Adaptive Adjustment Method and System for the Complex Refractive Index of Aerosol Based on the Cross Structure

Abstract

This application belongs to the field of atmospheric detection and remote sensing technology, and specifically relates to an adaptive adjustment method and system for the complex refractive index of aerosols based on a cross-shaped structure. The present invention proposes a cross-shaped structure function model and a comprehensive weight factor to fully consider the complexity of the spatial variation of the coastline. When constructing the aerosol lookup table, the imaginary part of the complex refractive index of the aerosol is adaptively adjusted according to the weight factor, thereby improving the accuracy of the inversion of the atmospheric aerosol optical thickness in the coastal area.

Description

Technical Field

[0001] The present application belongs to the field of atmospheric detection and remote sensing technology, and specifically relates to an aerosol complex refractive index adaptive adjustment and system based on a crisscross structure. Background Art

[0002] The structure function method is a method that uses satellite image ambiguity to invert aerosol optical thickness. It assumes that the surface reflectivity is constant, ignores multiple scattering between the surface and the atmosphere, and is not restricted by the surface reflectivity. However, in existing technical literature, the structure function model used considers limited spatial directions when calculating the difference between pixels, and does not take into account the regional differences in the imaginary part of the aerosol complex refractive index. There is a lack of research on offshore areas, especially coastal areas. The limitations of these models, especially their application in coastal areas, have become a problem that needs to be solved in the field of satellite remote sensing inversion of atmospheric aerosols.

[0003] Compared with deserts and urban areas, the coastline is a curve with no regular distribution, and the surface reflectivity at the junction of land and sea varies significantly. The traditional pixel differences in several directions are difficult to cover the complexity of the coastline. In the satellite data grid, each pixel has eight adjacent pixels, and the lines between the center point of the pixel and the center points of the eight adjacent pixels form a cross. Taking the differences of the eight adjacent pixels into account in the structure function model can cover the complex changes of the coastline. In addition, aerosols in coastal areas are affected by both land-based anthropogenic sources and sea salt aerosols. The spatial and temporal distribution of aerosol optical properties varies significantly compared to inland areas, especially the imaginary part of the complex refractive index. However, in the existing technical literature, the regional differences in the imaginary part of the aerosol complex refractive index are rarely considered. When constructing the aerosol lookup table, the sun angle, satellite angle and aerosol optical parameters are set to several fixed values, and multiple linear interpolations are performed when inverting the aerosol. Although this method has higher accuracy and efficiency in inland areas, from the perspective of error transmission, multiple interpolations will bring errors into the inversion results of aerosol optical thickness. However, this method is not applicable in nearshore and coastal areas. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes an adaptive adjustment system for the complex refractive index of aerosols based on a crisscross structure, which takes into account the regional changes in the sun angle, satellite angle and aerosol optical parameters, and improves the inversion accuracy of atmospheric aerosol optical thickness in offshore areas. The technical solution is as follows:

[0005] A method for adaptively adjusting the complex refractive index of an aerosol based on a pixilated structure comprises the following steps:

[0006] S1. Obtain satellite images of the area to be inverted;

[0007] S2. Preprocessing of L1 satellite remote sensing data in the area to be inverted;

[0008] S3. Constructing a cross-shaped structure function model to calculate the apparent reflectivity structure function value of the image to be inverted and the surface reflectivity structure function value;

[0009] S4. Calculate the weight factor for each pixel of the surface reflectance image, and calculate the window movement law to obtain the comprehensive weight factor after resampling;

[0010] S5. Adaptively adjust the imaginary part of the aerosol complex refractive index based on the comprehensive weight factor to construct a lookup table for atmospheric aerosol optical thickness and atmospheric transmittance;

[0011] S6. Substitute the ratio of the apparent reflectance to the structure function value of the surface reflectance into the fitted formula of atmospheric transmittance and optical thickness to realize the inversion of aerosol optical thickness using the structure function method.

[0012] Preferably, in step S1, the historical surface reflectance is used as a clear image, and the seasonal changes in the phenology of surface vegetation are taken into consideration. When downloading the surface reflectance products of the area to be inverted in the adjacent years, they need to be stored separately according to the seasons; the historical surface reflectance products are synthesized by minimum value by season to obtain surface reflectance images of the four seasons of spring, summer, autumn and winter.

