Methane parameter estimation method, system, equipment, medium and product

Abstract

The invention provides a methane parameter estimation method, system and device, a medium and a product, and relates to the technical field of satellite remote sensing, and the method comprises the following steps: obtaining radiance image data and effective wind speed data corresponding to a target area; acquiring a background covariance matrix, an earth surface albedo correction factor and a background covariance correction factor according to the radiance image data; according to the radiance image data, the background covariance matrix, the earth surface albedo correction factor, the background covariance correction factor and the target spectral data, performing methane column concentration inversion processing to obtain methane column concentration increment image data corresponding to the target area, the target spectral data is obtained based on a pixel mean value and a unit absorption spectrum coefficient corresponding to the radiance image data; and obtaining a methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data. According to the method, the methane parameter estimation efficiency and accuracy are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing technology, and in particular, to a method, system, device, medium and product for estimating methane parameters. Background Art

[0002] Methane ( ) is an important greenhouse gas, and its global warming potential is 27 to 30 times that of carbon dioxide. The greenhouse effect of methane not only causes the temperature on the earth's surface to rise, but also triggers a series of environmental problems such as extreme weather events and sea level rise.

[0003] Currently, the method of ground monitoring facilities is the most direct way to continuously estimate anthropogenic emissions at point sources and regional scales. However, due to spatial limitations, ground monitoring facilities cannot automatically observe methane source leaks and the morphology of methane leakage plumes over a large area, resulting in large pollution due to the failure to timely observe methane leakage sources.

[0004] Based on satellite remote sensing data, existing research has deeply explored the estimation of column concentrations of methane point sources. These methods usually first use radiance data and variants of various matched filtering algorithms to calculate the column concentration of methane. However, most existing methods cannot directly obtain the calculation result of the emission rate from the radiance image, reducing the efficiency and accuracy of accounting for the methane emission rate of point sources. Therefore, there is an urgent need for a method, system, device, medium and product for estimating methane parameters to solve the above problems. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a method, system, device, medium and product for estimating methane parameters.

[0006] The present invention provides a method for estimating methane parameters, including: Obtaining radiance image data and effective wind speed data corresponding to a target area; According to the radiance image data, obtaining a background covariance matrix, a surface albedo correction factor and a background covariance correction factor; Performing inversion processing on the column concentration of methane according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor and target spectral data to obtain methane column concentration increment image data corresponding to the target area, wherein the target spectral data is obtained based on the pixel mean value and the unit absorption spectral coefficient corresponding to the radiance image data; According to the methane column concentration increment image data and the effective wind speed data, obtaining an estimation result of methane parameters in the target area.

[0007] A method for estimating methane parameters provided by the present invention, the obtaining the radiance image data and the effective wind speed data corresponding to the target area includes: Obtain the original spectral data of the target area, and perform data preprocessing on the original spectral data to obtain the radiance image data, wherein the data preprocessing at least includes radiometric correction processing and orthorectification processing; Based on the observation time and geographic coordinate information corresponding to the original spectral data, determine the surface wind speed data corresponding to the target area, and calculate the effective wind speed data corresponding to the target area according to the surface wind speed data and the wind speed empirical coefficient.

[0008] A method for estimating methane parameters provided by the present invention, the obtaining the background covariance matrix, the surface albedo correction factor and the background covariance correction factor according to the radiance image data includes: Calculate the background covariance values between each pixel according to the pixel values of each pixel in the radiance image data; Construct the background covariance matrix according to the background covariance values; Perform atmospheric correction processing on the radiance image data, and obtain the surface albedo correction factor based on the radiance image data after atmospheric correction processing; perform error correction processing on the background covariance matrix, and obtain the background covariance correction factor based on the background covariance matrix after error correction processing.

[0009] A method for estimating methane parameters provided by the present invention, the performing column concentration inversion processing of methane according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor and the target spectral data, and obtaining the methane column concentration increment image data corresponding to the target area includes: Obtain the radiance image deviation according to the difference between each pixel in the radiance image data and the pixel mean value; Obtain the unit absorption spectral coefficient according to the unit absorption spectral absorption table corresponding to the radiance image data, wherein the unit absorption spectral absorption table is matched from a preset unit coefficient spectral coefficient database based on the observation angle, observation time, surface elevation, full width at half maximum and central wavelength of the radiance image data; Obtain the target spectral data according to the product of the unit absorption spectral coefficient and the pixel mean value; Based on the calculation formula of methane column concentration increment constructed by the matched filtering algorithm, methane column concentration inversion processing is performed on the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data to obtain the methane column concentration increment image data.

[0010] According to a methane parameter estimation method provided by the present invention, obtaining the methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data includes: Obtaining the column concentration data of methane in the target area according to the methane column concentration increment image data and the radiance image data; Performing filtering processing on the methane column concentration increment image data to obtain the filtered methane column concentration increment image data, and obtaining the methane plume area based on the methane plume range in the filtered methane column concentration increment image data; Calculating the methane plume length according to the methane plume area; Calculating the total mass of the methane plume based on the number of corresponding pixels within the methane plume range in the filtered methane column concentration increment image data; Calculating the methane emission rate according to the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data; Constructing the methane parameter estimation result in the target area based on the column concentration data and the methane emission rate.

