Method for estimating landfill methane emissions based on satellite remote sensing observations and wind field information

Abstract

The application discloses a landfill methane emission estimation method based on satellite remote sensing observation and wind field information, and belongs to the technical field of remote sensing atmospheric environment monitoring and greenhouse gas emission inversion, and comprises the following steps: acquiring TROPOMI satellite XCH4 column concentration data and performing quality control; performing vector rotation on the satellite observation field by using ERA5 wind direction data; performing area weighted resampling and synthesis on the rotated data to obtain annual average methane column concentration distribution; determining a seed point at the center of the research area, generating a methane plume mask by using a region growing algorithm, and calculating a methane enhancement value; calculating a tuning wind speed by using ERA5 wind speed data, constructing a transverse integration strip along the wind direction, and calculating a strip emission contribution; and obtaining a methane emission estimation result through abnormal value screening and average calculation. By using the above method, the landfill methane emission source can be effectively identified, the estimation stability is improved, and data support is provided for greenhouse gas emission monitoring and inventory evaluation.

Description

Technical Field

[0001] This invention relates to the field of remote sensing atmospheric environment monitoring and greenhouse gas emission inversion technology, and in particular to a method for estimating methane emissions from landfills based on satellite remote sensing observations and wind field information. Background Technology

[0002] Methane (CH4) is a significant greenhouse gas influencing recent global climate change, possessing a high potential for global warming, and its atmospheric concentration has been steadily increasing in recent decades. Since methane has an atmospheric lifetime of approximately ten years, reducing anthropogenic methane emissions can produce significant climate mitigation effects in a relatively short period, effectively slowing the rate of global temperature rise. Existing research indicates that human activities contribute over 60% of global methane emissions, with the waste disposal sector accounting for approximately 12% of total anthropogenic methane emissions, making it a crucial area with significant emission reduction potential. Municipal solid waste landfills, especially poorly managed ones and open dumps, typically contribute more than half of waste-related methane emissions at the urban scale due to the large amounts of methane produced by the anaerobic decomposition of waste. However, significant uncertainties remain in the quantitative assessment of methane emissions at the urban and facility scales.

[0003] Traditional methane emission estimations often employ bottom-up inventory methods, such as calculating landfill emissions based on the first-order decay model proposed by the Intergovernmental Panel on Climate Change (IPCC). However, bottom-up methods rely on emission factors and activity data, resulting in high uncertainty. Furthermore, they tend to oversimplify the description of complex biochemical processes within landfills, potentially leading to systematic underestimation of emissions. Existing research has shown significant discrepancies between inventory-based emission estimates and atmospheric inversion or satellite observations, necessitating the validation and supplementation of emission inventories using independent observational methods.

[0004] With the development of satellite remote sensing technology, top-down observation methods have provided a new technical approach for methane emission monitoring. Satellite or airborne hyperspectral imagers can identify and quantify methane plumes from individual emission sources by detecting anomalies in atmospheric methane column concentrations, demonstrating high detection and quantification capabilities at the facility scale. However, such high spatial resolution sensors typically have small observation swaths and low revisit frequencies, making it difficult to conduct long-term continuous monitoring of persistent emission sources. In contrast, regional flux imagers carried by atmospheric composition observation satellites have larger observation swaths and higher temporal coverage. For example, the Troposphere Monitor (TROPOMI) can provide daily global coverage methane column concentration observation data, providing an important data foundation for long-term monitoring of emission sources such as landfills. How to stably identify and quantify methane enhancement signals in the urban waste sector using TROPOMI data remains a technical challenge in current methane remote sensing monitoring. Therefore, there is an urgent need to develop a method for estimating methane emissions from landfills based on satellite methane column concentration observations, so as to achieve robust quantitative estimation of emission sources from municipal solid waste landfills and provide reliable technical support for greenhouse gas emission monitoring, emission inventory assessment and regional carbon cycle research. Summary of the Invention

[0005] The purpose of this invention is to provide a method for estimating methane emissions from landfills based on satellite remote sensing observations and wind field information, so as to achieve robust quantitative estimation of emission sources from municipal solid waste landfills and provide reliable technical support for greenhouse gas emission monitoring, emission inventory assessment and regional carbon cycle research.

