A method for inverting remote sensing data with overlapping strong and weak greenhouse gas absorption lines

Abstract

This invention relates to a method for inverting remote sensing data from overlapping strong and weak absorption lines of greenhouse gases, belonging to the field of satellite remote sensing technology. Specifically, it includes the following steps: Step 1: Constructing an optical system, including a satellite platform, camera 1, and camera 2; camera 1 and camera 2 are mounted at the bottom of the satellite platform; camera 1 and camera 2 are mirrored; the imaging spectral band of camera 1 moves from the weak absorption spectral band to the strong absorption spectral band along the positive push-broom direction to the negative push-broom direction; the imaging spectral band of camera 2 is in the opposite direction; the optical system moves horizontally from left to right to achieve horizontal push-brooming of greenhouse gases on the ground, obtaining a dual-camera fused image; the dual-camera differential absorption spectroscopy algorithm is used to invert the dual-camera fused image to obtain the greenhouse gas column concentration; this invention, based on the spectral characteristics of strong and weak absorption lines of greenhouse gases, adopts a detector focal plane segmentation method, and the dual cameras achieve differential absorption and inversion in the same region, which can guide the design of greenhouse gas detection micro / nano satellite payloads.

Description

Technical Field

[0001] This invention belongs to the field of satellite remote sensing technology and relates to a method for inverting remote sensing data of overlapping strong and weak absorption lines of greenhouse gases. Background Technology

[0002] Global greenhouse gas remote sensing has become a key research area in both science and application. Greenhouse gas detection primarily employs optical hyperspectral imaging systems, including grating spectrometry, Fourier interferometry, and FP interferometry. Currently, various types of greenhouse gas hyperspectral detection payloads in orbit and under development use spotting or pushbroom methods to detect gases in specific regions and areas. A typical SCIAMACHY payload can detect greenhouse gases such as CO2, weighing 198 kg and consuming 122 W. With the development of large-scale, high-density constellations, the design of lightweight, compact, and highly integrated payload platforms has become a trend in microsatellite technology. Therefore, traditional imaging modes and systems result in heavy, high-powered payloads that are unsuitable for the integrated development and application of greenhouse gas detection microsatellites. Summary of the Invention

[0003] The technical problem solved by this invention is to overcome the shortcomings of the prior art and propose a remote sensing data inversion method based on the overlapping of strong and weak absorption lines of greenhouse gases. Based on the spectral characteristics of strong and weak absorption lines of greenhouse gases, a focal plane segmentation method is adopted for the detector, and dual cameras are used to realize differential absorption and inversion in the same region, which can be used to guide the design of greenhouse gas detection micro-nano satellite payloads.

[0004] The solution of the present invention is:

[0005] A method for inverting remote sensing data with overlapping strong and weak greenhouse gas absorption lines includes:

[0006] An optical system is constructed, including a satellite platform, camera 1, and camera 2; camera 1 and camera 2 are mounted on the bottom of the satellite platform.

[0007] The optical system moves horizontally from left to right to achieve horizontal sweeping of greenhouse gases on the ground and obtain dual-camera fusion imaging;

[0008] The greenhouse gas column concentration was obtained by inverting the dual-camera fusion imaging using a dual-camera differential absorption spectroscopy algorithm.

[0009] In the aforementioned method for inverting remote sensing data based on the overlapping of strong and weak greenhouse gas absorption lines, the satellite platform is set horizontally; cameras 1 and 2 are installed axially parallel, and the optical axes of cameras 1 and 2 both point vertically toward the ground; the fields of view of cameras 1 and 2 coincide on the ground.

[0010] In the above-mentioned remote sensing data inversion method for overlapping strong and weak absorption lines of greenhouse gases, camera 1 and camera 2 are set up in a mirror image configuration; the imaging spectrum of camera 1 from the positive push-broom direction to the negative push-broom direction is from the weak absorption spectrum to the strong absorption spectrum; the imaging spectrum of camera 2 from the positive push-broom direction to the negative push-broom direction is from the strong absorption spectrum to the weak absorption spectrum.

[0011] In the aforementioned method for inverting remote sensing data with overlapping strong and weak greenhouse gas absorption lines, the specific method for performing inversion calculations on the images using a dual-camera differential absorption spectroscopy algorithm is as follows:

[0012] S1. Extract the differential absorption spectrum from the detection spectrum of the dual-camera fusion imaging;

[0013] S2. Fit the differential absorption spectrum to obtain the greenhouse gas column concentration.

