CN115112586A - Pasture methane emission estimation method under multi-source data fusion - Google Patents
Pasture methane emission estimation method under multi-source data fusion Download PDFInfo
- Publication number
- CN115112586A CN115112586A CN202210763296.7A CN202210763296A CN115112586A CN 115112586 A CN115112586 A CN 115112586A CN 202210763296 A CN202210763296 A CN 202210763296A CN 115112586 A CN115112586 A CN 115112586A
- Authority
- CN
- China
- Prior art keywords
- pasture
- data
- cloud
- estimating
- amount
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000004927 fusion Effects 0.000 title claims abstract description 17
- 238000012549 training Methods 0.000 claims abstract description 16
- 238000007599 discharging Methods 0.000 claims abstract description 13
- 238000012937 correction Methods 0.000 claims abstract description 7
- 238000013517 stratification Methods 0.000 claims abstract description 5
- 230000005855 radiation Effects 0.000 claims description 28
- 238000004422 calculation algorithm Methods 0.000 claims description 15
- 230000003595 spectral effect Effects 0.000 claims description 13
- 230000005540 biological transmission Effects 0.000 claims description 12
- 238000001658 differential optical absorption spectrophotometry Methods 0.000 claims description 8
- 238000002310 reflectometry Methods 0.000 claims description 8
- 238000010521 absorption reaction Methods 0.000 claims description 4
- 239000000443 aerosol Substances 0.000 claims description 4
- 238000009395 breeding Methods 0.000 claims description 4
- 230000001488 breeding effect Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 230000010287 polarization Effects 0.000 claims description 4
- 210000001747 pupil Anatomy 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 abstract description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
- Y02P90/845—Inventory and reporting systems for greenhouse gases [GHG]
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Pathology (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Medical Informatics (AREA)
- Computing Systems (AREA)
- Evolutionary Computation (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to a method for estimating the methane emission of a pasture under multi-source data fusion, which sequentially comprises the following steps: step S1, calculating CH by quantitative inversion 4 Column concentration; step S2, obtaining CH by using quantitative inversion 4 CH for column concentration correction mode simulation 4 Method of column concentration and estimating near-surface CH based on vertical stratification information in the model 4 Column concentration; step S3, extracting pasture CH from historical list data 4 Discharge capacity; step S4, training CH based on historical multi-source data 4 Emission model, inputting updated remote sensing, mode data, estimating new CH 4 And (4) discharging the amount. The invention discloses a method for estimating the methane emission of a pasture under multi-source data fusion, which is used for estimating historical CH 4 Extrapolating emission inventory data over time while couplingThe model simulation result and the real-time satellite observation inversion result are obtained, the accuracy of the background value of the list data is kept, the timeliness of the satellite data and the high space-time coverage of the model data are introduced.
Description
Technical Field
The invention relates to the technical field related to quantitative remote sensing and data fusion, in particular to a method for estimating pasture methane emission under multi-source data fusion.
Background
Timely and effective calculation of CH 4 The discharge amount of (A) is a main problem to be solved by the patent.
At present, with CH 4 The gas-related data are mainly divided into 3 types, the first is CH calculated from bottom to top by historical statistical investigation 4 The second is CH calculated from top to bottom by combining remote sensing observation data with quantitative remote sensing inversion algorithm 4 Concentration data, third is CH predicted by simulation of emission inventory data of coupled history according to atmospheric physical and chemical modes 4 Concentration data.
Each of these three data has advantages and disadvantages, among them: the list data is directly displacement data, while the remote sensing inversion and the mode simulation are concentration data, and the discharge amount can be obtained by further calculation; comparing the accuracy of the data, wherein the accuracy of the list data is highest, the remote sensing is inferior, and the mode is worst; from the spatiotemporal analysis, the mode spatiotemporal is the best, the list space is full-covered but the timeliness is poor, and the remote sensing timeliness is medium but the space is lacked.