[0006] To achieve the above objectives, this invention provides a method for estimating methane emissions from landfills based on satellite remote sensing observations and wind field information, comprising the following steps: S1. Obtaining data retrieved from the TROPOMI satellite. The annual column concentration data is processed and quality control is performed. The quality identification parameter qa value is used for filtering, and effective pixels with a qa value of 0 are retained to obtain effective methane column concentration raster data. S2. Obtain 10-meter wind direction data from ERA5 reanalysis data, and perform vector rotation processing on the satellite observation field based on the wind direction data to unify the wind direction at different observation times to the same reference direction; S3. The rotated methane column concentration data is sampled with area weighting and then synthesized on a unified spatial grid to obtain the annual average methane column concentration raster data of the study area. S4. Set a 5×5 pixel window at the center of the study area, calculate the maximum methane column concentration within the pixel window, and determine the pixel where the maximum methane column concentration is located as the seed point for plume detection. S5. Perform statistical analysis on the annual average methane column concentration raster data obtained in step S3, and calculate the global statistical median of methane column concentration in the study area. and global statistical standard deviation ; Let the seed point determined in step S4 be the starting position, use the eight-connected region growth method to detect the plume region, determine the neighboring pixels that meet the preset threshold conditions as plume pixels and include them in the plume region, and generate a methane plume mask. S6. Based on the methane plume mask obtained in step S5, distinguish the target pixels from the background pixels, and calculate the methane enhancement value in the plume region. ; S7. Obtain 10-meter wind speed data from the ERA5 reanalysis data and tune the wind speed according to the date of the corresponding TROPOMI image. Calculation; S8. Construct horizontal integral strips along the wind direction with the target pixel as the center; calculate the methane enhancement value by mass integration within the horizontal integral strips to obtain the emission contribution of each horizontal integral strip. ; S9. Perform outlier screening on the emission contribution of the strips obtained in step S8, remove abnormal strips that deviate from the statistical distribution range, and calculate the average emission contribution of the filtered strips to obtain the final methane emission estimation result.

[0007] Preferably, the method used in step S1 is to obtain the data retrieved from the TROPOMI satellite. Single-year column concentration data, followed by quality control processing, specifically including: S11. Construct a spatial window of size 0.6°×0.6° centered on the center of the study area, and select all TROPOMI observation data that have spatial intersection with this spatial window throughout the year; S12. Use the quality index field xch4_quality_flag to perform quality control filtering on the observed data, and retain valid pixels with a qa value of 0.

[0008] Preferably, step S2 specifically includes: S21. Based on the time field of each TROPOMI image, obtain the 10-meter orthogonal wind direction component wind speed data from the corresponding hourly ERA5 reanalysis data. S22. Calculate the 10-meter wind direction and 10-meter wind speed data at the corresponding hour, using the following formula: ; ; in, Represents the true wind direction. Represents actual wind speed. Represents the east-west component of wind speed (positive values ​​are from west to east – westerly winds). This represents the north-south component of the wind speed (positive values ​​indicate winds moving from south to north – southerly winds).

[0009] Preferably, in step S3, the concentration data of the rotated methane column is sampled using area-weighted sampling, specifically as follows: The rotated methane column concentration data is weighted and sampled to a 0.01° resolution regular grid. A weighted average of the methane column concentration is calculated by determining the area overlap ratio between effective pixels and the uniform spatial grid. The calculation method is as follows: ; in, To standardize the methane column concentration in the spatial grid, For the first methane column concentration per effective pixel, The area of ​​overlap between the effective pixel and the uniform spatial grid. The number of pixels participating in resampling.

