A method for calculating vertical column density of a rotating scanning carbon monitoring satellite based on slant path radiance information

Abstract

The application is a kind of vertical column concentration calculation method of rotating scanning carbon monitoring satellite based on slant path radiance information. The application relates to the technical field of monitoring satellite vertical column concentration calculation, the application analyzes the observation geometry of satellite in the side view scanning process, calculates the path integral of each area greenhouse gas column concentration on the observation path, and expresses the radiance as the function of each area greenhouse gas column concentration and wavelength; the path integral is discretized according to the ground resolution requirement, the nonlinear equation of radiance about different area gas column concentration is obtained, and the nonlinear least square optimization method is used to solve the vertical column concentration of each area different component greenhouse gas under the condition that the number of detection instrument spectral band is rich enough. The application realizes the solution of slant path radiance to vertical column concentration based on hyperspectral detection instrument data, and proves the feasibility of obtaining the greenhouse gas column concentration of rotating scanning new system satellite.

Description

Technical Field

[0001] This invention relates to the field of vertical column concentration calculation technology for monitoring satellites, and is a method for calculating vertical column concentration of rotating scanning carbon monitoring satellites based on slant path radiance information. Background Technology

[0002] To address the current challenge of resolution and swath width constraints in carbon dioxide monitoring satellites, the patent "An Ultra-Wide Coverage Multi-Mode Carbon Monitoring Satellite Based on Payload Vertical Orbit Rotation Scanning, Construction Method, and Monitoring Method" proposes a method for achieving ultra-wide swath detection over a thousand kilometers using payload vertical orbit rotation scanning, which can strongly support global carbon inventory. However, under this new system, the satellite's vertical orbit sweep angle can reach 65°, and the observation zenith angle of ground points can reach up to 74°. The radiance obtained from long-range observations inevitably couples with greenhouse gas vertical column concentration information at different latitudes and longitudes. Therefore, this paper designs a method for calculating vertical column concentration of a rotating scanning carbon monitoring satellite based on slant-range radiance information. This method can invert the radiance observed over long ranges to the vertical column concentration in each region along the observation path, effectively demonstrating the feasibility of the rotating scanning system, strongly supporting the overall satellite design and application, and possessing high academic value and engineering significance. Summary of the Invention

[0003] To address the shortcomings of existing technologies and solve the aforementioned problems, this invention proposes a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information. This invention aims to solve the problem of retrieving the vertical column concentration of greenhouse gases from the radiance observed over a long slant path to the concentration in each region along the observation path.

[0004] This invention provides a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information. The invention provides the following technical solution:

[0005] A method for calculating vertical column concentration from a rotating scanning carbon monitoring satellite based on slant path radiance information includes the following steps:

[0006] The observation geometry of the satellite during the side-looking scan process is analyzed, the path integral of the greenhouse gas column concentration in each region along the observation path is calculated, and the radiance is expressed as a function of the greenhouse gas column concentration in each region and the wavelength.

[0007] The path integral is discretized according to the ground resolution requirements to obtain a nonlinear equation for radiance with respect to the gas column concentration in different regions. When the number of detector spectral bands is sufficiently rich, a nonlinear least squares optimization method is used to solve for the vertical column concentration of greenhouse gases of different components in each region.

[0008] Preferably, let the Earth's radius be R, the satellite's orbital altitude be H, the altitude of the top of the atmosphere be h, and the side angle be θ. The satellite's line of sight is a straight line BS, intersecting the top of the atmosphere at point A. Then, the distance from point C on the line of sight AB, with a geocentric angular distance φ from the line SO connecting the satellite and the Earth, to the Earth's center O is obtained using the sine theorem for triangle SOC.

[0009]

[0010] Similarly, the geocentric distances between points A and B and SO are respectively:

[0011]

[0012] From geometric relations, the angle between the perpendicular line OC and AB is:

[0013]

[0014] The length of the infinitesimal element of the line segment AB with a geocentric angle distance of dφ from point C is:

[0015]

[0016] Preferably, the geocentric distance is set as... The column concentration of greenhouse gases is According to the radiative transfer equation, the radiance L of the line of sight AB is a nonlinear function of wavelength λ and column concentration with respect to the path integral, i.e.:

[0017]

[0018] Preferably, the above integral is discretized. Let the discretization step size be Δ, and let η0 = mΔ, η = nΔ. Then the above equation is discretized as:

[0019]

[0020] Where δ i =δ(iΔ).