In view of the above drawbacks, the designer actively makes research and innovation to create a method for estimating the methane emission in the pasture under the multi-source data fusion, so that the method has industrial utilization value.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method for estimating the methane emission of a pasture under multi-source data fusion.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for estimating pasture methane emission under multi-source data fusion sequentially comprises the following steps:
step S1, calculating CH by quantitative inversion 4 Column concentration;
step S2, obtaining CH by using quantitative inversion 4 Column concentration calibration mode simulated CH 4 Method of column concentration and estimating near-surface CH based on vertical stratification information in the model 4 Column concentration;
step S3, extracting pasture CH from historical list data 4 Discharge capacity;
step S4, training CH based on historical multi-source data 4 Emission model, inputting updated remote sensing, mode data, estimating new CH 4 And (4) discharging the amount.
As a further improvement of the present invention, step S1 includes the following steps in sequence:
step S11, cloud parameter inversion: cloud amount estimation is carried out according to an O2-O2 absorption band cloud parameter inversion algorithm of 477 nm wave band, the algorithm assumes that cloud is an opaque Lambert reflecting surface, the influence of atmospheric polarization effect and atmospheric profile is considered, a cloud height and cloud amount lookup table about observation angle, earth surface reflectivity, cloud amount and cloud pressure factors is constructed on the basis of zenith albedo of a high-spectral-resolution atmospheric radiation transmission model in an ultraviolet-visible light wave band, and the total amount and other parameters of O2-O2 obtained by utilizing the cloud amount lookup table and satellite actual observation difference inversion are utilized to obtain cloud amount information of a satellite actual observation position;
step S12, CH 4 Total quantity inversion of the inclined column: calibrating original spectral data according to a spectral calibration equation, and performing satellite zenith hyperspectral surveying measurement by using a near-infrared 1200-1400 nm window based on a Differential Optical Absorption Spectroscopy (DOAS) algorithm to obtain solar radiation and earth observation radiation reflected to a satellite sensor to invert CH (channel) of the whole light path 4 Total amount of batter post;
step S13, CH 4 The total amount of diagonal bars is converted into the total amount of vertical bars: establishing CH by utilizing high spectral resolution atmospheric radiation transmission model 4 And an atmospheric quality factor lookup table of a plurality of parameters including a profile, an observation geometric angle, a surface reflectivity, an aerosol and a cloud calculates an atmospheric quality factor AMF of a satellite corresponding angle, and converts the total amount of the inverted oblique column into the total amount of the vertical column.
As a further improvement of the present invention, in step S11, the satellite inversion cloud top height and equivalent radiation cloud amount result are input as a radiation transmission model, and the calculation of the zenith radiation and the atmospheric quality factor at the satellite entrance pupil is performed.
As a further improvement of the present invention, in step S13, a reference area method is used to correct background errors that vary with latitude and observation angle.
As a further improvement of the present invention, step S3 specifically includes: screening out CH relevant to animal husbandry emission from a historical list 4 Arranging the grid data of the list, and removing non-pasture areas according to the position information of the pasture to obtain CH covering the pasture 4 Grid data are discharged, and finally, the breeding industry information of the grid where the pasture is located is introduced, and CH of the non-pasture is removed 4 And (4) discharging the amount.
As a further improvement of the present invention, step S4 includes the following steps in sequence:
step S41, extracting CH from the mode near-surface concentration data and the historical meteorological field data after the historical emission list and the historical inversion concentration data are corrected according to space-time matching 4 Concentration-emission training sample set;
step S42, constructing machine learning model, training CH using training sample data 4 An emission model;
step S43, inputting satellite correction near-ground CH of several years in sequence 4 Concentration data and meteorological field data, estimating pasture CH for years 4 And (4) discharging the amount.