[0010] Preferably, the preset threshold condition in step S5 is determined through the following steps: S51. Based on the methane column concentration in the study area, a probability density function curve is constructed using kernel density estimation (KDE). The inflection point on the high-concentration side of this curve is then used as the initial threshold coefficient. This is used to initially distinguish the background field from the enhanced plume signal; S52, using the initial threshold coefficient Based on this, a dynamic scan is performed within a preset range at a fixed step size to construct a candidate set of threshold coefficients; S53. Under the premise of satisfying the statistical separability constraint, select the threshold coefficient that makes the change in the number of pixels in the feather region the most gradual (i.e., the pixel number gradient is the smallest) as the optimal threshold. The following formula is used as the final judgment rule: .

[0011] Preferably, in step S6, the methane enhancement value The calculation method is as follows: .

[0012] Preferably, in step S7, the wind speed is adjusted. The calculation method is as follows: ; in, To adjust the wind speed, For the first The wind speed corresponding to this observation was 10 meters per second. NThe number of observations used in the calculation.

[0013] Preferably, in step S8, the contribution of strip emissions... The calculation method is as follows: ; in, Contribution to strip emissions To adjust the wind speed, For the first The methane enhancement value of each pixel, The width of the pixel in the direction perpendicular to the wind direction. This represents the number of pixels within the strip.

[0014] Preferably, in step S9, the method for outlier screening and the calculation of the final methane emission estimation result is as follows: Calculate the mean of the emission contributions of all strips. and the standard deviation of the emission contribution of all strips The strip emission contribution that meets the following conditions will be retained: ; The final methane emission estimate is obtained by averaging the emission contributions from the retained strips: ; in, For the first Emission contribution of each strip The number of stripes required to meet the retention criteria. The set of stripes that satisfy the retention criteria.

[0015] Therefore, this invention employs the aforementioned method for estimating landfill methane emissions based on satellite remote sensing observations and wind field information. This method can identify methane emission hotspots in urban landfills using methane column concentration data retrieved from satellites, enabling automatic extraction and quantitative estimation of landfill emission sources. By combining wind direction rotation, region growing algorithms, and transverse strip integration methods, the stability of methane plume identification and the accuracy of emission estimation are improved. Simultaneously, outlier removal reduces the impact of random noise on the results. Verification results show that the emission estimation results obtained by this method have good consistency with hyperspectral observation data, demonstrating high reliability and application value.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a flowchart of the landfill methane emission estimation method based on satellite remote sensing observation and wind field information according to the present invention; Figure 2This is a schematic diagram of the annual average methane column concentration and enhanced signal extraction in an embodiment of the landfill methane emission estimation method based on satellite remote sensing observation and wind field information of the present invention. Figure 3 This is a statistical chart of strip emission estimation results from an embodiment of the landfill methane emission estimation method based on satellite remote sensing observation and wind field information of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] Example This embodiment selects the Midong Municipal Solid Waste Landfill in Urumqi, Xinjiang Uygur Autonomous Region, China as the research site.

[0021] like Figure 1 As shown, the method for estimating methane emissions from landfills based on satellite remote sensing observations and wind field information includes the following steps: S1. Obtaining data retrieved from the TROPOMI satellite. The annual column concentration data was processed and quality control was performed. The quality labeling parameter qa value (xch4_quality_flag) was used for filtering, and effective pixels with a qa value of 0 were retained to obtain effective methane column concentration raster data. In this embodiment, the 2023 Tropomi satellite data was obtained through the BREMEN platform. For column concentration data, a spatial window of size 0.6°×0.6° was constructed with the center of the study area as the center, and all TROPOMI observation data that spatially intersect with this spatial window throughout the year were selected. The observation data were screened for quality control using the quality index field xch4_quality_flag, and valid pixels with a qa value of 0 were retained to obtain the 2023 methane column concentration time series dataset for this location.