[0011] According to a methane parameter estimation method provided by the present invention, the calculation formula of the methane column concentration increment is specifically: ; Wherein, represents the methane column concentration increment image data, represents the pixel in the radiance image data, represents the radiance image data, represents the pixel mean value of the radiance image data, represents the background covariance matrix corrected by the background covariance correction factor, represents the target spectral data, represents the th surface albedo correction factor corresponding to the pixel; The calculating the methane emission rate according to the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data includes: Input the methane column concentration increment image data, the total mass of the methane plume, the length of the methane plume, and the effective wind speed data into the methane emission rate calculation formula to obtain the methane emission rate. The specific methane emission rate calculation formula is as follows: ; ; ; Wherein, represents the total mass of the methane plume, represents the methane mass coefficient, represents the number of pixels within the methane plume range, represents the effective wind speed data; represents the length of the methane plume, and the length of the methane plume is calculated based on the square root of the methane plume area; represents the methane emission rate, and are the wind speed empirical coefficients, represents the surface wind speed data.

[0012] The present invention also provides a methane parameter estimation system, including: A data acquisition module for acquiring radiance image data and effective wind speed data corresponding to a target area; A first processing module for obtaining a background covariance matrix, a surface albedo correction factor, and a background covariance correction factor according to the radiance image data; A second processing module for performing methane column concentration inversion processing according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and target spectral data to obtain methane column concentration increment image data corresponding to the target area, wherein the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient; A methane parameter estimation module for obtaining a methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the methane parameter estimation method as described in any one of the above.

[0014] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the methane parameter estimation method as described in any one of the above.

[0015] The present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the methane parameter estimation method as described in any one of the above.

[0016] The methane parameter estimation method, system, device, medium and product provided by the present invention obtain the radiance image data and effective wind speed data of the target area, then calculate the background covariance matrix, surface albedo correction factor and background covariance correction factor based on the radiance image data, and further use the target spectral data, radiance image data and correction factors obtained from the image pixel mean and unit absorption spectral coefficient to perform methane column concentration inversion to obtain the methane column concentration increment image data. Finally, by combining the methane column concentration increment image data with the effective wind speed data, the methane parameter estimation result of the target area is obtained, thereby effectively improving the efficiency and accuracy of methane parameter estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart of the methane parameter estimation method provided by the present invention; Figure 2 It is a schematic overall flowchart of inverting methane column concentration and estimating methane emission rate based on ZY-1 satellite provided by the present invention; Figure 3 It is a methane column concentration increment map obtained by inverting a single ZY-1 image using the matched filtering algorithm provided by the present invention; Figure 4 It is a plume image after magnifying a local area in the methane column concentration increment map provided by the present invention; Figure 5 It is a schematic structural diagram of the methane parameter estimation system provided by the present invention; Figure 6 It is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Satellite remote sensing technology has become the core means of monitoring atmospheric methane concentration by virtue of its wide spatial coverage and high-efficiency data acquisition ability. With the progress of space-based retrieval technology for obtaining CH4 from satellite observations, such as the applications of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and the Greenhouse Gases Observing Satellite (GOSAT), it has become possible to conduct top-down observations of global methane emissions. However, the spatial resolution of these technologies is relatively coarse (about 50 km), making it difficult to distinguish the magnitudes of different anthropogenic emission sources, which is crucial for accurately identifying the causes and consequences of changes in atmospheric methane concentration.

[0021] Although the Tropospheric Monitoring Instrument (TROPOMI) has a spatial resolution of several kilometers and can identify high-emission areas, it has not reached the level of directly identifying and mitigating local emission sources. To bridge this scale gap, airborne instruments based on the same technology can be used, such as the Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG), whose high spatial resolution (a few meters) can complete the retrieval of absorption features even at a medium spectral resolution (5 nm to 10 nm). Utilizing the absorption feature of methane in the shortwave infrared band around 2300 nm, the methane concentration can be retrieved at a spatial resolution far lower than 5 m, generating a methane column concentration enhancement map for the detection and quantification of methane point sources.

[0022] Currently, the on-orbit land hyperspectral and multispectral imagers have shown great potential in the remote sensing monitoring and emission quantification of methane point sources, such as the Gaofen-5 satellite (GF-5), the Ziyuan-1 satellite (ZY-1), as well as satellites such as GHGSat, PRISMA, CarbonMapper, EMIT, and EnMAP. Although satellite remote sensing technology has advantages such as wide coverage, strong timeliness, and data transparency, making up for the deficiencies of traditional ground monitoring, the existing algorithms for satellite methane monitoring still have problems such as high time consumption, poor inversion accuracy, and inability to directly calculate the instantaneous emission rate of methane plumes. In addition, the satellite network has not achieved all-weather and full-region monitoring, and the monitoring accuracy needs to be further improved.

