Atmospheric carbon monoxide concentration inversion method based on domestic hyper-spectral satellite remote sensing

Abstract

The invention discloses an atmospheric carbon monoxide concentration inversion method based on domestic hyper-spectral satellite remote sensing, which can be combined with a new instrument with better radiation performance and higher spatial sampling performance to provide a rapid and accurate inversion method, on one hand, the influence of cloud and aerosol on CO inversion precision is considered, and on the other hand, the CO inversion precision is improved; the inversion precision and the coverage rate of the atmospheric CO concentration are improved; and on the other hand, nonlinear influence factors in the fitting process are processed by utilizing an iterative algorithm in the inversion process, and a double-flow radiation transmission model which is more efficient in numerical value is adopted under the condition of meeting feasibility, so that the reliability and rationality of inversion data are ensured, and the data inversion precision and rate are improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring, and in particular to a method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing. Background Art

[0002] Carbon monoxide (CO) is an important atmospheric trace gas and a major atmospheric pollutant in some regions. The main sources of CO are the burning of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. Since CO has a lifetime of weeks to months, it is a good tracer for studying long-range transport processes. In addition, CO is one of the highest priority chemicals measured by the Monitoring Atmospheric Composition and Climate (MACC) project of the Global Monitoring for Environment and Security (GMES) program.

[0003] The first spaceborne measurements of CO were made on four flights of the Space Shuttle between 1981 and 1999 using the MAPS (Satellite Air Pollution Measurement) instrument, and in 1996 and 1997 using the IMG (Interferometric Monitor of Greenhouse Gases) instrument on the Advanced Earth Observing System (ADEOS). Since 2000, long-term datasets of global CO have been provided by the MOPITT (Atmospheric Pollution Measurement) instrument. New spaceborne instruments are needed in the future to ensure the measurement of CO in the near-infrared spectral range. In recent years, more and more satellites have taken on the task of extending these long-term global CO datasets. The main scientific goal of these missions is to participate in the development and improvement of the processes and data assimilation of air quality models to support their operational services, including air quality forecasts and protocol monitoring. This scientific goal requires the combination of new instruments with better radiometric performance and higher spatial sampling performance, as well as the development of fast and accurate inversion methods. However, the speed and accuracy of the inversion of atmospheric CO concentrations still need to be improved.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing, which can achieve rapid and accurate inversion of atmospheric CO concentration.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] A method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing, including:

[0008] Obtain the input data required for carbon monoxide concentration inversion, including: spectral data in the near-infrared spectrum range collected by satellites, meteorological data, prior data simulated by atmospheric chemical transport models, and surface height information;

[0009] Invert the vertical column concentration of methane in the unscattered radiation transfer model based on the input data, and compare it with the prior data to determine the influence of clouds and aerosols on the scattering of the atmosphere, and select the observation data that meets the requirements;

[0010] Combined with the selected observation data, a physics-based inversion method is used to infer the carbon monoxide concentration from the spectral data in the near-infrared spectral range collected by the satellite.

[0011] A domestic hyperspectral satellite remote sensing atmospheric carbon monoxide concentration inversion system, including:

[0012] An input data acquisition unit is used to acquire the input data required for carbon monoxide concentration inversion, including: spectral data in the near-infrared spectrum range collected by satellites, meteorological data, prior data simulated by the atmospheric chemical transport model, and surface height information;

[0013] A scattering influence calculation unit, used for inverting the vertical column concentration of methane in a non-scattering radiation transfer model based on the input data, and comparing it with prior data to determine the influence of clouds and aerosols on the scattering of the atmosphere, and screening out observation data that meet the requirements;

[0014] The inversion unit is used to combine the screened observation data and use a physics-based inversion method to spectral data in the near-infrared spectral range collected by the satellite to estimate the carbon monoxide concentration.

[0015] A processing device, comprising: one or more processors; a memory for storing one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0017] A readable storage medium stores a computer program, which implements the above method when the computer program is executed by a processor.

