A method and device for atmospheric correction of a Fengyun satellite spectral imager based on a 6S radiation transmission model, a medium and a product

Abstract

The application discloses a FY satellite spectral imager atmospheric correction method and device based on 6S radiation transmission mode, medium and product, and relates to the field of atmospheric correction. The method comprises the following steps: determining the threshold range of a first parameter according to MERSI observation data and determining the threshold range of a second parameter according to atmospheric reanalysis data; generating a target coefficient by applying a 6S radiation transmission mode and a spectral response function of MERSI; constructing a lookup table; obtaining matching data values of the second parameter corresponding to each pixel in target MERSI observation data by applying an interpolation algorithm; determining the target coefficient of each pixel in the target MERSI observation data from the lookup table; obtaining the corrected reflectivity of each pixel by applying the 6S radiation transmission mode; and correcting the true color quick view of the visible light channel according to the corrected reflectivity of each pixel to obtain a corrected true color composite image. The application can perform atmospheric correction on MERSI wide-width global images.

Description

TECHNICAL FIELD

[0001] The application relates to the field of atmospheric correction, in particular to a Fengyun satellite spectral imager atmospheric correction method, device, medium and product based on a 6S radiation transmission mode. BACKGROUND

[0002] In the application process of remote sensing data, atmospheric correction is needed to remove the influence of atmospheric component absorption and scattering on the reflectivity measured by a satellite sensor. Since the 1970s, researches on atmospheric correction have been carried out for more than 40 years, and many atmospheric correction methods have been proposed. Common methods include the invariant target method based on statistics, the histogram matching method, the dark pixel method based on a simplified radiation transmission model, and the MODTRAN, FLAASH, ATCOR and 6S radiation transmission model based on radiation transmission theory. There are two methods for applying the radiation transmission model. One is to directly correct each pixel, and the other is to pre-establish an atmospheric correction lookup table. Direct correction is relatively simple, but it is extremely time-consuming and is not suitable for high-time-efficiency large-scale image correction. At present, there are precedents for using 6S and other radiation transmission models for atmospheric correction, but they are mostly applied to local small-range atmospheric correction research, and there are few real-time atmospheric correction algorithm researches on MERSI wide global images of the Fengyun-3 series satellite. SUMMARY

[0003] The application aims to provide a Fengyun satellite spectral imager atmospheric correction method, device, medium and product based on a 6S radiation transmission mode, which can perform atmospheric correction on MERSI wide global images.

[0004] To achieve the above object, the application provides the following scheme.

[0005] A Fengyun satellite spectral imager atmospheric correction method based on a 6S radiation transmission mode, characterized in that the method comprises the following steps.

[0006] MERSI observation data and atmospheric reanalysis data are acquired;

[0007] A threshold range of a first parameter is determined according to the MERSI observation data, and a threshold range of a second parameter is determined according to the atmospheric reanalysis data; the first parameter includes an observation zenith angle, a solar zenith angle, a relative azimuth angle, a satellite azimuth angle, a solar azimuth angle and a relative ground surface height; the relative ground surface height is a relative tropopause height based on a MERSI long-wave infrared channel brightness temperature; the second parameter includes an ozone column content, a 550nm aerosol optical thickness and a total precipitable water content;

[0008] According to the first parameter, the threshold range of the second parameter and the satellite observed reflectivity, a target coefficient corresponding to the payload visible light to near-infrared channel is generated by applying a 6S radiation transfer model and a spectral response function of MERSI; the target coefficient includes a transmittance coefficient, a scattering reflectivity coefficient and a spherical albedo coefficient;

[0009] According to the first parameter, the second parameter and the corresponding target coefficient, a lookup table is constructed;

[0010] A data set of the first parameter of target MERSI observation data and a data set of the second parameter of target atmospheric reanalysis data on the same day are acquired;

[0011] An interpolation algorithm is applied to the data set of the second parameter of the target atmospheric reanalysis data to obtain a matching data value of the second parameter corresponding to each pixel in the target MERSI observation data;

[0012] According to the data set of the first parameter and the matching data value of the second parameter, a target coefficient of each pixel in the target MERSI observation data is determined from the lookup table;

[0013] According to the target coefficient of each pixel in the target MERSI observation data and the satellite observed reflectivity, a 6S radiation transfer model is applied to obtain a corrected reflectivity of each pixel;

[0014] According to the corrected reflectivity of each pixel, a visible light channel true color fast view is corrected to obtain a corrected true color composite image.