By means of the scheme, the invention at least has the following advantages:
the invention discloses a method for estimating the methane emission of a pasture under multi-source data fusion, which is used for estimating historical CH 4 And the emission list data is subjected to time extrapolation, and a mode simulation result and a real-time satellite observation inversion result are coupled, so that the accuracy of the background value of the list data is kept, the timeliness of the satellite data and the high space-time coverage of the mode data are introduced.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flowchart illustrating step S1 according to the first embodiment of the present invention;
FIG. 3 is a flowchart illustrating step S2 according to the first embodiment of the present invention;
FIG. 4 is a flowchart illustrating step S3 according to the first embodiment of the present invention;
fig. 5 is a flowchart illustrating step S4 in the first embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in figures 1-5, the first and second parts,
a method for estimating pasture methane emission under multi-source data fusion sequentially comprises the following steps:
step S1, calculating CH by quantitative inversion 4 Column concentration;
step S2, obtaining CH by using quantitative inversion 4 Column concentration calibration mode simulated CH 4 Method of column concentration and estimating near-surface CH based on vertical stratification information in the model 4 Column concentration;
step S3, extracting pasture CH from historical list data 4 Discharge capacity;
step S4, training CH based on historical multi-source data 4 Emission model, inputting updated remote sensing, mode data, estimating new CH 4 And (4) discharging the amount.
Preferably, step S1 includes the following steps in sequence:
step S11, cloud parameter inversion: cloud amount estimation is carried out according to an O2-O2 absorption band cloud parameter inversion algorithm of 477 nm wave band, the algorithm assumes that cloud is an opaque Lambert reflecting surface, the influence of atmospheric polarization effect and atmospheric profile is considered, a cloud height and cloud amount lookup table about observation angle, earth surface reflectivity, cloud amount and cloud pressure factors is constructed on the basis of zenith albedo of a high-spectral-resolution atmospheric radiation transmission model in an ultraviolet-visible light wave band, and the total amount and other parameters of O2-O2 obtained by utilizing the cloud amount lookup table and satellite actual observation difference inversion are utilized to obtain cloud amount information of a satellite actual observation position;
step S12, CH 4 Total amount of the batter columns is inverted: calibrating original spectral data according to a spectral calibration equation, and obtaining solar radiation and earth observation radiation reflected to a satellite sensor to invert the total amount of CH4 batter posts of the whole light path by utilizing satellite zenith hyperspectral sounding measurement of a near-infrared 1200-1400 nm window based on a Differential Optical Absorption Spectroscopy (DOAS) algorithm;
step S13, CH 4 The total amount of diagonal bars is converted into the total amount of vertical bars: establishing CH by utilizing high spectral resolution atmospheric radiation transmission model 4 And an atmospheric quality factor lookup table of a plurality of parameters including a profile, an observation geometric angle, a surface reflectivity, an aerosol and a cloud calculates an atmospheric quality factor AMF of a satellite corresponding angle, and converts the total amount of the inverted oblique column into the total amount of the vertical column.
Preferably, in step S11, the result of the satellite inversion cloud top height and the equivalent radiation cloud amount are input as a radiation transmission model, and the zenith radiation and the atmospheric quality factor at the satellite entrance pupil are calculated.
Preferably, in step S13, a reference area method is used to correct background errors that vary with latitude and observation angle.
Preferably, step S3 specifically includes: screening CH4 emission list grid data related to animal husbandry emission from a history list catalog, removing non-pasture areas according to the position information of the pasture to obtain CH4 emission grid data covering the pasture, finally introducing the breeding industry information of the grid where the pasture is located, and removing the CH4 emission of the non-pasture.
Preferably, step S4 includes the following steps in sequence:
step S41, extracting CH from the mode near-surface concentration data and the historical meteorological field data after the historical emission list and the historical inversion concentration data are corrected according to space-time matching 4 Concentration-drained training sample sets;
step S42, constructing machine learning model, training CH using training sample data 4 An emission model;
step S43, inputting satellite correction near-ground CH of several years in sequence 4 Concentration data and meteorological field data, estimating pasture CH of a plurality of years 4 And (4) discharging the amount.
