Rotary scanning carbon monitoring satellite vertical column concentration calculation method based on slant range radiance information

Abstract

The invention relates to a rotational scanning carbon monitoring satellite vertical column concentration calculation method based on slant range radiance information. The invention relates to the technical field of monitoring satellite vertical column concentration calculation, and the method comprises the following steps: analyzing observation geometry of a satellite in a side-looking scanning process, calculating a path integral of greenhouse gas column concentration of each region on an observation path, and expressing radiance as a function of greenhouse gas column concentration and wavelength of each region; discretizing path integration according to ground resolution requirements to obtain a nonlinear equation of radiance about gas column concentrations in different regions, and solving greenhouse gas vertical column concentrations of different components in each region by adopting a nonlinear least square optimization method under the condition that the number of spectrum segments of a detector is abundant enough. According to the method, the solution from the slant range radiance to the vertical column concentration based on the data of the hyperspectral detector is realized, and the feasibility of obtaining the greenhouse gas column concentration of the novel rotary scanning satellite is proved.

Description

Technical Field

[0001] The invention relates to the technical field of vertical column concentration calculation of monitoring satellites, in particular to a vertical column concentration calculation method of a rotating scanning carbon monitoring satellite based on slant range radiance information. Background Art

[0002] In order to solve the problem of the resolution and width of the current carbon dioxide monitoring satellites being constrained by each other, the patent "An ultra-wide coverage multi-mode carbon monitoring satellite based on payload vertical orbit rotation scanning, construction method and monitoring method" proposes to achieve ultra-wide detection of thousands of kilometers through the method of payload vertical orbit rotation scanning, which can strongly support the global carbon inventory. However, under this new system, the satellite vertical orbit swing scanning angle can reach 65°, and the observation zenith angle of ground objects can reach up to 74°. The radiance observed under large slant range inevitably couples the vertical column concentration information of greenhouse gases at different longitudes and latitudes. Therefore, a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information is designed, which can realize the inversion from the radiance observed at large slant range to the vertical column concentration in each area of ​​the observation path, thereby effectively demonstrating the feasibility of the rotating scanning system, strongly supporting the overall design and application of the satellite, and has high academic value and engineering significance. Summary of the invention

[0003] In view of the shortcomings of the prior art and to solve the above-mentioned problems, the present invention proposes a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant-range radiance information. In order to solve the problem of inversion from large-slant-range observed radiance to the vertical column concentration of greenhouse gases in each region of the observation path, the present invention proposes a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant-range radiance information.

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

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

[0006] Analyze the observation geometry of the satellite during the side-view scanning process, calculate the path integral of the greenhouse gas column concentration in each area on the observation path, and express the radiance as a function of the greenhouse gas column concentration and wavelength in each area;

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

[0008] Preferably, assuming that the radius of the earth is R, the altitude of the satellite orbit is H, the altitude of the top of the atmosphere is h, and the side angle is θ, the satellite observation line of sight is a straight line BS, which intersects the top of the atmosphere at point A. Then, the distance from point C on the line of sight AB, which has an angular distance φ from the center of the earth to the center of the earth on the satellite-ground line SO, to the center of the earth O is obtained by the sine theorem of the triangle SOC:

[0009]

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

[0011]

[0012] From the geometric relationship, we know that the angle between the perpendicular line OC and AB is:

[0013]

[0014] The infinitesimal length of the line segment AB at point C with an angular distance dφ from the center of the earth is:

[0015]

[0016] Preferably, the geocentric angular distance is The column concentration of greenhouse gases at According to the radiation transfer equation, the radiance L of the line of sight AB is a nonlinear function of the wavelength λ and the column concentration with respect to the path integral, that is:

[0017]

[0018] Preferably, the above integral is discretized, assuming that the discrete step length is Δ, and η0=mΔ, η=nΔ, then the above equation is discretized as:

[0019]

[0020] where δ i =δ(iΔ).

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

[0022] Preferably, when R=6378km, H=700km, h=10km, θ=65°, it can be calculated that η0=12.3765°, η=12.7841°. For the ground resolution of 1kmΔ=0.009°, m=1375, n=1420 are calculated. For carbon dioxide, there are 46 variables to be solved.

