A method and device for simulating top-of-atmosphere radiation using greenhouse gas passive loads

Abstract

The present application provides a method and device for simulating top-of-atmosphere radiation from a greenhouse gas passive payload. The method provided in the present application includes: determining the satellite radiation band based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation; calculating a first radiation amount after the surface reflection modulates the initial solar radiation using surface reflectivity data within the radiation band; determining the total molecular optical thickness based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum within the radiation band and the distribution of different molecular number densities in the atmosphere, and calculating a first radiation attenuation due to molecular absorption at the top of the atmosphere; determining the aerosol optical thickness based on the scattering coefficient and extinction coefficient of aerosol particles within the radiation band and the distribution of aerosol particle density in the atmosphere, and calculating a second radiation attenuation due to aerosol at the top of the atmosphere; and subtracting the first and second radiation attenuations from the first radiation amount to obtain the radiation intensity at the top of the atmosphere.

Description

Technical Field

[0001] The present application relates to the technical field of atmospheric environment remote sensing monitoring, and in particular to a method and device for simulating top-of-atmosphere radiation of a greenhouse gas passive payload. Background Art

[0002] Top-of-atmosphere radiation simulation plays a crucial role in modern remote sensing technology, particularly in greenhouse gas monitoring and climate change research. Top-of-atmosphere radiation simulation accurately describes the propagation of electromagnetic waves through the atmosphere, encompassing the physical processes of absorption and scattering by atmospheric molecules, aerosols, and clouds, as well as surface reflection. These simulations enable the study of the atmospheric radiation balance, energy budget, changes in gas concentrations, and the distribution of Earth and solar radiation, providing important decision-making support for fields such as meteorology, energy, and agriculture. For remote sensing satellites, accurate radiation transmission simulation is fundamental to obtaining reliable remote sensing data.

[0003] Currently, existing top-of-atmosphere radiation simulations rely on atmospheric radiative transfer models, such as the 6S model. While these models can account for both molecular absorption and aerosol scattering, they suffer from low spectral resolution, making them inadequate for high-precision remote sensing tasks such as greenhouse gas monitoring. This limitation makes it difficult for traditional atmospheric radiative transfer models to accurately simulate the absorption effects of greenhouse gases, hindering accurate assessments of greenhouse gas concentrations and environmental changes.

[0004] Therefore, a method is urgently needed to solve the problem that the existing atmospheric radiation transfer model cannot simultaneously simulate molecular absorption and aerosol scattering with high spectral resolution, so as to accurately simulate the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, and provide strong support for the evaluation of greenhouse gas passive load performance. Summary of the Invention

[0005] In view of this, the present application provides a method and device for simulating top-of-atmosphere radiation of a greenhouse gas passive payload, which is used to solve the problem that the existing atmospheric radiation transmission model cannot simultaneously simulate molecular absorption and aerosol scattering with high spectral resolution, and to achieve accurate simulation of the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, providing strong support for the performance evaluation of greenhouse gas passive payloads.

[0006] Specifically, this application is implemented through the following technical solutions:

[0007] In a first aspect, the present application provides a method for simulating top-of-atmosphere radiation of a greenhouse gas passive load, the method comprising:

[0008] Based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, the radiation band that the satellite can detect is determined;

[0009] Acquiring surface reflectance data based on the spatial distribution characteristics of the surface reflectance in the radiation band and the surface type;

[0010] Utilizing the surface reflectivity data, calculating the modulation of initial solar radiation by surface reflection through a reflection model to generate a first radiation amount of sunlight after reflection from the surface; the initial solar radiation is generated based on energy distribution characteristics of solar radiation;

[0011] establishing an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determining the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere; and calculating the first radiation attenuation of the top of the atmosphere due to molecular absorption based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics;

[0012] Calculating the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determining the aerosol optical depth based on the scattering coefficient, extinction coefficient, and the distribution of the aerosol particle density in the atmosphere; and calculating the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical depth;

[0013] The first radiation attenuation amount and the second radiation attenuation amount are subtracted from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0014] The second aspect of the present application provides a greenhouse gas passive load top of atmosphere radiation simulation device, the device includes a determination module, an acquisition module, a generation module and a calculation module; wherein,

[0015] The determination module is configured to determine the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation;

[0016] The acquisition module is used to acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type;

[0017] The generating module is configured to use the surface reflectivity data to calculate the modulation of the initial solar radiation by the surface reflection through a reflection model, thereby generating a first radiation amount of sunlight after being reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation;

[0018] The calculation module is configured to establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere; and calculate the first radiation attenuation of the molecular absorption at the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics;

[0019] The calculation module is further configured to calculate a scattering coefficient and an extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine an aerosol optical depth based on the scattering coefficient, the extinction coefficient, and the distribution of aerosol particle density in the atmosphere; and calculate a second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical depth.

[0020] The acquisition module is further configured to subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0021] This application provides a method and apparatus for simulating top-of-atmosphere radiation from a greenhouse gas passive payload. First, based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, the application first determines the radiation bands that the satellite can detect. This selection of radiation bands takes into account not only the energy distribution of solar radiation but also the capabilities of the satellite's sensors, ensuring the selection of detailed hyperspectral data covering multiple bands. This setup provides high-spectral-resolution band data for subsequent molecular absorption and aerosol scattering simulations. Furthermore, by further utilizing surface reflectance data, combined with the spatial distribution characteristics of surface reflectance and the surface type, a reflection model is used to calculate the modulation of solar radiation by surface reflection. This process not only provides a more refined initial value for radiation intensity but also lays the foundation for calculating radiation attenuation at high spectral resolution, further enhancing the accuracy of the simulation of reflection effects. Secondly, during the molecular absorption simulation, the application establishes an absorption model for each molecule based on the Doppler broadening and collisional broadening characteristics of the molecular absorption lines within the radiation band to achieve high-spectral-resolution simulations. To enable accurate absorption simulation at high spectral resolution, the absorption model considers the absorption characteristics of different wavelengths, enabling precise calculation of the absorption effect within each wavelength. High spectral resolution data distinguishes the absorption characteristics of each wavelength, ensuring that greenhouse gas absorption of radiation can be accurately quantified within a narrower wavelength range. High spectral resolution data allows the absorption effect of each molecule to be captured within a narrower wavelength range, avoiding the blurring effects associated with wide-band processing. For example, the absorption characteristics of carbon dioxide and water vapor vary significantly across wavelengths. High spectral resolution enables these differences to be accurately captured, improving the accuracy of molecular absorption simulations. Thirdly, this application simulates the scattering and extinction properties of aerosols by calculating the optical parameters of aerosol particles and then using complex mathematical formulas such as the Mie scattering formula and the Riccati-Bessel function. Aerosols scatter radiation differently across different wavelengths. By calculating the scattering coefficient and extinction coefficient for each aerosol particle and combining them with the distribution of aerosol particle density in the atmosphere, the scattering and extinction effects of aerosols can be accurately simulated within each wavelength range. High-spectral-resolution data provides detailed aerosol optical properties for each waveband, which is crucial for the high-precision calculation of aerosol optical depth. Combining scattering and extinction coefficients, the aerosol characteristics at different altitudes are further combined to accurately calculate aerosol optical depth. High-spectral-resolution data ensures a detailed simulation of aerosol scattering and absorption effects at each altitude, thereby providing accurate aerosol radiation attenuation. Finally, after obtaining detailed simulation data for surface reflection, molecular absorption, and aerosol scattering and absorption, these effects are combined to calculate the radiation intensity at the top of the atmosphere.Since each link is carefully calculated under high spectral resolution data, from surface reflection to atmospheric molecular absorption, and then to aerosol scattering, the final radiation intensity result is of high accuracy, achieving accurate simulation of the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, providing strong support for the evaluation of greenhouse gas passive load performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of the greenhouse gas passive load top of atmosphere radiation simulation method provided in Example 1 of the present application;

[0023] Figure 2 This is a schematic diagram of the structure of the greenhouse gas passive load top of atmosphere radiation simulation device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0024] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0025] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] Specific embodiments are given below to introduce the technical solutions of the present application in detail.

[0028] Figure 1 This is a flow chart of the greenhouse gas passive load top of atmosphere radiation simulation method provided in Example 1 of this application. Figure 1 The method provided in this embodiment may include:

[0029] S101. Based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, determine the radiation bands that the satellite can detect.

[0030] Specifically, the spectral response characteristics of a satellite payload refer to its ability to respond to radiation of different wavelengths. Each satellite payload has specific spectral response characteristics, which determine the wavelengths of radiation it can effectively detect. For example, some instruments may be particularly sensitive to visible light, but less responsive to infrared or ultraviolet bands. The energy distribution characteristics of solar radiation refer to the distribution of radiation intensity from a radiation source (such as the sun) across different wavelengths. The energy distribution of solar radiation varies at different wavelengths.

[0031] Furthermore, satellite remote sensing missions typically aim to obtain information about the Earth's surface or atmosphere. Different land objects or atmospheric components have different absorption and scattering characteristics for radiation of different wavelengths. Therefore, it is necessary to identify appropriate radiation bands to maximize observation effectiveness. For example, greenhouse gas absorption peaks typically occur in the infrared band, while vegetation reflectance peaks occur in the visible and near-infrared bands. By selecting the appropriate band, satellites can more effectively monitor specific land objects or atmospheric components.

[0032] In a specific implementation, determining the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation includes:

[0033] (1) Based on the spectral response characteristics of the satellite payload, a multi-band spectral response function is established.

[0034] Specifically, the multi-band spectral response function is a mathematical description of the satellite payload's ability to respond to radiation in different bands, characterizing the satellite payload's response characteristics to radiation at multiple wavelengths (or bands).

[0035] In a specific implementation, the multi-band spectral response function is established based on the spectral response characteristics of the satellite payload, including: determining parameters related to the satellite payload according to the spectral response characteristics of the satellite payload; the parameters characterize the response capability of the satellite payload in different bands and the sensitivity to radiation in different bands; based on the parameters, combined with the application requirements of the satellite payload and the absorption characteristics of greenhouse gases, determining the appropriate spectral range and sampling interval; based on the selected spectral range and sampling interval, spectrally sampling the Gaussian function to obtain a multi-band spectral response function.

[0036] Specifically, satellite payload-related parameters characterize the satellite payload's sensitivity and response to radiation within different wavelength ranges. These parameters include the satellite payload's wavelength range, sensitivity, noise level, and transmittance. A Gaussian function is often used to represent the shape of the satellite payload's response. The Gaussian function mathematically calculates and determines the response strength for each wavelength band.

[0037] In specific implementation, the spectral response characteristics of the satellite payload are obtained based on the technical documentation or experimental data provided by the satellite payload manufacturer. Parameters related to the satellite payload's performance, such as the instrument's wavelength range, sensitivity, noise level, and transmittance, are extracted from the spectral response characteristics. Based on the spectral response characteristics of the satellite payload, the sensitivity of the satellite payload to radiation in different wavelength bands is defined, and the response intensity of the satellite payload in each wavelength band is calculated. Furthermore, the mission objectives of the satellite payload are comprehensively considered, including the monitoring target (such as greenhouse gases, where the satellite payload needs to cover the specific absorption band of greenhouse gases) and the requirement for high spectral resolution. In addition, the absorption characteristics of greenhouse gases are considered, and the location and intensity of their absorption lines are studied for different greenhouse gases. For example, the absorption lines of CO2 are primarily located in the mid-infrared region (approximately 15μm), while the absorption lines of methane are located near 3.3μm. The absorption characteristics of the target gas in different wavelength bands are determined, and those wavelength bands with significant absorption features near the target gas's absorption band are selected. The absorption intensity of the greenhouse gas, that is, the degree of radiation absorption by the gas at different concentrations, must also be considered. When determining the spectral range, it should cover all relevant greenhouse gas absorption characteristics. For example, if simultaneous monitoring of CO2 and methane is desired, coverage of the 2-15 μm range may be necessary. Furthermore, the selection of the spectral range must take into account atmospheric transmission characteristics. Certain bands may be affected by absorption or scattering by other gases in the atmosphere (such as water vapor). Therefore, these bands should be avoided when selecting bands to avoid affecting the extraction of greenhouse gas concentrations. When determining the sampling interval, to ensure sufficient resolution of the greenhouse gas absorption characteristics, an appropriate sampling interval is required. The sampling interval should be small enough to ensure that the satellite payload can capture the absorption peaks and details of the target gas. This interval should be fine enough to obtain a sufficient number of data points within the spectral range. Using the selected spectral range and sampling interval, a Gaussian function is fitted and sampled to the spectral response. The spectral response of each band is combined with its corresponding Gaussian curve. The response data for each band is then combined to form a complete multi-band spectral response function.

[0038] (2) Based on the energy distribution characteristics of solar radiation within the spectral range, a high spectral resolution solar light source simulation model is constructed; the simulation model represents the spectral distribution of solar radiation.

[0039] Specifically, the high spectral resolution solar light source simulation model is modeled based on the spectral distribution of solar radiation and can simulate the intensity or energy density of solar radiation in different wavelength ranges.

[0040] In this embodiment, the high-spectral-resolution solar light source simulation model is a Kurucz solar spectrum model with a resolution of 0.001 nm, covering a spectral range of 200 nm to 3000 nm. Since over 99% of the solar radiation spectrum at the upper boundary of the Earth's atmosphere lies between wavelengths of 0.15 and 4.0 μm, approximately 50% of solar radiation energy is in the visible spectrum (between wavelengths of 0.4 and 0.76 μm), 7% in the ultraviolet spectral region (wavelengths < 0.4 μm), and 43% in the infrared spectral region (wavelengths > 0.76 μm), with maximum energy occurring at a wavelength of 0.475 μm. Because the wavelength of solar radiation is much smaller than that of terrestrial and atmospheric radiation (approximately 3 to 120 μm), solar radiation is often referred to as shortwave radiation and longwave radiation. The Kurucz solar spectrum is observed using a ground-based Fourier transform spectrometer, which can clearly capture subtle features in the solar spectrum, achieving an ultra-high spectral resolution of 0.001 nm. In terms of data processing, the collected solar FTS spectral data was processed, including setting continuity levels and plotting spectra to ensure data accuracy and reliability. Furthermore, the total opacity of the spectrum was quickly calculated to improve data processing efficiency. Kurucz also employed a semi-empirical method to fit the central intensity spectrum calculated from the model with the actual observed spectrum to generate a flux spectrum, providing theoretical support for understanding spectral formation.

[0041] In specific implementation, based on the spectral distribution characteristics of solar radiation, the spectral energy distribution of solar radiation is calculated for each wavelength band. Using hyperspectral sampling technology, the spectral distribution is modeled according to sufficiently fine wavelength intervals, and the intensity distribution data of solar radiation in each band is generated to obtain a high spectral resolution solar light source simulation model.

[0042] (3) Combining the multi-band spectral response function with the high spectral resolution solar light source simulation model to determine the radiation bands that can be detected by the satellite.

[0043] In practical implementation, the established multi-band spectral response function is combined with the solar radiation spectrum model. For each spectral band, the satellite's detectable radiation intensity is calculated by performing a convolution or product operation on the multi-band spectral response function and the solar radiation intensity data. Based on the radiation intensity data, by comparing the radiation intensity of different bands with the satellite's response capability, the effective radiation band range is determined, thereby determining the bands that the satellite payload can effectively detect.

[0044] The method provided in this example first determines the radiation bands detectable by the satellite, based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation. This selection of radiation bands takes into account not only the energy distribution of solar radiation but also the capabilities of the satellite sensor, ensuring the selection of detailed hyperspectral data covering multiple bands. This setup provides high-spectral-resolution band data for subsequent molecular absorption and aerosol scattering simulations.

[0045] S102: Acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type.

[0046] Specifically, surface reflectivity refers to the ability of the surface to reflect electromagnetic waves (such as solar radiation) that strike it, and is typically between 0 and 1. The spatial distribution of surface reflectivity refers to how surface reflectivity is distributed across geographic space. This spatial distribution significantly impacts surface reflectivity, reflecting how surface reflectivity varies across different locations or regions, including cities, forests, lakes, and deserts. Surface type refers to the different physical features and cover types of the Earth's surface. Surface type determines the characteristics of surface reflectivity, and surface types typically include forests, deserts, and urban areas. Surface reflectivity data contains various types of information related to surface reflectivity, including reflectivity values ​​(reflectivity values ​​in different wavelength bands), spatial distribution (the spatial distribution pattern of reflectivity, which may be represented in a map or raster data format, typically for a region or globally), temporal information (reflecting changes in reflectivity over time), and surface type.

[0047] In a specific implementation, obtaining surface reflectance data based on the spatial distribution characteristics of the surface reflectance in the radiation band and the surface type includes:

[0048] (1) Obtaining overall surface reflectance data; the overall surface reflectance data includes surface reflectance data at multiple bands, multiple surface types, and multiple spatial resolutions.

[0049] Specifically, the global surface reflectance data includes surface reflectance data in multiple bands, multiple surface types, and multiple spatial resolutions. The global surface reflectance data can be obtained by sensors carried on satellite payloads.

[0050] For example, in this embodiment, the overall surface reflectivity data comes from the bidirectional reflectance distribution function and albedo (BRDF / Albedo) data measured by MODIS on the Terra and Aqua satellites, including two spatial resolutions of 0.05° and 500m, and its temporal resolution is 16 days. It contains seven detection bands in total, and the surface types include evergreen coniferous forest, evergreen broad-leaved forest, deciduous coniferous forest, deciduous broad-leaved forest, mixed forest, closed shrubland, open shrubland, forest steppe, savanna, grassland, wetland, cultivated land, city, Antarctic snowfield, desert, ocean waters, tundra, fresh snow, sea ice, etc.

[0051] (2) Based on the radiation band, first surface reflectance data of the corresponding band is obtained by screening from the overall surface reflectance data.

[0052] Specifically, in combination with the above description, the overall surface reflectivity data includes surface reflectivity data of multiple bands. In this embodiment, only the surface reflectivity data of the radiation band needs to be obtained, so screening is required.

[0053] In specific implementation, based on the determined radiation band, the corresponding field (such as band) in the overall surface reflectance data is retrieved, the band in the field is set as the radiation band for extraction, and the first surface reflectance data that meets these bands is screened. For example, if reflectance data of a specific band (such as the near-infrared band) is required, all surface reflectance data of this band will be extracted from the overall dataset.

[0054] (3) For the second surface reflectivity data other than the first surface reflectivity data in the overall surface reflectivity data, the second surface reflectivity data is converted into third surface reflectivity data of a corresponding band based on a conversion coefficient between different bands; the second surface reflectivity data and the first surface reflectivity data have corresponding bands different from each other.

[0055] Specifically, the second surface reflectance data and the first surface reflectance data have different wavelength bands, while other information may be the same or different (such as surface type and spatial resolution). Different wavelength bands can be converted using a linear equation constructed using a conversion system. The conversion coefficient is typically determined based on a physical model or empirical formula (such as a conversion coefficient obtained through regression analysis), and the conversion coefficient varies between different wavelength bands.

[0056] In specific implementation, for the second surface reflectivity data in the overall surface reflectivity data that is not in the radiation band, the second surface reflectivity data of each band is converted into the third surface reflectivity data of the radiation band based on the second surface reflectivity data of each band and the conversion coefficient between the band and the radiation band.

[0057] For example, in one embodiment, the radiation band is a near-infrared band. For the second surface reflectivity data in the ultraviolet band, the second surface reflectivity data is converted into the third surface reflectivity data in the radiation band based on the conversion coefficient 1; for the second surface reflectivity data in the visible light band, the second surface reflectivity data is converted into the third surface reflectivity data in the radiation band based on the conversion coefficient 2.

[0058] (4) Determine the target surface type based on the simulation requirements, and select the fourth surface reflectivity data corresponding to the surface type from the first surface reflectivity data and the third surface reflectivity data based on the target surface type.

[0059] In specific implementation, the target surface type (such as city, forest, agriculture, etc.) is selected according to the simulation requirements, and the corresponding fields (surface type) in the first surface reflectance data and the third surface reflectance data are retrieved, and matched according to the surface type identifier or classification label to extract the fourth surface reflectance data of the target surface type.

[0060] (5) Determine the target spatial resolution based on the simulation requirements, and filter the surface reflectance data of the corresponding spatial resolution from the fourth surface reflectance data based on the target spatial resolution.

[0061] In specific implementation, the target spatial resolution is determined based on the required satellite image resolution (e.g., 10 meters, 30 meters, etc.) in the simulation. Based on the target spatial resolution, a data resampling technique (e.g., nearest neighbor interpolation, bilinear interpolation, or nearest neighbor interpolation) is used to select matching spatial resolutions from the fourth surface reflectance data to obtain surface reflectance data of the corresponding spatial resolution.

[0062] The method provided in this embodiment achieves high-precision and high-efficiency acquisition of surface reflectance data by combining data from multiple bands, multiple surface types, and different spatial resolutions, ensuring the accuracy of radiation and greenhouse gas simulations at high spectral resolution. First, by acquiring overall surface reflectance data, covering reflectance data from multiple bands, different surface types, and multiple spatial resolutions, the reflectance characteristics of the surface can be comprehensively described. Data from different bands supports detailed analysis of the reflectance characteristics of the surface in different bands, which is particularly important for simulations with high spectral resolution, as high spectral resolution requires accurate capture of subtle changes in radiation, thereby effectively improving the accuracy of the simulation. The diversity of surface types takes into account the different reflectance characteristics of different surfaces (such as forests, deserts, cities, etc.) in different bands. Second, by screening and converting reflectance data from different bands, the problem of mismatched or missing reflectance data in different bands can be addressed in practice. In particular, by using conversion coefficients between different bands, the second surface reflectance data is converted to reflectance data from the corresponding band, avoiding the problem of insufficient data in certain bands. This approach ensures that effective surface reflectance data can be obtained in all bands, ensuring the continuity and integrity of the simulation. Furthermore, by screening specific surface types and spatial resolution data according to simulation requirements, the simulation results can be further optimized. For example, after determining the target surface type according to the simulation requirements, the surface reflectance data that matches it is obtained from the screened first surface reflectance data and third surface reflectance data. This process ensures that only the data most relevant to the simulation target is used, avoiding unnecessary data redundancy and improving the efficiency of the simulation. In terms of spatial resolution, selecting appropriate spatial resolution data according to simulation requirements not only ensures the refinement of the simulation results, but also optimizes the calculation process and avoids the computational burden caused by excessively high resolution.

[0063] S103. Utilizing the surface reflectivity data, the modulation of the initial solar radiation by the surface reflection is calculated through a reflection model to generate a first radiation amount of the sunlight after being reflected from the surface; the initial solar radiation is generated based on energy distribution characteristics of the solar radiation.

[0064] Specifically, initial solar radiation refers to the original electromagnetic energy distribution radiated by the sun to Earth outside the atmosphere. Initial solar radiation varies with wavelength, primarily concentrated in the visible and near-infrared bands, and is expressed as a solar spectrum. Initial solar radiation is unaffected by Earth's atmosphere and has a relatively stable distribution.

[0065] In specific implementation, a high-spectral-resolution solar source simulation model built based on the energy distribution characteristics of solar radiation is combined with existing observation data (such as the AM0 spectrum) and the characteristics of solar blackbody radiation to generate initial solar radiation through interpolation and fitting methods. Based on the determined radiation band, the initial solar radiation is adjusted to the spectral resolution and unit form of the corresponding band. According to the reflection model, the initial solar radiation and the surface reflectivity are calculated band by band, and the product of the initial solar radiation and the surface reflectivity in each radiation band is determined as the first radiation amount after the sunlight is reflected from the surface.

[0066] S104. Establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, and calculate the first radiation attenuation of the molecular absorption at the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics.

[0067] Specifically, molecular absorption lines refer to the absorption characteristics of molecules for radiation of a specific wavelength (or frequency). Doppler broadening characteristics refer to the broadening of absorption lines due to molecular thermal motion. When molecules perform thermal motion in a gas, the molecules moving along the direction of light propagation will shift the absorption frequency due to the Doppler effect. The broadened absorption lines have a Gaussian distribution, and the line width is related to the gas temperature and molecular mass. The higher the temperature or the smaller the molecular mass, the more significant the broadening effect. Collision broadening characteristics refer to the broadening of absorption lines due to collisions between gas molecules. Molecules are disturbed by the outside world during the collision process, which shortens the lifetime of the energy level and causes the absorption lines to broaden. The broadened absorption lines have a Lorentz distribution, and the line width is proportional to the molecular pressure and the collision frequency. The higher the pressure, the more frequent the collisions, and the more significant the broadening effect.

[0068] Furthermore, the absorption model characterizes the absorption properties of molecules, reflecting the shape and intensity of their absorption lines. The total molecular optical depth (MOD) describes the overall effect of a specific molecule on radiation absorption in the atmosphere. It measures the degree to which radiation energy is attenuated by absorption and scattering along the light propagation path. The MOD is the integral of the number density of absorbing molecules and the absorption cross section at each altitude. It should be noted that in this example, the primary focus is on greenhouse gases, so the molecules are greenhouse gases with optical absorption properties.

[0069] In a specific implementation, the absorption model of each molecule is established based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band, including: calculating the Gaussian distribution function of the molecular absorption cross-section based on the Doppler broadening characteristics of the molecular absorption spectrum in the radiation band; calculating the Lorentz distribution function of the molecular absorption cross-section based on the collision broadening characteristics of the molecular absorption spectrum in the radiation band; and performing Voigt convolution on the Gaussian distribution function and the Lorentz distribution function to determine the frequency distribution of the absorption cross-section of each molecule, thereby obtaining the absorption model of each molecule.

[0070] Specifically, based on the Doppler broadening characteristics of the molecular absorption line in the radiation band, the Gaussian distribution function of the molecular absorption cross section is calculated. In specific implementation, the Doppler broadening parameter is calculated based on the molecular temperature, molecular mass, and the center frequency of the absorption line, namely:

[0071]

[0072] Wherein, the Δv D is the Doppler broadening parameter; v0 is the center frequency of the absorption line; k is the Boltzmann constant; T is the molecular temperature; m is the molecular mass; and c is the speed of light.

[0073] Furthermore, the Gaussian distribution function of the molecular absorption cross section is constructed based on the Doppler broadening parameter, the frequency in the radiation band, and the center frequency of the absorption line, that is:

[0074]

[0075] Wherein, G(v) is the Gaussian distribution function of the molecular absorption cross section; Δv D is the Doppler broadening parameter; v is the frequency in the radiation band; and v0 is the center frequency of the absorption line.

[0076] Based on the collision broadening characteristics of the molecular absorption spectrum in the radiation band, the Lorentz distribution function of the molecular absorption cross section is calculated. In specific implementation, the collision broadening parameters are calculated based on the molecular pressure and molecular temperature, that is:

[0077]

[0078] Wherein, γ is the collision broadening parameter; γ0 is the collision broadening parameter under reference conditions; P is the molecular pressure; P0 is the pressure under reference conditions; T is the molecular temperature; T0 is the temperature under reference conditions; and n is the dependence index.

[0079] Furthermore, the Lorentz distribution function of the molecular absorption cross section is constructed based on the collision broadening parameter, the frequency in the radiation band, and the center frequency of the absorption line, that is:

[0080]

[0081] Wherein, L(v) is the Lorentz distribution function of the molecular absorption cross section; γ is the collision broadening parameter; v is the frequency in the radiation band; and v0 is the center frequency of the absorption line.

[0082] Furthermore, after calculating the Gaussian distribution function and the Lorentzian distribution function of the molecular absorption cross section, Voigt convolution is performed with the Gaussian distribution function and the Lorentzian distribution function as input. The convolution is calculated using a numerical integration method (such as fast Fourier transform or partition integration) to determine the frequency distribution of the molecular absorption cross section. The frequency distribution of the molecular absorption cross section can be expressed as:

[0083]

[0084] Wherein, V(v) is the frequency distribution of the molecular absorption cross section; G(v′) is the Gaussian distribution function of the molecular absorption cross section; L(vv′) is the Lorentz distribution function of the molecular absorption cross section; v is the frequency in the radiation band; and v′ is the frequency within the integral range.

[0085] After obtaining the frequency distribution of the molecular absorption cross section, based on the molecular absorption spectrum, the frequency distribution of the molecular absorption cross section is combined with the absorption intensity of the molecular absorption cross section (such as the transition intensity of the absorption line) to obtain the absorption cross section of the molecule at different frequencies, that is, the molecular absorption model.

[0086] In specific implementation, the method of determining the total molecular optical depth based on the absorption model of each molecule and the distribution of the number density of different molecules in the atmosphere includes: determining the number of similar molecules in each altitude layer based on the distribution of the number density of different molecules in the atmosphere; for each altitude layer, calculating the absorption amount of each type of molecule based on the number of similar molecules and the absorption model, and determining the sum of the absorption amounts of different types of molecules as the total molecular absorption amount of each altitude layer; adding the total molecular absorption amounts of all altitude layers to obtain the total molecular absorption amount, and calculating the total molecular optical depth based on the total molecular absorption amount.

[0087] Specifically, according to the distribution of the number density of different molecules in the atmosphere, the atmosphere is divided into multiple altitude layers according to altitude. For each altitude layer, according to the distribution of the number density of molecules, the same type of molecules in the altitude layer are determined, and the number of similar molecules is obtained. Based on the molecular absorption model, the number of similar molecules in each altitude layer is combined with the absorption model, and the product of the absorption amount of each molecule and the number of similar molecules is determined as the absorption amount of each type of molecule in the altitude layer. For each altitude layer, the total molecular absorption amount of each altitude layer is summarized, and the absorption amounts of different types of molecules are added together to obtain the total molecular absorption amount of the altitude layer. The total molecular absorption amounts of all altitude layers are summed to obtain the total molecular absorption amount of the entire atmosphere. Further, after obtaining the total molecular absorption amount, the quotient of the total molecular absorption amount and the initial solar radiation intensity is determined as the total molecular optical depth.

[0088] In a specific implementation, the total molecular absorption amounts of all height layers are added together to obtain the total molecular absorption amount, and the total molecular optical thickness is calculated based on the total molecular absorption amount, including: adding the total molecular absorption amounts of each height layer at the same horizontal coordinate to obtain the total molecular absorption amount at each horizontal coordinate; calculating the total molecular optical thickness at each horizontal coordinate based on the total molecular absorption amount and the equivalent absorption efficiency; and correcting the abnormal thickness area based on the change in the distribution trend of the total molecular optical thickness at each horizontal coordinate to obtain the corrected total molecular optical thickness.

[0089] Specifically, for each altitude layer, the total molecular absorption of the layer is calculated based on the molecular absorption model and the molecular number density of the altitude layer. At the same horizontal coordinate (latitude and longitude position), the total molecular absorption of all altitude layers is accumulated to obtain the total molecular absorption at the horizontal coordinate. Based on the relationship between the total molecular absorption and the equivalent absorption efficiency, the corresponding formula (through the relationship formula between absorption efficiency and optical thickness) is used to calculate the total molecular optical thickness at each horizontal coordinate. The total molecular optical thickness data under all horizontal coordinates are analyzed to identify abnormal areas, which may represent data errors or special atmospheric phenomena. For the identified abnormal thickness areas, interpolation, smoothing algorithms or setting reasonable thresholds can be used for correction so that the optical thickness data of these areas are more in line with physical laws and general trends. After completing the correction of the abnormal areas, the final corrected total molecular optical thickness is obtained.

[0090] The method provided in this embodiment ensures the vertical resolution of molecular absorption by gradually accumulating the total molecular absorption at each altitude layer under the same horizontal coordinate, and can accurately reflect the contribution of different altitude layers to the optical properties. At the same time, the introduction of equivalent absorption efficiency converts complex spectral absorption characteristics into simplified parameters, which not only retains the physical meaning of the calculation but also improves the calculation efficiency and applicability. By using the distribution trend changes of the total molecular optical thickness to correct abnormal areas, it can effectively identify and correct deviations caused by noise, sensor errors or special environmental conditions, and ensure the authenticity and consistency of the results. This trend analysis combined with correction method enhances the robustness of the model, making it more tolerant to local anomalies. In addition, by combining layered accumulation and correction, it can adapt to a variety of complex simulation needs, such as atmospheric radiation analysis for specific areas, different climate conditions or spectral ranges, and improve the flexibility and adaptability of the overall calculation. While ensuring the accuracy of the results, this setting optimizes the calculation process and provides a method for calculating the total molecular optical thickness that is both efficient, reliable and adaptable, laying a solid foundation for subsequent radiation simulation.

[0091] Furthermore, after determining the total optical thickness of the molecule, the first radiation attenuation of the top of the atmosphere due to molecular absorption is determined based on the exponential relationship between the optical thickness and the radiation attenuation:

[0092] I(v)=I0exp(-τ)

[0093] Wherein, I(v) is the first radiation attenuation; I0 is the initial solar radiation; and τ is the total molecular optical thickness.

[0094] S105. Calculate the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band; determine the aerosol optical depth based on the scattering coefficient, extinction coefficient, and the distribution of the aerosol particle density in the atmosphere; and calculate the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical depth.

[0095] Specifically, the optical properties of aerosol particles are used to describe the interaction between aerosol particles and electromagnetic radiation (such as sunlight) during optical or radiation transmission. Common optical properties include scattering cross section (describing how aerosol particles scatter radiation), absorption cross section (describing how aerosol particles absorb radiation), aerosol radius (the size of aerosol particles, which directly affects the scattering and absorption characteristics of aerosol particles), and refractive index (describing the optical properties of the interaction between aerosol particles and light). The scattering coefficient refers to the intensity of light scattered by aerosols in the medium, which represents the intensity of scattered radiation caused by aerosol particles in a unit volume. The scattering coefficient also includes the backscattering coefficient. The extinction coefficient refers to the attenuation of radiation intensity when radiation passes through aerosols. Aerosol optical depth is used to describe the overall impact of aerosols in the atmosphere on radiation. It is the integral of the extinction coefficient along the observation path, which represents the attenuation of radiation due to aerosol scattering and absorption when passing through the atmosphere.

[0096] In a specific implementation, the calculation of the scattering coefficient and the extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band includes:

[0097] (1) Determine the optical characteristic parameters of aerosol particles in the radiation band.

[0098] Specifically, the optical characteristic parameters include the aerosol particle radius, the operating wavelength of the laser, the negative refractive index of the aerosol particles, etc. The aerosol particle radius is obtained through experiments or aerosol distribution models, the operating wavelength of the laser is known, and the negative refractive index of the aerosol particles is obtained through experiments.

[0099] (2) Calculate the scale parameter based on the optical characteristic parameters.

[0100] Specifically, the scale parameter is calculated from the aerosol particle radius and the laser working step size, that is:

[0101]

[0102] Wherein, x is the scale parameter; r is the radius of the aerosol particle; and λ is the working step of the laser.

[0103] (3) Initializing the Riccati-Bessel function based on the recursive relationship of the Riccati-Bessel function, and using the logarithmic inverse recursive method to solve the first Riccati-Bessel function and the second Riccati-Bessel function of each order; the first Riccati-Bessel function represents the propagation of light waves after being scattered by aerosol particles; the second Riccati-Bessel function represents the reflection phenomenon of light waves on the interface.

[0104] Specifically, Riccati-Bessel functions are often used to describe scattering and diffraction phenomena in wave propagation, particularly electromagnetic wave propagation around circular or spherical objects. The first Riccati-Bessel function characterizes the propagation of light waves on aerosol particles, primarily describing the propagation of light waves away from the particles. The second Riccati-Bessel function characterizes the reflection of light waves at interfaces, describing the reflection of light waves at the boundaries of particles.

[0105] The first and second Riccati-Bessel functions are usually calculated by iteration, that is:

[0106]

[0107] ξ n (x)=ψ n (x)-iχ n (x)

[0108] Wherein, x is the scale parameter; ψ n (x) is the value of the second Riccati-Bessel function at the iteration number n; n is the iteration number; ξ n (x) is the value of the first Riccati-Bessel function at the iteration number n.

[0109] It should be noted that when performing iterative calculations, there may be problems such as slow convergence and long computation time. Therefore, in this embodiment, the logarithmic reciprocal recursion method is used to calculate the Riccati-Bessel function:

[0110]

[0111] Among them, the D n (x) is the logarithmic reciprocal of the second Riccati-Bessel function; the ψ n (x) is the value of the second Riccati-Bessel function at the iteration number n; x is the scale parameter; and n is the iteration number.

[0112] (4) Calculating a first Mie scattering coefficient and a second Mie scattering coefficient based on the negative refractive index of the particle, the scale parameter, the first Riccati-Bessel function, and the second Riccati-Bessel function using a Mie scattering formula.

[0113] Specifically, when calculating the first Mie scattering coefficient, in the numerator, the negative refractive index of the aerosol particle is multiplied by the value of the second Riccati-Bessel function at the first target position, then multiplied by the derivative of the second Riccati-Bessel function at the second target position, and then the value of the second Riccati-Bessel function at the second target position multiplied by the derivative of the second Riccati-Bessel function at the first target position is subtracted. In the denominator, the negative refractive index of the aerosol particle is multiplied by the value of the first Riccati-Bessel function at the first target position, then multiplied by the derivative of the first Riccati-Bessel function at the second target position, and then the value of the first Riccati-Bessel function at the second target position multiplied by the derivative of the second Riccati-Bessel function at the first target position is subtracted. The first target position is the product of the negative refractive index of the aerosol particle and the scale parameter, and the second target position is the scale parameter.

[0114] In specific implementation, the first Mie scattering coefficient can be calculated according to the following formula:

[0115]

[0116] Wherein, an is the first Mie scattering coefficient; m is the negative refractive index of aerosol particles; X is the scale parameter; ψ n (mx) is the value of the second Riccati-Bessel function at the first target position; n (x) is the derivative of the second Riccati-Bessel function at the second target position; n (x) is the value of the second Riccati-Bessel function at the second target position; n (mx) is the derivative of the second Riccati-Bessel function at the first target position; the ξ′ n (x) is the derivative of the first Riccati-Bessel function at the second target position; n (x) is the value of the first Riccati-Bessel function at the second target position.

[0117] Furthermore, when calculating the second Mie scattering coefficient, in the numerator, the value of the second Riccati-Bessel function at the first target position is multiplied by the derivative of the second Riccati-Bessel function at the second target position, and then the negative refractive index of the aerosol particle and the multiplication of the value of the second Riccati-Bessel function at the second target position and the derivative of the second Riccati-Bessel function at the first target position are subtracted; in the denominator, the value of the first Riccati-Bessel function at the first target position is multiplied by the derivative of the first Riccati-Bessel function at the second target position, and then the negative refractive index of the aerosol particle and the multiplication of the value of the first Riccati-Bessel function at the second target position and the derivative of the second Riccati-Bessel function at the first target position are subtracted.

[0118] In specific implementation, the second Mie scattering coefficient can be calculated according to the following formula:

[0119]

[0120] Among them, the b n is the second Mie scattering coefficient; m is the negative refractive index of the aerosol particles; X is the scale parameter; ψ n (mx) is the value of the second Riccati-Bessel function at the first target position; n (x) is the derivative of the second Riccati-Bessel function at the second target position; n (x) is the value of the second Riccati-Bessel function at the second target position; n (mx) is the derivative of the second Riccati-Bessel function at the first target position; the ξ′ n (x) is the derivative of the first Riccati-Bessel function at the second target position; n (x) is the value of the first Riccati-Bessel function at the second target position.

[0121] (5) Calculating the scattering coefficient and extinction coefficient of each aerosol particle based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter.

[0122] In specific implementation, the scattering coefficient is calculated based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter:

[0123]

[0124] Among them, the Q scais the scattering coefficient; X is the scale parameter; a n is the first Mie scattering coefficient; the b n is the second Mie scattering coefficient; n is the order.

[0125] Calculate the backscattering coefficient based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter:

[0126]

[0127] Among them, the Q back is the backscattering coefficient; X is the scale parameter; a n is the first Mie scattering coefficient; the b n is the second Mie scattering coefficient; n is the order.

[0128] In specific implementation, the small light coefficient is calculated based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter:

[0129]

[0130] Among them, the Q ext is the extinction coefficient; X is the scale parameter; a n is the first Mie scattering coefficient; the b n is the second Mie scattering coefficient; n is the order.

[0131] In a specific implementation, the aerosol optical depth is determined based on the scattering coefficient, the extinction coefficient and the distribution of the aerosol particle density in the atmosphere, including: obtaining the total scattering coefficient of the aerosol at each altitude layer based on the scattering coefficient of each aerosol particle and the distribution of the aerosol particle density in the atmosphere; obtaining the total extinction coefficient of the aerosol at each altitude layer based on the extinction coefficient of each aerosol particle and the distribution of the aerosol particle density in the atmosphere; and determining the aerosol optical depth by integration using the sum of the total scattering coefficient and the total extinction coefficient as the integrand, the thickness of each altitude layer as the integration step, and the altitude range of the aerosol distribution as the integration interval.

[0132] Specifically, the atmosphere is divided into multiple layers based on the distribution of aerosol particle density. For each layer, the scattering coefficients of all aerosol particles in that layer are integrated based on the aerosol particle density distribution and the scattering coefficient of each aerosol particle. The total scattering coefficient for each layer is then calculated by summing the scattering coefficients of all aerosol particles in all layers.

[0133] In specific implementation, the total scattering coefficient can be calculated according to the following formula:

[0134]

[0135] Wherein, β is the total scattering coefficient; N0 is the aerosol particle number density; σ is r m The standard deviation of r max is the maximum radius of aerosol particles; the r min is the minimum radius of aerosol particles; the Q back is the backscattering coefficient; λ is the operating wavelength of the laser; r m is the median radius of the aerosol number concentration spectrum distribution.

[0136] Furthermore, based on the distribution of aerosol particle density in the atmosphere, the atmosphere is divided into multiple altitude layers according to altitude. For each altitude layer, the extinction coefficient of all aerosol particles in that altitude layer is calculated by integration based on the aerosol particle density distribution and the extinction coefficient of each aerosol particle. The extinction coefficients of all aerosol particles in all altitude layers are then added together to obtain the total extinction coefficient of aerosols at each altitude layer.

[0137] In specific implementation, the total extinction coefficient can be calculated according to the following formula:

[0138]

[0139] Wherein, α is the total extinction coefficient; N0 is the aerosol particle number density; σ is r m The standard deviation of r max is the maximum radius of aerosol particles; the r min is the minimum radius of aerosol particles; the Q back is the backscattering coefficient; λ is the operating wavelength of the laser; r m is the median radius of the aerosol number concentration spectrum distribution.

[0140] After the total scattering coefficient and total extinction coefficient are calculated, the sum of the total scattering coefficient and the total extinction coefficient is calculated for each altitude layer. The integration interval (i.e., the altitude range from low to high layers) and the thickness of each altitude layer (i.e., the altitude difference of each layer) are determined according to the distribution of aerosol particle density. The sum of the total scattering coefficient and the total extinction coefficient of all altitude layers is used as the integrand, and the numerical integration method (such as the trapezoidal method or the Simpson method) is used for integration:

[0141]

[0142] Among them, the τ aerosol is the aerosol optical depth; the σ total (h i ) is the sum of the total scattering coefficient and the total extinction coefficient at different altitudes; iis the thickness of each altitude layer; i is the altitude range of aerosol distribution.

[0143] Furthermore, after obtaining the aerosol optical depth, the second transmittance of radiation passing through the aerosol is calculated based on the aerosol optical depth. The product of the initial solar radiation intensity and the second attenuation rate is determined as the second radiation attenuation of the aerosol at the top of the atmosphere. The sum of the second transmittance and the second attenuation rate is 1.

[0144] S106: Subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0145] Specifically, in conjunction with the above description, the first radiation quantity refers to the radiation intensity of solar radiation before it reaches the top of the atmosphere after being reflected from the Earth's surface and scattered and absorbed by the various layers of the atmosphere. The first radiation attenuation quantity refers to the radiation attenuation calculated based on the total optical depth of molecular absorption and scattering. The second radiation attenuation quantity refers to the radiation attenuation calculated based on the scattering and absorption properties of aerosols.

[0146] Since surface reflection directly affects the ground radiation intensity, molecular absorption determines the heat distribution of radiation in the atmosphere, and aerosol scattering and absorption further affect the propagation path and intensity of radiation in the atmosphere. If any one factor is ignored, it may lead to misjudgment of the radiation intensity and radiation attenuation at the top of the atmosphere, thereby affecting the analysis and prediction of issues such as atmospheric composition, surface characteristics and climate change. Therefore, it is necessary to comprehensively consider the effects of these three factors. In this step, the first radiation amount is subtracted from the two radiation attenuation amounts to obtain the final top of the atmosphere radiation intensity, and the greenhouse gas passive load performance is evaluated based on the size of the top of the atmosphere radiation intensity.

[0147] This embodiment provides a method for simulating top-of-atmosphere radiation from a greenhouse gas passive payload. First, based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, the present application first determines the radiation bands that the satellite can detect. This selection of radiation bands takes into account not only the energy distribution of solar radiation but also the capabilities of the satellite's sensors, ensuring the selection of detailed hyperspectral data covering multiple bands. This setup provides high-spectral-resolution band data for subsequent molecular absorption and aerosol scattering simulations. Furthermore, by further utilizing surface reflectance data, combined with the spatial distribution characteristics of surface reflectance and the surface type, a reflection model is used to calculate the modulation of solar radiation by surface reflection. This process not only provides a more refined initial value for radiation intensity but also lays the foundation for calculating radiation attenuation at high spectral resolution, further enhancing the accuracy of the simulation of reflection effects. Secondly, during the molecular absorption simulation, the present application achieves high-spectral-resolution simulation by establishing an absorption model for each molecule based on the Doppler broadening and collisional broadening characteristics of the molecular absorption lines within the radiation band. To enable accurate absorption simulation at high spectral resolution, the absorption model considers the absorption characteristics of different wavelengths, enabling precise calculation of the absorption effect within each wavelength. High spectral resolution data distinguishes the absorption characteristics of each wavelength, ensuring that greenhouse gas absorption of radiation can be accurately quantified within a narrower wavelength range. High spectral resolution data allows the absorption effect of each molecule to be captured within a narrower wavelength range, avoiding the blurring effects associated with wide-band processing. For example, the absorption characteristics of carbon dioxide and water vapor vary significantly across wavelengths. High spectral resolution enables these differences to be accurately captured, improving the accuracy of molecular absorption simulations. Thirdly, this application simulates the scattering and extinction properties of aerosols by calculating the optical parameters of aerosol particles and then using complex mathematical formulas such as the Mie scattering formula and the Riccati-Bessel function. Aerosols scatter radiation differently across different wavelengths. By calculating the scattering coefficient and extinction coefficient for each aerosol particle and combining them with the distribution of aerosol particle density in the atmosphere, the scattering and extinction effects of aerosols can be accurately simulated within each wavelength range. High-spectral-resolution data provides detailed aerosol optical properties for each waveband, which is crucial for the high-precision calculation of aerosol optical depth. Combining scattering and extinction coefficients, the aerosol characteristics at different altitudes are further combined to accurately calculate aerosol optical depth. High-spectral-resolution data ensures a detailed simulation of aerosol scattering and absorption effects at each altitude, thereby providing accurate aerosol radiation attenuation. Finally, after obtaining detailed simulation data for surface reflection, molecular absorption, and aerosol scattering and absorption, these effects are combined to calculate the radiation intensity at the top of the atmosphere.Because each step is meticulously calculated using high-spectral-resolution data, from surface reflection to absorption by atmospheric molecules to aerosol scattering, the resulting radiation intensity results are highly accurate. This allows for precise simulation of the absorption and scattering effects of greenhouse gases and particulate matter, providing strong support for the evaluation of greenhouse gas passive loading performance. Fourthly, the combined use of multi-band, multi-surface-type, and spatially resolved data enables highly accurate and efficient acquisition of surface reflectance data, ensuring the accuracy of radiation and greenhouse gas simulations at high spectral resolution. First, by acquiring global surface reflectance data covering multiple bands, different surface types, and various spatial resolutions, a comprehensive description of the surface's reflectance properties can be achieved. Data from diverse bands enables detailed analysis of the surface's reflectance characteristics in different bands, which is particularly important for high-spectral-resolution simulations, as high spectral resolution requires precise capture of subtle variations in radiation, effectively improving simulation accuracy. The diversity of surface types accounts for the varying reflectance characteristics of different surfaces (e.g., forests, deserts, and urban areas) in different bands. Secondly, by screening and converting reflectance data from different bands, the problem of mismatched or missing reflectance data in different bands can be addressed in practice. In particular, by using conversion coefficients between different bands, the second surface reflectance data is converted to reflectance data from the corresponding band, avoiding the problem of insufficient data in certain bands. This approach ensures that valid surface reflectance data can be obtained in all bands, ensuring the continuity and integrity of the simulation. Furthermore, by screening specific surface types and spatial resolution data based on simulation requirements, simulation results can be further optimized. For example, after determining the target surface type based on simulation requirements, matching surface reflectance data is obtained from the screened first and third surface reflectance data. This process ensures that only the data most relevant to the simulation target is used, avoiding unnecessary data redundancy and improving simulation efficiency. In terms of spatial resolution, selecting appropriate spatial resolution data based on simulation requirements not only ensures refined simulation results but also optimizes the calculation process, avoiding the computational burden caused by excessively high resolution. Fifthly, this application significantly improves the accuracy and reliability of the top-of-atmosphere radiation simulation results by introducing the high-resolution Kurucz solar spectrum model and Voigt convolution technology. First, by adopting the high-resolution Kurucz solar spectrum model to characterize the characteristics of solar radiation, its fine spectral resolution can more accurately describe the energy distribution characteristics of solar radiation. Traditional solar radiation models may ignore subtle changes in radiation intensity in certain bands, while the Kurucz model can depict the radiation characteristics of different bands and different spectral details in detail, especially in specific bands related to greenhouse gas absorption. Accurate solar radiation data is the basis for top-of-atmosphere radiation simulation and greenhouse gas absorption simulation.Secondly, the introduction of Voigt convolution technology plays a vital role in the modeling of the molecular absorption line shape of greenhouse gases. The molecular absorption spectrum of greenhouse gases has Doppler effect and collision effect, which will cause the shape of the absorption spectrum to change, thereby affecting the absorption intensity and wavelength distribution. Voigt convolution technology combines Gaussian distribution and Lorentz distribution, which can more accurately simulate the actual absorption characteristics of greenhouse gas molecules, especially at high spectral resolution. This technology can capture subtle changes in molecular absorption spectra. This kind of detailed simulation helps to improve the prediction of the radiation transfer characteristics of greenhouse gases, especially when the concentration of greenhouse gases is low or changes rapidly. Through the combination of these two technologies, the present invention significantly improves the accuracy and reliability of the top of the atmosphere radiation simulation model, which can not only more accurately predict the radiation absorption characteristics of greenhouse gases, but also provide more accurate radiation data support for greenhouse gas concentration monitoring, air quality assessment and climate model optimization.

[0148] Corresponding to the aforementioned embodiment of a greenhouse gas passive payload top of atmosphere radiation simulation method, the present application also provides an embodiment of a greenhouse gas passive payload top of atmosphere radiation simulation device.

[0149] Figure 2 This is a schematic diagram of the structure of the greenhouse gas passive load top of atmosphere radiation simulation device provided in Example 2 of this application. Figure 2 The device provided in this embodiment includes a determination module 210, an acquisition module 220, a generation module 230 and a calculation module 240; wherein,

[0150] The determination module 210 is configured to determine the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation;

[0151] The acquisition module 220 is configured to acquire surface reflectance data based on the spatial distribution characteristics of the surface reflectance in the radiation band and the surface type;

[0152] The generating module 230 is configured to use the surface reflectivity data to calculate the modulation of the initial solar radiation by the surface reflection through a reflection model, thereby generating a first radiation amount of sunlight after reflection from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation;

[0153] The calculation module 240 is configured to establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere; and calculate the first radiation attenuation of the molecular absorption at the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics;

[0154] The calculation module 240 is further configured to calculate a scattering coefficient and an extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine an aerosol optical depth based on the scattering coefficient, the extinction coefficient, and the distribution of aerosol particle density in the atmosphere, and calculate a second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical depth.

[0155] The acquisition module 220 is further configured to subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0156] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0157] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0158] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0159] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for simulating top-of-atmosphere radiation of greenhouse gas passive loads, characterized in that: The method comprises: Based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, the radiation band that the satellite can detect is determined; Acquiring surface reflectance data based on the spatial distribution characteristics of the surface reflectance in the radiation band and the surface type; Utilizing the surface reflectivity data, calculating the modulation of initial solar radiation by surface reflection through a reflection model to generate a first radiation amount of sunlight after reflection from the surface; the initial solar radiation is generated based on energy distribution characteristics of solar radiation; establishing an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determining the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere; and calculating the first radiation attenuation of the top of the atmosphere due to molecular absorption based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics; Calculating the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determining the aerosol optical depth based on the scattering coefficient, extinction coefficient, and the distribution of the aerosol particle density in the atmosphere; and calculating the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical depth; The first radiation attenuation amount and the second radiation attenuation amount are subtracted from the first radiation amount to obtain the top of atmosphere radiation intensity.

2. The method according to claim 1, characterized in that The absorption model of each molecule is established based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band, including: Calculating the Gaussian distribution function of the molecular absorption cross section based on the Doppler broadening characteristics of the molecular absorption spectrum in the radiation band; Calculating the Lorentz distribution function of the molecular absorption cross section based on the collision broadening characteristics of the molecular absorption spectrum in the radiation band; The Gaussian distribution function is subjected to Voigt convolution with the Lorentzian distribution function to determine the frequency distribution of the absorption cross section of each molecule and obtain the absorption model of each molecule.

3. The method according to claim 1, characterized in that The determination of the total molecular optical depth based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere includes: Based on the distribution of number densities of different molecules in the atmosphere, determine the number of similar molecules in each altitude layer; For each altitude layer, based on the number of molecules of the same type and the absorption model, the absorption amount of each type of molecule is calculated, and the sum of the absorption amounts of different types of molecules is determined as the total absorption amount of molecules at each altitude layer; The total molecular absorption of all height layers is added together to obtain the total molecular absorption, and the total molecular optical depth is calculated based on the total molecular absorption.

4. The method according to claim 1, wherein The calculating of the scattering coefficient and the extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band includes: Determining optical characteristic parameters of aerosol particles in the radiation band; Calculating a scale parameter based on the optical characteristic parameters; Initialize the Riccati-Bessel function based on the recursive relationship of the Riccati-Bessel function, and use the logarithmic reciprocal recursion method to solve the first and second Riccati-Bessel functions of each order; the first Riccati-Bessel function represents the propagation of light waves after being scattered by aerosol particles; the second Riccati-Bessel function represents the reflection phenomenon of light waves at the interface; Calculating a first Mie scattering coefficient and a second Mie scattering coefficient using a Mie scattering formula based on the negative refractive index of the particle, the scale parameter, the first Riccati-Bessel function, and the second Riccati-Bessel function; The scattering coefficient and extinction coefficient of each aerosol particle are calculated based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter.

5. The method according to claim 1, wherein Determining the aerosol optical depth based on the scattering coefficient, the extinction coefficient, and the distribution of aerosol particle density in the atmosphere includes: Based on the scattering coefficient of each aerosol particle and the distribution of aerosol particle density in the atmosphere, the total scattering coefficient of aerosol at each altitude layer is obtained; Based on the extinction coefficient of each aerosol particle and the distribution of aerosol particle density in the atmosphere, the total extinction coefficient of aerosol at each altitude layer is obtained; The aerosol optical depth is determined by integration, with the sum of the total scattering coefficient and the total extinction coefficient as the integrand, the thickness of each altitude layer as the integration step, and the altitude range of the aerosol distribution as the integration interval.

6. The method according to claim 1, characterized in that The obtaining of surface reflectance data based on the spatial distribution characteristics of the surface reflectance in the radiation band and the surface type includes: Acquire overall surface reflectance data; the overall surface reflectance data includes surface reflectance data at multiple bands, multiple surface types, and multiple spatial resolutions; Filtering the overall surface reflectivity data based on the radiation band to obtain first surface reflectivity data of a corresponding band; For second surface reflectance data other than the first surface reflectance data in the overall surface reflectance data, converting the second surface reflectance data into third surface reflectance data of a corresponding band based on a conversion coefficient between different bands; the second surface reflectance data and the first surface reflectance data have corresponding bands different from each other; Determine a target surface type based on simulation requirements, and filter the first surface reflectivity data and the third surface reflectivity data based on the target surface type to obtain fourth surface reflectivity data corresponding to the surface type; A target spatial resolution is determined based on simulation requirements, and surface reflectivity data of a corresponding spatial resolution is obtained by screening the fourth surface reflectivity data based on the target spatial resolution.

7. The method according to claim 1, characterized in that Determining the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation includes: Based on the spectral response characteristics of satellite payloads, a multi-band spectral response function is established; Based on the energy distribution characteristics of solar radiation within the spectral range, a high spectral resolution solar light source simulation model is constructed; the simulation model represents the spectral distribution of solar radiation; The multi-band spectral response function is combined with the high spectral resolution solar light source simulation model to determine the radiation bands that can be detected by the satellite.

8. The method according to claim 7, characterized in that The multi-band spectral response function is established based on the spectral response characteristics of the satellite payload, including: Determining parameters related to the satellite payload based on the spectral response characteristics of the satellite payload; the parameters characterize the satellite payload's response capability in different bands and its sensitivity to radiation in different bands; Based on the parameters, combined with the application requirements of the satellite payload and the absorption characteristics of greenhouse gases, determine the appropriate spectral range and sampling interval; Based on the selected spectral range and sampling interval, the Gaussian function is spectrally sampled to obtain a multi-band spectral response function.

9. The method according to claim 3, characterized in that The method of adding the total molecular absorption of all height layers to obtain the total molecular absorption, and calculating the total molecular optical depth based on the total molecular absorption, comprises: Add up the total molecular absorption at each altitude layer at the same horizontal coordinate to obtain the total molecular absorption at each horizontal coordinate; Based on the total molecular absorption and equivalent absorption efficiency, the total molecular optical thickness at each horizontal coordinate is calculated; Based on the change in the molecular total optical thickness distribution trend at each horizontal coordinate, the abnormal thickness area is corrected to obtain the corrected molecular total optical thickness.

10. A greenhouse gas passive load top of atmosphere radiation simulation device, characterized in that: The device includes a determination module, an acquisition module, a generation module and a calculation module; wherein, The determination module is configured to determine the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation; The acquisition module is used to acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type; The generating module is configured to use the surface reflectivity data to calculate the modulation of the initial solar radiation by the surface reflection through a reflection model, thereby generating a first radiation amount of sunlight after being reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation; The calculation module is configured to establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere; and calculate the first radiation attenuation of the molecular absorption at the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics; The calculation module is further configured to calculate a scattering coefficient and an extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine an aerosol optical depth based on the scattering coefficient, the extinction coefficient, and the distribution of aerosol particle density in the atmosphere; and calculate a second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical depth. The acquisition module is further configured to subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

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