CN115310550B - Atmospheric carbon dioxide dry air column concentration calculation method and system - Google Patents
Atmospheric carbon dioxide dry air column concentration calculation method and system Download PDFInfo
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Abstract
The invention provides a method and a system for calculating the concentration of an atmospheric carbon dioxide dry air column, and relates to atmospheric carbon dioxide (CO) 2 ) The technical field of monitoring. The method comprises collecting XCO of three remote sensing monitoring in the research area 2 Preprocessing such as meshing, space-time matching and the like is performed on the data set and the environment covariate data; with environment covariates, for these three XCO 2 Fusing the data sets to obtain XCO 2 Fusing data; XCO is to be XCO 2 Fusing data as dependent variables, taking environment covariates as independent variables, and establishing a machine learning model based on an XGBoost algorithm; inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set. The invention fuses three remote sensing monitoring XCO 2 Data set and reconstruction of XCO using machine learning model 2 Provides support for the carbon accounting of "two-carbon actions".
Description
Technical Field
The invention relates to an atmospheric carbon dioxide dry air column concentration (XCO) 2 ) The technical field of monitoring, in particular to a method for detecting the position of a target objectMethod and system for calculating concentration of atmospheric carbon dioxide dry air column.
Background
Remote sensing monitoring using sensors mounted on satellites or space stations, is to obtain XCO 2 Important means of space-time distribution mainly include satellite orbital carbon observation No. 2 (OCO-2), greenhouse Gas Observation Satellite (GOSAT), and sensor orbital carbon observation No. 3 (OCO-3) mounted on international space stations. The OCO-2 and OCO-3 sensors are almost identical, and three high-resolution spectrometers are mounted to measure the reflected solar light spectra near 0.76,1.61, and 2.06 microns, respectively. GOSAT is equipped with a spectrometer for measuring the reflected solar spectrum at 0.76,1.61 and 2.06 microns, and also measures the reflected solar spectrum in the 5.56-14.3 micron band.
XCO for satellite monitoring 2 The method is relatively sparse in space, and the missing part can be filled by using a model simulation method. Current reconstruction full-field XCO 2 The method comprises the following steps of Carbon Tracker model simulation, and monitoring CO by using ground, airship, satellite and the like 2 Concentration, for bottom-up estimated atmospheric CO 2 The concentration is corrected. However, due to computer power limitations, the resulting XCO is simulated 2 The data resolution is low, typically only 3 ° x 2 ° on the global scale. At the same time, the method relies on carbon emission list, however, statistics, collection and correction of emission list data take a long time, thus leading to current XCO 2 There is some lag in the computation of the spatiotemporal distribution of the model simulation.
In recent years, studies have been made on the use of statistical methods in combination with satellite monitoring XCO 2 Data reconstruction XCO 2 Space-time distribution but due to XCO 2 The satellite monitoring data of (2) are distributed relatively sparsely in space, and XCO is obtained by using a Kriging interpolation method 2 Is relatively low. According to the previous research, the machine learning of the reconstructed air pollutant space-time distribution often has higher accuracy compared with the kriging interpolation. Reconstructing the atmospheric pollutants through machine learning modeling by utilizing the relevance between the atmospheric pollutants and environment covariates such as weather, altitude, land utilization type and the likeAnd (5) full-domain space-time distribution. Meanwhile, the machine learning method is often used for fusion application of homologous data, and can well make up for the limitation of a single data source.
The invention applies the machine learning method to a plurality of remote sensing monitoring XCO 2 Fusion of data sets and reconstruction of XCO 2 Is distributed in space-time in the whole domain, overcomes XCO 2 Monitoring has the problem of a large amount of missing data.
Disclosure of Invention
The invention aims to provide a method and a system for calculating the concentration of an atmospheric carbon dioxide dry air column, which are used for overcoming the problem in the prior art that XCO 2 Monitoring has the problem of a large amount of missing data.
In a first aspect, an embodiment of the present application provides a method for calculating a concentration of an atmospheric carbon dioxide dry air column, including the steps of:
collecting XCO monitored by three remote sensing in research area 2 The method comprises the steps of preprocessing a data set and environment covariate data;
XCO for three remote sensing monitoring 2 Fusing the data sets to obtain XCO 2 Fusing data;
by XCO 2 Fusing the data and the environment covariate data, and training to obtain an XGBoost model;
inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set.
In the implementation process, XCO (X-ray diffraction) of three remote sensing monitoring in a research area is collected 2 Preprocessing the data set and the environment covariate data, and performing gridding, space-time matching and the like; XCO for three remote sensing monitoring 2 Fusing the data sets to obtain XCO 2 Fusing data; using XCO 2 Fusing the data and the environment covariate data, and training to obtain an XGBoost model; inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set. XCO for three remote sensing monitoring 2 Data fusion with XCO 2 With weather, altitude, land use type, road density,Correlation among environment covariates such as vegetation normalization index and the like is calculated to obtain XCO through machine learning modeling 2 Is used for reconstructing a data set by comprehensive domain space-time distribution, and overcomes XCO 2 Monitoring has the problem of a large amount of missing data.
Based on the first aspect, in some embodiments of the invention, XCO for three telemetry monitors 2 Data fusion is carried out on the data set to obtain XCO 2 The procedure for fusing data was as follows:
input:
XCO of OCO-2 2 Monitor data a= { a 1 ，...，a j ，...，a J }；
XCO of OCO-3 2 Monitor data b= { B 1 ，...，b j ，...，b J }；
XCO of GOSAT 2 Monitor data c= { C 1 ，…，c j ，…，c J }；
Environment covariate dataset D= { D 1 ，...，d j ，...，d J }；
Note that: wherein d is j Representing the set of values of all environment covariates at j, but not the jth environment covariate. d, d j And a j 、b j C j Spatially and spatially correspond to each other, i.e. observations of three sensors on the same day, on the same grid. A is due to the different degree of deletion of OCO-2, OCO-3 and GOSAT data j 、b j C j There may be missing values but not at the same time.
And (3) outputting: fusion dataset e= { E 1 ，...，e j ，...，e J }；
The method comprises the following steps:
(1) fusion A and B is O:
wherein o= { O 1 ，...，o j ，...，o J }，o j ＝f 1 (a j ，b j ) Wherein a is j ，b j At least one of which is non-empty;
f 1 is a fusion function:
(2) linear transformation of GOSAT dataset C:
S＝{j|O j non-empty and C j Non-null };
M＝{c s |s∈S}，N＝{o s |s∈S}；
establishing a linear model f 3 ：N←M；
Based on f 3 Transformation c j ：
J is from 1 to J:
when c j Non-empty and
c′ j ＝f 3 (c j )；
the transformed dataset was C '= { C' 1 ，...，c′ j ，...，c′ J }；
(3) Taking the union of the data set O and the data set C' to obtain the final XCO 2 The dataset E is fused.
Based on the first aspect, XCO is used in some embodiments of the invention 2 The steps of establishing the XGBoost model by fusing the data and the environment covariate data are as follows:
XCO is to be XCO 2 And (5) taking the fusion data as a dependent variable, taking an environment covariate as an independent variable, and training to obtain an XGBoost model.
Based on the first aspect, in some embodiments of the present invention, preprocessing environment covariate data of a study area includes the steps of:
carrying out space division on a research area to obtain a space grid with 1km resolution;
assigning values to the predefined grids by adopting methods such as space resampling and the like for the environment covariate data;
in a second aspect, embodiments of the present application provide an atmospheric carbon dioxide dry air column concentration calculation system, comprising:
the data acquisition module is used for collecting XCO (X-ray computer operation) monitored by three remote sensing in the research area 2 Data sets and environment covariate data;
data preprocessing module for XCO of three remote sensing monitoring in research area 2 The data set and the environment covariate data are subjected to preprocessing such as meshing, space-time matching and the like, and a preprocessed data set is obtained;
the data fusion module is used for XCO (X-ray computer operation) for three remote sensing monitoring 2 Data fusion is carried out on the data set to obtain XCO 2 Fusing the data sets;
XGBoost model building module for XCO-based 2 Training the fusion data and the environment covariate data to obtain an XGBoost model;
the concentration calculation module is used for inputting the environment covariate data into the XGBoost model to calculate and obtain XCO 2 Is used for reconstructing the data set.
In the implementation process, XCO (X-ray diffraction) of three remote sensing monitoring in a research area is collected 2 Preprocessing the data set and the environment covariate data, and performing gridding, space-time matching and the like; XCO for three remote sensing monitoring 2 Fusing the data sets to obtain XCO 2 Fusing data; using XCO 2 Fusing the data and the environment covariate data, and training to obtain an XGBoost model; inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set. XCO for three remote sensing monitoring 2 Data fusion with XCO 2 Correlation with environment covariates such as meteorological, elevation, land utilization type, road density, vegetation normalization index and the like, and calculating to obtain XCO through machine learning modeling 2 Is used for reconstructing a data set by comprehensive domain space-time distribution, and overcomes XCO 2 Monitoring has the problem of a large amount of missing data.