The first embodiment of the present invention:
step S1 referring to FIG. 2, the invention discloses a method for quantitatively inverting CH based on satellite remote sensing spectral data 4 Column concentration algorithm:
step S11, cloud parameter inversion
For CH 4 For quantitative remote sensing inversion of total amount, accuracy of cloud parameters directly affects CH 4 The accuracy of the inversion, so the identification of the cloud pixels and the cloud amount estimation are CH 4 The method comprises the first step of a satellite remote sensing inversion process. The cloud quantity estimation is carried out according to an O2-O2 absorption band cloud parameter inversion algorithm of 477 nm wave band, the algorithm assumes that cloud is an opaque Lambert reflecting surface, the influence of atmospheric polarization effect and atmospheric profile is fully considered, and the algorithm is based on the zenith of a high spectral resolution atmospheric radiation transmission model in ultraviolet-visible light wave bandAnd (3) constructing a cloud height and cloud amount lookup table about factors such as an observation angle, surface reflectivity, cloud amount and cloud pressure by the albedo, and acquiring cloud amount information at an actual observation position of the satellite by using the total O2-O2 amount and other parameters obtained by the cloud parameter lookup table and the actual satellite observation difference inversion. In addition, the satellite inversion cloud top height and equivalent radiation cloud amount results can also be input as a radiation transmission model, and the zenith radiation and the atmospheric quality factor AMF (air Mass factor) at the entrance pupil of the satellite are calculated.
Step S12, CH 4 Total amount of batter column inversion
And calibrating the original spectral data according to a spectral calibration equation. Based on a Differential Optical Absorption Spectroscopy (DOAS) algorithm, by utilizing satellite zenith hyperspectral sounding measurement of a near-infrared 1200-1400 nm window, the CH of the whole light path is inverted by obtaining solar radiation and earth observation radiation reflected to a satellite sensor 4 Total amount of diagonal bars.
Step S13, CH 4 Conversion of total amount of diagonal columns into total amount of vertical columns
Establishing CH by utilizing high spectral resolution atmospheric radiation transmission model 4 And (3) an atmospheric quality factor AMF lookup table of a plurality of parameters such as a profile, an observation geometric angle, surface reflectivity, aerosol and cloud, calculating an atmospheric quality factor AMF of a satellite corresponding angle, and converting the total quantity of the inverted oblique column into the total quantity of the vertical column. Finally, a reference area method is used to correct background errors that vary with latitude and observation angle.
Step S2 referring to FIG. 3, the present invention discloses a CH inversion method using satellite quantitative remote sensing 4 Total column concentration correction mode simulated CH 4 Total column concentration and estimating near-surface CH based on vertical stratification information in the model 4 And (4) concentration.
Step S3 referring to FIG. 4, the present invention discloses a method for extracting a pasture CH from inventory data 4 A method of discharging data.
Screening two kinds of CH related to animal husbandry discharge, namely ENF and MNM from a history list 4 Emissions inventory grid data. According to the position information of the pasture, the non-pasture area is removed to obtain CH covering the pasture 4 The mesh data is discharged.Finally, introducing the breeding industry information of the grid where the pasture is located, and removing CH of the non-pasture 4 And (4) discharging the amount.
Step S4 referring to FIG. 5, the invention discloses a training CH based on historical multi-source data 4 Emission model, inputting updated remote sensing, mode data, estimating new CH 4 A method of discharging the amount of the exhaust gas.