[0023] Currently, based on the data of the Advanced Hyperspectral Imager (AHSI) on ZY-1, existing studies have explored methods for estimating the column concentration of methane point sources. These methods first use radiance data to calculate the methane column concentration based on various variants of the matched filtering algorithm. However, most existing methods cannot directly calculate the emission rate from the radiance image, which greatly reduces the efficiency of accounting for the methane emission rate of point sources. In addition, since most existing studies are based on satellites such as GF-5 and EMIT, their image coverage is limited. As a series of ZY-1 satellites can be used as a supplement to satellite data to improve image coverage, there is currently a lack of a method for accounting for the methane emission rate using the AHSI images of ZY-1 satellites.

[0024] In view of the problems existing in the above-mentioned prior art, the present invention provides a method for retrieving the column concentration of methane and estimating the emission rate. This method can be based on the image data of ZY-1 satellites and can solve the problems existing in the prior art, such as the slow rate of retrieving the column concentration of methane and the inability to directly calculate the methane plume emission rate, and improve the utilization efficiency of satellites, increase the coverage of the satellite network, and improve the ability of satellites to automatically monitor methane emissions and leaks. It should be noted that the image data used in the present invention is not limited to that from ZY-1 satellites, and the image data collected by other satellites equipped with hyperspectral imagers is also applicable to the methane parameter estimation method provided by the present invention.

[0025] Figure 1 The flow chart of the methane parameter estimation method provided by the present invention is as follows Figure 1 As shown, the present invention provides a methane parameter estimation method, including: Step 101, obtaining the radiance image data and effective wind speed data corresponding to the target area.

[0026] In the present invention, the target area is a specific geographical area for research or monitoring, which may be a city, an ecological reserve, a farmland area or other locations. Radiance is the radiation energy received by the sensor from the ground object target, and the radiance image data is a data set obtained by imaging the target area through sensors on remote sensing satellites, airplanes or other platforms. These data are presented in the form of images, and each pixel point contains the radiance information of the ground object at that position, reflecting the reflection or emission characteristics of the ground object in different bands. In the present invention, the radiance image data is obtained through satellite remote sensing equipment. During the acquisition process, it is necessary to ensure the accurate calibration and calibration of the sensor to ensure the reliability and accuracy of the data.

[0027] In the present invention, the effective wind speed data can be obtained in various ways, including ground meteorological station observations, meteorological satellite remote sensing, radar monitoring, etc. Ground meteorological stations can provide fixed-point and continuous wind speed observation data; meteorological satellites can, through remote sensing technology, obtain large-scale and continuous wind speed distribution information; radar monitoring can, under specific conditions, provide high-resolution wind speed data.

[0028] Step 102: Obtain the background covariance matrix, surface albedo correction factor, and background covariance correction factor according to the radiance image data.

[0029] In the present invention, the radiance image data of the target area is obtained by the sensors on the remote sensing satellite. Each pixel point in the radiance image data contains the radiance information of the ground object at that position.

[0030] In remote sensing data processing, the background covariance matrix is used to describe the correlation between different bands (or variables) in the image data, reflecting the statistical characteristics of background noise or natural variations in the image. In the present invention, it can be obtained by performing statistical analysis on the radiance image data. Uniform areas in the image (such as water bodies, bare soil, etc.) can be selected as the background areas, and the covariance between the pixel values in these areas is calculated, and then the background covariance matrix is constructed.

[0031] Furthermore, the surface albedo correction factor can be obtained based on methods such as ground measured data, remote sensing inversion algorithms, or model simulations. The surface albedo correction factor can be used to correct the radiance image data to obtain more accurate surface albedo information. At the same time, the background covariance correction factor is obtained by preprocessing the radiance image data (such as filtering, denoising, etc.), or estimated based on statistical models or machine learning algorithms. The background covariance correction factor can improve the accuracy and stability of the background covariance matrix, thereby improving the effect of remote sensing data processing and analysis.

[0032] Step 103: Perform methane column concentration inversion processing according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data to obtain the methane column concentration increment image data corresponding to the target area, where the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient.

[0033] In the present invention, the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient, and is used to describe the absorption characteristics of methane in specific bands. Since methane has obvious absorption characteristics in specific bands (such as the short-wave infrared band), the column concentration of methane can be deduced by measuring the radiance changes in these bands.

[0034] In the present invention, using the input data (radiance image data, background covariance matrix, surface albedo correction factor, background covariance correction factor, and target spectral data), through specific inversion algorithms (such as differential absorption spectroscopy, spectral matching method, etc.), the methane column concentration of each pixel in the target area is calculated. During the inversion process, the influences of various factors such as surface albedo, atmospheric scattering, and sensor noise need to be fully considered and corrected through correction factors to improve the accuracy of the inversion.

[0035] Furthermore, through the inversion process, the obtained methane column concentration increment image data is presented in the form of an image, and each pixel in the methane column concentration increment image data corresponds to a methane column concentration value. Then, comparing the methane column concentration increment image data with the previous methane column concentration image data, the newly obtained image data reflects the change in the methane column concentration in the target area, that is, the increment information.

[0036] Step 104, according to the methane column concentration increment image data and the effective wind speed data, obtain the estimation result of the methane parameters in the target area.

[0037] In the present invention, the methane column concentration increment image data shows the change in the methane column concentration in the target area. Each pixel in this increment image corresponds to a methane column concentration value, and these values reflect the total amount of methane in the vertical air column at this position. From the methane column concentration increment image data, the column concentration information of methane can be directly obtained, which can be used to evaluate the methane emissions and distribution.