[0013] Preferably, in step S2, the preprocessing includes geometric correction, radiation calibration, and cloud detection;

[0014] During geometric correction, the projection and coordinate system parameters should be set according to the location of the study area;

[0015] Radiometric calibration uses the image's own correction coefficients to calculate and obtain the corresponding apparent reflectance;

[0016] Cloud detection uses the threshold method to identify whether it is a cloud pixel by pixel. Cloud pixels are 1 and non-cloud pixels are 0. The cloud mask is stored as a binary image, and only 0-valued pixels are retained in subsequent processing.

[0017] Preferably, in step S3, assuming that the surface reflectivity remains unchanged and ignoring multiple scattering between the surface and the atmosphere, the ratio of the image structure function value of turbid weather to that of clear weather is the atmospheric transmittance, and thus the aerosol optical thickness is inverted according to the radiation transfer model. The cross-shaped structure function model is as follows:

[0018] ;

[0019] in, Indicates the minimum value; Indicates the row number, Indicates the number of columns, represents the pixel spacing, Indicates the model calculation window size. Indicates the image reflectance.

[0020] Preferably, the cross-shaped structure function performs multiple pixel-by-pixel traversals within the calculation window. When the interval is starting from the th row and th column of the calculation window, calculate the difference between each pixel and the pixels at an interval of in 8 directions until the pixel interval increases to , and then calculate the mean of the multiple traversals.

[0021] Preferably, in step S4:

[0022] S41. Calculate the sea-land distance based on the coastline position and pixel position , use to represent the weight contributed by the ocean. Based on , the weight is segmented as follows:

[0023] ;

[0024] Among them: represents the contribution of one unit of the ocean, represents the number of ocean pixels. When the pixel is an ocean pixel, the sea-land distance is positive, and when the pixel is a land pixel, the sea-land distance is negative; for the pixels on the coastline, its weight is proportional to the number of ocean pixels among the adjacent eight pixels;

[0025] S42. Use to represent the weight of industrial and living areas. The weight of industrial land is the largest, set to , the weight value of the residential area is less than that of the office building. The residential area is set to , and the office building is set to , where represents the contribution of one unit of industrial and living areas;

[0026] S43. Obtain the resampled comprehensive weight factor according to the movement rule of the structure function method model window, where , represents the proportion of the ocean contribution, represents the proportion of industrial and living areas, and the values are as follows:

[0027] ;

[0028] When the land pixel is more than 20 KM away from the coastline, the ocean contribution is ignored. When the ocean pixel is more than 20 KM away from the coastline, the industrial and living contribution is ignored.

[0029] Preferably, in step S5, after determining the calculation window size of the structure function model, resampling is simultaneously performed on the solar zenith angle, solar azimuth angle, satellite zenith angle, and satellite azimuth angle. When constructing the look-up table using the 6S model, the resampled observed geometric values are directly input.

[0030] Atmospheric model: The custom mode is adopted. According to the time of the image to be retrieved, the vertical profile data of atmospheric pressure, temperature, water vapor content, and ozone content corresponding to the season are input.

[0031] Aerosol model: The Junge spectrum is selected. The Junge spectrum index and complex refractive index need to be input. The imaginary part of the complex refractive index is adaptively changed using the comprehensive weight factor obtained in step S4. The final form of the comprehensive weight factor is , and the adaptively adjusted imaginary part is , where represents the ocean contribution coefficient, represents the industrial and domestic contribution coefficient, represents the imaginary part of sea salt type aerosol particles, represents the imaginary part of continental type aerosol particles.

[0032] Preferably, in step S5, regarding the sensor code, 6S comes with pre-configured sensor bands. For sensor bands not covered, the corresponding spectral response function needs to be converted and added to the program. 6S simulates the solar electromagnetic radiation transmission process between 0.25μm and 4.0μm, and divides the electromagnetic wave at intervals of 2.5nm to obtain 1501 wavelength values , so when adding bands, only the starting wavelength value and of the band to be added need to be determined at the corresponding wavelength values in by the nearest neighbor interpolation method and , where and represent the positions of the interpolated starting wavelength values in the 1501 elements, and the spectral response values between and are obtained by bilinear interpolation , and the spectral response values at other positions are set to 0. After specifying the observed geometry, atmospheric model, aerosol model, and sensor code, a look-up table containing atmospheric transmittance and aerosol optical thickness at 550nm is constructed.