Firstly, extracting CH from historical emission lists, mode near-ground concentration data after historical satellite inversion concentration data correction and historical meteorological field data according to space-time matching 4 Concentration-emission training sample set. Secondly, a machine learning model is constructed, and CH is trained by using training sample data 4 And (4) an emission model. Finally, the satellite correction near-surface CH of 19, 20, 21 and 22 years is input in sequence 4 Concentration data and meteorological field data, estimating pasture CH of 19-22 years 4 And (4) discharging the amount.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly referring to the number of technical features being grined. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection: either mechanically or electrically: the terms may be directly connected or indirectly connected through an intermediate member, or may be a communication between two elements.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A method for estimating pasture methane emission under multi-source data fusion is characterized by sequentially comprising the following steps:
step S1, calculating CH by quantitative inversion 4 Column concentration;
step S2, obtaining CH by using quantitative inversion 4 Column concentration calibration mode simulated CH 4 Method of column concentration and estimating near-surface CH based on vertical stratification information in the patterns 4 Column concentration;
step S3, extracting pasture CH from historical list data 4 Discharge capacity;
step S4, training CH based on historical multi-source data 4 Emission model, inputting updated remote sensing, mode data, estimating new CH 4 And (4) discharging the amount.
2. The method for estimating the methane emission amount of the pasture under the multi-source data fusion of claim 1, wherein the step S1 comprises the following steps in sequence:
step S11, cloud parameter inversion: cloud amount estimation is carried out according to an O2-O2 absorption band cloud parameter inversion algorithm of 477 nm wave band, the algorithm assumes that cloud is an opaque Lambert reflecting surface, the influence of atmospheric polarization effect and atmospheric profile is considered, a cloud height and cloud amount lookup table about observation angle, earth surface reflectivity, cloud amount and cloud pressure factors is constructed on the basis of zenith albedo of a high-spectral-resolution atmospheric radiation transmission model in an ultraviolet-visible light wave band, and the total amount and other parameters of O2-O2 obtained by utilizing the cloud amount lookup table and satellite actual observation difference inversion are utilized to obtain cloud amount information of a satellite actual observation position;
step S12, CH 4 Total amount of the batter columns is inverted: calibrating original spectral data according to a spectral calibration equation, and obtaining solar radiation and earth observation radiation reflected to a satellite sensor to invert the total amount of CH4 batter posts of the whole light path by utilizing satellite zenith hyperspectral sounding measurement of a near-infrared 1200-1400 nm window based on a Differential Optical Absorption Spectroscopy (DOAS) algorithm;
step S13, CH 4 The total amount of diagonal bars is converted into the total amount of vertical bars: and establishing an atmospheric quality factor lookup table about a plurality of parameters including a CH4 profile, an observation geometric angle, a ground surface reflectivity, aerosol and cloud by using a high-spectral-resolution atmospheric radiation transmission model, calculating an atmospheric quality factor AMF of a satellite corresponding angle, and converting the total amount of the inverted oblique columns into the total amount of the vertical columns.
3. The method for estimating the methane emission in the pasture under the multi-source data fusion of claim 2, wherein in the step S11, the satellite inversion cloud top height and the equivalent radiation cloud amount result are input as a radiation transmission model, and the zenith radiation and the atmospheric quality factor at the entrance pupil of the satellite are calculated.
4. The method for estimating methane emission from pasture under multi-source data fusion of claim 2, wherein in step S13, a reference area method is used to correct background errors varying with latitude and observation angle.
5. The method for estimating the methane emission amount of the pasture under the multi-source data fusion according to claim 1, wherein the step S3 specifically comprises: screening out CH relevant to animal husbandry emission from a historical list 4 Arranging the grid data of the list, and removing non-pasture areas according to the position information of the pasture to obtain CH covering the pasture 4 Discharge grid data, mostThen introducing the breeding industry information of the grid where the pasture is positioned, and removing CH of the non-pasture 4 And (4) discharging the amount.