[0038] Furthermore, by combining the methane column concentration increment image data and the effective wind speed data, the emission rate of methane can be calculated. The emission rate is a key parameter for evaluating the intensity and activity level of methane emission sources. In the present invention, the emission rate can be estimated through parameters such as the change rate of methane column concentration and wind speed. For example, using the Gaussian diffusion model or other atmospheric transport models, combining the spatial distribution of methane column concentration with the wind speed field to inversely calculate the emission rate of methane.

[0039] The methane parameter estimation method provided by the present invention, by obtaining the radiance image data and the effective wind speed data of the target area, then calculating the background covariance matrix, surface albedo correction factor, and background covariance correction factor based on the radiance image data, and further using the target spectral data, radiance image data, and correction factors obtained from the image pixel mean and unit absorption spectral coefficient to perform methane column concentration inversion to obtain the methane column concentration increment image data. Finally, combining the methane column concentration increment image data and the effective wind speed data to obtain the estimation result of the methane parameters in the target area, thereby effectively improving the efficiency and accuracy of methane parameter estimation.

[0040] Based on the above embodiments, the obtaining of the radiance image data and the effective wind speed data corresponding to the target area includes: Obtain the original spectral data of the target area, and perform data preprocessing on the original spectral data to obtain the radiance image data, wherein the data preprocessing at least includes radiometric correction processing and orthorectification processing. Based on the observation time and geographic coordinate information corresponding to the original spectral data, determine the surface wind speed data corresponding to the target area, and calculate the effective wind speed data corresponding to the target area according to the surface wind speed data and the wind speed empirical coefficient.

[0041] In the present invention, first, the original spectral data (L0-level image) is obtained through a remote sensing satellite (such as AHSI carried by ZY-1 satellite) or other remote sensing platforms. These unprocessed spectral data contain the reflection or emission information of the ground objects in the target area at different bands.

[0042] Furthermore, data preprocessing is performed on the original spectral data to eliminate noise, distortion, and errors in the data, and improve the quality and usability of the data. In the present invention, the data preprocessing process at least includes radiometric correction processing and orthorectification processing. Among them, radiometric correction is used to eliminate the influence of factors such as sensor response, atmospheric transmission, and solar radiation on the spectral data, and convert the original spectral data into radiance data. Radiance is the radiation energy received by the sensor from the ground object target and is a basic physical quantity in remote sensing analysis. Orthorectification is used to eliminate the image distortion caused by factors such as sensor viewing angle and terrain undulation, and project the image onto an orthographic plane so that the position of the ground object on the image is consistent with the actual geographical location.

[0043] After radiometric correction and orthorectification processing, the original spectral data is converted into radiance image data (L1-level image). The radiance image data is presented in the form of an image, and each pixel point contains the radiance information of the ground object at that position, reflecting the reflection or emission characteristics of the ground object at different bands.

[0044] Surface wind speed is an important factor affecting various processes such as the diffusion of atmospheric pollutants and the change of meteorological conditions. In the present invention, through the observation time and geographic coordinate information corresponding to the original spectral data, the surface wind speed data corresponding to the target area can be downloaded or obtained from a meteorological database (such as GEOS-fp).

[0045] The wind speed empirical coefficient is obtained based on a large amount of observational data and experience, and is used to convert the surface wind speed data into effective wind speed data. The effective wind speed data is the wind speed value after considering the influence of various factors (such as terrain, vegetation, buildings, etc.) on the wind speed. The present invention calculates the effective wind speed data through the surface wind speed data and the wind speed empirical coefficient, thereby more accurately reflecting the actual wind speed situation in the target area.

[0046] Based on the above embodiments, the obtaining of the background covariance matrix, the surface albedo correction factor, and the background covariance correction factor according to the radiance image data includes: Calculating the background covariance values between each pixel according to the pixel values of each pixel in the radiance image data; Constructing the background covariance matrix according to the background covariance values; Performing atmospheric correction processing on the radiance image data, and obtaining the surface albedo correction factor based on the radiance image data after atmospheric correction processing; performing error correction processing on the background covariance matrix, and obtaining the background covariance correction factor based on the background covariance matrix after error correction processing.

[0047] In the present invention, the radiance image data can be obtained through a remote sensing satellite or other platforms, and includes the radiance information of the ground objects in the target area in different bands. Each pixel point in the radiance image data has the radiance values of one or more bands.

[0048] The background covariance is used to describe the covariance of the radiance values of different pixels in the same band in the image, that is, the correlation between them. The present invention can obtain the background covariance values by calculating the covariance of the radiance values of each pixel in the radiance image data in the same band, and these values reflect the statistical characteristics of the background noise or natural variations among the pixels in the image.

[0049] Each element in the background covariance matrix represents the background covariance value of two pixels in the radiance image data in the same band. The present invention organizes the background covariance values between all pixel pairs into a matrix form, and can construct the background covariance matrix.