[0014] In the aforementioned method for inverting remote sensing data with overlapping strong and weak greenhouse gas absorption lines, the method for extracting the differential absorption spectrum in step S1 is as follows:

[0015] Let the emission spectrum of the solar light source be I0(λ); let the spectrum received by the optical system after atmospheric absorption be I(i,λ); where i is the spatial dimension index.

[0016] Calculate the natural logarithm R(i,λ) of the ratio of I(i,λ) to I0(λ); R(i,λ) = σ(i,λ)c i L+P(i,λ); where, σ(i,λ)c i L represents the differential absorption spectrum; P(i,λ) represents the extinction caused by the absorption of spectral intensity by the narrow band of the gas, Rayleigh scattering, and Mie scattering by the aerosol.

[0017] A polynomial fitting method is used to eliminate P(i,λ) in the ratio spectrum R(i,λ);

[0018] After eliminating P(i,λ), the difference spectrum of R(i,λ) is denoted as A(i,λ), and A(i,λ) = σ(i,λ)c i L.

[0019] In the aforementioned method for inverting remote sensing data with overlapping strong and weak absorption lines of greenhouse gases, the spectrum I(i,λ) received by the optical system after atmospheric absorption is the integration of the detection spectra of camera 1 and camera 2 in the i-th spatial dimension, which includes both strong absorption bands and weak absorption bands.

[0020] In the remote sensing data inversion method described above, which involves overlapping strong and weak absorption lines of greenhouse gases, the differential absorption spectrum σ(i,λ)c i In L, σ(i,λ) is the standard difference absorption cross section of the greenhouse gas, indicating the absorption of light intensity per unit length by a unit concentration of the gas; ci Let L be the greenhouse gas column concentration in the i-th spatial dimension to be solved; L represents the optical path length, i.e., the known orbital height.

[0021] In the aforementioned method for inverting remote sensing data with overlapping greenhouse gas absorption lines, the differential absorption spectrum σ(i,λ)c i Compared to L, the extinction P(i,λ) caused by narrow-band absorption of spectral intensity by gas, Rayleigh scattering and Mie scattering by aerosols changes more rapidly with wavelength.

[0022] In the aforementioned method for inverting remote sensing data with overlapping strong and weak absorption lines of greenhouse gases, a polynomial fitting is performed on the contrast spectrum R(i,λ) to obtain the fitting curve. This fitting curve subtracts the slow changes in the spectrum, including broadband absorption by atmospheric molecules, Rayleigh scattering and Mie scattering, as well as low-frequency background interference and system noise. What remains is the difference spectrum A(i,λ).

[0023] In the aforementioned method for inverting remote sensing data with overlapping strong and weak absorption lines of greenhouse gases, the specific method for fitting the differential absorption spectrum in step S2 is as follows:

[0024] By fitting the differential absorption spectrum A(i,λ) to the differential absorption cross section σ(i,λ) of the gas molecules, the greenhouse gas column concentration c in the i-th spatial dimension can be obtained. i .

[0025] The advantages of this invention compared to the prior art are:

[0026] (1) This invention discloses a remote sensing payload imaging mode design with overlapping strong and weak absorption lines of greenhouse gases. The payload is lightweight and small, and is suitable for micro-nano satellite configuration.

[0027] (2) This invention discloses a remote sensing payload imaging mode design with overlapping strong and weak absorption lines of greenhouse gases. Dual cameras can realize differential absorption of strong and weak spectral lines in the same area, which is convenient for data inversion and application.

[0028] (3) In the data processing, the present invention uses a dual-camera differential absorption spectroscopy algorithm to realize the inversion calculation of greenhouse gas column concentration. Attached Figure Description

[0029] Figure 1 This is a flowchart of the greenhouse gas data inversion process of the present invention;

[0030] Figure 2 This is a schematic diagram of the optical system of the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the embodiments.

[0032] This invention provides a method for inverting remote sensing data based on the overlapping of strong and weak absorption lines of greenhouse gases. Based on the spectral characteristics of strong and weak absorption lines of greenhouse gases, the method adopts a detector focal plane segmentation approach and uses dual cameras to achieve differential absorption and inversion in the same region, which can be used to guide the design of greenhouse gas detection micro-nano satellite payloads.