6. The method for estimating the methane emission amount of the pasture under the multi-source data fusion of claim 1, wherein the step S4 comprises the following steps in sequence:
step S41, extracting CH from the mode near-surface concentration data and the historical meteorological field data after the historical emission list and the historical inversion concentration data are corrected according to space-time matching 4 Concentration-drained training sample sets;
step S42, constructing machine learning model, training CH using training sample data 4 An emission model;
step S43, inputting satellite correction near-ground CH of several years in sequence 4 Concentration data and meteorological field data, estimating pasture CH of a plurality of years 4 And (4) discharging the amount.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210763296.7A CN115112586A (en) | 2022-07-01 | 2022-07-01 | Pasture methane emission estimation method under multi-source data fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210763296.7A CN115112586A (en) | 2022-07-01 | 2022-07-01 | Pasture methane emission estimation method under multi-source data fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115112586A true CN115112586A (en) | 2022-09-27 |
Family
ID=83331473
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210763296.7A Pending CN115112586A (en) | 2022-07-01 | 2022-07-01 | Pasture methane emission estimation method under multi-source data fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115112586A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116189813A (en) * | 2023-04-18 | 2023-05-30 | 南京大学 | Method for estimating rice field methane emission flux based on satellite atmospheric methane concentration |
| CN116486931A (en) * | 2023-06-21 | 2023-07-25 | 上海航天空间技术有限公司 | Full-coverage atmospheric methane concentration data production method and system coupled with physical mechanism |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106124374A (en) * | 2016-07-22 | 2016-11-16 | 中科宇图科技股份有限公司 | Atmospheric particulates remote-sensing monitoring method based on data fusion |
| WO2019246280A1 (en) * | 2018-06-19 | 2019-12-26 | Seekops Inc. | Emissions estimate model algorithms and methods |
| CN111523717A (en) * | 2020-04-15 | 2020-08-11 | 北京工业大学 | Inversion estimation method for atmospheric pollutant emission list |
| CN112231983A (en) * | 2020-10-23 | 2021-01-15 | 河南大学 | Brain-like cross-modal recognition and parallel processing method of pollution precursor emission source for remote sensing space-time big data |
| CN112884079A (en) * | 2021-03-30 | 2021-06-01 | 河南大学 | Method for estimating near-surface nitrogen dioxide concentration based on Stacking integrated model |
| CN113159141A (en) * | 2021-04-02 | 2021-07-23 | 北京首创大气环境科技股份有限公司 | Remote sensing estimation method for high-resolution near-real-time PM2.5 concentration |
-
2022
- 2022-07-01 CN CN202210763296.7A patent/CN115112586A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106124374A (en) * | 2016-07-22 | 2016-11-16 | 中科宇图科技股份有限公司 | Atmospheric particulates remote-sensing monitoring method based on data fusion |
| WO2019246280A1 (en) * | 2018-06-19 | 2019-12-26 | Seekops Inc. | Emissions estimate model algorithms and methods |
| CN111523717A (en) * | 2020-04-15 | 2020-08-11 | 北京工业大学 | Inversion estimation method for atmospheric pollutant emission list |
| WO2021208393A1 (en) * | 2020-04-15 | 2021-10-21 | 北京工业大学 | Inversion estimation method for air pollutant emission inventory |
| CN112231983A (en) * | 2020-10-23 | 2021-01-15 | 河南大学 | Brain-like cross-modal recognition and parallel processing method of pollution precursor emission source for remote sensing space-time big data |
| CN112884079A (en) * | 2021-03-30 | 2021-06-01 | 河南大学 | Method for estimating near-surface nitrogen dioxide concentration based on Stacking integrated model |
| CN113159141A (en) * | 2021-04-02 | 2021-07-23 | 北京首创大气环境科技股份有限公司 | Remote sensing estimation method for high-resolution near-real-time PM2.5 concentration |
Non-Patent Citations (4)
| Title |
|---|
| 程巳阳 等: "基于直射太阳光红外吸收光谱技术的大气中CH_4柱浓度遥测研究", 量子电子学报, vol. 31, no. 01, 15 January 2014 (2014-01-15), pages 18 - 24 * |