[0033] Preferably, in step S6, an exponential function is used for fitting, and the fitting formula is as follows:

[0034] ,

[0035] ;

[0036] Wherein represents the atmospheric transmittance, represents the atmospheric aerosol optical depth at 550 nm, and are fitting coefficients, wherein is negative; during the fitting process, the ratio of the structure function value of the apparent reflectance to that of the surface reflectance is , and are coefficients determined by fitting, and the aerosol optical depth can be obtained.

[0037] An aerosol complex refractive index adaptive adjustment system based on a cross structure, comprising a data acquisition unit, a data processing unit and an output unit;

[0038] The data acquisition unit: acquires satellite images of the area to be inverted;

[0039] The data processing unit: performs processing on the data, constructs a cross structure function model, calculates the structure function value of the apparent reflectance of the image to be inverted and the structure function value of the surface reflectance; calculates the weight factor for each pixel of the surface reflectance image, and obtains the resampled comprehensive weight factor according to the movement rule of the calculation window of the structure function model; adaptively adjusts the imaginary part of the aerosol complex refractive index based on the comprehensive weight factor, constructs a look-up table of the atmospheric aerosol optical depth and the atmospheric transmittance; substitutes the ratio of the structure function value of the apparent reflectance to that of the surface reflectance into the formula of the atmospheric transmittance and the optical depth obtained by fitting, and realizes the inversion of the aerosol optical depth by the structure function method;

[0040] The output unit: visually outputs the result.

[0041] Compared with the prior art, the beneficial effects of the present application are as follows:

[0042] 1. More detailed spatial structure: The cross structure model is adopted, and the pixel differences in eight directions are considered simultaneously, with a wide coverage of the spatial structure;

[0043] 2. Considering the mixing of multi-source aerosol particles: Since the coastal area is affected by both the ocean and the land, the comprehensive weight factor is used to consider the multi-source aerosol mixing effect brought by the ocean and industrial life simultaneously, and the aerosol complex refractive index can be adaptively adjusted;

[0044] 3. Precise observation geometry: The present application generates a targeted atmospheric aerosol look-up table for each pixel. After determining the movement rule of the structure function model, the resampling of the solar zenith angle and azimuth angle, and the satellite zenith angle and azimuth angle is completed simultaneously. Compared with the traditional technology, the error transmission of the aerosol optical depth is reduced. Brief Description of the Drawings

[0045] Figure 1 is a technical flow chart for inverting the atmospheric aerosol content using the cross-shaped structure function model;

[0046] Figure 2 is a comparison chart of the structure function models. (A) is the one-direction model, (B) is the three-direction model, (C) is the urban model, and (D) is the cross model;

[0047] Figure 3 is a comparison chart of the ratios of the four structure function models;

[0048] Figure 4 is a comparison chart of the inversion accuracy of the 550nm atmospheric aerosol optical thickness. (A) is the one-direction model, (B) is the three-direction model, (C) is the urban model, and (D) is the cross model. Detailed Implementation Manner

[0049] The technical solution of the present application will be described in detail below through specific embodiments and the accompanying drawings. It should be understood that the specific features in the embodiments of the present application are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. The specific technical features can be combined with each other.

[0050] An adaptive adjustment method for the aerosol complex refractive index based on a cross structure includes the following steps:

[0051] S1. Obtain satellite images of the area to be inverted;

[0052] Use the historical surface reflectance as the clear image. Considering the seasonal changes in the phenology of surface vegetation, when downloading the surface reflectance products of adjacent years in the area to be inverted, they need to be stored separately according to seasons; perform minimum value synthesis on the historical surface reflectance products seasonally to obtain surface reflectance images for the four seasons of spring, summer, autumn, and winter.