[0022] S2. Obtain 10-meter wind direction data from ERA5 reanalysis data, and perform vector rotation processing on the satellite observation field based on the wind direction data to unify the wind direction at different observation times to the same reference direction; In this embodiment, ERA5 reanalysis data (including 10-meter wind direction and 10-meter wind speed data) with a spatial resolution of 0.25°×0.25° is obtained through the Climate Data Store platform. Based on the time field of each TROPOMI image, the 10-meter orthogonal wind direction component wind speed data from the corresponding hourly ERA5 reanalysis data is obtained. The formula for calculating the 10-meter wind direction and 10-meter wind speed data at the corresponding hour is: ; ; in, Represents the true wind direction. Represents actual wind speed. Represents the east-west component of wind speed (positive values ​​are from west to east – westerly winds). This represents the north-south component of the wind speed (positive values ​​indicate winds moving from south to north – southerly winds).

[0023] Using the center of the study area (the center of the Midong landfill) as the rotation center, the coordinates of the TROPOMI observation pixels are rotated to unify all observation data spatially to the same reference wind direction (north in this embodiment). This step can effectively eliminate the influence of wind direction differences on plume structure identification on different observation dates, thereby improving the stability of multi-temporal data overlay analysis.

[0024] S3. The rotated methane column concentration data is sampled with area weighting and then synthesized on a unified spatial grid to obtain the annual average methane column concentration raster data of the study area. In this embodiment, a 0.6°×0.6° study area is constructed with the center of the study area as the reference point, and a regular raster grid with a spatial resolution of 0.01°×0.01° is established. The rotated methane column concentration data is weighted and sampled to the 0.01° resolution regular raster. By calculating the area overlap ratio between the effective pixels and the uniform spatial grid, a weighted average of the methane column concentration is calculated. The calculation method is as follows: ; in, To standardize the methane column concentration in the spatial grid, For the first methane column concentration per effective pixel, The area of ​​overlap between the effective pixel and the uniform spatial grid. The number of pixels participating in resampling.

[0025] All valid observation data for the year are overlaid and averaged to generate an annual average methane column concentration distribution map of the study area.

[0026] S4. Set a 5×5 pixel window at the center of the study area, calculate the maximum methane column concentration within the pixel window, and determine the pixel where the maximum methane column concentration is located as the seed point for plume detection. S5. Perform statistical analysis on the annual average methane column concentration raster data obtained in step S3, and calculate the global statistical median of methane column concentration in the study area. and global statistical standard deviation ; Let the seed point determined in step S4 be the starting position, use the eight-connected region growth method to detect the plume region, determine the neighboring pixels that meet the preset threshold conditions as plume pixels and include them in the plume region, and generate a methane plume mask. The preset threshold condition in step S5 is determined through the following steps: S51. Based on the methane column concentration in the study area, a probability density function curve is constructed using kernel density estimation (KDE). The inflection point on the high-concentration side of this curve is then used as the initial threshold coefficient. In this embodiment, the initial value is obtained based on the histogram statistical analysis of the raster image. This is used to initially distinguish the background field from the enhanced plume signal; S52, using the initial threshold coefficient Based on the baseline, within the preset range (e.g.) to Dynamic scanning is performed at fixed step sizes (e.g., 0.01) to construct a candidate set of threshold coefficients. In this embodiment, the preset range is selected as the interval [1.19, 2.69]. S53. Under the premise of satisfying the statistical separability constraint, select the threshold coefficient that makes the change in the number of pixels in the feather region the most gradual (i.e., the pixel number gradient is the smallest) as the optimal threshold. This embodiment The following formula is used as the final judgment rule: .

[0027] If the conditions are met, the pixel is determined to belong to the methane plume and added to the plume region. Then, the neighbor search continues with this pixel as the new starting position until no new neighboring pixels that meet the conditions are found, thus obtaining the complete methane plume mask region.

[0028] S6. Based on the methane plume mask obtained in step S5, distinguish the target pixels from the background pixels, and calculate the methane enhancement value in the plume region. ; Methane enhancement value The calculation method is as follows: ; like Figure 2 The diagram shown illustrates the annual average methane column concentration and enhanced signal extraction obtained in this embodiment.

[0029] S7. Obtain the 10-meter wind speed data from the ERA5 reanalysis data and perform statistical analysis on these wind speeds. To reduce the impact of wind speed fluctuations on the emission estimation results, the harmonic averaging method is used to calculate the tuned wind speed. The calculation formula is as follows: ; in, To adjust the wind speed, For the first The wind speed corresponding to this observation was 10 meters per second. N The number of observations used in the calculation.

[0030] S8. Construct horizontal integral strips along the wind direction with the target pixel as the center; calculate the methane enhancement value by mass integration within the horizontal integral strips to obtain the emission contribution of each horizontal integral strip. ; In this embodiment, the width of each strip is 0.01°, and the emission contribution of each strip is... The calculation method is as follows: ; in, Contribution to strip emissions To adjust the wind speed, For the first The methane enhancement value of each pixel, The width of the pixel in the direction perpendicular to the wind direction. This represents the number of pixels within the strip.

[0031] S9. Perform outlier screening on the emission contribution of the strips obtained in step S8, remove abnormal strips that deviate from the statistical distribution range, and calculate the average emission contribution of the filtered strips to obtain the final methane emission estimation result.

[0032] The method for outlier screening and the calculation of the final methane emission estimation results is as follows: Calculate the mean of the emission contributions of all strips. and the standard deviation of the emission contribution of all strips The strip emission contribution that meets the following conditions will be retained: ; The final methane emission estimate is obtained by averaging the emission contributions from the retained strips: ; in, For the first Emission contribution of each strip The number of stripes required to meet the retention criteria. The set of stripes that satisfy the retention criteria.

[0033] like Figure 3 The figure shown is a statistical chart of the strip emission estimation results in this embodiment.

[0034] Through the above steps, the estimated methane emissions from the Midong municipal solid waste landfill in 2023 were obtained.

[0035] Therefore, this invention employs the aforementioned method for estimating landfill methane emissions based on satellite remote sensing observations and wind field information. This method can identify methane emission hotspots in urban landfills using methane column concentration data retrieved from satellites, enabling automatic extraction and quantitative estimation of landfill emission sources. By combining wind direction rotation, region growing algorithms, and transverse strip integration methods, the stability of methane plume identification and the accuracy of emission estimation are improved. Simultaneously, outlier removal reduces the impact of random noise on the results. Verification results show that the emission estimation results obtained by this method have good consistency with hyperspectral observation data, demonstrating high reliability and application value.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for estimating methane emissions from landfills based on satellite remote sensing observations and wind field information, characterized in that, Includes the following steps: S1. Obtaining data retrieved from the TROPOMI satellite. The annual column concentration data is processed and quality control is performed. The quality identification parameter qa value is used for filtering, and effective pixels with a qa value of 0 are retained to obtain effective methane column concentration raster data. S2. Obtain 10-meter wind direction data from ERA5 reanalysis data, and perform vector rotation processing on the satellite observation field based on the wind direction data to unify the wind direction at different observation times to the same reference direction; S3. The rotated methane column concentration data is sampled with area weighting and then synthesized on a unified spatial grid to obtain the annual average methane column concentration raster data of the study area. S4. Set a 5×5 pixel window at the center of the study area, calculate the maximum methane column concentration within the pixel window, and determine the pixel where the maximum methane column concentration is located as the seed point for plume detection. S5. Perform statistical analysis on the annual average methane column concentration raster data obtained in step S3, and calculate the global statistical median of methane column concentration in the study area. and global statistical standard deviation ; Let the seed point determined in step S4 be the starting position. Use the eight-connected region growth method to detect the plume region. The neighboring pixels that meet the preset threshold conditions are identified as plume pixels and included in the plume region to generate a methane plume mask. S6. Based on the methane plume mask obtained in step S5, distinguish the target pixels from the background pixels, and calculate the methane enhancement value in the plume region. ; S7. Obtain 10-meter wind speed data from the ERA5 reanalysis data and tune the wind speed according to the date of the corresponding TROPOMI image. Calculation; S8. Construct horizontal integral strips along the wind direction with the target pixel as the center; calculate the methane enhancement value by mass integration within the horizontal integral strips to obtain the emission contribution of each horizontal integral strip. ; S9. Perform outlier screening on the emission contribution of the strips obtained in step S8, remove abnormal strips that deviate from the statistical distribution range, and calculate the average emission contribution of the filtered strips to obtain the final methane emission estimation result. In S7, the wind speed is adjusted. The calculation method is as follows: ； in, To adjust the wind speed, For the first The wind speed corresponding to this observation was 10 meters per second. N The number of observations used in the calculation.

2. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, In step S1, the data obtained from the inversion of the TROPOMI satellite is retrieved. Single-year column concentration data, followed by quality control processing, specifically including: S11. Construct a spatial window of size 0.6°×0.6° centered on the center of the study area, and select all TROPOMI observation data that have spatial intersection with this spatial window throughout the year; S12. Use the quality index field xch4_quality_flag to perform quality control filtering on the observed data, and retain valid pixels with a qa value of 0.

3. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, Step S2 specifically includes: S21. Based on the time field of each TROPOMI image, obtain the 10-meter orthogonal wind direction component wind speed data from the corresponding hourly ERA5 reanalysis data. S22. Calculate the 10-meter wind direction and 10-meter wind speed data at the corresponding hour, using the following formula: ； ； in, Represents the true wind direction. Represents actual wind speed. Represents the east-west component of wind speed. This represents the north-south wind speed component.

4. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, In step S3, the area-weighted sampling of the rotated methane column concentration data is performed, specifically as follows: The rotated methane column concentration data is weighted and sampled to a 0.01° resolution regular grid. A weighted average of the methane column concentration is calculated by determining the area overlap ratio between effective pixels and the uniform spatial grid. The calculation method is as follows: ； in, To standardize the methane column concentration in the spatial grid, For the first methane column concentration per effective pixel, For the first The area of ​​overlap between each effective cell and the uniform spatial grid. The number of pixels participating in resampling.

5. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, The preset threshold condition in step S5 is determined through the following steps: S51. Based on the methane column concentration in the study area, a probability density function curve is constructed using kernel density estimation. The inflection point on the high-concentration side of the probability density function curve is then used as the initial threshold coefficient. ; S52, using the initial threshold coefficient Based on this, a dynamic scan is performed within a preset range at a fixed step size to construct a candidate set of threshold coefficients; S53. Under the premise of satisfying the statistical separability constraint, select the threshold coefficient that makes the change in the number of pixels in the plume region the most gradual as the optimal threshold. The following formula is used as the final judgment rule: 。 6. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, In step S6, the methane enhancement value The calculation method is as follows: 。 7. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, In step S8, the contribution of strip emissions The calculation method is as follows: ； in, Contribution to strip emissions To adjust the wind speed, For the first The methane enhancement value of each pixel, The width of the pixel in the direction perpendicular to the wind direction. This represents the number of pixels within the strip.

8. The method for estimating landfill methane emissions based on satellite remote sensing observation and wind field information according to claim 1, characterized in that, In step S9, the outlier screening and processing method and the calculation method for the final methane emission estimation result are as follows: Calculate the mean of the emission contributions of all strips. and the standard deviation of the emission contribution of all strips The strip emission contribution that meets the following conditions will be retained: ； The final methane emission estimate is obtained by averaging the emission contributions from the retained strips: ； in, For the first Emission contribution of each strip The number of stripes required to meet the retention criteria. The set of stripes that satisfy the retention criteria.

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