[0023] Preferably, for a hyperspectral detector with more than 300 spectral bands, the inversion of three component parameters of carbon dioxide, methane, and aerosol optical thickness AOD can be carried out simultaneously, with the number of variables not exceeding 150, and the nonlinear least squares method is used for solution.

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

[0025] An analysis module, wherein the analysis module analyzes the observation geometry of the satellite during the side-view scanning process, calculates the path integral of the greenhouse gas column concentration in each region on the observation path, and expresses the radiance as a function of the greenhouse gas column concentration in each region and the wavelength;

[0026] A calculation module discretizes the path integral according to the ground resolution requirement to obtain a nonlinear equation of the radiance with respect to the gas column concentration in different regions, and adopts a nonlinear least squares optimization method to solve the vertical column concentration of greenhouse gases of different components in each region when the number of detector spectral bands is sufficient.

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

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information is implemented.

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

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

[0031] The present invention proposes a method for calculating vertical column concentration of a rotating scanning carbon monitoring satellite based on slant radiance information. The present invention solves the problem that the radiance observed by the payload under the large side swing angle observation condition of the rotating scanning satellite is coupled with the greenhouse gas column concentration information in different regions, realizes the solution of slant radiance to vertical column concentration based on hyperspectral detector data, and proves the feasibility of obtaining greenhouse gas column concentration of the rotating scanning new system satellite. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

[0034] Figure 2 It is a schematic diagram of a line segment microelement at point C of the present invention;

[0035] Figure 3 It is the satellite observation geometry simulation flow chart;

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

[0037] Figure 5 It is a schematic diagram of observation geometry calculation;

[0038] Figure 6 It is the flow chart of the vector radiation transfer model;

[0039] Figure 7 It is the flow chart of gas absorption cross section calculation. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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 scope of protection of the present invention.

[0041] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0042] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] In addition, 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 is described in detail below in conjunction with specific embodiments. Specific embodiment one:

[0046] according to Figure 1-Figure 7 As shown, the specific optimization technical solution adopted by the present invention to solve the above 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 range radiance information.

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

[0048] Analyze the observation geometry of the satellite during the side-view scanning process, calculate the path integral of the greenhouse gas column concentration in each area on the observation path, and express the radiance as a function of the greenhouse gas column concentration and wavelength in each area;

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

[0051] The difference between the second embodiment of the present invention and the first embodiment is that:

[0052] Assume that the radius of the earth is R, the height of the satellite orbit is H, the height of the top of the atmosphere is h, and the side angle is θ. The satellite observation line of sight is a straight line BS, which intersects the top of the atmosphere at point A. Then the distance from point C on the line of sight AB with an angular distance φ from the center of the earth to the center of the earth is obtained by the sine theorem of triangle SOC:

[0053]

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

[0055]

[0056] From the geometric relationship, we know that the angle between the perpendicular line OC and AB is:

[0057]

[0058] The infinitesimal length of the line segment AB at point C with an angular distance dφ from the center of the earth is:

[0059] Specific embodiment three:

[0061] The difference between the third embodiment of the present invention and the second embodiment is that:

[0062] Assume the geocentric angular distance is The column concentration of greenhouse gases at According to the radiation transfer equation, the radiance L of the line of sight AB is a nonlinear function of the wavelength λ and the column concentration with respect to the path integral, that is:

[0063] Specific embodiment four:

[0065] The difference between the fourth embodiment of the present invention and the third embodiment is that:

[0066] Discretize the above integral, set the discrete step length to Δ, and record η0 = mΔ, η = nΔ, then the above equation is discretized as:

[0067]

[0068] where δ i =δ(iΔ). Specific embodiment five:

[0070] The difference between the fifth embodiment of the present invention and the fourth embodiment is that:

[0071] If the number of observed spectral segments is greater than n-m+1, the unknown greenhouse gas column concentration on the path is solved using a nonlinear least squares algorithm. Specific embodiment six:

[0073] The difference between the sixth embodiment of the present invention and the fifth embodiment is that:

[0074] When R=6378km, H=700km, h=10km, θ=65°, we can calculate η0=12.3765°, η=12.7841°. For the ground resolution of 1kmΔ=0.009°, we can calculate m=1375, n=1420. For carbon dioxide, there are 46 variables to be solved. Specific embodiment seven:

[0076] The difference between the seventh embodiment of the present invention and the sixth embodiment is that:

[0077] For hyperspectral detectors with more than 300 spectral bands, the inversion of three component parameters of carbon dioxide, methane, and aerosol optical thickness (AOD) can be carried out simultaneously. The number of variables does not exceed 150, and the nonlinear least squares method is used for solution. Specific embodiment eight:

[0079] The difference between the eighth embodiment of the present invention and the seventh embodiment is that:

[0080] The present invention provides a vertical column concentration calculation system for a rotating scanning carbon monitoring satellite based on slant range radiance information, the system comprising:

[0081] An analysis module, wherein the analysis module analyzes the observation geometry of the satellite during the side-view scanning process, calculates the path integral of the greenhouse gas column concentration in each region on the observation path, and expresses the radiance as a function of the greenhouse gas column concentration in each region and the wavelength;

[0082] A calculation module discretizes the path integral according to the ground resolution requirement to obtain a nonlinear equation of the radiance with respect to the gas column concentration in different regions, and adopts a nonlinear least squares optimization method to solve the vertical column concentration of greenhouse gases of different components in each region when the number of detector spectral bands is sufficient. Specific embodiment nine:

[0084] The difference between the ninth embodiment of the present invention and the eighth embodiment is that:

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

[0087] The difference between the tenth embodiment of the present invention and the ninth embodiment is that:

[0088] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information is implemented. Specific embodiment eleven:

[0090] The difference between the eleventh embodiment of the present invention and the tenth embodiment is that:

[0091] In order to verify the observation efficiency of the rotating scanning carbon monitoring satellite, the generation and verification of satellite simulation data sets are carried out under the conditions of considering the characteristics of the real earth, satellite and camera. Specifically, it includes three parts: the generation of ground object observation geometry based on a strict imaging model, the simulation of observation radiance based on a strict radiation transfer model, and the testing and accuracy verification of greenhouse gas inversion algorithms. The implementation methods of each part are as follows:

[0092] (1) Geometry generation of ground object observation based on strict imaging model

[0093] Based on the OpenSceneGraph (OSG) 3D rendering engine, a 3D scene including the earth, satellite and payload is established. The graphics rendering pipeline of the OSG engine is used to complete the strict mapping from the coordinates of the ground objects to the pixel coordinates to obtain the simulated image. The simulation process and the scene are as follows: Figure 3 , Figure 4 As shown. At this time, the coordinates of the sub-satellite point in the geographic coordinate system G are [G1G2G3] T After the ground object point has gone through seven coordinate transformation processes, including the ground-fixed coordinate system E, inertial coordinate system I, orbital coordinate system B, body coordinate system S, camera installation coordinate system C and image plane coordinate system P, the coordinates of its corresponding image point in the image plane system are

[0094]

[0095] Among them, M BA It is the homogeneous coordinate transformation matrix from system A to system B. All matrices in the above transformation are reversible matrices, so the geographic coordinates, earth-fixed coordinates and inertial coordinates of each pixel can be solved according to the pixel coordinates of each point in the image and the depth buffer data in the OSG engine.

[0096] Based on the object point position, satellite position and sun ray vector, the observation geometry information can be calculated, such as Figure 5 shown.

[0097] In the same specific reference system (taking J2000 geocentric inertial system as an example), the normalized solar ray vector (from the center of the earth to the sun) i = (i x ,i y ,i z ), the unit vector of the z-axis of the inertial system is N J2000 , the coordinates of a point on the ground (x, y, z), the normalized normal vector of the point is n = (n x ,n y ,n z ), the satellite position is (x s ,y s ,z s ), then the vector coordinates of the ground object point pointing to the satellite position are (x s ,y s ,z s )-(x,y,z), normalized to unit vector r. Let e=N J2000 ×n is the local due east direction, N=n×e is the due north direction, then the solar / observation zenith angle is defined as the angle between the solar / observation vector and the local normal, and the solar / observation azimuth angle is defined as the angle between the projection vector of the solar / observation vector on the ground pointing clockwise from the local due north direction. According to the above definitions, the solar zenith angle and the observation 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 on the ground is

[0101] i ⊥ =i-(n·i)n

[0102] r ⊥ =r-(n·r)n

[0103] The solar azimuth and observation azimuth are

[0104]

[0105] (2) Observational radiance simulation based on a rigorous radiative transfer model

[0106] The forward model process of satellite remote sensing describes the relationship between atmospheric parameters, including aerosols, greenhouse gases, atmospheric temperature, humidity, wind pressure and other information, and the observed physical quantities obtained by the satellite at the top of the atmosphere. The vector radiation transfer model describes the changes in radiation intensity and polarization state in the atmosphere, and is a key element in the joint inversion of atmospheric parameters using satellite scalar and polarization observations. The vector radiation transfer model used in this embodiment is the linear vectorized radiation transfer model LINTRAN developed by the Netherlands Space Research Center. The basic form of the equation is

[0107]

[0108] Among them, I(τ,Ω) is the Stokes vector, including 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, including scattered light and intrinsic radiation.

[0109] In the calculation of radiation transfer, 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 reflection characteristics (BRDF parameters and BPDF proportional coefficient), atmospheric temperature and pressure profile. These independent variables jointly affect the transmission and scattering process 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 atmosphere models are key elements of radiation transfer simulation.

[0110] ①Aerosol model

[0111] The aerosol model mainly constructs the conversion process from the microscopic physical and chemical properties of aerosols to their optical properties. In practical applications, it can be divided into two categories: fine mode and coarse mode. To describe the size of aerosol particles, the scale distribution of each mode can be quantified using the log-normal distribution. To describe the vertical distribution characteristics of aerosols in the atmosphere, the Gaussian distribution function is used to simulate, so that the number density N of each aerosol mode in the kth layer is 0,k Given by:

[0112] N 0,k =N aer h(z k )Δz k

[0113]

[0114] Among them, N aer is the vertically integrated column number density, A is the normalization constant, z k is the height of the k-th floor, ω aer is the width of the aerosol height distribution, z aer is the average aerosol height.

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

[0116]

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

[0118] ②Surface model

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

[0120]

[0121] Where D is divided by D 11 = Empty matrix other than 1, r 11 is the bidirectional reflectance distribution function (BRDF), which is used to describe the radiation reflection characteristics of the surface under different incident and observation geometric conditions. The expression is given by the Ross-Li model:

[0122]

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

[0124] R pol is the bidirectional polarization distribution function (BPDF), which is used to explain the surface polarization reflectivity and is expressed as

[0125]

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

[0127] ③Atmospheric model

[0128] In the standard setting, the standard atmosphere model is discretized into 15 uniform vertical layers (the spacing between 0-20km is 2km, the spacing between 20-36km is 4km, and there is 1 layer above 36km). The vertical atmospheric profiles of temperature, H2O, CO2 and CH4 are used as input. The absorption coefficient and broadening of greenhouse gases in different wavelength ranges and different spectral resolutions are calculated from the HITRAN (high-resolution transmission molecular absorption database) gas absorption spectrum database. First, it is necessary to read the absorption spectrum database, then set the gas type to be calculated, read the temperature and pressure grid data, set the band range and spectral resolution data, select the type of absorption linearity, and finally calculate the absorption cross section of the gas type and output it in a standardized manner. The processing flow chart is as follows Figure 7 shown.

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

[0130] According to the above simulation analysis process, this study targets ultra-wide observation data and combines satellite observation geometry information to comprehensively consider different aerosol optical thicknesses, different aerosol modes, including aerosol effective radius, effective variance, spherical ratio, aerosol layer height, and a surface model of bare soil and vegetation mixture, and then couples the US standard atmospheric profile to simulate satellite observation radiance. The XCO2 data used is obtained based on atmospheric assimilation model calculations. Combined with the radiation transfer model and the corresponding atmospheric and surface data, radiance simulation is performed.

[0131] (3) Greenhouse gas inversion algorithm testing and accuracy verification

[0132] The optimal estimation OE algorithm is used to invert the typical area. The cost function is constructed based on the forward transmission model and the prior state vector and its covariance and other parameters. The Tikhonov-Phillips regularization constraint and Levenberg-Marquardt iteration method are used to carry out iterative inversion of greenhouse gas parameters. The specific spectral range, spectral resolution, spectral sampling rate and signal-to-noise ratio considered and the inversion results are shown in Tables 1 and 2. For the marginal areas, the inversion accuracy of XCO2 can reach 1ppm, and the inversion accuracy of XCH4 can reach 4.4ppb, which fully verifies the ability of the rotating scanning satellite to achieve high-precision inversion of greenhouse gases.