[0053] S2. Preprocess the L1-level satellite remote sensing data of the area to be inverted;

[0054] Preprocessing mainly includes geometric correction, radiometric calibration, and cloud detection. Whether the L1 data is geometrically corrected depends on the instructions for use of the satellite sensor data. For example, the L1B data of MODIS (MODerate-resolution Imaging Spectroradiometer) is not geometrically corrected, while the L1C data of Sentinel-2 and the L1B data of VIIRS (Visible Infrared Imaging Radiometer Suite) are geometrically corrected. During geometric correction, the projection and coordinate system parameters should be set according to the location of the study area. Albers conformal projection and WGS-84 coordinate system are usually selected in China. Radiometric calibration can be calculated using the correction coefficients of the image itself to obtain the corresponding apparent reflectance. Cloud detection is mainly done by using the threshold method to identify whether it is a cloud pixel by pixel. Cloud pixels are 1 and non-cloud pixels are 0. The cloud mask is stored as a binary image, and only 0-value pixels are retained in subsequent processing.

[0055] S3. Construct a cross-shaped structure function model to calculate the apparent reflectivity structure function value of the image to be inverted and the surface reflectivity structure function value.

[0056] In order to invert aerosols over high reflectivity surfaces, Tanre proposed a structure function method that uses the ambiguity of satellite images in turbid weather to invert aerosol optical thickness, which is not restricted by the surface reflectivity. The structure function method requires satellite images of clear weather as a reference, and calculates the structure function value after removing the atmospheric effect. Assuming that the surface reflectivity remains unchanged and ignoring multiple scattering between the surface and the atmosphere, the ratio of the image structure function value of turbid weather to that of clear weather is the atmospheric transmittance, and then the aerosol optical thickness is inverted according to the radiation transfer model, as shown in formula (1):

[0057] (1);

[0058] in, is the structure function value of the surface reflectance obtained by atmospheric correction of the clear weather image, is the structure function value of the apparent reflectivity of the image to be inverted, , , and are the solar zenith angle and the satellite zenith angle, respectively.

[0059] According to formula (1), the core of the structure function method lies in the calculation of the structure function value. There are three commonly used structure function models, as shown in formulas (2), (3), and (4):

[0060] (2);

[0061] (3);

[0062] (4);

[0063] Wherein, represents the row number, represents the column number, represents the pixel interval, represents the size of the model calculation window, represents the image reflectance, represents the maximum value of the pixel interval, represents the minimum value of the pixel interval.

[0064] Formulas (2), (3), and (4) respectively correspond to the unidirectional model, the three-direction model, and the urban model. The unidirectional model calculates the pixel difference with the pixel at the interval in the row direction for each pixel within the window. The three-direction model calculates the pixel difference with the pixels at the intervals in the row direction, column direction, and diagonal direction for each pixel within the window. The urban model calculates the pixel difference with the pixel at a certain distance for each pixel within the window. The coastline is an irregular curve with complex spatial angle changes. To comprehensively cover the structural characteristics of the coastline, this application proposes a cross-shaped structure model, and the calculation formula is as follows:

[0065] (5);

[0066] Wherein, represents the minimum value in

[0067] Compared with other structure models, the cross-shaped structure proposed in this patent will perform multiple pixel-by-pixel traversals within the calculation window. When the interval is , starting from the th row and the th column of the calculation window, calculate the difference between each pixel and the pixels at the interval in 8 directions until the pixel interval increases to , and then calculate the mean value of the multiple traversals.

[0068] S4. Calculate the weight factor for each pixel of the surface reflectance image and calculate the window movement rule to obtain the resampled comprehensive weight factor.

[0069] The comprehensive weight factor proposed in this application takes into account both the ocean contribution and the impact of industrial and domestic life.

[0070] First, use the professional software ARCGIS of the geographic information system to calculate the sea-land distance according to the coastline position and the pixel position. This application uses to represent the weight of the ocean contribution, based on the weights are segmented as follows:

[0071] ;

[0072] The unit is KM, representing the ocean contribution of one unit, indicating the number of ocean pixels. When the pixel is ocean, the sea-land distance is positive, and when the pixel is land, the sea-land distance is negative. For the pixels on the coastline, their weights are proportional to the number of ocean pixels among the adjacent eight pixels.

