Biogenic volatile organic compound emission list calculation method based on multi-source satellite data

Abstract

The invention discloses a biogenic volatile organic compound emission list calculation method based on multi-source satellite data, which comprises the following steps: acquiring a target multi-source satellite remote sensing data set, and carrying out data unification and standardization processing; mapping the processed data into a grid system, and constructing an effective data matrix; based on the leaf area index and the vegetation coverage rate, obtaining an effective leaf area index of unit vegetation coverage; based on the land coverage type and the mapping matrix W, for each grid (x, y), obtaining a coverage score of each vegetation type; obtaining an emission activity factor and a standard emission factor of the compound i; and obtaining the emission flux of the unit grid based on the effective leaf area index, the coverage score of the vegetation type and the emission factor. According to the method, the latest vegetation data is obtained based on the multi-type satellite data, and the photosynthetically active radiation absorption amount of each layer of leaves is calculated based on the more accurate leaf density, so that the calculation precision of the discharge amount of photosensitive compounds such as isoprene is remarkably improved.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of atmospheric environmental science and remote sensing applications, specifically to a method for calculating biogenic volatile organic compound (BVOC) emission inventories based on multi-source satellite data. This method utilizes multi-source satellite remote sensing data to update the input parameters of an atmospheric chemical model, thereby generating a high-resolution biogenic volatile organic compound (BVOC) emission inventory. Background Technology

[0002] Biogenic volatile organic compounds (BVOCs) are key precursors in atmospheric chemical processes, significantly contributing to the formation of tropospheric ozone and secondary organic aerosols, severely impacting regional air quality and climate change. Currently, one tool for estimating BVOC emissions is the MEGAN model. However, most existing studies directly use the default vegetation-driven data provided with the official MEGAN model, which is based on a global dataset from 2008. This means the model input fails to reflect significant changes in vegetation distribution in recent years, resulting in high uncertainty in the final calculated BVOC emission inventory. Summary of the Invention

[0003] To address the problem of outdated vegetation input data in existing BVOCs emission inventory compilation methods, this invention provides a method for calculating biogenic volatile organic compound emission inventories based on multi-source satellite data.

[0004] This invention discloses a method for calculating biogenic volatile organic compound (VOC) emission inventories based on multi-source satellite data, including: Step 1: Obtain the target multi-source satellite remote sensing dataset, construct a unified spatial projection coordinate system and temporal resolution for the multi-source satellite remote sensing dataset; resample the multi-source satellite remote sensing dataset with the unified spatial projection coordinate system and temporal resolution and project it onto the standardized intermediate state matrix data; Step 2: Establish a vectorized standard grid G ​​covering the target area and define the grid index (x, y); map the intermediate matrix data from Step 1 to the grid system G(x, y) to construct an effective data matrix; Step 3: Based on the leaf area index and vegetation coverage, obtain the effective leaf area index per unit of vegetation coverage; Step 4: Based on the land cover type and mapping matrix W, obtain the cover score for each vegetation type for each grid (x, y); Step 5: Obtain the emission activity factor and standard emission factor of compound i; Step 6: Based on the effective leaf area index, vegetation type coverage fraction, and emission factor, obtain the emission flux per unit grid.

[0005] As a further improvement of the present invention, in step 1, the multi-source satellite remote sensing dataset includes vegetation cover rate (VCF), leaf area index (LAI), and land cover type data (GrowthForm).

[0006] As a further improvement of the present invention, in step 1... An Albers equal-area projection coordinate system was constructed, and data processing algorithms were used to resample and reproject heterogeneous data from multi-source satellite remote sensing datasets. For continuous data VCF and LAI, a bilinear interpolation algorithm was used to ensure smooth numerical transitions. For discrete classification data GrowthForm, the nearest neighbor pixel method was used to merge multiple vegetation types into five categories: trees, shrubs, grasses, crops, and others.

[0007] As a further improvement of the present invention, in step 3, the formula for calculating the effective leaf area index per unit vegetation cover is: LAIv = LAI / VCF In the formula: LAIv: Effective leaf area index per unit vegetation cover; LAI: Leaf Area Index; VCF: Vegetation coverage, with a value range of 0-1.

[0008] As a further improvement of the present invention, in step 4, the formula for calculating the cover fraction of each vegetation type is as follows: χj =Σ(Pk×Wk,j) In the formula: χj: Cover score of vegetation type j; Pk: The percentage of land cover type k in satellite data; Wk,j: Mapping weight coefficient, which is 1 when the k-th land belongs to vegetation type j, and 0 otherwise.

[0009] As a further improvement of the present invention, in step 6, the formula for calculating the emission flux per unit grid is: Fi = γi×ρ×Σ (εi,j × χj) In the formula: Fi: Emission flux of compound i within the grid, in μg·m⁻²·h⁻¹; γi: Emission activity factor of compound i, used to characterize the impact of environmental conditions on emissions; ρ: Canopy production and loss factor, usually taken as 1.0; εi,j: Standard emission factor of compound i emitted by vegetation type j; χj: Coverage score of vegetation type j in this grid.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention obtains the latest vegetation data based on multiple types of satellite data, and calculates the photosynthetically active radiation absorption of each layer of leaves based on more accurate leaf density, thereby significantly improving the calculation accuracy of the emission of photosensitive compounds such as isoprene. Attached Figure Description

[0011] Figure 1 This is a flowchart of the method for calculating the bio-based volatile organic compound emission inventory based on multi-source satellite data disclosed in this invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The present invention will now be described in further detail with reference to the accompanying drawings: Taking the treatment of the 2020 national BVOCs emission inventory as an example, such as Figure 1 As shown, the method for calculating the bio-source volatile organic compound emission inventory based on multi-source satellite data of the present invention specifically includes: Step 1: Download 2020 vegetation cover, leaf area index (LAI), and vegetation type data from the NASA Open Database. Construct an Albers iso-area projection coordinate system and use data processing algorithms to resample and reproject the heterogeneous data: for continuous data (VCF and LAI), use bilinear interpolation to ensure smooth numerical transitions; for discrete categorical data (GrowthForm), use the nearest neighbor method to merge 17 vegetation types into five categories: trees, shrubs, herbaceous plants, crops, and others. Finally, standardize all data.

[0014] Step 2: Construct a 1km×1km high-resolution grid, accurately map the data from Step 1 into the grid, identify and remove outliers, and construct an effective data matrix.

[0015] Step 3: Based on the leaf area index and vegetation cover, obtain the effective leaf area index per unit of vegetation cover. The calculation formula is as follows: LAIv = LAI / VCF Step 4: Based on the land cover type and mapping matrix W, for a unit grid (x, y), obtain the cover score for each vegetation type. The calculation formula is as follows: χj =Σ(Pk×Wk,j) Step 5: Download the list of compound emission factors and standard emission factors from the Zenodo database; Step 6: Based on the effective leaf area index, vegetation type cover fraction, and emission factor, obtain the emission flux per unit grid. The calculation formula is as follows: Fi = γi × ρ × Σ (εi,j × χj) The total BVOC emissions in China in 2020 were calculated to be 15.66 Tg. Isoprene was the compound with the largest emissions, with an annual emission of 4.77 Tg, contributing 30.46% to China's BVOC emissions. Monoterpenes were the second largest, with emissions of 3.09 Tg. Sesquiterpenes had the lowest emissions, at only 0.48 Tg, due to their relatively low emission rate. Other VOCs included more than 100 compounds, with a wide variety of types, contributing 7.32 Tg to China's annual BVOC emissions, accounting for 46.7% of China's total BVOC emissions.

[0016] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating biogenic volatile organic compound (VOC) emission inventories based on multi-source satellite data, characterized in that, include: Step 1: Obtain the target multi-source satellite remote sensing dataset, construct an equal-area projection coordinate system, resample the target multi-source satellite remote sensing dataset, and project it onto the standardized intermediate matrix data; Step 2: Establish a vectorized standard grid G ​​covering the target area and define the grid index (x, y); map the intermediate matrix data from Step 1 to the grid system G(x, y) to construct an effective data matrix; Step 3: Based on the leaf area index and vegetation coverage, obtain the effective leaf area index per unit of vegetation coverage; Step 4: Based on the land cover type and mapping matrix W, obtain the cover score for each vegetation type for each grid (x, y); Step 5: Obtain the emission activity factor and standard emission factor of compound i; Step 6: Based on the effective leaf area index, vegetation type coverage fraction, and emission factor, obtain the emission flux per unit grid.

2. The method for calculating the biogenic volatile organic compound emission inventory based on multi-source satellite data as described in claim 1, characterized in that, In step 1, the multi-source satellite remote sensing dataset includes vegetation cover rate (VCF), leaf area index (LAI), and land cover type data (GrowthForm).

3. The method for calculating the biogenic volatile organic compound emission inventory based on multi-source satellite data as described in claim 2, characterized in that, In step 1, An Albers equal-area projection coordinate system was constructed, and data processing algorithms were used to resample and reproject heterogeneous data from multi-source satellite remote sensing datasets. For continuous data VCF and LAI, a bilinear interpolation algorithm was used to ensure smooth numerical transitions. For discrete classification data GrowthForm, the nearest neighbor pixel method was used to merge multiple vegetation types into five categories: trees, shrubs, grasses, crops, and others.

4. The method for calculating the biogenic volatile organic compound emission inventory based on multi-source satellite data as described in claim 2, characterized in that, In step 3, the formula for calculating the effective leaf area index per unit of vegetation cover is: LAIv = LAI / VCF In the formula: LAIv: Effective leaf area index per unit vegetation cover; LAI: Leaf Area Index; VCF: Vegetation coverage, with a value range of 0-1.

5. The method for calculating the biogenic volatile organic compound emission inventory based on multi-source satellite data as described in claim 1, characterized in that, In step 4, the formula for calculating the cover fraction of each vegetation type is as follows: χj =Σ(Pk×Wk,j) In the formula: χj: Cover score of vegetation type j; Pk: The percentage of land cover type k in satellite data; Wk,j: Mapping weight coefficient, which is 1 when the k-th land belongs to vegetation type j, and 0 otherwise.

6. The method for calculating the biogenic volatile organic compound emission inventory based on multi-source satellite data as described in claim 1, characterized in that, In step 6, the formula for calculating the emission flux per unit grid is: Fi = γi×ρ×Σ (εi,j × χj) In the formula: Fi: Emission flux of compound i within the grid; γi: Emission activity factor of compound i; ρ: Canopy production and loss factor; εi,j: Standard emission factor of compound i emitted by vegetation type j; χj: Coverage score of vegetation type j in this grid.

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