[0018] It can be seen from the technical solution provided by the present invention that a new instrument with better radiation performance and higher spatial sampling performance can be combined to provide a fast and accurate inversion method. On the one hand, the influence of clouds and aerosols on the CO inversion accuracy is taken into account, and the inversion accuracy and coverage of atmospheric CO concentration are improved; on the other hand, an iterative algorithm is used in the inversion process to deal with nonlinear influencing factors in the fitting process, and a more numerically efficient dual-stream radiation transfer model is adopted under the condition of satisfying feasibility, which ensures the reliability and rationality of the inversion data and improves the data inversion accuracy and rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 A flowchart of a method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing provided in an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of an atmospheric carbon monoxide concentration inversion system based on domestic hyperspectral satellite remote sensing provided in an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of a processing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of the present invention.

[0024] First, the terms that may be used in this article are explained as follows:

[0025] The terms "include", "comprises", "contains", "has" or other descriptions with similar semantics should be interpreted as non-exclusive inclusion. For example, including certain technical feature elements (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, procedures, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or products, etc.) should be interpreted as including not only certain technical feature elements explicitly listed, but also other technical feature elements known in the art that are not explicitly listed.

[0026] The term "consisting of..." means excluding any technical feature elements not explicitly listed. If this term is used in a claim, it will make the claim closed, so that it does not contain technical feature elements other than the technical feature elements explicitly listed, except for the conventional impurities related to them. If this term only appears in a clause of a claim, it only limits the elements explicitly listed in the clause, and the elements recorded in other clauses are not excluded from the overall claim.

[0027] The following is a detailed description of a scheme for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing provided by the present invention. The contents not described in detail in the embodiments of the present invention belong to the prior art known to professionals in the field. If the specific conditions are not specified in the embodiments of the present invention, they are carried out according to the conventional conditions in the field or the conditions recommended by the manufacturer. If the manufacturer is not specified in the reagents or instruments used in the embodiments of the present invention, they are all conventional products that can be purchased commercially.

[0028] Embodiment 1

[0029] The embodiment of the present invention provides a method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing, such as Figure 1 As shown, the method mainly includes the following steps:

[0030] (1) Obtain the input data required for carbon monoxide concentration inversion, including: spectral data in the near-infrared spectral range collected by satellites, meteorological data, prior data simulated by atmospheric chemical transport models, and surface height information.

[0031] In the embodiment of the present invention, the spectral data within the near-infrared spectrum range collected by the satellite includes: solar irradiance and earth irradiance spectral data.

[0032] In the embodiment of the present invention, the meteorological data includes: atmospheric pressure, temperature and humidity data.

[0033] In the embodiment of the present invention, the priori data simulated by the atmospheric chemical transport model include: atmospheric carbon monoxide and methane (CH 4 )’s vertical distribution data.

[0034] (2) Inverting the vertical column concentration of methane in the non-scattered radiation transfer model based on the input data, and comparing it with the prior data to determine the impact of clouds and aerosols on the scattering of the atmosphere, and screen out the observation data that meets the requirements.

[0035] In an embodiment of the present invention, the vertical column concentration of methane is inverted using a standard least squares algorithm in a non-scattered radiation transfer model based on the input data, and compared with the vertical distribution data of methane concentration in prior data, and the methane absorption characteristics are used to filter out the observation results of high clouds and optically thick clouds to obtain observation data that meets the requirements.

[0036] like Figure 1 As shown, only the difference between the vertical column concentration of methane inferred and the prior data ΔCH 4When the difference is smaller, continue with the following steps; specifically, a threshold can be set in advance. When the difference is not greater than the threshold, it can be considered that the difference is small, and then continue with the subsequent process, thereby filtering out the observation results of high clouds and optically thick clouds. Here, the high clouds and optically thick clouds mainly refer to when ΔCH 4 When the cloud layer observation result is greater than a threshold value (eg, 20%), the cloud layer observation result is filtered out. The specific value of the threshold value can be set by the user according to the actual situation or experience.

[0037] (3) Taking into account the effects of clouds and aerosols on atmospheric scattering, a physics-based inversion method is used to infer the carbon monoxide concentration from the spectral data in the near-infrared spectral range collected by satellites.

[0038] In the embodiment of the present invention, in addition to being able to estimate the carbon monoxide concentration, the atmospheric water (H 2 O) content, surface albedo and spectral calibration information of the measured spectrum. The surface albedo and spectral calibration information of the measured spectrum involved here are corrected through algorithm iteration, and participate in the intermediate calculation and can also be output as a result.

[0039] In the embodiment of the present invention, estimating the carbon monoxide concentration mainly includes: estimating the carbon monoxide column concentration (CO total column), the column average kernel function and the random error.