[0021] Preferably, if the number of observed spectral segments is greater than n-m+1, then the unknown greenhouse gas column concentration on this path is solved using a nonlinear least squares algorithm.

[0022] Preferably, when R = 6378 km, H = 700 km, h = 10 km, and θ = 65°, η0 = 12.3765° and η = 12.7841° can be calculated. For a ground resolution of 1 km and Δ = 0.009°, m = 1375 and n = 1420 can be calculated. For carbon dioxide as a single gas, there are a total of 46 variables to be solved.

[0023] Preferably, for hyperspectral detectors with more than 300 spectral bands, the inversion of three component parameters—carbon dioxide, methane, and aerosol optical thickness (AOD)—can be carried out simultaneously, with no more than 150 variables, and the solution is performed using a nonlinear least squares method.

[0024] A system for calculating vertical column concentration from a rotating scanning carbon monitoring satellite based on slant path radiance information, the system comprising:

[0025] The analysis module analyzes the observation geometry of the satellite during the side-looking scan process, calculates the path integral of the greenhouse gas column concentration in each region along the observation path, and expresses the radiance as a function of the greenhouse gas column concentration in each region and the wavelength.

[0026] The calculation module discretizes the path integral according to the ground resolution requirements to obtain a nonlinear equation for radiance with respect to the gas column concentration in different regions. When the number of spectral bands of the detector is sufficiently rich, a nonlinear least squares optimization method is used to solve for the vertical column concentration of greenhouse gases of different components in each region.

[0027] A computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information.

[0028] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information.

[0029] The present invention has the following beneficial effects:

[0030] Compared with the prior art, the present invention:

[0031] This invention proposes a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information. This invention addresses the problem of greenhouse gas column concentration information in different regions being coupled with payload-observed radiance under large side-swing angle observation conditions of rotating scanning satellites. It achieves the solution from slant path radiance data to vertical column concentration based on hyperspectral imager data, demonstrating the feasibility of obtaining greenhouse gas column concentrations from a new rotating scanning satellite system. Attached Figure Description

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the satellite observation geometry of the present invention;

[0034] Figure 2 This is a schematic diagram of the line segment micro-element at point C of the present invention;

[0035] Figure 3 This is a flowchart of the satellite observation geometry simulation.

[0036] Figure 4 It is a three-dimensional simulation scene;

[0037] Figure 5 This is a schematic diagram of observational geometry calculation;

[0038] Figure 6 This is a flowchart of the vector radiative transfer model;

[0039] Figure 7 This is a flowchart for calculating the gas absorption cross section. Detailed Implementation

[0040] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0042] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0043] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0044] The present invention will be described in detail below with reference to specific embodiments. Specific Implementation Example 1:

[0046] according to Figures 1-7 As shown, the specific optimized technical solution adopted by the present invention to solve the above-mentioned technical problems is: The present invention relates to a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information.

[0047] A method for calculating vertical column concentration from a rotating scanning carbon monitoring satellite based on slant path radiance information includes the following steps:

[0048] The observation geometry of the satellite during the side-looking scan process is analyzed, the path integral of the greenhouse gas column concentration in each region along the observation path is calculated, and the radiance is expressed as a function of the greenhouse gas column concentration in each region and the wavelength.

[0049] The path integral is discretized according to the ground resolution requirements to obtain a nonlinear equation for radiance with respect to the gas column concentration in different regions. When the number of detector spectral bands is sufficiently rich, a nonlinear least squares optimization method is used to solve for the vertical column concentration of greenhouse gases of different components in each region. Specific Implementation Example 2:

[0051] The difference between Embodiment 2 and Embodiment 1 of the present invention lies only in:

[0052] Let the Earth's radius be R, the satellite's orbital altitude be H, the altitude of the top of the atmosphere be h, and the satellite's line of sight be a straight line BS intersecting the top of the atmosphere at point A when the side angle is θ. Then, the distance from point C on the line of sight AB, with a geocentric angular distance φ from the line SO connecting the satellite and the Earth, to the Earth's center O can be obtained using the sine theorem for triangle SOC.