[0050] Atmospheric correction processing can be used to eliminate the influence of the atmosphere on remote sensing images, including atmospheric scattering, absorption, and emission, etc. Through atmospheric correction processing, the present invention can convert radiance image data into surface albedo or surface radiance data, more accurately reflecting the true situation of the surface. Further, the surface albedo refers to the ratio of the solar radiation reflected by the surface to the incident solar radiation. Since the surface albedo is affected by various factors (such as surface type, vegetation cover, soil moisture, etc.), correction factors are needed to eliminate these influences. Based on the radiance image data after atmospheric correction processing, the present invention can calculate the surface albedo correction factor, which is then used to correct the surface albedo.

[0051] Further, due to the influence of various factors (such as sensor noise, changes in atmospheric conditions, etc.), there may be errors in the background covariance matrix. Through error correction processing, these errors can be eliminated or reduced, improving the accuracy of the background covariance matrix. Based on the background covariance matrix after error correction processing, the present invention can calculate the background covariance correction factor, and the background covariance correction factor is used to further correct the background covariance matrix to eliminate or reduce the influence of the errors in the matrix on subsequent analysis.

[0052] Specifically, the background covariance matrix, surface albedo correction factor, and background covariance correction factor in the above embodiments can be obtained through the following calculation formulas: ; ; ; where, is the pixel mean value, is the number of pixels, is the pixel value corresponding to the th pixel in the radiance image data, is the background covariance matrix corrected by the background covariance correction factor; is the original covariance matrix, that is, the background covariance matrix; is a very small positive number, is the identity matrix.

[0053] For the th pixel, the calculation formula for its corresponding surface albedo correction factor is as follows: ; where, is the radiation spectrum of the th pixel, is the spectral mean value of the background distribution.

[0054] Based on the above embodiments, the process of performing column concentration inversion processing of methane according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data to obtain the methane column concentration increment image data corresponding to the target area includes: Obtain the radiance image deviation according to the difference between each pixel in the radiance image data and the pixel mean value; Obtain the unit absorption spectral coefficient according to the unit absorption spectral absorption table corresponding to the radiance image data, where the unit absorption spectral absorption table is matched from a preset unit coefficient spectral coefficient database based on the observation angle, observation time, surface elevation, full width at half maximum, and central wavelength of the radiance image data; Obtain the target spectral data according to the product of the unit absorption spectral coefficient and the pixel mean value; Perform column concentration inversion processing of methane on the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data based on the methane column concentration increment calculation formula constructed by the matched filtering algorithm to obtain the methane column concentration increment image data.

[0055] In the present invention, for each band in the radiance image data, the mean value of the radiance values of all pixels can be calculated. Then, by calculating the difference between the radiance value of each pixel and the pixel mean value of the corresponding band, the radiance image deviation can be obtained, and this deviation reflects the difference between each pixel and the overall mean value.

[0056] The unit absorption spectral absorption table contains the coefficients of the unit absorption spectrum under different conditions (such as observation angle, observation time, surface elevation, full width at half maximum, and central wavelength), which describes the absorption characteristics of methane in a specific band. In the present invention, according to the information such as the observation angle, observation time, surface elevation, full width at half maximum (abbreviated as FWHM), and central wavelength of the radiance image data, the corresponding unit absorption spectral absorption table is matched from a preset unit coefficient spectral coefficient database. Then, from the matched unit absorption spectral absorption table, the required unit absorption spectral coefficient can be obtained. Then, by calculating the product of the unit absorption spectral coefficient and the pixel mean value, the target spectral data can be obtained. The target spectral data reflects the absorption situation of methane in the radiance image data.

[0057] Furthermore, through the matched filtering algorithm, signals matching a specific pattern (such as the absorption spectrum of methane) are extracted. Specifically, in the present invention, based on the matched filtering algorithm, a calculation formula for the increment of methane column concentration can be constructed, which takes into account multiple data such as radiance image data, background covariance matrix, surface albedo correction factor, background covariance correction factor, and target spectral data; then, these data are input into the calculation formula for the increment of methane column concentration to perform the inversion process of methane column concentration, thereby calculating the increment of methane column concentration for each pixel point in the target area and obtaining the increment image data of methane column concentration.

[0058] Based on the above embodiments, obtaining the methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data includes: Obtaining the methane column concentration data in the target area according to the methane column concentration increment image data and the radiance image data; Performing filtering processing on the methane column concentration increment image data to obtain the filtered methane column concentration increment image data, and obtaining the methane plume area based on the methane plume range in the filtered methane column concentration increment image data; Calculating the methane plume length according to the methane plume area; Calculating the total mass of the methane plume based on the number of corresponding pixels within the methane plume range in the filtered methane column concentration increment image data; Calculating the methane emission rate according to the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data; Constructing the methane parameter estimation result in the target area based on the column concentration data and the methane emission rate.

[0059] In the present invention, by combining the methane column concentration increment image data and the radiance image data, the methane column concentration data for each pixel point in the target area can be calculated through a specific algorithm (such as differential absorption spectroscopy, spectral matching method) or a machine learning model.

[0060] Furthermore, filtering the methane column concentration increment image data can eliminate noise and outliers, improving the accuracy and reliability of the data. The filtered image data is smoother and easier to analyze. In the filtered methane column concentration increment image data, the range of the methane plume can be identified, that is, the area where the methane concentration is significantly higher than the background. Based on the range of the methane plume, the present invention can calculate the area of the methane plume, which reflects the influence range of the methane emission source. Then, according to the square root of the methane plume area, the methane plume length is calculated; and based on the number of corresponding pixels within the methane plume range, the total mass of the methane plume can be estimated.

[0061] In the present invention, the methane plume length can be obtained by analyzing the shape and distribution of the plume, which is usually defined as the extension distance of the plume in the main diffusion direction. Then, based on the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data, through the integrated mass enhancement algorithm (Integrated Mass Enhancement, abbreviated as IME), an integrated mass enhancement model (i.e., the methane emission rate calculation formula) is constructed to obtain the methane emission rate, which reflects the mass of methane released from the emission source per unit time.

[0062] Finally, based on the column concentration data and the methane emission rate, the methane parameter estimation results in the target area can be constructed, including the total methane emission, the distribution of emission sources, the emission intensity, etc.

[0063] Based on the above embodiments, the specific formula for the methane column concentration increment is as follows: ; Wherein, represents the methane column concentration increment image data, represents the pixel in the radiance image data, represents the radiance image data, represents the pixel mean value of the radiance image data, represents the background covariance matrix corrected by the background covariance correction factor, represents the target spectral data, represents the th surface albedo correction factor corresponding to the pixel; Calculating the methane emission rate according to the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data includes: Input the methane column concentration increment image data, the total mass of the methane plume, the length of the methane plume, and the effective wind speed data into the methane emission rate calculation formula to obtain the methane emission rate. The specific methane emission rate calculation formula is as follows: ; ; ; Wherein, represents the total mass of the methane plume, represents the methane mass coefficient, represents the number of pixels within the methane plume, represents the effective wind speed data; represents the length of the methane plume, and the length of the methane plume is calculated based on the square root of the methane plume area; represents the methane emission rate, and are the wind speed empirical coefficients, represents the surface wind speed data.

[0064] Figure 2 Figure Figure 2 shows the overall flow chart of retrieving methane column concentration and estimating methane emission rate based on ZY-1 satellite provided by the present invention. Referring to Figure 2 as shown, based on the L0-level images of ZY-1 AHSI, the original images are obtained. These original images are subjected to data preprocessing steps such as radiometric calibration and orthorectification to obtain the L1-level radiance images. Further, through code extraction, the image data of the shortwave infrared band to be processed, as well as auxiliary parameters such as the observation angle, observation time, surface elevation, full width at half maximum, and central wavelength, can be obtained. These auxiliary parameters can be used to match the molecular absorption data in the preset molecular absorption database, and the unit absorption spectrum lookup table of methane can be calculated using the radiative transfer model. Then, based on the unit absorption spectrum data in the unit absorption spectrum lookup table, the subsequent matching filter inversion process is carried out. In the present invention, the band range of the above-mentioned image data is 2250 nm to 2350 nm.

[0065] Furthermore, the specific calculation formula for calculating the effective wind speed is: ; Wherein, represents the surface wind speed data, which is synthesized from the wind speed components in the east-west direction u and the north-south direction v in the reanalysis wind speed data of GEOS-fp; and are the wind speed empirical coefficients, which can be obtained by fitting with the real wind speed through large eddy simulation. In the present invention, and 。

[0066] Further, the radiance image data obtained in the above process is inversed by a matched filter with the corresponding unit absorption spectrum data in the unit absorption spectrum lookup table. The specific calculation formula for the methane column concentration increment involved in the inversion process is as follows: 。

[0067] Based on the above methane column concentration increment calculation formula, a methane column concentration increment image and methane column concentration data corresponding to the methane column concentration increment image can be obtained.

[0068] Further, based on the effective wind speed data calculated from the 10-meter wind speed data, the methane emission rate is estimated through a comprehensive mass enhancement model. The specific calculation formula for the methane emission rate corresponding to the comprehensive mass enhancement model is as follows: ; ; wherein is the methane mass coefficient. In the present invention, = 5.155×10 -3 kg / ppb, is obtained by considering the spatial resolution of ZY-1 AHSI to be 30 meters, assuming the atmospheric layer height is 8 km, and converting from volume mixing ratio to mass according to Avogadro's law. is the comprehensive mass enhancement of the plume (unit: kg), that is, the total mass of the methane plume. Further, after obtaining the total mass of the methane plume, the emission rate is obtained by calculating the effective wind speed data. In the present invention, represents the methane emission rate (unit: kg / h), that is, the instantaneous emission rate of the methane plume; is the plume length (unit: m), defined as the square root of the plume mask area.

[0069] In the embodiment, the calculation method of the methane mass coefficient is specifically as follows: Since the gas scale height is 8 km (the gas is distributed within this height range, and the satellite observation image and its pixels can be approximated as a rectangle, there is a methane air column). If the methane concentration in this air column is 1 ppb (parts per billion), the volume of the air column can be obtained: V = 30m × 30m × 8000m = 7,200,000m 3 ; Next, calculate the volume of methane (volume concentration of 1 ppb): ; Under standard conditions, the volume of 1 mole of gas is 22.4 L. Therefore, the amount of substance of methane is: ; The molar mass of methane is 16.04 g / mol. Therefore, the mass of methane is: ; Furthermore, the methane mass coefficient is obtained as = .

[0070] Figure 3 This is the methane column concentration increment map obtained by inverting a single scene of ZY-1 imagery using the matched filtering algorithm provided by the present invention. As shown in Figure 3 , there are many methane plumes in the methane column concentration increment map. By combining the morphology of the plumes and the ground facilities, the target plumes can be monitored in a targeted manner. At the same time, <00> Figure 3 there are also some extreme values and artifacts caused by sensor quality, algorithm accuracy, and actual meteorological factors. These interferences can be excluded by combining the wind direction and the actual surface conditions.

[0071] Figure 4 This is the plume image after magnifying a local area in the methane column concentration increment map provided by the present invention. As shown in Figure 3 and Figure 4 , Figure 4 shows Figure 3 the relevant information of the methane plume and emission rate after magnifying the local area of the dashed box in . After magnification, due to the high spatial resolution performance of ZY-1 AHSI, the morphology of the plume is clearly visible, enabling researchers and engineers to directly locate the methane leakage or emission source based on the high-resolution satellite map, and thus take targeted emission control measures.

[0072] The method for inverting methane column concentration and estimating emission rate based on ZY-1 satellite provided by the present invention, as a supplement to the methane satellite observation network, improves the coverage rate of the methane satellite observation network; and using the matched filtering algorithm, it can quickly calculate the methane column concentration increment, thereby obtaining a high-resolution methane plume map. At the same time, using the comprehensive quality enhancement model can also relatively accurately calculate the instantaneous emission rate of each methane plume, greatly improving the observation efficiency in satellite methane observation.

[0073] Next, the methane parameter estimation system provided by the present invention will be described. The methane parameter estimation system described below can be correspondingly referred to the methane parameter estimation method described above.

[0074] Figure 5The structural schematic diagram of the methane parameter estimation system provided by the present invention is as follows Figure 5 As shown, the present invention provides a methane parameter estimation system, including a data acquisition module 501, a first processing module 502, a second processing module 503, and a methane parameter estimation module 504. Among them, the data acquisition module 501 is used to obtain the radiance image data and effective wind speed data corresponding to the target area; the first processing module 502 is used to obtain the background covariance matrix, surface albedo correction factor, and background covariance correction factor according to the radiance image data; the second processing module 503 is used to perform methane column concentration inversion processing according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data, and obtain the methane column concentration increment image data corresponding to the target area, where the target spectral data is obtained based on the pixel mean value and unit absorption spectral coefficient corresponding to the radiance image data; the methane parameter estimation module 504 is used to obtain the methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data.

[0075] The methane parameter estimation system provided by the present invention obtains the radiance image data and effective wind speed data of the target area, then calculates the background covariance matrix, surface albedo correction factor, and background covariance correction factor based on the radiance image data, and further uses the target spectral data, radiance image data, and correction factors obtained from the image pixel mean value and unit absorption spectral coefficient to perform methane column concentration inversion to obtain the methane column concentration increment image data. Finally, by combining the methane column concentration increment image data and the effective wind speed data, the methane parameter estimation result of the target area is obtained, thereby effectively improving the efficiency and accuracy of methane parameter estimation.

[0076] The system provided by the embodiments of the present invention is used to execute the above method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.

[0077] Figure 6 The structural schematic diagram of the electronic device provided by the present invention is as follows Figure 6As shown in the figure, the electronic device may include: a processor 601, a communications interface 602, a memory 603, and a communication bus 604. Among them, the processor 601, the communications interface 602, and the memory 603 complete communication with each other through the communication bus 604. The processor 601 may call logic instructions in the memory 603 to execute a methane parameter estimation method, which includes: obtaining radiance image data and effective wind speed data corresponding to a target area; obtaining a background covariance matrix, a surface albedo correction factor, and a background covariance correction factor according to the radiance image data; performing column concentration inversion processing of methane according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and target spectral data to obtain methane column concentration increment image data corresponding to the target area, where the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient; and obtaining an estimation result of methane parameters in the target area according to the methane column concentration increment image data and the effective wind speed data.

[0078] In addition, when the logic instructions in the above-mentioned memory 603 are implemented in the form of software functional units and sold or used as an independent product, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0079] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methane parameter estimation method provided by each of the above methods. The method includes: obtaining radiance image data and effective wind speed data corresponding to a target area; obtaining a background covariance matrix, a surface albedo correction factor, and a background covariance correction factor according to the radiance image data; performing methane column concentration inversion processing according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and target spectral data to obtain methane column concentration increment image data corresponding to the target area, where the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient; and obtaining a methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data.

[0080] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is configured to execute the methane parameter estimation method provided by each of the above embodiments. The method includes: obtaining radiance image data and effective wind speed data corresponding to a target area; obtaining a background covariance matrix, a surface albedo correction factor, and a background covariance correction factor according to the radiance image data; performing methane column concentration inversion processing according to the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and target spectral data to obtain methane column concentration increment image data corresponding to the target area, where the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient; and obtaining a methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data.

[0081] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0082] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating methane parameters, characterized in that, Including: Obtaining radiance image data and effective wind speed data corresponding to a target area; According to the radiance image data, obtaining a background covariance matrix, a surface albedo correction factor, and a background covariance correction factor; Performing column concentration inversion processing of methane based on the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and target spectral data to obtain methane column concentration increment image data corresponding to the target area, wherein the target spectral data is obtained based on the pixel mean value and the unit absorption spectral coefficient corresponding to the radiance image data; According to the methane column concentration increment image data and the effective wind speed data, obtaining an estimation result of methane parameters in the target area.

2. The methane parameter estimation method according to claim 1, wherein The obtaining of the radiance image data and the effective wind speed data corresponding to the target area includes: Obtaining the original spectral data of the target area, and performing data preprocessing on the original spectral data to obtain the radiance image data, wherein the data preprocessing at least includes radiometric correction processing and orthorectification processing; Based on the observation time and geographical coordinate information corresponding to the original spectral data, determining the surface wind speed data corresponding to the target area, and calculating the effective wind speed data corresponding to the target area according to the surface wind speed data and the wind speed empirical coefficient.

3. The methane parameter estimation method according to claim 2, wherein The obtaining of the background covariance matrix, the surface albedo correction factor, and the background covariance correction factor according to the radiance image data includes: Calculating the background covariance values between each pixel according to the pixel values of each pixel in the radiance image data; Constructing the background covariance matrix according to the background covariance values; Performing atmospheric correction processing on the radiance image data, and obtaining the surface albedo correction factor based on the radiance image data after atmospheric correction processing; performing error correction processing on the background covariance matrix, and obtaining the background covariance correction factor based on the background covariance matrix after error correction processing.

4. The methane parameter estimation method according to claim 2 or 3, characterized in that, The performing of the column concentration inversion processing of methane based on the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data to obtain the methane column concentration increment image data corresponding to the target area includes: Obtaining a radiance image deviation according to the difference between each pixel in the radiance image data and the pixel mean value; Obtaining the unit absorption spectral coefficient according to the unit absorption spectral absorption table corresponding to the radiance image data, wherein the unit absorption spectral absorption table is matched from a preset unit coefficient spectral coefficient database based on the observation angle, observation time, surface elevation, full width at half maximum, and central wavelength of the radiance image data; Obtaining the target spectral data according to the product of the unit absorption spectral coefficient and the pixel mean value; Based on the calculation formula of methane column concentration increment constructed by the matched filtering algorithm, methane column concentration inversion processing is performed on the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data to obtain the methane column concentration increment image data.

5. The methane parameter estimation method according to claim 4, characterized in that, Obtaining the methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data includes: Obtaining the methane column concentration data in the target area according to the methane column concentration increment image data and the radiance image data; Performing filtering processing on the methane column concentration increment image data to obtain the filtered methane column concentration increment image data, and obtaining the methane plume area based on the methane plume range in the filtered methane column concentration increment image data; Calculating the methane plume length according to the methane plume area; Calculating the total mass of the methane plume based on the number of pixels corresponding to the methane plume range in the filtered methane column concentration increment image data; Calculating the methane emission rate according to the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data; Constructing the methane parameter estimation result in the target area based on the column concentration data and the methane emission rate.

6. The methane parameter estimation method according to claim 5, wherein The specific calculation formula of the methane column concentration increment is: ； Among them, represents the methane column concentration increment image data, represents the pixel in the radiance image data, represents the radiance image data, represents the pixel mean value of the radiance image data, represents the background covariance matrix corrected by the background covariance correction factor, represents the target spectral data, represents the th surface albedo correction factor corresponding to the pixel; Calculating the methane emission rate according to the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data includes: Inputting the methane column concentration increment image data, the total mass of the methane plume, the methane plume length, and the effective wind speed data into the methane emission rate calculation formula to obtain the methane emission rate. The specific methane emission rate calculation formula is: ； ； ； Among them, represents the total mass of the methane plume, represents the methane mass coefficient, represents the number of pixels within the methane plume, represents the effective wind speed data; represents the length of the methane plume, and the length of the methane plume is calculated based on the square root of the area of the methane plume; represents the methane emission rate, and is the wind speed empirical coefficient, represents the surface wind speed data.

7. A methane parameter estimation system, characterized in that, Including: A data acquisition module for acquiring the radiance image data and the effective wind speed data corresponding to the target area; A first processing module for obtaining the background covariance matrix, the surface albedo correction factor, and the background covariance correction factor according to the radiance image data; A second processing module for performing methane column concentration inversion processing on the radiance image data, the background covariance matrix, the surface albedo correction factor, the background covariance correction factor, and the target spectral data to obtain the methane column concentration increment image data corresponding to the target area, where the target spectral data is obtained based on the pixel mean value corresponding to the radiance image data and the unit absorption spectral coefficient; A methane parameter estimation module for obtaining the methane parameter estimation result in the target area according to the methane column concentration increment image data and the effective wind speed data.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, wherein, When the processor executes the computer program, it implements the methane parameter estimation method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the methane parameter estimation method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the methane parameter estimation method according to any one of claims 1 to 6.

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