[0015] Optionally, according to the corrected reflectivity of each pixel, a visible light channel true color fast view is corrected to obtain a corrected true color composite image, specifically including:

[0016] A visible light channel of the visible light channel true color fast view is normalized to obtain a normalized global image;

[0017] According to the corrected reflectivity of each pixel, a histogram equalization method is applied to obtain a corrected true color composite image.

[0018] Optionally, a nearest interpolation algorithm is applied to the data set of the second parameter of the target atmospheric reanalysis data to obtain a matching data value of the second parameter corresponding to each pixel in the target MERSI observation data.

[0019] Optionally, according to the first parameter, the second parameter and the corresponding target coefficient, a lookup table is constructed, specifically including:

[0020] applying an interpolation algorithm to the first parameter, the second parameter and the corresponding target coefficient to obtain the first parameter, the second parameter and the target coefficient after difference;

[0021] constructing a lookup table according to the correspondence between the first parameter after difference, the second parameter after difference and the target coefficient after difference.

[0022] Optionally, a bilinear interpolation algorithm is applied to the first parameter, the second parameter and the corresponding target coefficient to obtain the first parameter, the second parameter and the target coefficient after difference.

[0023] A computer device comprises a memory, a processor to store a computer program on the memory and run the computer program on the processor, and the processor executes the computer program to implement the steps of the method for atmospheric correction of a Fengyun satellite spectral imager based on a 6S radiation transfer model according to any one of the above.

[0024] A computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the method for atmospheric correction of a Fengyun satellite spectral imager based on a 6S radiation transfer model according to any one of the above.

[0025] A computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps of the method for atmospheric correction of a Fengyun satellite spectral imager based on a 6S radiation transfer model according to any one of the above.

[0026] According to the embodiments of the present application, the following technical effects are achieved.

[0027] The present application can quickly realize atmospheric correction of global MERSI visible light and near-infrared channels and generate true color image products and reflectivity products by using MERSI L1 data and CAMS reanalysis data. Compared with other atmospheric correction methods, the present application has a wider spatial application range, uses the relative reflectivity height converted from the infrared channel brightness temperature, which is closer to the actual situation compared with the common ground elevation as input for cloud reflectivity correction, and uses CAMS reanalysis data to make the atmospheric condition closer to real time, and the correction accuracy is better. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1A specific flowchart of actual application of the 6S radiation transmission mode-based atmospheric correction method for the Fengyun satellite spectral imager provided in Embodiment 1 of the present application is shown in the figure.

[0030] Figure 2 The reflectance probability density distribution graphs before and after correction of the true color channels 1, 2 and 3 are shown in the figure. Figure 2 (a) in the figure is the reflectance probability density distribution graph before and after correction of the true color channel 1. Figure 2 (b) in the figure is the reflectance probability density distribution graph before and after correction of the true color channel 2. Figure 2 (c) in the figure is the reflectance probability density distribution graph before and after correction of the true color channel 3.

[0031] Figure 3 A flowchart of the 6S radiation transmission mode-based atmospheric correction method for the Fengyun satellite spectral imager provided in Embodiment 1 of the present application is shown in the figure.

[0032] Figure 4 An internal structure diagram of the computer device is shown in the figure. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0034] The purpose of the present application is to provide a 6S radiation transmission mode-based atmospheric correction method, device, medium and product for the Fengyun satellite spectral imager, which can perform atmospheric correction on MERSI wide global images.

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0036] Embodiment 1

[0037] As shown in the figures, Figure 1 and Figure 3 the 6S radiation transmission mode-based atmospheric correction method for the Fengyun satellite spectral imager in the present embodiment includes:

[0038] Step S1: Obtain MERSI observation data and atmospheric reanalysis data.

[0039] Step S2: determining a threshold range of a first parameter according to the MERSI observation data and a threshold range of a second parameter according to the atmospheric reanalysis data; the first parameter includes an observation zenith angle, a solar zenith angle, a relative azimuth angle, a satellite azimuth angle, a solar azimuth angle and a relative surface height; wherein the relative surface height is a relative tropopause height based on a brightness temperature of a MERSI long-wave infrared channel; the second parameter includes an ozone column content, an aerosol optical thickness at 550nm and a total precipitable water.

[0040] Step S3: generating a target coefficient corresponding to a visible light to near infrared channel of a payload according to the threshold ranges of the first parameter and the second parameter and a satellite observation reflectivity, applying a 6S radiation transfer model and a spectral response function of MERSI; the target coefficient includes a transmittance coefficient, a scattering reflectivity coefficient and a spherical albedo coefficient.

[0041] Step S4: constructing a lookup table according to the first parameter, the second parameter and the corresponding target coefficient.

[0042] S4 specifically includes:

[0043] Step S41: applying an interpolation algorithm to the first parameter, the second parameter and the corresponding target coefficient to obtain a first parameter, a second parameter and a target coefficient after interpolation.

[0044] Specifically, a bilinear interpolation algorithm is applied to the first parameter, the second parameter and the corresponding target coefficient to obtain a first parameter, a second parameter and a target coefficient after interpolation.

[0045] Step S42: constructing a lookup table according to a corresponding relationship between the first parameter after interpolation, the second parameter after interpolation and the target coefficient after interpolation.

[0046] Step S5: obtaining a data set of a first parameter of target MERSI observation data and a data set of a second parameter of target atmospheric reanalysis data on the same day.

[0047] Step S6: applying an interpolation algorithm to the data set of the second parameter of the target atmospheric reanalysis data to obtain a matching data value of the second parameter corresponding to each pixel in the target MERSI observation data.

[0048] Specifically, a nearest neighbor interpolation algorithm is applied to the data set of the second parameter of the target atmospheric reanalysis data to obtain a matching data value of the second parameter corresponding to each pixel in the target MERSI observation data.

[0049] Step S7: determining a target coefficient of each pixel in the target MERSI observation data from the lookup table according to the data set of the first parameter and the matching data value of the second parameter.

[0050] Step S8: According to the target coefficient of each pixel in the target MERSI observation data and the satellite observation reflectivity, a 6S radiation transfer model is applied to obtain the corrected reflectivity of each pixel.

[0051] Step S9: According to the corrected reflectivity of each pixel, a true color fast view in a visible light channel is corrected to obtain a corrected true color composite image.

[0052] S9 specifically includes:

[0053] Step S91: The visible light channel of the true color fast view in the visible light channel is normalized to obtain a normalized global image.

[0054] Step S92: According to the corrected reflectivity of each pixel, a histogram equalization method is applied to obtain a corrected true color composite image.

[0055] The dimension of satellite observation data is three-dimensional, the xy direction is the spatial distribution of pixels, and the z direction is the channel number of pixels. That is, each pixel has N channels. Since the first, second and third channels of the FY satellite visible light are red, green and blue, only the reflectivity of the first, second and third channels of each pixel is needed to make a true color composite image.

[0056] In practical applications, the specific implementation process of the present application includes:

[0057] Step 1: Using Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition reanalysis data, statistical analysis is performed to obtain the probability distribution of global ozone column content, 550 nm aerosol optical thickness and total precipitable water to determine the appropriate lookup table distribution and upper and lower boundaries. In order to compensate for the deviation of the 6S radiation transfer model in the calculation of the photometric path length in the high zenith angle region, a relative reflection layer height based on the brightness temperature of the MERSI long-wave infrared channel is defined to replace the surface height. The lookup table parameters include the observation zenith angle, the solar zenith angle, the relative azimuth angle, the ozone column content, the total precipitable water, the 550 nm aerosol optical thickness, and the relative reflection layer height. For the observation effective spatial geometry of each type of medium resolution spectral imager (MERSI) and the above-mentioned other parameters, the corresponding lookup table segment threshold and range are planned and determined. Among them, the relative azimuth angle refers to the absolute value of the difference between the satellite azimuth angle and the solar azimuth angle.

[0058] Step 2: According to the principle of the 6S radiation transfer model, in the future correction process, according to the formula: The atmospheric correction result can be obtained, wherein, ρ TOA is the satellite observation reflectivity, and ρ sThe satellite observes the physical quantity in the visible band, which is the reflectance. The satellite observed reflectance refers to the original reflectance data of the satellite L1 level data after calibration calculation, which is equivalent to the apparent reflectance of the top of the atmosphere in the physical sense. The satellite observed reflectance is the original data of the satellite.

[0059] For the look-up table range defined in step 1, the transmittance coefficient (x a ), scattering reflectance coefficient (x b ) and spherical albedo coefficient (x c ) look-up tables corresponding to the visible light to near-infrared channel of the load are generated by using the 6S radiation transfer model combined with the spectral response function of each type of MERSI.

[0060] Step 3: Calculate the index using the MERSI observation data and the CAMS reanalysis data, retrieve the look-up table generated in step 2, obtain the x a , x b and x c coefficients corresponding to the pixel, and calculate the corrected ground surface reflectance p s using the formula in step 2. In the application of the look-up table, the observation zenith angle, the sun zenith angle, the satellite azimuth angle, the sun azimuth angle and the relative ground height converted by the brightness temperature in the data set need to be extracted as part of the look-up conditions. At the same time, the ozone column content, 550 nm aerosol optical thickness and total precipitable water in the CAMS reanalysis data set on the same day need to be extracted and spatially matched to each pixel point of the satellite observation after the near interpolation as another part of the look-up conditions. Since the angle interval of the look-up table is much larger than the angle change between the pixels, in order to improve the resolution of the look-up table, the look-up table is bilinearly interpolated to make it continuous, so as to realize more accurate correction in the angle. The atmospheric correction coefficients obtained by look-up table are brought into x a , x b and x c in the above formula, and the reflectance is brought into p TOA to calculate the corrected ground surface reflectance p s data set.

[0061] Step 4: After obtaining the corrected ground surface reflectance, the generation of the true color quick view of the visible light channel needs to normalize the visible light channel to unify the overall reflectance distribution of the global image, but this operation will cause the phenomenon of color deviation and brightness distortion in the true color synthesis image. In order to correct this phenomenon, the histogram equalization method is used to stretch, and by mapping the reflectance range in 0-1 to the pixel range of 0-255, the error mapping of reflectance to color and the overall image contrast are effectively improved.

[0062] This invention establishes a universal framework for the MERSI visible and near-infrared channels of the Fengyun-3 series satellites. It can be rapidly deployed on the MERSI systems of each generation of Fengyun-3 satellites, providing a reuse solution for existing and future Fengyun-3 satellites. Utilizing MERSI L1 level data and CAMS reanalysis data, it can quickly perform atmospheric correction on global MERSI visible and near-infrared channels, generating true-color image products and reflectance products. It can process all 1km resolution global data within a single day within 15 minutes. Compared to other atmospheric correction methods, this invention has a wider spatial applicability. Using the relative reflectance layer height derived from infrared channel brightness temperature conversion provides a more realistic assessment of cloud reflectance correction compared to commonly used surface elevation. Furthermore, the use of CAMS reanalysis data ensures more real-time atmospheric conditions and better correction accuracy.

[0063] This invention reduces the computational burden and resource consumption of physical models by employing a lookup table construction and a smoothing method using bilinear inverse distance interpolation. Compared to directly using the 6S radiative transfer model, the lookup table algorithm with interpolation achieves similar accuracy while the optimized atmospheric correction algorithm effectively reduces computation time. Based on existing computing resources, the average computation time per pixel per core is reduced from approximately 0.1 seconds to approximately 0.0001 seconds. The parallel computation speed for a single 5-minute block of data (2000×2048 pixels) is reduced to an average of 18 seconds. Correction processing for all daytime data can be controlled within 15 minutes, providing support for future operational and engineering applications.

[0064] For global figures, such as Figure 2 As shown, taking the reflectance probability density distribution maps of true color channels 1, 2, and 3 before and after correction as an example, the spectral distribution of each channel after correction is wider than that before correction, indicating that atmospheric correction has calculated more reflectance values ​​in each channel, that is, obtained richer surface information. Visual comparison shows that the surface visibility after atmospheric correction is significantly improved, the contrast is improved, the sense of layering is better, and richer ground features are displayed.

[0065] Example 2

[0066] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps of the atmospheric correction method for the Fengyun satellite spectral imager based on the 6S radiative transfer mode in Embodiment 1.

[0067] Example 3

[0068] A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for Fengyun satellite spectral imager atmospheric correction based on 6S radiation transfer mode in embodiment 1.

[0069] Embodiment 4

[0070] A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for Fengyun satellite spectral imager atmospheric correction based on 6S radiation transfer mode in embodiment 1.

[0071] Embodiment 5

[0072] A computer device, which can be a database, can have an internal structure diagram as shown in the figure. Figure 4 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store transactions to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the method for Fengyun satellite spectral imager atmospheric correction based on 6S radiation transfer mode in embodiment 1.

[0073] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0075] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0076] The principles and implementation modes of the present application are described by using specific examples in this paper, and the above-mentioned embodiments are only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for atmospheric correction of a Fengyun satellite spectral imager based on a 6S radiative transfer model, characterized in that, The method comprises: obtaining MERSI observation data and atmospheric reanalysis data; determining a threshold range of a first parameter according to the MERSI observation data and a threshold range of a second parameter according to the atmospheric reanalysis data; the first parameter comprises an observation zenith angle, a solar zenith angle, a relative azimuth angle, a satellite azimuth angle, a solar azimuth angle and a relative ground height; wherein the relative ground height is a relative tropopause height based on a brightness temperature of a MERSI long-wave infrared channel, used to compensate for a deviation in the calculation of a photometric path length of a 6S radiation transfer model in a high zenith angle region; the second parameter comprises an ozone column content, an aerosol optical thickness at 550 nm and a total precipitable water content; applying a 6S radiation transfer model and a spectral response function of MERSI to generate a target coefficient corresponding to a visible light to near-infrared channel of a payload according to the threshold ranges of the first parameter and the second parameter and satellite observation reflectivity; the target coefficient comprises a transmittance coefficient, a scattering reflectance coefficient and a spherical albedo coefficient; constructing a lookup table according to the first parameter, the second parameter and the corresponding target coefficient; obtaining a data set of the first parameter of target MERSI observation data and a data set of the second parameter of target atmospheric reanalysis data on the same day; applying an interpolation algorithm to the data set of the second parameter of the target atmospheric reanalysis data to obtain matching data values of the second parameter corresponding to each pixel in the target MERSI observation data; determining the target coefficient of each pixel in the target MERSI observation data from the lookup table according to the data set of the first parameter and the matching data values of the second parameter; applying a 6S radiation transfer model to obtain a corrected reflectivity of each pixel according to the target coefficient of each pixel in the target MERSI observation data and the satellite observation reflectivity; correcting a true color quick view of a visible light channel according to the corrected reflectivity of each pixel to obtain a corrected true color composite image.

2. The 6S radiative transfer model based atmospheric correction method for FY-3 satellite spectral imager according to claim 1, characterized in that, correcting a true color quick view of a visible light channel according to the corrected reflectivity of each pixel to obtain a corrected true color composite image, specifically comprising: normalizing a visible light channel of the true color quick view of the visible light channel to obtain a normalized global image; applying a histogram equalization method to obtain a corrected true color composite image according to the corrected reflectivity of each pixel.

3. The 6S radiative transfer model based atmospheric correction method for FY-3 satellite spectral imager according to claim 1, characterized in that, applying a nearest interpolation algorithm to the data set of the second parameter of the target atmospheric reanalysis data to obtain matching data values of the second parameter corresponding to each pixel in the target MERSI observation data.

4. The 6S radiative transfer model based atmospheric correction method for FY-3 satellite spectral imager according to claim 1, characterized in that, constructing a lookup table according to the first parameter, the second parameter and the corresponding target coefficient, specifically comprising: applying an interpolation algorithm to the first parameter, the second parameter and the corresponding target coefficient to obtain the first parameter, the second parameter and the target coefficient after differencing; constructing a lookup table according to the corresponding relationship between the first parameter after differencing, the second parameter after differencing and the target coefficient after differencing.

5. The method according to claim 4, wherein, A bilinear interpolation algorithm is applied to the first parameter, the second parameter and the corresponding target coefficient to obtain the first parameter, the second parameter and the target coefficient after difference.

6. A computer apparatus comprising: The memory, the processor to store the computer program stored on the memory and can run on the processor, characterized in that the processor executes the computer program to realize the steps of the method for the Fengyun satellite spectral imager atmospheric correction based on the 6S radiation transfer mode in any one of claims 1-5.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the steps of the method for the Fengyun satellite spectral imager atmospheric correction based on the 6S radiation transfer mode in any one of claims 1-5.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method for the Fengyun satellite spectral imager atmospheric correction based on the 6S radiation transfer mode in any one of claims 1-5. The computer program is executed by the processor to realize the steps of the method for the Fengyun satellite spectral imager atmospheric correction based on the 6S radiation transfer mode in any one of claims 1-5.

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