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CN-122019924-A - Biological source volatile organic compound emission list calculation method based on multi-source satellite data

CN122019924ACN 122019924 ACN122019924 ACN 122019924ACN-122019924-A

Abstract

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

Inventors

  • CHEN DONGSHENG
  • YANG HONGYUAN

Assignees

  • 北京工业大学

Dates

Publication Date
20260512
Application Date
20260211

Claims (6)

  1. 1. The method for calculating the biological source volatile organic compound emission list based on the multi-source satellite data is characterized by comprising the following steps of: Step 1, acquiring a target multi-source satellite remote sensing data set, constructing an equal-area projection coordinate system, resampling the target multi-source satellite remote sensing data set, and projecting the resampled target multi-source satellite remote sensing data set to standardized intermediate state matrix data; step 2, establishing a vectorization standard grid G covering a target area, defining a grid index (x, y), mapping the intermediate state matrix data in the step 1 into a grid system G (x, y), and constructing an effective data matrix; Step 3, obtaining an effective leaf area index of unit vegetation coverage based on the leaf area index and vegetation coverage; Step 4, obtaining coverage scores of vegetation types for each grid (x, y) based on the land coverage types and the mapping matrix W; Step 5, obtaining the emission activity factor and the standard emission factor of the compound i; And 6, obtaining the emission flux of the unit grid based on the effective leaf area index, the coverage fraction of the vegetation type and the emission factor.
  2. 2. The method of claim 1, wherein in step 1, the multi-source satellite remote sensing dataset includes vegetation coverage VCF, leaf area index LAI, and land cover type data GrowthForm.
  3. 3. The method for calculating a list of emissions of biological source volatile organic compounds based on multi-source satellite data according to claim 2, wherein in said step 1, The method comprises the steps of constructing Albers equal area projection coordinate systems, carrying out resampling and reprojection on heterogeneous data of a multi-source satellite remote sensing dataset by utilizing a data processing algorithm, wherein a bilinear interpolation algorithm is adopted for continuous data VCF and LAI to ensure smooth transition of numerical values, and a nearest neighbor pixel method is adopted for discrete classification data GrowthForm to combine various vegetation types into trees, shrubs, vegetation, crops and other five major categories.
  4. 4. The method for calculating a list of biogenic volatile organic compounds emissions based on multi-source satellite data as claimed in claim 2, wherein in said step 3, the calculation formula of the effective leaf area index per unit vegetation cover is: LAIv = LAI / VCF wherein: LAIv effective leaf area index of unit vegetation cover; LAI: leaf area index; VCF, vegetation coverage, the value range is 0-1.
  5. 5. The method for calculating a list of biogenic volatile organic compounds emissions based on multi-source satellite data according to claim 1, wherein in the step 4, the coverage score of each vegetation type is calculated by the following formula: χj =Σ(Pk×Wk,j) wherein: Coverage fraction of vegetation type j; Pk, the duty ratio of the k-th type of land coverage in the satellite data; Wk, j is a mapping weight coefficient, 1 is taken when the k-th land belongs to the vegetation type j, and otherwise 0 is taken.
  6. 6. The method for calculating a list of emissions of biological source volatile organic compounds based on multi-source satellite data according to claim 1, wherein in said step 6, the formula for calculating the emission flux per unit grid is: Fi = γi×ρ×Σ (εi,j × χj) wherein: fi, emission flux of compound i in the grid; γi, emission activity factor of compound i; ρ, canopy production and loss factor; epsilon i, j standard emission factor for vegetation type j emission compound i; chi j the coverage score of vegetation type j in the grid.

Description

Biological source volatile organic compound emission list calculation method based on multi-source satellite data Technical Field The invention relates to the technical field of intersection of atmospheric environment science and remote sensing application, in particular to a method for calculating a biogenic volatile organic compound emission list based on multi-source satellite data, which updates input parameters of an atmospheric model by utilizing the multi-source satellite remote sensing data so as to manufacture a high-resolution biogenic volatile organic compound emission list (BVOCs). Background Biological source volatile organic compounds (BVOCs) are key precursors in atmospheric chemistry, contributing significantly to the generation of ozone in the stratosphere and secondary organic aerosols, severely affecting regional air quality and climate change. One of the tools currently estimating BVOCs emissions is the MEGAN model, but most of the existing studies directly follow the default vegetation driving data of the MEGAN model official self-contained, the default data is made based on the global dataset of 2008, which results in that model inputs cannot reflect significant changes in vegetation distribution in recent years, resulting in a high uncertainty in the final calculated BVOCs emissions inventory. Disclosure of Invention The invention provides a biological source volatile organic compound emission list calculation method based on multi-source satellite data, which solves the problem that vegetation input data are old in the existing BVOCs emission list manufacture. The invention discloses a biological source volatile organic compound emission list calculation method based on multi-source satellite data, which comprises the following steps: step 1, acquiring a target multi-source satellite remote sensing data set, and constructing a unified space projection coordinate system and time resolution for the multi-source satellite remote sensing data set; step 2, establishing a vectorization standard grid G covering a target area, defining a grid index (x, y), mapping the intermediate state matrix data in the step 1 into a grid system G (x, y), and constructing an effective data matrix; Step 3, obtaining an effective leaf area index of unit vegetation coverage based on the leaf area index and vegetation coverage; Step 4, obtaining coverage scores of vegetation types for each grid (x, y) based on the land coverage types and the mapping matrix W; Step 5, obtaining the emission activity factor and the standard emission factor of the compound i; And 6, obtaining the emission flux of the unit grid based on the effective leaf area index, the coverage fraction of the vegetation type and the emission factor. As a further improvement of the present invention, in the step 1, the multi-source satellite remote sensing dataset includes vegetation coverage VCF, leaf area index LAI, and land cover type data GrowthForm. As a further development of the invention, in said step 1, The method comprises the steps of constructing Albers equal area projection coordinate systems, carrying out resampling and reprojection on heterogeneous data of a multi-source satellite remote sensing dataset by utilizing a data processing algorithm, wherein a bilinear interpolation algorithm is adopted for continuous data VCF and LAI to ensure smooth transition of numerical values, and a nearest neighbor pixel method is adopted for discrete classification data GrowthForm to combine various vegetation types into trees, shrubs, vegetation, crops and other five major categories. As a further improvement of the present invention, in the step 3, the calculation formula of the effective leaf area index of the unit vegetation cover is: LAIv = LAI / VCF wherein: LAIv effective leaf area index of unit vegetation cover; LAI: leaf area index; VCF, vegetation coverage, the value range is 0-1. As a further improvement of the present invention, in the step 4, a calculation formula of the coverage score of each vegetation type is: χj =Σ(Pk×Wk,j) wherein: Coverage fraction of vegetation type j; Pk, the duty ratio of the k-th type of land coverage in the satellite data; Wk, j is a mapping weight coefficient, 1 is taken when the k-th land belongs to the vegetation type j, and otherwise 0 is taken. As a further improvement of the present invention, in the step 6, the calculation formula of the discharge flux of the unit mesh is: Fi = γi×ρ×Σ (εi,j × χj) wherein: fi is the emission flux of the compound i in the grid, and the unit is mug.m-2.h-1; gammat is an emission activity factor of compound i, used to characterize the effect of environmental conditions on emissions; ρ is a canopy production and loss factor, typically 1.0; epsilon i, j standard emission factor for vegetation type j emission compound i; chi j the coverage score of vegetation type j in the grid. Compared with the prior art, the invention has the beneficial effects that: According to the i