[0033] A dual-camera greenhouse gas data inversion method based on detector mirror distribution, such as Figure 1 Specifically, the process includes the following steps:

[0034] Step 1: Construct the optical system, including a satellite platform, camera 1, and camera 2. Cameras 1 and 2 are mounted at the bottom of the satellite platform. The satellite platform is horizontally positioned; cameras 1 and 2 are mounted axially parallel, with their optical axes pointing perpendicularly to the ground; the fields of view of cameras 1 and 2 coincide on the ground. Cameras 1 and 2 are mirrored; the imaging spectrum of camera 1, along the push-broom positive direction to the push-broom negative direction, ranges from weak absorption to strong absorption; the imaging spectrum of camera 2, along the push-broom positive direction to the push-broom negative direction, ranges from strong absorption to weak absorption. Figure 2 As shown.

[0035] The optical system moves horizontally from left to right to achieve horizontal sweeping of greenhouse gases on the ground, obtaining dual-camera fusion imaging.

[0036] The greenhouse gas column concentration was obtained by inverting the dual-camera fusion imaging using a dual-camera differential absorption spectroscopy algorithm.

[0037] The specific method for inverting the image using the dual-camera differential absorption spectroscopy algorithm is as follows:

[0038] S1. Extract the differential absorption spectrum from the detection spectrum of the dual-camera fusion imaging.

[0039] The extraction method for differential absorption spectra is as follows:

[0040] Let the emission spectrum of the solar source be I0(λ); let the spectrum received by the optical system after atmospheric absorption be I(i,λ); i is the spatial dimension number; the spectrum I(i,λ) received by the optical system after atmospheric absorption is the integration of the detection spectra of machine 1 and camera 2 in the i-th spatial dimension, which includes both strong absorption and weak absorption bands.

[0041] Calculate the natural logarithm R(i,λ) of the ratio of I(i,λ) to I0(λ); R(i,λ) = σ(i,λ)c i L+P(i,λ); where, σ(i,λ)c i L represents the differential absorption spectrum; P(i,λ) represents the extinction caused by the absorption of spectral intensity by the narrow band of the gas, Rayleigh scattering, and Mie scattering by the aerosol.

[0042] Differential absorption spectrum σ(i,λ)c i In L, σ(i,λ) is the standard difference absorption cross section of the greenhouse gas, indicating the absorption of light intensity per unit length by a unit concentration of the gas; c i Let L be the greenhouse gas column concentration in the i-th spatial dimension to be solved; L represents the optical path length, i.e., the known orbital height.

[0043] Differential absorption spectrum σ(i,λ)c i Compared to L, the extinction P(i,λ) caused by narrow-band absorption of spectral intensity by gas, Rayleigh scattering and Mie scattering by aerosols changes more rapidly with wavelength.

[0044] A polynomial fitting method is used to eliminate P(i,λ) in the ratio spectrum R(i,λ).

[0045] The differential spectrum R(i,λ) is fitted with a polynomial to obtain the fitting curve. This fitting curve subtracts the slow changes in the spectrum, including the broadband absorption of atmospheric molecules, Rayleigh scattering and Mie scattering, as well as low-frequency background interference and system noise. What remains is the differential spectrum A(i,λ).

[0046] After eliminating P(i,λ), the difference spectrum of R(i,λ) is denoted as A(i,λ), and A(i,λ) = σ(i,λ)c i L.

[0047] S2. Fit the differential absorption spectrum to obtain the greenhouse gas column concentration.

[0048] The specific method for fitting differential absorption spectra is as follows:

[0049] By fitting the differential absorption spectrum A(i,λ) to the differential absorption cross section σ(i,λ) of the gas molecules, the greenhouse gas column concentration c in the i-th spatial dimension can be obtained. i .

[0050] Example

[0051] The greenhouse gas in this embodiment is CH4. Taking the continuous spectrum of CH4 gas absorption bands 1635-1664nm (weak) and 1664-1670nm (strong) as an example.

[0052] By selecting the detector focal plane, the hyperspectral imaging system design of camera 1 and camera 2 can be realized. The spectral band of ground projection camera 1 along the push-broom positive direction to the negative direction is from the weak absorption band to the strong absorption band, that is, 1635-1670nm, and the spectral band of camera 2 along the push-broom positive direction to the negative direction is from the strong absorption band to the weak absorption band, that is, 1670-1635nm.