[0073] Secondly, using the industrial land classification in the land use data, factories, residential areas, and office buildings are determined. This application uses to represent the weight of industrial life. The weight of industrial land is the largest, set as The weight value of the residential area is less than that of the office building. The residential area is set as and the office building is set as where represents the industrial life contribution of one unit.

[0074] Finally, according to the moving rule of the structural function method model window, the resampled comprehensive weight factor is obtained, where ; represents the proportion of the ocean contribution, represents the proportion of industrial life, and the values are as follows:

[0075] ;

[0076] When the land pixel is more than 20 KM away from the coastline, the ocean contribution is ignored. When the ocean pixel is more than 20 KM away from the coastline, the industrial life contribution is ignored.

[0077] S5. According to the comprehensive weight factor, adaptively adjust the imaginary part of the aerosol complex refractive index, and construct a lookup table of atmospheric aerosol optical thickness and atmospheric transmittance.

[0078] A lookup table is constructed using the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model. In addition to the angular information of the sun and the satellite, atmospheric model, aerosol model, aerosol content, and sensor code need to be given. The present invention uses curve fitting to invert the atmospheric aerosol optical depth, which has relatively high requirements for the lookup table generated by the radiative transfer model. Usually, when constructing the lookup table, only several solar zenith angles, solar azimuth angles, satellite zenith angles, and satellite azimuth angles are considered, and the change of the observation geometry within the region is not considered. A default value is used throughout the region. When this angle does not exist in the lookup table, interpolation is performed. However, this process will introduce errors into the subsequent inversion of the atmospheric aerosol optical depth. Therefore, after determining the calculation window size of the structure function model, the present invention resamples the solar zenith angle, solar azimuth angle, satellite zenith angle, and satellite azimuth angle simultaneously, and directly inputs the resampled observation geometry values when constructing the lookup table using the 6S model.

[0079] For the atmospheric model, a custom model is adopted. According to the time of the image to be inverted, the vertical profile data of atmospheric pressure, temperature, water vapor content, and ozone content in the corresponding season are input.

[0080] For the aerosol model, the Junge spectrum is selected. The Junge spectrum index and complex refractive index need to be input. The real part of the complex refractive index changes little. In this application, the imaginary part of the complex refractive index is adaptively changed mainly by using the comprehensive weight factor obtained in step S4. The final form of the comprehensive weight factor is , represents the ocean contribution coefficient, represents the industrial and domestic contribution coefficient, and the adaptively adjusted imaginary part is further obtained, where represents the ocean contribution coefficient, represents the industrial and domestic contribution coefficient, represents the imaginary part of sea salt type aerosol particles, represents the imaginary part of continental type aerosol particles.

[0081] For the aerosol content module, the aerosol optical depth is selected in this application. Since there is no case where the aerosol content is zero, the parameter settings of the aerosol optical depth are: 0.001, 0.05, 0.1 - 1.0 (at intervals of 0.1), 1.0 - 2.0 (at intervals of 0.2), 2.5.

[0082] Regarding the sensor code, the 6S comes with 59 pre-configured sensor bands. For sensor bands not covered, the corresponding spectral response functions need to be converted and added to the program. 6S can simulate the solar electromagnetic radiation transmission process between 0.25μm and 4.0μm, and divide the electromagnetic wave at intervals of 2.5nm to obtain 1501 wavelength values. , so when adding a band, only the starting wavelength value of the band to be added ( and ) needs to be determined according to the nearest neighbor interpolation method in to obtain the corresponding wavelength values and , where and represent the positions of the interpolated starting wavelength values in the 1501 elements, and the spectral response values between and are obtained by bilinear interpolation, and the spectral response values at other positions are set to 0. After specifying the observation geometry, atmospheric model, aerosol model, and sensor code, a lookup table containing the atmospheric transmittance and the aerosol optical depth at 550nm is constructed. , the ratio of the structure function values of the apparent reflectance to the surface reflectance is substituted into the formula for the atmospheric transmittance and optical depth obtained by fitting to realize the inversion of the aerosol optical depth by the structure function method.

[0083] S6.