The embodiment of the invention has at least the following advantages or beneficial effects:
the embodiment of the invention provides a method and a system for calculating the concentration of an atmospheric carbon dioxide dry air column, which are implemented by collecting three air columns in a research areaXCO for remote sensing monitoring 2 Preprocessing the data set and the environment covariate data, and performing gridding, space-time matching and the like; XCO for three remote sensing monitoring 2 Data fusion is carried out on the data set to obtain XCO 2 Fusing data; using XCO 2 Fusing the data and the environment covariate data, and training to obtain an XGBoost model; inputting the environment covariate number set into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set. XCO for three remote sensing monitoring 2 Data sets are fused while using XCO 2 Correlation with environment covariates such as weather, altitude, land utilization type, road density, vegetation normalization index and the like, and calculating to obtain XCO through machine learning modeling 2 Is used for reconstructing a data set by comprehensive domain space-time distribution, and overcomes XCO 2 Monitoring has the problem of a large amount of missing data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for calculating the concentration of an atmospheric carbon dioxide dry air column provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a data fusion process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the distribution of each remote sensing data and the spatial and temporal composition of the fused data according to an embodiment of the present invention;
fig. 4 is a block diagram of a system for calculating the concentration of an atmospheric carbon dioxide dry air column according to an embodiment of the present invention.
Icon: 110-a data acquisition module; 120-a data preprocessing module; 130-a data fusion module; 140-XGBoost model building module; 150-concentration calculation module.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are 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 application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Examples
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
Referring to fig. 1-2, fig. 1 is a flowchart of a method for calculating a dry air column concentration of atmospheric carbon dioxide according to an embodiment of the present invention, and fig. 2 is a schematic diagram of a data fusion process according to an embodiment of the present invention. The method for calculating the concentration of the atmospheric carbon dioxide dry air column comprises the following steps:
step S110: collecting remote sensing monitoring XCO in research area 2 Data and environment covariate data. The XCO described above 2 The data comprises three remote sensing monitored XCO 2 Data set comprising XCO from satellite orbital carbon observation No. 2 (OCO-2) 2 Data, XCO from a greenhouse Gas Observation Satellite (GOSAT) 2 Data, and XCO from sensor orbital carbon observation No. 3 (OCO-3) mounted on international space station 2 Data. The data sets include weather, population density, planet boundary layer height, land utilization type, normalized vegetation index, altitude, road information, etc. Wherein the meteorological data comes from the European middle weather forecast center, and the land use type data comes from the European space agencyThe climate change institute, the elevation data comes from the space plane radar topography mission, and the population density data comes from the gridded world population.
The environmental covariate data of the study area is preprocessed. The preprocessing is to process environment covariate data into a predefined 1km grid through a gridding method, a space-time matching method and the like for training and calculating a machine learning model, wherein the data set usually comprises tens to hundreds of variables. The method specifically comprises the following steps:
firstly, space division is carried out on a research area, and a space grid with 1km resolution is obtained.
And then, assigning the environment covariate data to a predefined 1km grid by adopting methods such as meshing, space-time matching and the like to obtain a preprocessed environment covariate data set.
Step S120: XCO for three remote sensing monitoring 2 Data fusion is carried out on the data set to obtain XCO 2 The data are fused, and the steps are as follows:
input:
XCO of OCO-2 2 Monitor data a= { a 1 ，...，a j ，...，a J }；
XCO of OCO-3 2 Monitor data b= { B 1 ，...，b j ，...，b J }；
XCO of GOSAT 2 Monitor data c= { C 1 ，…，c j ，…，c J }；
Environment covariate dataset D= { D 1 ，...，d j ，...，d J }；
Note that: wherein d is j Representing the set of values of all environment covariates at j, but not the jth environment covariate. d, d j And a j 、b j C j Spatially and spatially correspond to each other, i.e. observations of three sensors on the same day, on the same grid. A is due to the different degree of deletion of OCO-2, OCO-3 and GOSAT data j 、b j C j There may be missing values but not at the same time.
And (3) outputting: fusion dataset e= { E 1 ，...，e j ，...，e J }；
The method comprises the following steps:
(1) fusion A and B is O:
wherein o= { O 1 ，...，o j ，...，o J }，o j ＝f 1 (a j ，b j ) Wherein a is j ，b j At least one of which is non-empty;
f 1 is a fusion function:
(2) linear transformation of GOSAT dataset C:
S＝{j|O j non-empty and C j Non-null };
M＝{c s |s∈S}，N＝{o s |s∈S}；
establishing a linear model f 3 ：N←M；
Based on f 3 Transformation c j ：
J is from 1 to J:
when c j Non-empty and
c′ j ＝f 3 (c j )；
the transformed dataset was C '= { C' 1 ，...，c′ j ，...，c′ J }；
(3) Taking the union of the data set O and the data set C' to obtain the final XCO 2 The dataset E is fused.