[0053]

[0054] Similarly, the geocentric distances between points A and B and SO are respectively:

[0055]

[0056] From geometric relations, the angle between the perpendicular line OC and AB is:

[0057]

[0058] The length of the infinitesimal element of the line segment AB with a geocentric angle distance of dφ from point C is:

[0059] Specific Implementation Example 3:

[0061] The difference between Embodiment 3 and Embodiment 2 of the present invention lies only in:

[0062] Let the geocentric distance be The column concentration of greenhouse gases is According to the radiative transfer equation, the radiance L of the line of sight AB is a nonlinear function of wavelength λ and column concentration with respect to the path integral, i.e.:

[0063] Specific Implementation Example 4:

[0065] The only difference between Embodiment 4 and Embodiment 3 of the present invention is that:

[0066] Discretizing the above integral, let the discretization step size be Δ, and denote η0 = mΔ and η = nΔ, then the above equation is discretized as follows:

[0067]

[0068] Where δ i =δ(iΔ). Specific Implementation Example 5:

[0070] The difference between Embodiment 5 and Embodiment 4 of the present invention lies only in:

[0071] If the number of observed spectral segments is greater than n-m+1, the unknown greenhouse gas column concentrations along this path are solved using a nonlinear least squares algorithm. Specific Implementation Example Six:

[0073] The difference between Embodiment Six and Embodiment Five of the present invention lies only in:

[0074] When R = 6378 km, H = 700 km, h = 10 km, and θ = 65°, we can calculate η0 = 12.3765° and η = 12.7841°. For a ground resolution of 1 km and Δ = 0.009°, we can calculate m = 1375 and n = 1420. For carbon dioxide as a single gas, there are a total of 46 variables to be solved. Specific Implementation Example 7:

[0076] The difference between Embodiment Seven and Embodiment Six of the present invention lies only in:

[0077] For hyperspectral detectors with more than 300 spectral bands, the inversion of three component parameters—carbon dioxide, methane, and aerosol optical thickness (AOD)—can be carried out simultaneously, with no more than 150 variables, and the solution is obtained using a nonlinear least squares method. Specific Implementation Example 8:

[0079] The difference between Embodiment 8 and Embodiment 7 of the present invention lies only in:

[0080] This invention provides a system for calculating vertical column concentration from a rotating scanning carbon monitoring satellite based on slant path radiance information. The system includes:

[0081] The analysis module analyzes the observation geometry of the satellite during the side-looking scan process, calculates the path integral of the greenhouse gas column concentration in each region along the observation path, and expresses the radiance as a function of the greenhouse gas column concentration in each region and the wavelength.

[0082] The calculation module discretizes the path integral according to the ground resolution requirements to obtain a nonlinear equation for radiance with respect to the gas column concentration in different regions. When the number of spectral bands of the detector is sufficiently rich, a nonlinear least squares optimization method is used to solve for the vertical column concentration of greenhouse gases of different components in each region. Specific Implementation Example Nine:

[0084] The difference between Embodiment Nine and Embodiment Eight of the present invention lies only in:

[0085] The present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information. Specific Implementation Example 10:

[0087] The only difference between Embodiment 10 and Embodiment 9 of the present invention is that:

[0088] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information. Specific Implementation Example Eleven:

[0090] The only difference between Embodiment Eleven and Embodiment Ten of this invention is that:

[0091] To verify the observation performance of the rotating scanning carbon monitoring satellite, a satellite simulation dataset was generated and validated considering the characteristics of the real Earth, satellite, and camera. This process included three parts: geometric generation of ground features based on a rigorous imaging model, simulation of observed radiance based on a rigorous radiative transfer model, and testing and accuracy verification of the greenhouse gas retrieval algorithm. The implementation methods for each part are as follows:

[0092] (1) Geometric generation of ground object observations based on a rigorous imaging model

[0093] A 3D scene including Earth, satellite, and payload is built based on the OpenSceneGraph (OSG) 3D rendering engine. The OSG engine's graphics rendering pipeline is used to perform a strict mapping from ground coordinates to pixel coordinates to obtain a simulated image. The simulation process and scene are as follows: Figure 3 , Figure 4 As shown. At this moment, the coordinates in the geographic coordinate system G at the sub-satellite point are [G1G2G3]. T After undergoing seven coordinate transformations—from the Earth-fixed coordinate system E, the inertial coordinate system I, the orbital coordinate system B, the body coordinate system S, the camera mounting coordinate system C, and the image plane coordinate system P—the coordinates of the corresponding image point in the image plane system are:

[0094]

[0095] Where M BA This is the homogeneous coordinate transformation matrix from system A to system B. All matrices in the above transformation are invertible; therefore, the geographic coordinates, Earth-fixed system coordinates, and inertial system coordinates corresponding to each pixel can be calculated based on the pixel coordinates of each point in the image and the depth cache data in the OSG engine.