[0084] Simulated by the 6S radiative transfer model, it is found that there is a strong exponential relationship between the atmospheric transmittance and the aerosol optical depth. Therefore, an exponential function is used for fitting in this application, and the fitting formula is in the form of , where represents the atmospheric transmittance (taking values from 0 to 1, indicating a negative exponent), represents the atmospheric aerosol optical depth at 550nm, and are fitting coefficients, where is negative. During the fitting process, the ratio of the structure function values of the apparent reflectance to the surface reflectance is , and are coefficients determined by fitting, and the aerosol optical depth can be obtained.

[0085] To illustrate the implementation process of the present invention in detail, only the example of monitoring the atmospheric aerosol content in the Bohai Bay coastal zone using VIIRS data is taken to show the implementation process of inverting the atmospheric aerosol in the offshore area using the cross-shaped structure function model, as Figure 1 shown, and the implementation steps are as follows:

[0086] Step 1. Based on the date of the data to be inverted, obtain the VNP09 surface reflectance product of the corresponding season in the previous two years of the study area, and perform minimum value synthesis to obtain the surface reflectance image of the study area;

[0087] Step 2. Obtain VNP03 data of the same time as VNP02 data, and use ENVI software to complete the geometric correction of VNP02 data; use IDL to read the radiation calibration parameters of VNP02 to obtain the apparent reflectance, and use the threshold method to remove cloud pixels to obtain the apparent reflectance image of the study area, the sun and the sensor observation angle;

[0088] Step 3. Use the cross-shaped structure function model shown in formula (5) to calculate the structure function values ​​of the surface reflectance image and the apparent reflectance image respectively;

[0089] Step 4. Calculate the weight factor pixel by pixel for the surface reflectance image based on land use, UAV and high-resolution satellite data, and resample the weight factor according to the calculation window movement law of the structure function model;

[0090] Step 5. According to the comprehensive weight factor, the imaginary part of the aerosol complex refractive index is adaptively adjusted to construct a lookup table for the 550nm atmospheric aerosol optical thickness and the total atmospheric transmittance.

[0091] According to formula (1), the ratio of the apparent reflectance to the structure function value of the surface reflectance can be understood as the atmospheric transmittance of the image to be inverted. The atmospheric aerosol optical thickness at 550 nm corresponding to the atmospheric transmittance can be determined by using the curve fitting method.

[0092] In order to elaborate on the advantages of the cross-shaped structure function model, the cross-shaped structure function model is compared with three traditional structure function models, such as Figure 2 As shown. Among them, Figure 2 (A) is a single-direction model. Figure 2 The middle (B) is a three-direction model. Figure 2 The middle (C) is the city model. Figure 2 The middle (D) is the cross-shaped model proposed by the present invention. Obviously, the cross-shaped model takes the spatial structure into more detailed consideration.

[0093] according to Figure 1 The algorithm flow shown in the figure inverts the atmospheric aerosol optical thickness in the coastal area of ​​Bohai Bay on July 23, 2024. The structure function ratios of the four models are as follows: Figure 3 As shown, the fitting coefficient and The inversion results are compared with the aerosol products of MODIS, and the distribution of pixels with absolute inversion errors within 10%, 20% and 30% are plotted, as shown in Figure 2. Figure 4 shown.

[0094] This application does not consider the directionality of ocean reflectance. When calculating the ratio of the structure function of apparent reflectance to that of surface reflectance, there will be cases where the ratio is greater than or equal to 1, which contradicts the fact that the transmittance is less than 1. This application statistically analyzed the proportion of pixels with a ratio less than 1, and the proportions of the four models are 53.56%, 50.02%, 50.63%, and 54.52% respectively. Obviously, the pixel proportion of the cross shape is the highest.

[0095] Figure 4 In it, blue indicates that the absolute error is less than 10%, green indicates that the absolute error is less than 20%, and red indicates that the absolute error is less than 30%. It is not difficult to find that the cross shape proposed in this application has relatively high inversion accuracy in the nearshore land and ocean areas, and the results are more stable compared with the other three models.

