Method, system, medium and equipment based on simplified aerosol optical thickness inversion

Abstract

The invention discloses a simplified aerosol optical thickness inversion-based method, system, medium and equipment, and relates to the technical field of aerosol satellite remote sensing, and the method comprises the steps: obtaining surface multispectral remote sensing data and foundation actual measurement AOD data of different spatial resolutions; calculating aerosol spatial distribution heterogeneity under different spatial resolutions according to the earth surface multispectral remote sensing data, wherein the earth surface multispectral remote sensing data with the resolution corresponding to the maximum value is target data; based on the processed target data, on the basis of an SARA algorithm, using a Newton iteration algorithm to carry out AOD inversion; according to the AOD inversion method, the range of the single scattering albedo (SSA) and the range of the asymmetric parameter G are limited, the SSA and the G in all the limited ranges are tested according to the set step length based on two-layer circulation of an SARA algorithm, the SSA and the G which enable the inversion AOD and the foundation actual measurement AOD to be closest are found, and the AOD inversion result of a research area is obtained. The method can reduce the number of iterations and time consumption.

Description

Technical Field

[0001] The invention relates to the field of aerosol satellite remote sensing technology, and in particular to a method, system, medium and equipment based on simplified aerosol optical thickness inversion. Background Art

[0002] Aerosol optical depth (AOD) refers to the degree of influence of aerosol particles on solar radiation. It is a key parameter for the calculation of aerosol characteristic parameters and atmospheric correction. In recent years, with the development of satellite remote sensing technology, the development of aerosol inversion methods has also been driven. However, the complex surface environment, the uncertainty of cloud masking methods and the different calibration methods all have an impact on satellite inversion of AOD. Aerosol inversion methods can be mainly divided into polar orbit and geostationary orbit. Polar orbit satellite inversion methods include five categories: single-view method, multi-view method, polarization characteristic inversion method, radar inversion method and multi-sensor collaborative inversion method. Among the geostationary satellite inversion methods, the main satellites include GOES-W, GOES-E, Himawari-8 and Meteosat-8, which can produce multi-phase AOD products. Since they can simultaneously monitor dynamic aerosols and surface reflection information and have a constant view, many methods have been developed, among which the most classic is the GOES Aerosol / Smoke Product (GASP). So far, among the aerosol satellite remote sensing inversion methods, the single-view method is the most common. The contrast reduction method was developed in the early stage. This method is based on the contrast modulation function, the geometric position of the solar sensor, the radiance values ​​of two different targets under different albedo, the average surface reflectivity, and a specific aerosol model for retrieving AOD. However, a more mature method developed in the single-view method is the MODIS computational inversion method: from the earliest dark dense vegetation (DDV) method to the current dark target (DT), although this method works well in areas covered by dark vegetation, its accuracy is not good on bright ground. To address this problem, in 2004, scholars proposed the Deep Blue (DB) method, which can effectively improve the accuracy of the dark target (DT) method on bright ground.

[0003] Scholars have developed a simplified aerosol retrieval algorithm (SARA), which uses MODIS primary and secondary data to retrieve AOD at a spatial resolution of 500m without the need for a lookup table. Compared with the dark target method and the deep blue method, this method has higher accuracy and spatial coverage, and can improve the retrieval capability of urban AOD. However, the key parameters of this method, the single albedo and the geometric symmetry factor, are obtained from the AERONET station, so the implementation of this method is hindered in areas where the AERONET station is not available. Therefore, this defect of the SARA method has been studied and optimized by researchers. Among them, the researchers proposed to analyze the sensitivity of the single albedo and the geometric symmetry factor, and calculated the empirical values ​​of the single albedo and the symmetry factor, which have a high correlation with the MOD04 AOD product. Although this method solves the problem of AOD inversion in areas without AERONET parameters, the inversion accuracy of this method is lower than that of the SARA method. The researchers proposed to use the optical properties of aerosols and clouds (OPAC) to simulate the single scattering albedo and asymmetric coefficient, and used the improved method to invert the test area, and obtained the 500m AOD product in the area from 2012 to 2014. Compared with the 3kmAOD and 10kmAOD products, the accuracy has been significantly improved, which increases the wide applicability of the method. The SARA method uses fixed-point iteration to invert AOD in the iterative process, but this iterative method is not suitable for the nonlinear radiation transfer equation of aerosol inversion. It requires multiple iterations to reach convergence, and the inversion of high-resolution data is relatively time-consuming. In addition, the input data is not fine enough in the atmospheric inversion process, and these problems need to be solved urgently. Therefore, the SARA method needs to be improved. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the SARA iteration method is time-consuming and propose a method based on simplified aerosol optical thickness inversion, comprising the following steps:

[0005] S1. Obtain surface multispectral remote sensing data with different spatial resolutions, as well as ground-based measured aerosol optical depth data;

[0006] S2. Calculate the spatial distribution heterogeneity of aerosols at different spatial resolutions based on the surface multispectral remote sensing data, and select the surface multispectral remote sensing data with the resolution corresponding to the maximum aerosol spatial distribution heterogeneity as the target data;

[0007] S3. Process the target data, and based on the processed target data, use the Newton iteration algorithm to perform aerosol optical thickness inversion on the basis of a simplified aerosol inversion algorithm;

[0008] S4. Limit the range of single scattering albedo SSA and asymmetry parameter G. Based on the two-layer loop of the simplified aerosol inversion algorithm, test SSA and G within all restricted ranges according to the set step size, find the SSA and G that make the inverted aerosol optical thickness closest to the ground-based measured aerosol optical thickness, and use the closest SSA and G to obtain the aerosol optical thickness inversion result of the study area.

[0009] Furthermore, surface multispectral remote sensing data with different spatial resolutions are obtained from Sentinel-2 data.

[0010] Furthermore, processing the target data includes:

[0011] Band selection is performed on the target data, and the target data is divided into two levels: L1C and L2A. The L1C level data includes: reflectivity of the top atmosphere, solar zenith angle, satellite azimuth, and observation zenith angle; the L2A level data includes: reflectivity of the bottom atmosphere;

[0012] The target data after band selection is projected into the same coordinate system and resampled using bilinear interpolation; the atmospheric bottom reflectance mask data is grid-subdivided and clipped; and the processed target data is obtained.

[0013] Furthermore, when the ground-based measured aerosol optical thickness data are missing, the average data within one hour before and after the ground-based measured data are used to fill the gap.

[0014] Furthermore, the bands of the ground-based measured aerosol optical thickness data need to be interpolated, expressed as:

[0015] τ a =βλ -α

[0016]

[0017] Among them, τ a represents the known aerosol optical thickness value of the band, and β represents Turbidity coefficient, λ indicates the band to be interpolated, α indicates The index, τ(λ), represents the aerosol optical depth value of the requested band, λ a Indicates known bands.

[0018] Furthermore, based on the simplified aerosol inversion algorithm, the Newton iteration algorithm is used to invert the aerosol optical thickness, specifically:

[0019]

[0020]

[0021] in, represents the aerosol optical depth under Newton iteration, τ a represents the aerosol optical thickness of a known band, f(τ a ) represents the constructed a The function of f(τ a ) prime represents f(τ a ) for τ a The derivative of s represents the cosine of the solar zenith angle, μ v represents the cosine of the apparent zenith angle, w0 represents the single scattering albedo, g represents the asymmetry parameter, represents the scattering phase angle, represents the top of the atmospheric reflectivity, represents the Rayleigh reflectivity, represents the downward transmittance, represents the upper transmittance, represents the surface reflectivity, S (λ) represents the atmospheric backscatter ratio, express For τ a The derivative of express For τ a The derivative of R represents the Rayleigh optical depth, Indicates S (λ) For τ a The derivative of , λ represents the center value of the corresponding wavelength, θ s represents the sun's zenith angle, θ v represents the vertex angle, and φ represents the relative azimuth.

[0022] The present invention also proposes a system based on simplified aerosol optical thickness inversion, including: a data acquisition module, a data selection module, a data processing module, and a result acquisition module;

[0023] Data acquisition module, used to obtain surface multispectral remote sensing data of different spatial resolutions, as well as ground-based measured aerosol optical thickness data;

[0024] The data selection module is used to calculate the spatial distribution heterogeneity of aerosols at different spatial resolutions based on the surface multispectral remote sensing data, and select the surface multispectral remote sensing data with the resolution corresponding to the maximum aerosol spatial distribution heterogeneity as the target data;

[0025] The data processing module is used to process the target data. Based on the processed target data and the simplified aerosol inversion algorithm, the Newton iteration algorithm is used to invert the aerosol optical thickness.

[0026] The result acquisition module is used to limit the range of single scattering albedo SSA and asymmetry parameter G. Based on the two-layer cycle of the simplified aerosol inversion algorithm, SSA and G within all restricted ranges are tested according to the set step size to find the SSA and G that make the inverted aerosol optical thickness closest to the ground-based measured aerosol optical thickness. The closest SSA and G are used to obtain the aerosol optical thickness inversion result of the study area.

[0027] The present invention also proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method based on simplified aerosol optical thickness inversion is implemented.

[0028] The present invention also proposes an electronic device, comprising a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the above-mentioned method based on simplified aerosol optical thickness inversion.

[0029] The beneficial effects brought by the technical solution provided by the present invention are:

[0030] Based on SARA, the present invention modifies the SARA method to adopt fixed-point iteration to perform AOD inversion in the iterative process, and changes the nonlinear radiation transfer equation that is not suitable for aerosol inversion into a Newton iteration method that is suitable for nonlinear equations, thereby reducing the number of iterations and time consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of a method based on simplified aerosol optical thickness inversion according to an embodiment of the present invention;

[0032] Figure 2 is a block diagram of an electronic device in an exemplary embodiment of the present invention;

[0033] Figure 3 This is a diagram of the embodiment of the present invention using the AOD data of the A, B and C sites to verify the accuracy of SARA and XSARA (the method of the present invention), wherein: Figure 3 (a) is the performance verification of SARA using the AOD data of site A, (b) is the performance verification of SARA using the AOD data of site B, (c) is the performance verification of SARA using the AOD data of site C, (d) is the performance verification of XSARA using the AOD data of site A, (e) is the performance verification of XSARA using the AOD data of site B, and (f) is the performance verification of XSARA using the AOD data of site C. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0035] The flowchart of the method based on simplified aerosol optical thickness inversion in an embodiment of the present invention is as follows: Figure 1 , specifically including the following steps:

[0036] S1. Obtain surface multispectral remote sensing data of different spatial resolutions, as well as ground-based measured aerosol optical depth data.

[0037] Specifically, in a preferred embodiment of the present invention, surface multispectral remote sensing data of different spatial resolutions adopt Sentinel-2 series data, which is a polar-orbiting multispectral high-resolution imaging mission used for land monitoring to provide images of vegetation, soil and water cover, inland waterways and coastal areas.

[0038] Sentinel-2 data products are mainly divided into: Level-1B, Level-1C and Level-2A.

[0039] Level-1B is raw data, and Level-1B products require professional correction technology;

[0040] Level-1C is the applied radiometric and geometric correction (including orthorectification and spatial registration). Level 1C images are a set of 100 square kilometer tiles, each tile is about 500MB.

[0041] Level-2A is a product that has undergone atmospheric correction and orthorectification by Sen2Cor.

[0042] The Sentinel-2 satellite carries a multi-spectral instrument (MSI) that covers 13 spectral bands with ground resolutions of 10m, 20m and 60m.

[0043] The ground-based measured aerosol optical depth data (AOD) uses AERONETAOD data, and the accuracy is verified by using the average of the AOD inversion results within the 3km×3km range of the ground station and the average of the AOD results half an hour before and after the ground station data. In addition, when the ground station data is missing, the average of the data within one hour before and after the ground station data can be used.

[0044] The AERONETAOD data product does not have the Sentinel-2 band, so the satellite-derived AOD and ground-based measured AOD cannot be directly used for comparison. Therefore, in order to obtain the AOD in the same band as Sentinel-2, the band of the AERONETAOD data must be interpolated. It is assumed that the atmosphere is quasi-stationary on the observed time scale and τa and λ satisfy the following relationship:

[0045] τ a =βλ -α

[0046]

[0047] Among them, τ a represents the known aerosol optical thickness value of the band, and β represents Turbidity coefficient, λ indicates the band to be interpolated, α indicates The index, τ(λ), represents the aerosol optical depth value of the requested band, λ a Indicates known bands.

[0048] S2. Calculate the spatial distribution heterogeneity of aerosols at different spatial resolutions based on the surface multispectral remote sensing data, and select the surface multispectral remote sensing data with the resolution corresponding to the maximum average local variance of the spatial distribution heterogeneity of aerosols as the target data.

[0049] Specifically, in a preferred embodiment of the present invention, Sentinel-2 data with a resolution of 10m, 20m and 60m are used to calculate the heterogeneity of aerosol spatial distribution, and Sentinel-2 data with strong heterogeneity of aerosol spatial distribution is the target data, and the corresponding resolution is the optimal resolution. The local variance method is used to calculate the heterogeneity of aerosol spatial distribution.

[0050] S3. Process the target data, select the band of the target data, and divide the target data into two levels: L1C and L2A. The L1C level data includes: top atmosphere reflectivity (TOA), solar zenith angle (SZA), satellite azimuth angle (SAA), and observation zenith angle (VZA); the L2A level data includes: bottom atmosphere reflectivity (BOA).

[0051] The target data after band selection is projected into the same coordinate system and resampled using bilinear interpolation; the BOA mask data is grid-subdivided and cropped; and the processed target data is obtained.

[0052] In a preferred embodiment of the present invention, the band calculation expression used is "if (scl_cloud_high_proba>0 (high cloud probability is greater than 0) or scl_thin_cirrus>0 (thin cloud probability is greater than 0) or scl_cloud_medium_proba>0 (medium cloud probability is greater than 0) or scl_snow_ice>0 (snow ice probability is greater than 0) or ((B3-B8) / (B3+B8))>0.0) then set the pixel value to 0, otherwise, set the pixel value to 1", and the BOA mask data (ice, snow, cloud, water) is gridded and clipped. These bands are superimposed into a multi-band file. In the above statement, "scl_cloud_high_proba" indicates the possible existence of high clouds; "scl_thin_cirrus" indicates cirrus clouds; "scl_cloud_medium_proba" indicates medium cloud probability, and "scl_snow_ice" indicates ice and snow. "

[0053] (B3-B8) / (B3+B8))>0.0" is used to detect water bodies. When this condition is met, that is, (B3-B8) / (B3+B8) is greater than 0.0, it means that the pixel is a water body. At this time, the pixel value is set to 0 for masking. When this condition is not met, that is, (B3-B8) / (B3+B8) is less than or equal to 0.0, it means that the pixel is not a water body. At this time, the pixel value is set to 1 and retained for subsequent aerosol optical thickness (AOD) inversion. This condition is used to identify and mask water body pixels to ensure that only non-water body pixels participate in the inversion calculation of AOD. B3 and B8 represent different bands of remote sensing images. B3 represents the blue light band and B8 represents the near-infrared band.

[0054] The above steps mask clouds, snow, ice, and water bodies, retaining only valid pixels for subsequent aerosol optical thickness (AOD) inversion.

[0055] Based on the processed target data, the Newton iteration algorithm is used to invert the aerosol optical thickness on the basis of the simplified aerosol inversion algorithm. Fixed-point iteration usually only converges linear equations, while the Newton iteration algorithm is suitable for solving nonlinear equations and complex surface AOD inversion.

[0056] In a preferred embodiment of the present invention, the Newton iteration algorithm is specifically:

[0057]

[0058]

[0059] in, represents the aerosol optical depth under Newton iteration, τ arepresents the aerosol optical thickness of a known band, f(τ a ) represents the constructed a The function of f(τ a ) prime represents f(τ a ) for τ a The derivative of s represents the cosine of the solar zenith angle, μ v represents the cosine of the apparent zenith angle, w0 represents the single scattering albedo, g represents the asymmetry parameter, represents the scattering phase angle, represents the top of the atmospheric reflectivity, represents the Rayleigh reflectivity, represents the downward transmittance, represents the upper transmittance, represents the surface reflectivity, S (λ) represents the atmospheric backscatter ratio, express For τ a The derivative of express For τ a The derivative of R represents the Rayleigh optical depth, Indicates S (λ) For τ a The derivative of , λ represents the center value of the corresponding wavelength, θ s represents the sun's zenith angle, θ v represents the vertex angle, and φ represents the relative azimuth.

[0060] Scattering phase angle The calculation is as follows:

[0061]

[0062] Atmospheric pressure is calculated as follows:

[0063] P Z =P0*(1-Gr*Z*10 3 / (Cp*T)) Cp*M0 / R ,

[0064] Among them, P Z represents the surface atmospheric pressure, P0 represents the atmospheric pressure at sea level, Gr represents the gravitational acceleration of the earth, and the value is 9.807 (m / s^2). Z represents the elevation obtained from the SRTM DEM data, Cp represents the specific heat at constant pressure, T represents the standard temperature, M0 represents the molar mass of dry atmosphere, and R represents the atmospheric constant.

[0065] S4. Limit the range of single scattering albedo SSA and asymmetry parameter G. Based on the two-layer cycle of the simplified aerosol inversion algorithm, test SSA and G in all restricted ranges according to the set step size, find the SSA and G that make the inverted aerosol optical thickness closest to the ground-based measured aerosol optical thickness, and use the closest SSA and G to obtain the aerosol optical thickness inversion result of the study area. Refine the inversion mode and dynamically adjust the tolerance error to minimize the difference between AERONETAOD and the calculated AOD and achieve the optimal value.

[0066] In a preferred embodiment of the present invention, the range of the single scattering albedo SSA and the asymmetry parameter G is limited to [0.5-1.0], and the step size is set to 0.001.

[0067] The present invention also proposes a system based on simplified aerosol optical thickness inversion, comprising: a data acquisition module, a data selection module, a data processing module, and a result acquisition module.

[0068] The data acquisition module is used to obtain surface multispectral remote sensing data of different spatial resolutions, as well as ground-based measured aerosol optical thickness data.

[0069] The data selection module is used to calculate the spatial distribution heterogeneity of aerosols at different spatial resolutions based on the surface multispectral remote sensing data, and select the surface multispectral remote sensing data with the resolution corresponding to the maximum aerosol spatial distribution heterogeneity as the target data.

[0070] The data processing module is used to process the target data. Based on the processed target data and the simplified aerosol inversion algorithm, the Newton iteration algorithm is used to invert the aerosol optical thickness.

[0071] The result acquisition module is used to limit the range of single scattering albedo SSA and asymmetry parameter G. Based on the two-layer cycle of the simplified aerosol inversion algorithm, SSA and G within all restricted ranges are tested according to the set step size to find the SSA and G that make the inverted aerosol optical thickness closest to the ground-based measured aerosol optical thickness. The closest SSA and G are used to obtain the aerosol optical thickness inversion result of the study area.

[0072] In an exemplary embodiment, a computer-readable storage medium is included, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method based on simplified aerosol optical thickness inversion is implemented.

[0073] See also Figure 2 In an exemplary embodiment, an electronic device is also included, including at least one processor, at least one memory, and at least one communication bus.

[0074] Wherein, a computer program is stored in the memory, and the computer program includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through a communication bus to execute the above-mentioned method based on simplified aerosol optical thickness inversion.

[0075] Compared with the simplified aerosol inversion method (SARA), the AOD inversion (XSARA) of the present invention has higher accuracy and better consistency with AERONETAOD. In the final result, the present invention uses the AOD data of A, B and C to verify the accuracy of the method (XSARA) of the present invention. Figure 3 , Figure 3 This is a diagram of the embodiment of the present invention using the AOD data of the A, B and C sites to verify the accuracy of SARA and XSARA (the method of the present invention), wherein: Figure 3 (a) is the performance of SARA verified by using the AOD data of site A, (b) is the performance of SARA verified by using the AOD data of site B, (c) is the performance of SARA verified by using the AOD data of site C, (d) is the performance of XSARA verified by using the AOD data of site A, (e) is the performance of XSARA verified by using the AOD data of site B, and (f) is the performance of XSARA verified by using the AOD data of site C. The inversion results of SARA and XSARA have a high correlation with the ground station observation data, but the RMSE and MAE of SARA are larger than those of XSARA. In addition, the results of SARA exceed the EE interval more, while XSARA reaches the EE interval. At the same time, the correlation between the inversion results of XSARA and AERONETAOD data exceeds 0.98. In addition, the slopes of the fitting lines of the AOD inversion results based on XSARA and AERONETAOD data at sites A, B and C are 0.9662, 1.0022 and 1.0002, respectively, indicating that the linear fit of the two is good.