[0096] Based on the location of the object point, the satellite position, and the solar ray vector, observational geometric information can be calculated, such as... Figure 5 As shown.

[0097] The normalized solar ray vector (pointing from the Earth's center to the Sun) is defined in the same specific reference frame (taking the J2000 geocentric inertial frame as an example) as i = (i x i y i z The unit vector of the z-axis of the inertial frame is N. J2000 Given a point on the ground with coordinates (x, y, z), its normalized normal vector is n = (n x ,n y ,n z The satellite position is (x s ,y s ,z s If the vector coordinates of the ground point pointing to the satellite position are (x, y), then the vector coordinates of the ground point pointing to the satellite position are (x, y). s ,y s ,z s (x, y, z) is normalized to a unit vector r. Let e ​​= N J2000 Let ×n be the local east direction and N = n×e be the north direction. Then, the solar / observed zenith angle is defined as the angle between the solar / observed vector and the local normal, and the solar / observed azimuth angle is defined as the angle between the solar / observed vector projected onto the ground from the local north direction clockwise. According to these definitions, the solar zenith angle and the observed zenith angle are respectively...

[0098] θ i=arccos(n·i)

[0099] θ r =arccos(n·r)

[0100] The projection vector of the sun / observation vector onto the ground is

[0101] i ⊥ =i-(n·i)n

[0102] r ⊥ =r-(n·r)n

[0103] Then the solar azimuth and the observation azimuth are respectively

[0104]

[0105] (2) Simulation of observed radiance based on a rigorous radiative transfer model

[0106] The forward modeling process of satellite remote sensing describes the relationship between atmospheric parameters, including information on aerosols, greenhouse gases, atmospheric temperature, humidity, wind pressure, etc., and the physical quantities observed by the satellite at the top of the atmosphere. The vector radiative transfer model describes the changes in radiation intensity and polarization state in the atmosphere and is a key element in jointly inverting atmospheric parameters using satellite scalar and polarization observations. The vector radiative transfer model used in this embodiment is the linear vectorized radiative transfer model LINTRAN developed by the Netherlands Space Research Centre, and its basic equation is as follows:

[0107]

[0108] Where I(τ,Ω) is the Stokes vector, which includes I (total intensity), Q and U (describing linear polarization), and V (describing circular polarization), τ is the optical thickness, Ω is the propagation direction, K(τ,Ω) is the absorption and scattering matrix, which describes the absorption and scattering of radiation by the atmosphere and its effect on the polarization state, and J(τ,Ω) is the source term, which includes scattered light and intrinsic radiation.

[0109] In radiative transfer calculations, the independent variables involved include: observation geometry (such as solar zenith angle, observation zenith angle, azimuth angle), gas column concentration (such as CO2, H2O, etc.), aerosol characteristics (including particle spectrum distribution, complex refractive index, layer height distribution), surface reflectance characteristics (BRDF parameters and BPDF scaling factor), and atmospheric temperature and pressure profiles. These independent variables collectively affect the transmission and scattering processes of atmospheric radiation, thereby affecting the radiance (I) and degree of polarization (DoLP) observed by satellites. In addition to the observation geometry mentioned in (1), aerosol models, surface models, and atmospheric models are key elements in radiative transfer simulation.

[0110] ① Aerosol model

[0111] Aerosol models primarily construct the transformation process from the microscopic physical and chemical properties of aerosols to their optical properties, and in practical applications, they can be divided into two main categories: fine modes and coarse modes. To describe the size of aerosol particles, the scale distribution of each mode can be quantified using a log-normal distribution. To describe the vertical distribution characteristics of aerosols in the atmosphere, a Gaussian distribution function is used for simulation, such that the number density N of each aerosol mode in the k-th layer is... 0,k It is given by the following formula:

[0112] N 0,k =N aer h(z k )Δz k

[0113]

[0114] Where, N aer Let z be the column number density of the vertical integral, A be the normalization constant, and z be the column number density of the vertical integral. k Let ω be the height of floor k. aer z is the width of the aerosol height distribution. aer The average height of the aerosol.

[0115] The composition of aerosol particles differs in each mode, specifically reflected in the complex refractive index, a physical property parameter of the aerosol. The complex refractive index of each aerosol mode is defined by a linear combination of the two aerosol components; therefore, the complex refractive index as a function of wavelength is defined as...

[0116]

[0117] Where 0≤c l ≤1,m l (λ) is a wavelength-dependent complex refractive index, applicable to certain types of aerosols, such as inorganic salts, dust, black carbon, and organic matter.

[0118] ② Surface model

[0119] For the problem of remote sensing inversion over land, a surface bidirectional function is used, which includes the directional and polarization characteristics of the land surface:

[0120]

[0121] Where D is the value excluding D. 11 An empty matrix other than 1, r 11 The two-way reflectance distribution function (BRDF) describes the radiation reflectance characteristics of the Earth's surface under different incident and observation geometries. Its expression is given by the Ross-Li model:

[0122]

[0123] Where A is the isotropic scaling parameter, f geo and f vol They are geometric (Li-Sparse) and volumetric (Ross-Thick) kernels, respectively.

[0124] R pol The bidirectional polarization distribution function (BPDF) is used to explain the surface polarization reflectivity, and its expression is:

[0125]

[0126] Among them, B pol It is a scaling parameter (wavelength independent). F p (m,Θ) is the Fresnel scattering matrix with refractive index m = 1.5, v is taken as 0.1, and μ i ,μ r These are the cosines of the solar zenith angle and the observed zenith angle, respectively.

[0127] ③ Atmospheric model

[0128] In the standard setup, the standard atmospheric model is discretized into 15 uniform vertical layers (2km spacing between 0-20km, 4km spacing between 20-36km, and one layer above 36km). Atmospheric vertical profiles of temperature, H2O, CO2, and CH4 are used as input. Absorption coefficients and broadenings of greenhouse gases at different wavelength ranges and spectral resolutions are calculated from the HITRAN (high-resolution transmission molecular absorption database) gas absorption spectral database. The process involves first reading the absorption spectral database, then setting the gas species to be calculated, reading the temperature and pressure grid data, setting the wavelength range and spectral resolution, selecting the type of absorption linearity, and finally calculating the absorption cross-section for that gas species and outputting the standardized data. The processing flowchart is shown below. Figure 7 As shown.

[0129] The greenhouse gas concentration distribution simulation uses the WRF-GHG model, where greenhouse gas transport is treated as inert gases, not participating in any chemical reactions. Therefore, the scalar conservation equation and the mass conservation equation are consistent. In the scalar conservation equation, a fifth-order assessment of horizontal flux divergence and a third-order assessment of vertical flux are used, coupled with a third-order Runge-Kutta time integration scheme. The ARW dynamic kernel is used to calculate gas transport, employing terrain-tracked mass vertical coordinates and integrating compressible, non-hydrostatic Euler equations. Its advantage lies in the fact that initial and boundary conditions can be obtained from other global three-dimensional chemical transport models (such as GEOS-Chem, TM3, TM5, etc.).

[0130] Based on the simulation analysis process described above, this study focuses on ultra-wide swath observation data and, combined with satellite observation geometry information, comprehensively considers different aerosol optical thicknesses, different aerosol modes, specifically information such as effective aerosol radius, effective variance, sphericity ratio, and aerosol layer height, as well as a surface model of bare soil and vegetation mixture. It then couples this with the US standard atmospheric profile to simulate satellite-observed radiance. The XCO2 data used is obtained based on atmospheric assimilation models. Radiance simulation is then performed using a radiative transfer model and corresponding atmospheric and surface data.

[0131] (3) Testing and accuracy verification of greenhouse gas inversion algorithm

[0132] The optimal estimation (OE) algorithm was used to invert greenhouse gas parameters in typical regions. A cost function was constructed based on the forward transport model, prior state vectors, and their covariance. Iterative inversion of greenhouse gas parameters was carried out using Tikhonov-Phillips regularization constraints and the Levenberg-Marquardt iterative method. The specific spectral range, spectral resolution, spectral sampling rate, signal-to-noise ratio, and inversion results are shown in Tables 1 and 2. For the edge regions, the inversion accuracy for XCO2 reached 1 ppm, and for XCH4, it reached 4.4 ppb, fully validating the ability of the rotating scanning satellite to achieve high-precision greenhouse gas inversion.

[0133] Table 1 Simulation Analysis and Inversion Results of Sub-Satellite Point

[0134]

[0135] Table 2 Simulation analysis and inversion results of edge points (side view 65°)

[0136]

[0137]

[0138] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or N embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified. Any process or method described in the flowcharts or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logical functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain. The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or N wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM).Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory. It should be understood that various parts of the invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0139] The above description is merely a preferred embodiment of a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant path radiance information. The scope of protection for this method is not limited to the above embodiments; all technical solutions falling within this conceptual framework are within the scope of protection of this invention. It should be noted that for those skilled in the art, any improvements and variations made without departing from the principles of this invention should also be considered within the scope of protection of this invention.