[0096] An aerosol complex refractive index adaptive adjustment system based on a cross structure, comprising a data acquisition unit, a data processing unit, and an output unit;

[0097] The said data acquisition unit: acquires satellite images of the area to be inverted;

[0098] The said data processing unit performs relevant processing on the data, constructs a cross-shaped structure function model, calculates the structure function value of the apparent reflectance of the image to be inverted and the structure function value of the surface reflectance; calculates the weight factor for each pixel of the surface reflectance image, and obtains the resampled comprehensive weight factor according to the movement rule of the calculation window of the structure function model; adaptively adjusts the imaginary part of the aerosol complex refractive index based on the comprehensive weight factor, and constructs a look-up table of atmospheric aerosol optical thickness and atmospheric transmittance; substitutes the ratio of the structure function values of apparent reflectance and surface reflectance into the formula of the fitted atmospheric transmittance and optical thickness to realize the structure function method inversion of aerosol optical thickness.

[0099] The output unit: visually outputs the results.

[0100] The above is only the preferred implementation manner of this application. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the technical principle of this application, several improvements and deformations can still be made, and these improvements and deformations should also be regarded as the protection scope of this application.

Claims

1. A method for adaptively adjusting the complex refractive index of aerosol based on a 'Piece-shaped' structure, characterized in that: The following steps are involved: S1. Obtain satellite images of the area to be inverted; S2. Preprocessing of L1 satellite remote sensing data in the area to be inverted; S3. Constructing a cross-shaped structure function model to calculate the apparent reflectivity structure function value of the image to be inverted and the surface reflectivity structure function value; In step S3, assuming that the surface reflectivity remains unchanged and ignoring multiple scattering between the surface and the atmosphere, the ratio of the image structure function value of turbid weather to that of clear weather is the atmospheric transmittance, and the aerosol optical thickness is inverted according to the radiation transfer model. The cross-shaped structure function model is as follows: ； in, Indicates the minimum value; Indicates the row number, Indicates the number of columns, represents the pixel interval, Indicates the model calculation window size, represents the image reflectivity; The cross-shaped structure function performs multiple pixel-by-pixel traversals within the calculation window. When Row, No. Starting from the column, calculate the distance between each pixel and the 8 directions The difference between pixels until the pixel spacing increases to , and then calculate the mean of multiple traversals; S4. Calculate the weight factor for each pixel of the surface reflectance image, and calculate the window movement law to obtain the comprehensive weight factor after resampling; S5. Adaptively adjust the imaginary part of the aerosol complex refractive index based on the comprehensive weight factor to construct a lookup table for atmospheric aerosol optical thickness and atmospheric transmittance; S6. Substitute the ratio of the apparent reflectance to the structure function value of the surface reflectance into the fitted formula of atmospheric transmittance and optical thickness to realize the inversion of aerosol optical thickness using the structure function method.

2. The method for adaptively adjusting the aerosol complex refractive index based on the cross-shaped structure according to claim 1, characterized in that: In step S1, the historical surface reflectance is used as a clear image, and the seasonal changes in the surface vegetation phenology are taken into consideration. When downloading the surface reflectance products of the area to be inverted in the adjacent years, they need to be stored separately according to the seasons; the historical surface reflectance products are synthesized at the minimum value by season to obtain the surface reflectance images of the four seasons of spring, summer, autumn and winter.

3. The method for adaptively adjusting the aerosol complex refractive index based on the cross-shaped structure according to claim 1, characterized in that: In step S2, preprocessing includes geometric correction, radiometric calibration, and cloud detection; During geometric correction, the projection and coordinate system parameters should be set according to the location of the study area; Radiometric calibration uses the image's own correction coefficients to calculate and obtain the corresponding apparent reflectance; Cloud detection uses the threshold method to identify whether it is a cloud pixel by pixel. Cloud pixels are 1 and non-cloud pixels are 0. The cloud mask is stored as a binary image, and only 0-valued pixels are retained in subsequent processing.

4. The method for adaptively adjusting the aerosol complex refractive index based on the cross-shaped structure according to claim 1, characterized in that: In step S4: S41. Calculate the distance between land and sea based on the coastline position and pixel position ,use To express the weight of ocean contribution, based on The weights are set in sections: ； in: represents the ocean contribution of one unit, Indicates the number of ocean pixels. When the pixel is ocean, the distance between land and sea is is a positive value. When the pixel is land, the distance between land and sea is a negative number. For pixels on the coastline, their weight is proportional to the number of ocean pixels in the eight adjacent pixels. S42. Utilization To represent the weight of industrial life, the weight of industrial land is the largest, set , the weight of residential area is less than that of office building, so residential area is set as , the office building is set up ,in represents the contribution of a unit’s industrial life; S43. Obtain the comprehensive weight factor after resampling based on the movement law of the structure function method model window ,in , represents the proportion of ocean contribution, Indicates the proportion of industrial life, and the values ​​are as follows: ； When the land pixel is more than 20 km away from the coastline, the ocean contribution is ignored; when the ocean pixel is more than 20 km away from the coastline, the industrial life contribution is ignored.

5. The method for adaptively adjusting the aerosol complex refractive index based on the cross-shaped structure according to claim 1, characterized in that: In step S5, after determining the calculation window size of the structure function model, the solar zenith angle, solar azimuth angle, satellite zenith angle and satellite azimuth angle are simultaneously resampled, and when constructing a lookup table using the 6S model, the resampled observation geometry value is directly input; Atmospheric mode: Use the custom mode to input the vertical profile data of atmospheric pressure, temperature, water vapor content and ozone content of the corresponding season according to the time of the image to be inverted; Aerosol mode: Select Junge spectrum, you need to input the Junge spectrum index and complex refractive index, and use the comprehensive weight factor obtained in step S4 to adaptively change the imaginary part of the complex refractive index. The final form of the comprehensive weight factor is , the imaginary part after adaptive adjustment is ,in, represents the ocean contribution coefficient, represents the industrial life contribution coefficient, represents the imaginary part of sea salt aerosol particles, Represents the imaginary part of continental aerosol particles.

6. The method for adaptively adjusting the aerosol complex refractive index based on the cross-shaped structure according to claim 5, characterized in that: In step S5, regarding the sensor code, 6S ​​comes with configured sensor bands. For sensor bands that are not covered, the corresponding spectral response function needs to be converted and added to the program; 6S simulates the transmission process of solar electromagnetic radiation between 0.25μm and 4.0μm, and divides the electromagnetic waves into intervals of 2.5nm to obtain 1501 wavelength values , so when adding a band, you only need to add the starting value of the wavelength of the band to be added. and According to the nearest neighbor interpolation method, Determine the corresponding wavelength value in and ,in and Indicates the position of the interpolated wavelength starting value in 1501 elements, and is obtained by bilinear interpolation arrive Spectral response values ​​between , set the corresponding spectral values ​​of the remaining positions to 0; after given the observation geometry, atmospheric model, aerosol model and sensor code, a lookup table containing atmospheric transmittance and 550nm aerosol optical thickness is constructed.

7. The method for adaptively adjusting the aerosol complex refractive index based on the cross-shaped structure according to claim 1, characterized in that: In step S6, an exponential function is used for fitting, and the fitting formula is as follows: ， ； in represents the atmospheric transmittance, represents the atmospheric aerosol optical thickness at 550nm, and are the fitting coefficients, where is a negative number; during the fitting process, the ratio of the structure function value of the apparent reflectance to the surface reflectance is , and is the coefficient determined by fitting, and the aerosol optical thickness can be obtained.

8. A system for adaptively adjusting the complex refractive index of an aerosol based on a 'P' structure, using the method for adaptively adjusting the complex refractive index of an aerosol based on a 'P' structure according to any one of claims 1 to 7, characterized in that: It includes a data acquisition unit, a data processing unit and an output unit; The data acquisition unit is used to obtain satellite images of the area to be inverted; The data processing unit processes the data and constructs a cross-shaped structure function model to calculate the apparent reflectivity structure function value of the image to be inverted and the surface reflectivity structure function value; calculates the weight factor for each pixel of the surface reflectivity image, and obtains the comprehensive weight factor after resampling according to the calculation window movement law of the structure function model; according to the comprehensive weight factor, the imaginary part of the aerosol complex refractive index is adaptively adjusted to construct a lookup table of atmospheric aerosol optical thickness and atmospheric transmittance; the ratio of the structure function value of the apparent reflectivity to the surface reflectivity is brought into the fitted formula of atmospheric transmittance and optical thickness to realize the inversion of aerosol optical thickness by the structure function method; Output unit: Output the results visually.

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