[0053] Camera 1 and Camera 2 are configured on a micro-nano satellite platform, with their optical axes aligned with the ground and installed in parallel to ensure that the image planes of the two cameras overlap on the ground. Figure 2 In the diagram, ①②③ represent the ground application locations, specifically a location in the positive pushbroom direction, the center of the detector's ground projection, and a location in the negative pushbroom direction, respectively. As can be seen from the diagram, location ① on the ground contains both weak absorption spectral data from camera 1 and strong absorption data from camera 2. Similarly, location ③ on the ground contains both strong absorption spectral data from camera 1 and weak absorption data from camera 2.

[0054] In data processing, a dual-camera differential absorption spectroscopy algorithm is used to invert the CH4 concentration. This process mainly consists of two parts: first, extracting the differential absorption spectrum from the fused detection spectrum of the two cameras; and then fitting the differential absorption spectrum.

[0055] (1) Differential absorption spectroscopy extraction

[0056] Define the emission spectrum I0(λ) of the solar source; the spectrum I(i,λ) received by the detector after atmospheric absorption is the integration of the detection spectra of camera 1 and camera 2 in the i-th spatial dimension, which includes both strong absorption and weak absorption bands.

[0057] Calculate the natural logarithm R(i,λ) of the ratio of I(i,λ) to I0(λ).

[0058] According to Beer-Lambert's atmospheric extinction law, the ratio spectrum R(i,λ) can be decomposed into two parts:

[0059] R(i,λ)=σ(i,λ)c i L+P(i,λ) (1)

[0060] A portion of the extinction spectrum, which varies rapidly with wavelength, is caused by narrow-band absorption in the gas, i.e., differential absorption spectroscopy, denoted as σ(i,λ)c. i L. Where σ(i,λ) is the standard difference absorption cross section of the CH4 molecule, indicating the absorption of light intensity per unit length by a unit concentration of gas; c i Let represent the CH4 concentration in the i-th spatial dimension to be solved; L represents the optical path length, i.e., the known orbital height. A portion of the extinction changes slowly due to narrow-band absorption of the spectral intensity by the gas, Rayleigh scattering, and Mie scattering by the aerosol, denoted as P(i,λ). Mie scattering extinction in aerosols is inversely proportional to wavelength, while Rayleigh scattering extinction is inversely proportional to the fourth power of wavelength; both are low-order functions of wavelength, and scattering changes slowly with wavelength.

[0061] The slow-varying spectrum P(i,λ) in the ratio spectrum R(i,λ) is eliminated using a polynomial fitting method. A polynomial fitting curve is obtained from the ratio spectrum R(i,λ), which is considered to represent the slow-varying spectrum, including broadband absorption by atmospheric molecules, Rayleigh scattering and Mie scattering, as well as some low-frequency background interference and system noise. Subtracting P(i,λ) from the ratio spectrum leaves the differential absorption spectrum.

[0062] (a) The difference spectrum A(i,λ) after removing the slowly varying spectrum from R(i,λ) by polynomial fitting can be written as: Equation (1)

[0063] A(i,λ)=σ(i,λ)c i L (2)

[0064] (2) Differential absorption spectrum fitting

[0065] By fitting the differential absorption spectrum A(i,λ) to the differential absorption cross section σ(i,λ) of the gas molecules, the concentration c of these gas molecules can be determined. i .

[0066] The differential absorption cross section σ(i,λ) of gas molecules was obtained using the atmospheric radiative transfer simulation software SICATRAN. Based on the observation parameters and the standard differential absorption cross section provided by the HITRAN library, the differential absorption cross section for this observation condition was obtained.

[0067] Since the spectral intensity attenuation at each wavelength includes the influence of differential absorption of multiple atmospheric molecules, in order to remove the interference of other gas molecules, their concentrations need to be inverted simultaneously. The least squares method is used to achieve concentration inversion through data fitting. Assume that the methane detection band is affected by n gases, that is, the differential absorption spectrum A(i,λ) is a linear combination of the absorption cross sections σ(i,λ) of these n gases, as shown in equation (3). Let the number of data points in the spectrum be m (m>n), the value A(i,λ) of the i-th data point in the spectrum should be a linear combination of the absorption cross sections of these n gases at that point, that is...

[0068] A(i,λ)=u1σ1(i,λ)+u2σ2(i,λ)+...+u n σ n (i,λ) (3)

[0069] Where: u1, u2, ..., u n These are the fitting coefficients; for all data points, they can be represented by a matrix as follows:

[0070]

[0071] Since m > n, this system of equations is an overdetermined system. By solving this overdetermined system of equations, the fitting coefficients u1, u2, ..., u can be obtained.n Then, based on the fitting coefficients, the concentration c of the j-th gas at the i-th data point can be obtained. i,j =u i,j / L, where L is the track height.

[0072] This invention discloses an imaging mode design for a remote sensing payload featuring overlapping strong and weak absorption lines of greenhouse gases. The payload is lightweight and compact, making it suitable for deployment on micro- and nano-satellites. The dual-camera system enables differential absorption of strong and weak spectral lines in the same region, facilitating data inversion and application.

[0073] In data processing, this invention employs a dual-camera differential absorption spectroscopy algorithm to achieve the inversion calculation of greenhouse gas column concentration.

[0074] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A method for retrieving greenhouse gas absorption line overlap remote sensing data, characterized in that: The application relates to a satellite-based greenhouse gas column concentration measurement method and device. The optical system is horizontally moved from left to right to realize horizontal push scanning of the ground and obtain double-camera fusion imaging. The camera 1 and the camera 2 are mirror arranged; the imaging spectrum of the camera 1 is from a weak absorption spectrum to a strong absorption spectrum along a positive direction to a negative direction of push scanning; the imaging spectrum of the camera 2 is from a strong absorption spectrum to a weak absorption spectrum along the positive direction to the negative direction of push scanning; and the strong and weak absorption lines are overlapped. The double-camera differential absorption spectrum algorithm is used to perform inversion calculation on the double-camera fusion imaging to obtain the greenhouse gas column concentration. The satellite platform is horizontally arranged; the camera 1 and the camera 2 are axially and parallelly arranged, and the optical axes of the camera 1 and the camera 2 are vertically directed to the ground; and the fields of view of the camera 1 and the camera 2 are overlapped on the ground. The specific method for performing inversion calculation on the imaging by using the double-camera differential absorption spectrum algorithm is as follows:

2. The method according to claim 1, wherein: S1, extracting a differential absorption spectrum from a detection spectrum of double-camera fusion imaging; 3. The method according to claim 2, wherein: S2, fitting the differential absorption spectrum to obtain the greenhouse gas column concentration. In the S1, the differential absorption spectrum is extracted by the following method: The emission spectrum of a sunlight source is set as I0 (lambda); an optical system receives an atmospheric absorption spectrum as I (i, lambda); i is a spatial dimension number; 4. The method according to claim 3, wherein: A polynomial fitting method is used to eliminate P (i, lambda) in the ratio spectrum R (i, lambda); The atmospheric absorption spectrum I (i, lambda) received by the optical system is the integration of the detection spectrum of the camera 1 and the camera 2 at the i-th spatial dimension, and simultaneously contains a strong absorption spectrum and a weak absorption spectrum. R(i, λ) = σ(i, λ)c i L + P(i, λ); where σ(i, λ)c i L is the differential absorption spectrum; P(i, λ) is the extinction due to narrow-band pair absorption by the gas, Rayleigh scattering and Mie scattering by aerosols; The ratio spectrum R (i, lambda) is polynomially fitted to obtain a fitting curve, the fitting curve subtracts slow changes in the spectrum, including broadband absorption of atmospheric molecules, Rayleigh scattering and Mie scattering, low-frequency background interference and system noise, and the remaining is a differential spectrum A (i, lambda). After elimination of P(i, λ), the differential spectrum of R(i, λ) is noted A(i, λ), A(i, λ) = σ(i, λ)c i L; Difference absorption spectrum σ(i, λ)c i In L, σ(i, λ) is the standard difference absorption cross section of the greenhouse gas, indicating the absorption of light intensity per unit length by unit concentration of the gas; c i is the column concentration of the i-th spatial dimension of the greenhouse gas to be solved; L represents the optical path, i.e., the known orbital height.

5. The method according to claim 4, wherein: In the S2, the specific method for fitting the differential absorption spectrum is as follows:

6. The method according to claim 5, wherein: The differential absorption spectrum σ(i, λ)c i L is compared to the extinction P(i, λ) caused by the narrowband spectral absorption of the gas, Rayleigh scattering and Mie scattering of aerosols, which varies rapidly with wavelength.

7. The method according to claim 6, wherein: ​ 8. The method according to claim 3, wherein the method is characterized by: ​ fitting the differential absorption spectrum A(i, l) to the differential absorption cross section s(i, l) of the gas molecules, the column concentration c of the greenhouse gas in the i-th spatial dimension is retrieved i .

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