[0076] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method based on simplified aerosol optical thickness inversion, characterized in that: The following steps are involved: S1. Obtain surface multispectral remote sensing data with different spatial resolutions, as well as ground-based measured aerosol optical depth data; S2. Calculate the spatial distribution heterogeneity of aerosols at different spatial resolutions based on the surface multispectral remote sensing data, and select the surface multispectral remote sensing data with the resolution corresponding to the maximum aerosol spatial distribution heterogeneity as the target data; S3. Process the target data, and based on the processed target data, use the Newton iteration algorithm to perform aerosol optical thickness inversion on the basis of a simplified aerosol inversion algorithm; S4. Limit the range of single scattering albedo SSA and asymmetry parameter G. Based on the two-layer loop of the simplified aerosol inversion algorithm, test SSA and G within all restricted ranges according to the set step size, find the SSA and G that make the inverted aerosol optical thickness closest to the ground-based measured aerosol optical thickness, and use the closest SSA and G to obtain the aerosol optical thickness inversion result of the study area.

2. A method based on simplified aerosol optical thickness inversion according to claim 1, characterized in that: Acquire surface multispectral remote sensing data of different spatial resolutions from Sentinel-2 data.

3. A method based on simplified aerosol optical thickness inversion according to claim 2, characterized in that: Processing of target data includes: Band selection is performed on the target data, and the target data is divided into two levels: L1C and L2A. The L1C level data includes: reflectivity of the top atmosphere, solar zenith angle, satellite azimuth, and observation zenith angle; the L2A level data includes: reflectivity of the bottom atmosphere; The target data after band selection is projected into the same coordinate system and resampled using bilinear interpolation; the atmospheric bottom reflectance mask data is grid-subdivided and clipped; and the processed target data is obtained.

4. The method based on simplified aerosol optical thickness inversion according to claim 1, characterized in that: When the ground-based aerosol optical thickness data are missing, the average data within one hour before and after the ground-based measured data are used to fill the gap.

5. The method based on simplified aerosol optical thickness inversion according to claim 1, characterized in that: The bands of ground-based measured aerosol optical thickness data need to be interpolated, expressed as: t a =see -α Among them, τ a represents the aerosol optical thickness value of the known band, and β represents Turbidity coefficient, λ indicates the band to be interpolated, α indicates The index, τ(λ), represents the aerosol optical depth value of the requested band, λ a Indicates known bands.

6. The method based on simplified aerosol optical thickness inversion according to claim 1, characterized in that: Based on the simplified aerosol inversion algorithm, the Newton iteration algorithm is used to invert the aerosol optical thickness, specifically: in, represents the aerosol optical depth under Newton iteration, τ a represents the aerosol optical thickness of a known band, f(τ a ) represents the constructed a The function of f(τ a ) prime represents f(τ a ) for τ a The derivative of s represents the cosine of the solar zenith angle, μ v represents the cosine of the apparent zenith angle, w0 represents the single scattering albedo, g represents the asymmetry parameter, represents the scattering phase angle, ρ TOA (λ,θ s ,θ v , φ) represents the top of the atmospheric reflectivity, represents the Rayleigh reflectivity, represents the downward transmittance, represents the upper transmittance, represents the surface reflectivity, S (λ) represents the atmospheric backscatter ratio, express For τ a The derivative of express For τ a The derivative of R represents the Rayleigh optical depth, Indicates S (λ) For τ a The derivative of , λ represents the center value of the corresponding wavelength, θ s represents the sun's zenith angle, θ v represents the vertex angle, and φ represents the relative azimuth.

7. A system based on simplified aerosol optical thickness inversion, characterized in that: include: Data acquisition module, data selection module, data processing module, result acquisition module; Data acquisition module, used to obtain surface multispectral remote sensing data of different spatial resolutions, as well as ground-based measured aerosol optical thickness data; The data selection module is used to calculate the spatial distribution heterogeneity of aerosols at different spatial resolutions based on the surface multispectral remote sensing data, and select the surface multispectral remote sensing data with the resolution corresponding to the maximum aerosol spatial distribution heterogeneity as the target data; The data processing module is used to process the target data. Based on the processed target data and the simplified aerosol inversion algorithm, the Newton iteration algorithm is used to invert the aerosol optical thickness. The result acquisition module is used to limit the range of single scattering albedo SSA and asymmetry parameter G. Based on the two-layer cycle of the simplified aerosol inversion algorithm, SSA and G within all restricted ranges are tested according to the set step size to find the SSA and G that make the inverted aerosol optical thickness closest to the ground-based measured aerosol optical thickness. The closest SSA and G are used to obtain the aerosol optical thickness inversion result of the study area.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises computer-readable instructions, and the processor is configured to call the computer-readable instructions to execute the method according to any one of claims 1 to 6.

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