[0133] Table 1 Subsatellite point simulation analysis and inversion results

[0134]

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

[0136]

[0137]

[0138] In the description of this specification, the description of reference terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. Any process or method description in the flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code including one or more executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiment of the present invention includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by a person skilled in the art of the art to which the embodiments of the present invention belong. The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as a sequenced list of executable instructions for implementing the logic function, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or N wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM).In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, then editing, interpreting or processing in other suitable ways as necessary, and then storing it in a computer memory. It should be understood that the various parts of the present invention can be implemented with hardware, software, firmware, or a combination thereof. In the above-mentioned embodiment, N steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, any one of the following technologies known in the art or their combination can be used to implement: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0139] The above is only a preferred implementation of a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information. The protection scope of a method for calculating the vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information is not limited to the above embodiments. All technical solutions under this idea belong to the protection scope of the present invention. It should be pointed out that for those skilled in the art, several improvements and changes without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for calculating vertical column concentration of a rotating scanning carbon monitoring satellite based on slant range radiance information, characterized in that: The following steps are involved: Analyze the observation geometry of the satellite during the side-view scanning process, calculate the path integral of the greenhouse gas column concentration in each area on the observation path, and express the radiance as a function of the greenhouse gas column concentration and wavelength in each area; The path integral is discretized according to the ground resolution requirement to obtain a nonlinear equation of radiance related to gas column concentration in different regions. When the number of detector spectral bands is rich enough, the nonlinear least squares optimization method is used to solve the vertical column concentration of greenhouse gases of different components in each region.

2. The method according to claim 1, characterized in that: set up When the radius of the earth is R, the altitude of the satellite orbit is H, the altitude of the top of the atmosphere is h, and the side angle is θ, the satellite observation line of sight is a straight line BS, which intersects the top of the atmosphere at point A. Then the distance from point C on the line of sight AB with an angular distance φ from the center of the earth to the center of the earth O is obtained by the sine theorem of triangle SOC: Similarly, the angular distances between points A and B and SO are: From the geometric relationship, we know that the angle between the perpendicular line OC and AB is: The infinitesimal length of the line segment AB at point C with an angular distance dφ from the center of the earth is:

3. The method according to claim 2, characterized in that: Assume the geocentric angular distance is The column concentration of greenhouse gases at According to the radiation transfer equation, the radiance L of the line of sight AB is a nonlinear function of the wavelength λ and the column concentration with respect to the path integral, that is:

4. The method according to claim 3, characterized in that: Discretize the above integral, set the discrete step length to Δ, and record η0 = mΔ, η = nΔ, then the above equation is discretized as: among them i = δ(iΔ).

5. The method according to claim 4, characterized in that: Observation If the number of spectrum segments is greater than n-m+1, the unknown greenhouse gas column concentration on the path is solved using a nonlinear least squares algorithm.

6. The method according to claim 5, characterized in that: When R=6378km, H=700km, h=10km, θ=65°, we can calculate η0=12.3765°, η=12.7841°. For the ground resolution of 1kmΔ=0.009°, we can calculate m=1375, n=1420. For carbon dioxide, there are 46 variables to be solved.

7. The method according to claim 6, characterized in that: For hyperspectral detectors with more than 300 spectral bands, the inversion of three component parameters of carbon dioxide, methane, and aerosol optical thickness (AOD) can be carried out simultaneously. The number of variables does not exceed 150, and the nonlinear least squares method is used for solution.

8. A vertical column concentration calculation system for a rotating scanning carbon monitoring satellite based on slant range radiance information, characterized by: The system comprises: An analysis module, wherein the analysis module analyzes the observation geometry of the satellite during the side-view scanning process, calculates the path integral of the greenhouse gas column concentration in each region on the observation path, and expresses the radiance as a function of the greenhouse gas column concentration in each region and the wavelength; A calculation module discretizes the path integral according to the ground resolution requirement to obtain a nonlinear equation of the radiance with respect to the gas column concentration in different regions, and adopts a nonlinear least squares optimization method to solve the vertical column concentration of greenhouse gases of different components in each region when the number of detector spectral bands is sufficient.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to claims 1-7.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method of claims 1-7 is implemented.

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