| 胡春梓 等: "基于多源卫星遥感数据的河北省NO_2柱浓度时空变化", 科技通报, vol. 32, no. 11, 30 November 2016 (2016-11-30), pages 30 - 36 * |
| 陈良富 等: "近地面NO_2浓度卫星遥感估算问题", 环境监控与预警, vol. 08, no. 03, 15 June 2016 (2016-06-15), pages 1 - 5 * |
| 陈辉 等: "基于地理加权模型的我国冬季PM_(2.5)遥感估算方法研究", 环境科学学报, vol. 36, no. 06, 30 June 2016 (2016-06-30), pages 2142 - 2151 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116189813A (en) * | 2023-04-18 | 2023-05-30 | 南京大学 | Method for estimating rice field methane emission flux based on satellite atmospheric methane concentration |
| CN116189813B (en) * | 2023-04-18 | 2023-07-25 | 南京大学 | Method for estimating rice field methane emission flux based on satellite atmospheric methane concentration |
| CN116486931A (en) * | 2023-06-21 | 2023-07-25 | 上海航天空间技术有限公司 | Full-coverage atmospheric methane concentration data production method and system coupled with physical mechanism |
| CN116486931B (en) * | 2023-06-21 | 2023-08-29 | 上海航天空间技术有限公司 | Full-coverage atmospheric methane concentration data production method and system coupled with physical mechanism |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115112586A (en) | Pasture methane emission estimation method under multi-source data fusion | |
| WO2022166939A1 (en) | Method for improving estimation of leaf area index of early growth stage of wheat based on red-edge bands of sentinel-2 satellite images | |
| CN102788752B (en) | Non-destructive detection device and method of internal information of crops based on spectrum technology | |
| CN104266982B (en) | A kind of large area insect pest quantifies monitoring system | |
| CN102495005B (en) | Method for diagnosing crop water deficit through hyperspectral image technology | |
| CN110174359B (en) | Aviation hyperspectral image soil heavy metal concentration assessment method based on Gaussian process regression | |
| CN110222870B (en) | Regional winter wheat yield estimation method by assimilating satellite fluorescence data and crop growth model | |
| CN107796764A (en) | A kind of construction method of the wheat leaf area index appraising model based on three wave band vegetation indexs | |
| CN111751376A (en) | Rice nitrogen nutrition estimation method based on canopy image feature derivation | |
| CN116912690A (en) | Forest leaf area index inversion acquisition method and system based on data fusion | |
| CN115443889A (en) | Accurate irrigation method and device for crops | |
| CN112129709A (en) | Apple tree canopy scale nitrogen content diagnosis method | |
| CN113466143B (en) | Soil nutrient inversion method, device, equipment and medium | |
| CN112861712B (en) | Agricultural pest and disease monitoring method based on artificial intelligence and multi-temporal remote sensing | |
| CN116501925B (en) | Method, system, equipment and medium for predicting leaf area index under multi-source remote sensing data | |
| CN116486931B (en) | Full-coverage atmospheric methane concentration data production method and system coupled with physical mechanism | |
| CN117035066A (en) | Ground surface temperature downscaling method coupling geographic weighting and random forest | |
| Liu et al. | Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data. | |
| CN113537108B (en) | Crop chlorophyll content inversion method based on unmanned aerial vehicle hyperspectral remote sensing | |
| CN116310788A (en) | Plant leaf nitrogen content hyperspectral modeling method based on map fusion | |
| CN109540837A (en) | The method that near-infrared quickly detects Boehmeria nivea leaves wood fibre cellulose content | |
| CN115128013A (en) | Soil organic matter content space prediction evaluation method based on partition algorithm | |
| CN114065931A (en) | Land water vapor inversion method and system based on physical model and neural network fusion | |
| CN116091936A (en) | Agricultural condition parameter inversion method for fusing point-land block-area scale data | |
| CN114880933A (en) | Atmospheric temperature and humidity profile inversion method and system for non-exploration-site foundation microwave radiometer based on reanalysis data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |