribution.
Further, the step S4 includes:
firstly, judging according to iteration conditions: if the iteration condition is not met, S3, adjusting an aerosol model in scalar inversion according to the latest aerosol fine particle ratio, and continuing scalar inversion in S1 to obtain the total optical thickness of the iterated aerosol; meanwhile, the corrected aerosol fine particle polarization contribution is obtained by using a fine particle polarization contribution quantization model, the corrected aerosol fine particle polarization contribution is calculated to be used as a true value which is received by a satellite and only comprises the aerosol fine particle polarization contribution, and polarization inversion in S2 is carried out again to obtain the optical thickness of the iterated aerosol fine particle, so that the iteration of the aerosol fine particle ratio is realized; and if the iteration condition is met, the corresponding aerosol fine particle ratio and the total aerosol optical thickness are the aerosol parameter inversion result of the observed data.
The beneficial effects are that:
1. the method provided by the invention considers the contribution of coarse particles in polarization inversion, accurately distinguishes the contribution of coarse particles by constructing a coarse-fine particle polarization contribution quantization model, and avoids the problem of poor polarization inversion precision caused by neglecting coarse-mode aerosol particles in aerosol fine-particle optical thickness inversion.
2. The invention considers the mutual constraint between the scalar and polarization observation data of the multiple channels, and realizes the iterative acquisition of the high-precision aerosol fine particle ratio and the total optical thickness of the aerosol by continuously adjusting the fine particle ratio of the scalar inversion aerosol model and the contribution of the polarization inversion aerosol fine particles.
3. Compared with the traditional scalar and polarization inversion methods, the method combines the two inversion methods, is favorable for decoupling earth gas and weakening the preset deviation of the aerosol model, and improves the efficiency and the precision of parameter inversion.
Drawings
FIG. 1 is a flow chart of an aerosol parameter iterative inversion method of collaborative satellite borne scalar and polarization observation data of the present invention;
FIG. 2 is a scalar inversion flow chart;
FIG. 3 is a polarization inversion flow chart;
FIG. 4 is a flow chart of a fine particle aerosol atmospheric polarization reflectivity contribution correction model;
fig. 5 is an iterative optimization flow chart.
Detailed Description
The invention mainly aims to provide an atmospheric aerosol optical thickness and fine particle ratio iterative inversion method for synergized scalar and polarized satellite observation data. According to the method, based on aerosol scalar and polarization collaborative observation information, a fine particle polarization contribution quantization model is constructed, aerosol coarse and fine particle contributions in apparent reflectivity are decomposed and quantized, constraint is constructed, iteration optimization is conducted on the composition of coarse and fine particles in the aerosol model, and high-precision inversion of the total optical thickness and the fine particle ratio of the aerosol is achieved. The following diagram is a technical scheme of the invention.
As shown in fig. 1, the method for iterative inversion of the atmospheric aerosol optical thickness and fine particle ratio of the synergic scalar and polarized satellite observation data comprises the following steps:
s1: the method comprises the steps of calculating the surface scalar contribution in the satellite-borne scalar observation data, inverting the scalar lookup table based on aerosol parameter inversion of scalar observation, and inverting to obtain the total optical thickness of the aerosol by combining an aerosol model according to local information hypothesis.
S2: and separating the earth surface polarization contribution in the satellite-borne polarization observation data to obtain the polarization reflectivity of the atmosphere. The aerosol fine particle optical thickness is inverted by a polarization lookup table based on aerosol parameter inversion of polarization observation.
S3: and optimizing an aerosol model by adopting the aerosol total optical thickness and the aerosol fine particle ratio calculated by the aerosol fine particle optical thickness, and correcting the atmospheric polarization reflectivity of the fine particle aerosol based on the constructed fine particle polarization contribution quantization model.
S4: repeating the steps S1-S3 until the difference between the aerosol fine particle ratio calculated in the step S3 and the aerosol fine particle ratio of the aerosol model in the step S1 reaches a threshold value, and obtaining the optimal total optical thickness and aerosol fine particle ratio information, thereby realizing the aerosol multi-parameter inversion model construction of scalar and polarization collaborative observation information.
Specifically, the S1 includes:
(1) Scalar inversion principle:
assuming a uniform lambertian surface, regardless of gas absorption, the apparent reflectivity received by the satellite is expressed as:
，
wherein:、/>、/>respectively representing the zenith angle of the sun, the zenith angle observed by the satellite and the relative azimuth angle,representing the equivalent reflectivity of the large air path radiation item, < ->Is the atmospheric transmittance->Is the reflectivity of the earth surface>Is the albedo of the atmosphere downwards.
In scalar remote sensing inversion, the apparent reflectivity received by the satellite comprises two parts, namely atmospheric contribution and earth surface contribution. A key problem of aerosol inversion is therefore ground gas decoupling. As shown in fig. 2, a plurality of groups of atmospheric scalar radiation contributions are obtained by interpolation in a scalar lookup table based on geometric observation angles of remote sensing data, a plurality of groups of simulated apparent reflectivities are obtained by combining calculated earth surface reflectivities, simulated values of the apparent reflectivities are compared with actual values, a group of closest simulated values are selected by using an evaluation function, and the total optical thickness of aerosol corresponding to the simulated values is an inversion result.
(2) Scalar lookup table establishment:
the invention uses a scalar lookup table method to realize aerosol optical thickness inversion. The vector radiation transmission model 6SV is selected for the establishment of the scalar lookup table, and is developed by French atmospheric optics laboratory, and the vector radiation transmission equation is solved by using a primary and secondary scattering method, so that the vector radiation correction model is one of the well-developed atmospheric radiation correction models at present. By loop call to 6SV, set the variables: the channel, aerosol model, observation geometry, aerosol optical thickness, read radiation information and save, generate scalar lookup table.
The scalar lookup table 6SV model parameters are specifically set as follows:
a) Solar zenith angle: the value range of the zenith angle of the sun is 0-80 degrees, and the interval is 6 degrees.
b) The sensor observes zenith angle: the value range of the zenith angle observed by the sensor is 0-80 degrees, and the interval is 6 degrees.
c) Relative azimuth angle: the range of the relative azimuth angle of the sun and the sensor is 0-180 degrees, and the interval is 12 degrees.
d) Aerosol model: inputting custom coarse and fine particle aerosol model data meeting the bimodal normal distribution.
e) Aerosol optical thickness: the optical thickness of the aerosol at 550nm is input and is respectively 0.01, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5 and 2.0.
f) Observation channel: the satellite channels select 490nm and 670nm channels.
g) Surface reflectance: the reflectance was set to 0 regardless of the surface effect.
(3) Scalar surface reflectivity estimation method:
the estimation of the surface reflectivity is a key element of aerosol optical thickness inversion. The invention uses von Hoyningen-hue and other improved semi-empirical vegetation and bare soil linear combination models, scalar estimates the surface reflectivity to obtain scalar surface reflectivityThe calculation formula is as follows:
，
，
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the reflectivity of the near infrared band, ">Indicating red band reflectivity +.>Is an empirical weight coefficient +.>For normalizing vegetation index, < >>Is the wavelength. />、/>The spectral reflectivities of vegetation and bare soil are respectively represented, and corresponding data can be obtained through statistics of ground object reflection spectrum library data.
The step S2 comprises the following steps:
(1) Polarization inversion principle:
polarization is an important feature of electromagnetic waves. The physical characteristics of an atmospheric aerosol are closely related to its polarization characteristics and changes. In polarization remote sensing inversion, linear polarization is generally considered. The apparent polarization reflectivity received by a satellite can be expressed as:
，
in the method, in the process of the invention,、/>representing Stokes vector parameters, +.>Representing the sun zenith angle cosine value +.>Representing the solar constant. Watch (watch)The apparent polarization reflectivity includes two parts, atmospheric contribution and surface contribution, expressed as:
，
in the method, in the process of the invention,is atmospheric polarization reflectivity, < >>Is earth surface polarized reflectivity->Is the mass number of the atmosphere and->Is the optical thickness of atmospheric molecules, < >>Is the total optical thickness of the aerosol. />For the empirical factor, 0.5 is generally taken. Wherein the mass number of the atmosphere and the optical thickness of the atmosphere molecules can be calculated by an empirical formula:
，
，
in the method, in the process of the invention,representing the zenith angle of the sun>Indicate wavelength, & lt + & gt>Is the surface air pressure value.
Polarization inversion uses a 6SV established polarization look-up table to invert the aerosol fine particle optical thickness against the polarization reflectivity received by the actual satellite. The specific process is shown in fig. 3 and is substantially the same as the scalar inversion process. And obtaining a plurality of groups of simulated atmospheric polarization reflectivity values through interpolation of a scalar lookup table, subtracting the earth surface polarization contribution from the apparent polarization reflectivity received by the satellite to obtain a real atmospheric polarization reflectivity value, and determining a final aerosol fine particle optical thickness inversion result through an evaluation function.
(2) And (3) establishing a polarization lookup table:
the invention uses a polarization lookup table method to realize aerosol fine particle optical thickness inversion. The polarization look-up table is identical to the scalar look-up table construction method, and the variables are still set to: observation channel, aerosol model, solar zenith angle, observation zenith angle, relative azimuth angle, aerosol optical thickness. Except that the aerosol model needs to be set into a unimodal normal distribution of fine particle types, and the observation channels are 670nm and 865nm. And reading and storing the radiation information through circular calling of 6SV, and generating a polarization lookup table.
(3) The surface polarization reflectivity estimation method comprises the following steps:
the surface contribution is small in polarization inversion. It is generally believed that the surface polarization reflectivity does not change with wavelength. Surface polarization reflectance was estimated using semi-empirical models of Nadal and Breon:
，
，
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the reflectivity of the near infrared band, ">Indicating red band reflectivity +.>Is Fresnel scattering coefficient->、/>Representing the sun zenith angle cosine value and the observed zenith angle cosine value. />、/>Is an empirical coefficient, is obtained by normalizing vegetation index->And the surface coverage type, the specific parameter values are shown in table 1. And introducing an IGBP earth surface classification product of the MODIS-MCDQ1, performing spatial position matching of different data sources, and judging the earth surface type. The earth surface types are divided into four types of forests, shrubs, low vegetation and deserts. And obtaining different model parameters according to the earth surface type to calculate the earth surface polarization reflectivity value.
，
In the method, in the process of the invention,the refractive index is generally 1.5./>Representing the angle of refraction +.>Cosine value of>Representing incident angleThe cosine values of (2) are calculated as follows:
，
，
，
wherein, the liquid crystal display device comprises a liquid crystal display device,is a scattering angle>、/>、/>The solar zenith angle, the observation zenith angle and the relative azimuth angle are respectively adopted.
TABLE 1 model empirical parameter table
，
The step S3 comprises the following steps:
the invention aims to simulate the atmospheric contribution data of coarse particle, coarse particle and fine particle only of different input parameters by using a 6SV radiation transmission model, so as to realize the construction of a correction model of atmospheric polarization reflectivity contribution of fine particle aerosol. As shown in fig. 4, polarized atmospheric contributions at different geometric observations, different fine particle ratios, and different aerosol optical thicknesses were simulated for different aerosol types. The three atmosphere radiation parameter data of coarse and fine particle combination, fine particle only and coarse particle only obtained by simulation are the original data.
The parameters of 6SV are specifically set as follows:
a) Solar zenith angle: the value range of the zenith angle of the sun is 0-80 degrees, and the interval is 6 degrees.
b) Observing zenith angle: the value range of the zenith angle observed by the sensor is 0-80 degrees, and the interval is 6 degrees.
c) Relative azimuth angle: the range of the relative azimuth angle of the sun and the sensor is 0-180 degrees, and the interval is 12 degrees.
d) Aerosol model: inputting custom to meet the bimodal normal distribution to obtain the coarse and fine particle aerosol model data.
e) Aerosol optical thickness: the optical thickness of the aerosol at 550nm is input and is respectively 0.01, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5 and 2.0.
f) Observation channel: the satellite channels select 670nm and 865nm channels.
g) Fine particle ratio: the fine particle ratio was 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, respectively.
And processing the atmospheric radiation data acquired by the 6SV, removing abnormal values, and acquiring effective data. And carrying out exploration analysis based on the effective data, and establishing a lookup table according to the distribution condition of the effective data. The lookup table can find out the corresponding fine particle atmospheric contribution proportion according to the geometric observation value and the fine particle ratio, and the fine particle atmospheric polarization contribution value is accurately solved by combining the atmospheric polarization contribution.
The step S4 comprises the following steps:
aiming at the problems existing in the atmospheric aerosol parameter inversion, the invention utilizes the quantitative model of the atmospheric polarization reflectivity contribution of the fine particle aerosol to accurately solve the fine particle contribution based on scalar and polarization cooperative observation information, adjusts the aerosol fine particle ratio in the scalar inversion, and iteratively optimizes the inversion result so as to construct an aerosol multi-parameter inversion model of the scalar and polarization cooperative observation information, and the flow is shown in figure 5, and specifically comprises the following steps:
a) Scalar inversion obtains the total optical thickness of the aerosol, and polarization inversion obtains the fine particle optical thickness of the aerosol and atmospheric polarization contribution.
b) And obtaining the aerosol fine particle ratio according to the inversion result of the last step. The aerosol fine particle ratio calculation formula is expressed as:
，
in the middle ofRepresents aerosol fine particle optical thickness, +.>The optical thickness of the aerosol coarse particles is expressed, and the sum of the two is the total optical thickness of the aerosol.
c) And (3) carrying out iteration condition judgment on the inversion result, and if the inversion result does not meet the threshold condition, adjusting the aerosol fine particle ratio and carrying out scalar inversion. And simultaneously, obtaining atmospheric contribution proportion and atmospheric polarization contribution by utilizing a fine particle polarization contribution quantization model, obtaining adjusted fine particle atmospheric polarization contribution, and carrying out polarization inversion again.
d) Repeating the steps a), b) and c) until the inversion result meets a threshold value, wherein the inversion result is the final inversion result of the total optical thickness of the aerosol and the aerosol fine particle ratio.
In summary, the atmospheric aerosol optical thickness and fine particle ratio iterative inversion method of the synergic scalar and polarization satellite observation data provided by the invention can realize the acquisition of high-precision aerosol optical thickness and fine particle ratio parameters, and provides important data references for quantitative evaluation of the weather effect of aerosol and research on atmospheric environment change.
Claims (5)
1. An aerosol parameter iterative inversion method for coordinating satellite-borne scalar and polarization observation data is characterized by comprising the following steps:
s1: calculating the earth surface contribution in the satellite-borne scalar observation data, and inverting to obtain the total optical thickness of the aerosol by combining an aerosol model according to local information assumption based on a scalar lookup table for aerosol parameter inversion of scalar observation;
s2: separating earth surface contribution in satellite-borne polarization observation data to obtain the polarization reflectivity of the atmosphere, inverting the optical thickness of aerosol fine particles based on a polarization lookup table for aerosol parameter inversion of polarization observation;
s3: calculating aerosol fine particle ratio by adopting total aerosol optical thickness and aerosol fine particle optical thickness, optimizing an aerosol model, and correcting atmospheric polarization reflectivity of the fine particle aerosol based on a constructed fine particle polarization contribution quantization model;
s4: repeating the steps S1-S3 until the difference between the aerosol fine particle ratio calculated in the step S3 and the aerosol fine particle ratio of the aerosol model in the step S1 reaches a threshold value, namely obtaining the optimal total optical thickness of the aerosol and the aerosol fine particle ratio information, thereby realizing the aerosol multi-parameter inversion model construction of scalar and polarization collaborative observation information.
2. An aerosol parameter iterative inversion method for collaborative satellite borne scalar and polarization observation data according to claim 1,
in the process of establishing the scalar lookup table in the S1 and the polarization lookup table in the S2, the aerosol model comprises combinations of particles with different thicknesses, the observation geometric parameters comprise solar zenith angles, observation zenith angles and relative azimuth angles, and the channels comprise 490nm wave bands, 670nm wave bands and 865nm wave bands.
3. An aerosol parameter iterative inversion method for collaborative satellite borne scalar and polarization observation data according to claim 1,
the S1 adopts a scalar inversion method, and specifically comprises the following steps: solving the surface scalar contribution based on an empirical model method, and searching the atmospheric scalar contribution of different aerosol optical thicknesses of different aerosol models under the observation geometric condition of the remote sensing image by utilizing a scalar lookup table so as to obtain the simulated apparent reflectivity; comparing the simulated values of the apparent reflectances of the different aerosol optical thicknesses with the true values to obtain the total aerosol optical thickness closest to the true values;
the S2 adopts a polarization inversion method, and specifically comprises the following steps: solving earth surface polarization contribution based on an empirical model method, and searching atmospheric polarization contribution of different aerosol optical thicknesses of different fine particle aerosol models under the observation geometric condition of a remote sensing image by utilizing a polarization lookup table so as to obtain simulated apparent polarization reflectivity; and comparing the simulation value of apparent polarization reflectivity of different aerosol optical thicknesses with a true value to obtain the aerosol fine particle optical thickness closest to the true value.
4. An aerosol parameter iterative inversion method for collaborative satellite borne scalar and polarization observation data according to claim 3,
the specific method for constructing the fine particle polarization contribution quantization model in the S3 comprises the following steps:
and acquiring the atmospheric polarization contribution duty ratio of the aerosol fine particles with different combinations under different observation conditions by using a 6SV model, thereby realizing the correction of the aerosol fine particle polarization contribution.
5. The method of iterative inversion of aerosol parameters in conjunction with on-board scalar and polarization observation data according to claim 4, wherein said S4 comprises:
firstly, judging according to iteration conditions: if the iteration condition is not met, S3, adjusting an aerosol model in scalar inversion according to the latest aerosol fine particle ratio, and continuing scalar inversion in S1 to obtain the total optical thickness of the iterated aerosol; meanwhile, the corrected aerosol fine particle polarization contribution is obtained by using a fine particle polarization contribution quantization model, the corrected aerosol fine particle polarization contribution is calculated to be used as a true value which is received by a satellite and only comprises the aerosol fine particle polarization contribution, and polarization inversion in S2 is carried out again to obtain the optical thickness of the iterated aerosol fine particle, so that the iteration of the aerosol fine particle ratio is realized; and if the iteration condition is met, the corresponding aerosol fine particle ratio and the total aerosol optical thickness are the aerosol parameter inversion result of the observed data.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106225693A (en) * | 2016-08-29 | 2016-12-14 | 中国科学院遥感与数字地球研究所 | A kind of fine particle aerosol optical thickness and aerosol type Simultaneous Inversion method |
| CN108896450A (en) * | 2018-05-14 | 2018-11-27 | 中国科学院合肥物质科学研究院 | The atmospheric aerosol inversion method combined based on multiple angle multiple-pass polarization information with depth learning technology |
| CN110411927A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院遥感与数字地球研究所 | A kind of Fine Particles AOD and earth's surface polarized reflectance cooperate with inversion method |
| CN110705089A (en) * | 2019-09-27 | 2020-01-17 | 中国科学院遥感与数字地球研究所 | Fine-mode aerosol parameter inversion method |
| CN111753439A (en) * | 2020-07-09 | 2020-10-09 | 中国科学院空天信息创新研究院 | Aerosol optical thickness inversion method of domestic multi-angle polarization satellite sensor |
| US20210318253A1 (en) * | 2019-05-29 | 2021-10-14 | University Of Electronic Science And Technology Of China | Method for retrieving atmospheric aerosol based on statistical segmentation |
| CN114624731A (en) * | 2022-03-11 | 2022-06-14 | 桂林电子科技大学 | Inversion method for optical thickness of aerosol above cloud layer based on polarization remote sensing data |
-
2023
- 2023-07-21 CN CN202310899584.XA patent/CN116660106B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106225693A (en) * | 2016-08-29 | 2016-12-14 | 中国科学院遥感与数字地球研究所 | A kind of fine particle aerosol optical thickness and aerosol type Simultaneous Inversion method |
| CN108896450A (en) * | 2018-05-14 | 2018-11-27 | 中国科学院合肥物质科学研究院 | The atmospheric aerosol inversion method combined based on multiple angle multiple-pass polarization information with depth learning technology |
| US20210318253A1 (en) * | 2019-05-29 | 2021-10-14 | University Of Electronic Science And Technology Of China | Method for retrieving atmospheric aerosol based on statistical segmentation |
| CN110411927A (en) * | 2019-08-02 | 2019-11-05 | 中国科学院遥感与数字地球研究所 | A kind of Fine Particles AOD and earth's surface polarized reflectance cooperate with inversion method |
| CN110705089A (en) * | 2019-09-27 | 2020-01-17 | 中国科学院遥感与数字地球研究所 | Fine-mode aerosol parameter inversion method |
| CN111753439A (en) * | 2020-07-09 | 2020-10-09 | 中国科学院空天信息创新研究院 | Aerosol optical thickness inversion method of domestic multi-angle polarization satellite sensor |
| CN114624731A (en) * | 2022-03-11 | 2022-06-14 | 桂林电子科技大学 | Inversion method for optical thickness of aerosol above cloud layer based on polarization remote sensing data |
Non-Patent Citations (2)
| Title |
|---|
| 伽丽丽;马;陈兴峰;李莉;李正强;张洋;: "卫星近红外偏振通道反演气溶胶光学厚度的气溶胶模型影响", 红外与毫米波学报, no. 05, pages 569 - 577 * |
| 张洋: "基于多角度标量和偏振卫星数据的气溶胶光学参数反演研究", 《中国博士学位论文全文数据库》工程科技I辑, no. 04, pages 027 - 29 * |
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