Claims

1. A method for calculating vertical column concentration from a rotating scanning carbon monitoring satellite based on slant path radiance information, characterized by: Includes the following steps: The observation geometry of the satellite during the side-looking scan process is analyzed, the path integral of the greenhouse gas column concentration in each region along the observation path is calculated, and the radiance is expressed as a function of the greenhouse gas column concentration in each region and the wavelength. The path integral is discretized according to the ground resolution requirements to obtain a nonlinear equation for radiance with respect to the gas column concentration in different regions. When the number of detector spectral bands is sufficiently rich, a nonlinear least squares optimization method is used to solve for the vertical column concentration of greenhouse gases of different components in each region. Let the Earth's radius be R, the satellite's orbital altitude be H, the altitude of the top of the atmosphere be h, and the satellite's line of sight be a straight line BS intersecting the top of the atmosphere at point A when the side angle is θ. Then, the distance from point C on the line of sight AB, with a geocentric angular distance φ from the line SO connecting the satellite and the Earth, to the Earth's center O can be obtained using the sine theorem for triangle SOC. Similarly, the geocentric distances between points A and B and SO are respectively: From geometric relations, the angle between the perpendicular line OC and AB is: The length of the infinitesimal element of the line segment AB with a geocentric angle distance of dφ from point C is: ； Let the geocentric distance be The column concentration of greenhouse gases is According to the radiative transfer equation, the radiance L of the line of sight AB is a nonlinear function of wavelength λ and column concentration with respect to the path integral, i.e.: For hyperspectral detectors with more than 300 spectral bands, the inversion of three component parameters—carbon dioxide, methane, and aerosol optical thickness (AOD)—can be carried out simultaneously, with no more than 150 variables, and the solution can be obtained using a nonlinear least squares method.

2. The method according to claim 1, characterized in that: Discretize the above integral, and let the step size be . And record Then the above equation can be discretized as follows: in .

3. The method according to claim 2, characterized in that: Observation If the number of spectral segments is greater than n-m+1, the unknown greenhouse gas column concentrations along the path are solved using a nonlinear least squares algorithm.

4. The method according to claim 3, characterized in that: When R = 6378 km, H = 700 km, h = 10 km, and θ = 65°, it can be calculated that... , For a ground resolution of 1km In this case, we calculated m=1375 and n=1420. For carbon dioxide as a single gas, there are a total of 46 variables to be solved.

5. A vertical column concentration calculation system for a rotating scanning carbon monitoring satellite based on slant path radiance information, characterized in that: The system includes: The analysis module analyzes the observation geometry of the satellite during the side-looking scan process, calculates the path integral of the greenhouse gas column concentration in each region along the observation path, and expresses the radiance as a function of the greenhouse gas column concentration in each region and the wavelength. The calculation module discretizes the path integral according to the ground resolution requirements to obtain a nonlinear equation of radiance with respect to the gas column concentration in different regions. When the number of detector spectral bands is sufficiently rich, a nonlinear least squares optimization method is used to solve for the vertical column concentration of greenhouse gases of different components in each region. Let the Earth's radius be R, the satellite's orbital altitude be H, the altitude of the top of the atmosphere be h, and the satellite's line of sight be a straight line BS intersecting the top of the atmosphere at point A when the side angle is θ. Then, the distance from point C on the line of sight AB, with a geocentric angular distance φ from the line SO connecting the satellite and the Earth, to the Earth's center O can be obtained using the sine theorem for triangle SOC. Similarly, the geocentric distances between points A and B and SO are respectively: From geometric relations, the angle between the perpendicular line OC and AB is: The length of the infinitesimal element of the line segment AB with a geocentric angle distance of dφ from point C is: ； Let the geocentric distance be The column concentration of greenhouse gases is According to the radiative transfer equation, the radiance L of the line of sight AB is a nonlinear function of wavelength λ and column concentration with respect to the path integral, i.e.: For hyperspectral detectors with more than 300 spectral bands, the inversion of three component parameters—carbon dioxide, methane, and aerosol optical thickness (AOD)—can be carried out simultaneously, with no more than 150 variables, and the solution can be obtained using a nonlinear least squares method.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method as claimed in claims 1-4.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the method of claims 1-4.

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