[0040] In order to more clearly demonstrate the technical solution and technical effects provided by the present invention, the method provided by the embodiment of the present invention is described in detail with reference to specific embodiments below.

[0041] This paper focuses on clear sky observations over land and cloudy observations over land and ocean where low-altitude liquid water clouds exist. The shortwave infrared CO inversion algorithm is as follows Figure 1 The dynamic inputs include solar irradiance and earth irradiance in the near-infrared spectral range, numerical prediction model data: atmospheric pressure p, temperature T and humidity q, terrain height of the digital elevation model, and observations of atmospheric CO and CH from the prior atmospheric chemical transport model. 4 Concentration vertical distribution information, etc.

[0042] The first step is to screen the data, filter out the results of high clouds and optically thick clouds, and then invert the CO column concentration from the observed near-infrared spectrum through a fully physically based inversion method. The physically based inversion method requires first establishing a forward model F, which describes the observations as a function of the atmospheric state, including the atmospheric scattering effect.

[0043] y=F(x,t)+e y (1)

[0044] Where y represents a vector with measurements in the near-infrared spectral range (i.e., observations filtered out of high and optically thick clouds), the state vector x represents the parameters to be inverted (e.g., the CO column capacity, atmospheric water content, etc.), and t represents the adjusted parameters that affect the measurement but are not affected by the detection, except for the state vector. y Represents the observation error. Since the forward model is nonlinear on the state vector x, the Gauss-Newton iteration method is used to solve the inverse problem. For each iteration step, the forward model replaces the solution x of the previous iteration with 0 Linearization is performed using Taylor expansion:

[0045]

[0046] Among them, x 0 represents the inversion parameter of the previous iteration; 0 ) 2 ) represents the second-order and higher-order contributions of the expansion.

[0047] The forward model F simulates the top-of-atmosphere radiation R by TOA Convolved with the instrument spectral response function to simulate Earth radiation observations.

[0048] F i =ω i ·R TOA =∫ω i (λ)R TOA (λ)dλ(3)

[0049] Among them, F i is the i-th component of the forward model vector F, ω i Indicates that spectral pixel i is at a specified wavelength λ i The spectral instrument response under TOA (λ) is simulated on a fine internal spectral grid using a row-by-row radiative transfer model that requires as input the solar irradiance spectrum on the internal spectral grid, which is inverted from daily space-based solar measurements using a deconvolution method.

[0050] For the most advanced radiative transfer models, multiple scattering in multiple propagation directions including light polarization is considered, which is computationally too expensive to simulate for the application of the present invention. Therefore, the present invention adopts a numerically efficient two-stream radiative transfer model to describe the scattering of clouds and aerosols, neglecting atmospheric scattering.

[0051] In the forward model F, clouds and aerosols are represented by a scattering layer. It is assumed that the single scattering reflectivity and scattering phase function are constant in the scattering layer. In this case, the present invention uses aggregated vertical grids to optimize the numerical calculation efficiency of the dual-flow solver, calculates the absorption optical thickness on a 1km vertical grid, and then merges the upper and lower atmospheric layers of the scattering layer into one layer by integrating the optical thickness. For the optical properties of the scattering layer, it is assumed that the spectrally constant single scattering albedo χ = 0.9 and the asymmetric parameter of the scattering phase function g = 0.7. For the extinction optical thickness of the scattering layer, a simplified wavelength-dependent extinction optical thickness function of the scattering layer is used:

[0052]

[0053] In the forward model F, τ(λ) is used to calculate the optical properties of the scattering layer, and the central reference wavelength λ selected by the fitting window is 0 =2331nm, α=1.0 Although the radiative transfer solver is very efficient, the numerical cost of the forward model must be further reduced to facilitate data processing. The CH 4 , H 2 Molecular absorption cross sections of O, HDO and CO The sampling distance for pressure, temperature and spectrum is 5×10 -3 cm -1 Using this data set, the absorption cross section of each inversion layer is obtained by bilinear interpolation of pressure and temperature, and then the coarse spectrum sampling k is calculated using the generalized mean i The effective cross-section of each species

[0054]

[0055] Among them, in the forward model F, Used to calculate the absorption cross sections of each layer.

[0056] The spectral sampling interval is 3×10 -2 cm -1 . Where k represents the wave number, T i (k) is the spectral sampling k i-1 and k i+1 A normalized symmetric triangular weighting function between , with a peak at k i q represents a tuning parameter, and theoretically q can be any number in the real number domain. If q = 1, reproducing the arithmetic mean in equation (5) will introduce significant forward model errors in the inversion of the assumed spectral sampling, so the optimal value of q should be between 0 and 1.

[0057] The inversion tests were performed for different q values. Under clear sky conditions, the most accurate radiation simulation was obtained when q = 0.85, with an inversion deviation of less than 1% for CO. In general, the effective absorption cross section method increased the simulation speed of the forward model by six times compared with the row-by-row calculation on the spectral grid of the original spectral database.

[0058] In the inversion process of CO column concentration distribution, near-infrared spectroscopy measurement is very sensitive to the total amount of CO in the measured optical path. For clear sky atmosphere, within the measurement error range, only the total number of CO columns can be inferred from the measurement, and the relative vertical distribution information of CO cannot be obtained. In the case of clouds, the measurement loses its sensitivity to the amount of CO below the clouds. In order to correctly explain this, a regularized CO profile inversion is required to take into account the different sensitivities of measuring CO at different altitudes. To this end, the present invention adopts a first-order Tikhonov regularization technique embedded in a Gauss-Newton iterative scheme. For each iterative step, the least squares solution Given by formula (6):

[0059]

[0060] The above formula ignores the parameter t, and F(x,t) is recorded as F(x), ‖·‖ represents the Euclidean norm, and D y represents the error covariance matrix of the measurement y, ψ is the regularization parameter, and L 1 represents the first-order difference operator. Therefore, the regularization tends to be a constant solution, and the state vector x contains the profile x co , which can be represented by the reference profile σ ref Indicates that:

[0061]

[0062] The state vector x includes the water vapor column density C of the two isotopes in addition to the relative profile of CO. H2O and C HDO , surface albedo A s and its relationship with wavelength ΔA s The linear relationship between the effective cloud center height Z cld and the effective cloud optical depth τ cld The solution in equation (6) is regularized in order to infer one degree of freedom of the inverted CO profile signal from the measurements. Due to the influence of noise, the spaceborne infrared shortwave measurements are only sensitive to the amount of CO along the path of the observation light through the atmosphere. For equation (6), this corresponds to the regularization parameter ψ→∞. The solution to the minimization problem is the same as the unregularized least squares method:

[0063]

[0064] where the state vector contains the CO total column instead of the CO profile:

[0065] c=Cσ=∫σ(z)dz(9)

[0066] With the corresponding high degree integration operator C, all other elements of the state vector remain unchanged.

[0067] The solution to the least squares problem is:

[0068]

[0069] In the above formula is the true profile x true The smoothed version of , the degree of smoothness can be characterized by the average kernel (explained later).

[0070] in:

[0071]

[0072] A=(K T D y -1 K) -1 K T D y -1 (12)

[0073] in, is the forward model F about the state vector x at x 0 The Jacobian matrix at , is the symbol of the partial derivative, A is the gain matrix, and its definition is given by equation (12). Two important diagnostic information can be calculated during the inversion process. One is the error covariance matrix D x =AD y A T , which is used to describe the effect of measurement noise on the inversion parameters (including their correlations), and the column average kernel:

[0074]

[0075] Where d is the differential symbol.

[0076] It shows that the total amount of inversion column Sensitivity to changes in the atmospheric CO profile. Here, a column-averaged kernel function of the CO profile is provided, which consists of a partial column of each model layer. In a linear approximation, the column-averaged kernel compares the inverted total CO column with the true CO profile σ true Get in touch.

[0077]

[0078] Among them, ex represents the error contribution value, so, generally speaking, It does not represent the estimated value of the true column, but rather its difference or deviation. Referring to formula (14), the inversion deviation δ co The inversion accuracy of the simulated measurement is characterized by the total amount of the inversion column and the inversion noise τ co e y Corrected Λ col ρ true The difference between:

[0079]

[0080] Among them, η co is the CO row vector of the gain matrix A in equation (12), e y is the measurement noise. The inversion methods described so far focus on regularization methods for the ill-posed problem of inverting the CO profile in measurements in the near-infrared spectral range. The inversion is still susceptible to the influence of other elements in the state vector, and the measurement is insensitive to certain atmospheric environments, such as scenes covered by optically thick clouds, where the measurement is insensitive to the surface albedo, and for clear sky observations, where the measurement is insensitive to possible cloud height. Therefore, for these cases, some eigenvalues ​​of the normal matrix (K T D y -1 K) approaches zero, resulting in singularities in the inversion. To solve this problem, Tikhonov regularization is applied to the relevant elements of the state vector, namely:

[0081]

[0082] Where W is a diagonal weighting matrix. For all elements of the state vector related to the scattering layer and the surface albedo, that is, A s , ΔA s 、z cld , τ cld , whose diagonal elements are all 1 and otherwise 0.

[0083] In the present invention, the nonlinearity of the inversion is solved by an iterative algorithm, so a convergence criterion is required to terminate the iteration. For CO inversion, the convergence degree is defined as the reduction of the chi-square χ between two consecutive iterative steps. 2 The difference is defined as follows:

[0084]

[0085] Among them, e j is the standard deviation of the jth measurement, and e j 2 =D y,ii, one measurement corresponds to one inversion, and each inversion requires multiple iteration steps, e j Does not change with the iteration process, and is determined by each measurement result D y,ii In the generalized two-stream radiation transfer model, N uniform layers (each layer corresponds to an iteration process) are usually used to describe a vertically inhomogeneous atmosphere. N represents the maximum number of uniform layers under the model, and n is the layer index, n = 1, 2...N. is the chi-square of the nth level; F j is the jth component of the forward model vector F, x n is the inversion parameter of the nth layer, which is calculated by the above formula (16). n -x n-1 |<ε. The threshold of ε can only be determined in a reliable way through actual measurements during the commissioning phase of the satellite mission. In addition, iterations always follow the principle of the minimum number of iterations. If convergence is not achieved within a certain number of iterations, it indicates that the inversion has failed.

[0086] After convergence, we get That is the final inversion result, and the information contained therein, such as the CO column capacity and the atmospheric water content, are all the inversion results required by the present invention.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or by means of software plus necessary general hardware platforms. Based on such understanding, the technical solutions of the above embodiments can be embodied in the form of software products, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0088] Embodiment 2

[0089] The present invention also provides a domestic hyperspectral satellite remote sensing atmospheric carbon monoxide concentration inversion system, which is mainly used to implement the method provided in the above embodiment, such as Figure 2 As shown, the system mainly includes:

[0090] An input data acquisition unit is used to acquire the input data required for carbon monoxide concentration inversion, including: spectral data in the near-infrared spectrum range collected by satellites, meteorological data, prior data simulated by the atmospheric chemical transport model, and surface height information;

[0091] A scattering influence calculation unit, used for inverting the vertical column concentration of methane in a non-scattering radiation transfer model based on the input data, and comparing it with prior data to determine the influence of clouds and aerosols on the scattering of the atmosphere, and screening out observation data that meet the requirements;

[0092] The inversion unit is used to combine the screened observation data and use a physics-based inversion method to spectral data in the near-infrared spectral range collected by the satellite to estimate the carbon monoxide concentration.

[0093] Considering that the specific technical details involved in the system have been introduced in detail in the previous embodiments, they will not be repeated here.

[0094] Technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0095] Embodiment 3

[0096] The present invention also provides a processing device, such as Figure 3 As shown, it mainly includes: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the methods provided in the aforementioned embodiments.

[0097] Furthermore, the processing device also includes at least one input device and at least one output device; in the processing device, the processor, memory, input device, and output device are connected via a bus.

[0098] In the embodiment of the present invention, the specific types of the memory, input device and output device are not limited; for example:

[0099] The input device may be a touch screen, an image acquisition device, a physical button or a mouse, etc.;

[0100] The output device may be a display terminal;

[0101] The memory may be a random access memory (RAM) or a non-volatile memory, such as a disk memory.

[0102] Embodiment 4

[0103] The present invention also provides a readable storage medium storing a computer program, which implements the method provided in the above embodiment when the computer program is executed by a processor.

[0104] In the embodiment of the present invention, the readable storage medium is a computer-readable storage medium and can be set in the aforementioned processing device, for example, as a memory in the processing device. In addition, the readable storage medium can also be a U disk, a mobile hard disk, a read-only memory (ROM), a disk or an optical disk, etc., which can store program codes.

[0105] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing, characterized in that: include: Obtain the input data required for carbon monoxide concentration inversion, including: spectral data in the near-infrared spectrum range collected by satellites, meteorological data, prior data simulated by atmospheric chemical transport models, and surface height information; Invert the vertical column concentration of methane in the unscattered radiation transfer model based on the input data, and compare it with the prior data to determine the influence of clouds and aerosols on the scattering of the atmosphere, and select the observation data that meets the requirements; Combined with the selected observation data, a physics-based inversion method is used to infer the carbon monoxide concentration from the spectral data in the near-infrared spectral range collected by the satellite.

2. According to claim 1, a method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing is characterized in that: The spectral data within the near-infrared spectrum range collected by the satellite includes: solar irradiance and earth irradiance spectral data.

3. The method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing according to claim 1 is characterized in that: The meteorological data includes atmospheric pressure, temperature and humidity data.

4. The method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing according to claim 1 is characterized in that: The priori data simulated by the atmospheric chemical transport model include: vertical distribution data of atmospheric carbon monoxide and methane concentrations simulated by the atmospheric chemical transport model.

5. The method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing according to claim 1 is characterized in that: The vertical column concentration of methane is inverted in the non-scattered radiation transfer model based on the input data, and compared with the prior data to determine the influence of clouds and aerosols on the scattering of the atmosphere, and the observation data that meets the requirements are obtained, including: Based on the input data, the vertical column concentration of methane is inverted using a standard least squares algorithm in a non-scattered radiation transfer model, and compared with the vertical distribution data of methane concentration in the prior data, and the methane absorption characteristics are used to filter out the observation results of high cloud layers and optically thick cloud layers to obtain observation data that meets the requirements; wherein the high cloud layers and optically thick cloud layers refer to the observation results of clouds that are filtered out when the difference between the inverted vertical column concentration of methane and the prior data is greater than a threshold.

6. The method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing according to claim 1 is characterized in that: When using the physics-based inversion method, a forward model F is established, which describes the observations as a function of the atmospheric state, expressed as: y=F(x,t)+e y Where y represents a vector with the observed data that meets the requirements as components, the state vector x represents the parameters to be inverted, including the carbon monoxide concentration, t represents the adjustment parameters that affect the measurement but are not affected by the detection except the state vector, and e y represents the observation error; For each iteration step, the forward model linearizes the solution x0 of the previous iteration using Taylor expansion: Where x0 represents the inversion parameter of the previous iteration; ο((x-x0) 2 ) represents the second-order and higher-order contributions of the expansion, is the symbol of partial derivative; For each iteration step, the least squares solution for: In the above formula, the parameter t is ignored, F(x,t) is recorded as F(x), ‖·‖ represents the Euclidean norm, and D y represents the error covariance matrix of the measurement y, W is a diagonal weighting matrix whose diagonal elements are 1 for all elements of the state vector related to the scattering layer and the surface albedo, and 0 otherwise; ψ is the regularization parameter; When the iteration converges, the obtained This is the final inversion result.

7. The method for inverting atmospheric carbon monoxide concentration based on domestic hyperspectral satellite remote sensing according to claim 6 is characterized in that: Also includes: Set the convergence criterion. When the convergence criterion is met, it indicates that the iteration has converged; The degree of convergence is defined as the reduction in chi-square χ between two consecutive iteration steps. 2 The difference, when | n -x n-1 |<ε, it means that the convergence criterion is met, n is the number of uniform layers in the model, each layer corresponds to an iterative process, and ε is the threshold.

8. A domestic hyperspectral satellite remote sensing atmospheric carbon monoxide concentration inversion system, characterized in that: include: An input data acquisition unit is used to acquire the input data required for carbon monoxide concentration inversion, including: spectral data in the near-infrared spectrum range collected by satellites, meteorological data, prior data simulated by the atmospheric chemical transport model, and surface height information; A scattering influence calculation unit, used for inverting the vertical column concentration of methane in a non-scattering radiation transfer model based on the input data, and comparing it with prior data to determine the influence of clouds and aerosols on the scattering of the atmosphere, and screening out observation data that meet the requirements; The inversion unit is used to combine the screened observation data and use a physics-based inversion method to spectral data in the near-infrared spectral range collected by the satellite to estimate the carbon monoxide concentration.

9. A processing device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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