Step S130: using XCO 2 Fusing data as dependent variables, taking environment covariates as independent variables, and training to obtain a machine learning model based on an XGBoost algorithm;
step S140: inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set.
In the implementation process, XCO (X-ray diffraction) of three remote sensing monitoring in a research area is collected 2 Preprocessing the data set and the environment covariate data, and performing gridding, space-time matching and the like; XCO for three remote sensing monitoring 2 Fusing the data sets to obtain XCO 2 Fusing data; using XCO 2 Fusing the data and the environment covariate data, and training to obtain an XGBoost model; inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set. XCO for three remote sensing monitoring 2 Data sets are fused while using XCO 2 Correlation with environment covariates such as weather, altitude, land utilization type, road density, vegetation normalization index and the like, and calculating to obtain XCO through machine learning modeling 2 Is used for reconstructing the data set.
Referring to fig. 3, fig. 3 is a schematic diagram of a data fusion process according to an embodiment of the present invention, where a horizontal axis represents space and a vertical axis represents time. In the schematic, c=g+s represents XCO for GOSAT monitoring 2 The data set has data in the whole time interval P; o=t+s represents XCO monitored by OCO (OCO-2 and OCO-3) 2 Data set, only in time interval P 1 There is data. At P 1 XCO for GOSAT monitoring in time interval 2 XCO for monitoring with OCO 2 There is a distribution of data points. Due to the orbits of satellites and space stations, unfavorable meteorological conditions and the like, the satellite data of the daily scale is spatially missing, and the OCO is monitored by XCO 2 Covering only the O space-time interval, while GOSAT-monitored XCO 2 In the P period, only the space-time section C is covered, and the space-time section S is the portion where the two sections overlap in space. And (3) performing linear fitting on the GOSAT monitoring value and the OCO monitoring value in the S space-time interval, taking the GOSAT monitoring value as an independent variable and taking the OCO monitoring value as an independent variable, and establishing a linear model. Converting GOSAT monitoring value of G part into OCO monitoring value by using the linear model, so that the OCO monitoring value approximately covers G space interval, and the time interval covers the whole P period to realize multiple XCOs 2 Fusion of the data was monitored.
Based on the same inventive concept, the invention also provides an atmospheric carbon dioxide dry air column concentration calculating system, referring to fig. 4, and fig. 4 is a block diagram of an atmospheric carbon dioxide dry air column concentration calculating system according to an embodiment of the invention. The atmospheric carbon dioxide dry air column concentration calculation system comprises:
a data acquisition module 110 for collecting XCO for three remote sensing monitoring in the investigation region 2 Data sets and environment covariate data;
a data preprocessing module 120 for preprocessing XCO in the investigation region 2 Preprocessing the data and environment covariate data to obtain preprocessed XCO 2 A data set and an environment covariate data set;
XGBoost model building module 140 for XCO-based 2 Establishing an XGBoost model by fusing the data and the environment covariate data;
a concentration calculation module 150 for inputting the environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set.
In the implementation process, XCO (X-ray diffraction) of three remote sensing monitoring in a research area is collected 2 Preprocessing the data set and the environment covariate data, and performing gridding, space-time matching and the like; XCO for three remote sensing monitoring 2 Fusing the data sets to obtain XCO 2 Fusing data; using XCO 2 Fusing the data and the environment covariate data, and training to obtain an XGBoost model; inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set. XCO for three remote sensing monitoring 2 Data fusion with XCO 2 Correlation with environment covariates such as weather, altitude, land utilization type, road density, vegetation normalization index and the like, and calculating to obtain XCO through machine learning modeling 2 Is used for reconstructing the data set.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. The method for calculating the concentration of the atmospheric carbon dioxide dry air column is characterized by comprising the following steps of:
collecting XCO monitored by three remote sensing in research area 2 The method comprises the steps of preprocessing a data set and environment covariate data in an area; the XCO 2 The data set comprises OCO-2, OCO-3 and GOSAT;
XCO for three remote sensing monitoring 2 Fusing the data sets to obtain XCO 2 Fusing the data sets, comprising: input:
XCO of OCO-2 2 Monitor data a= { a 1 ，...，a j ，...，a J }；
XCO of OCO-3 2 Monitor data b= { B 1 ，...，b j ，...，b J }；
XCO of GOSAT 2 Monitor data c= { C 1 ，...，c j ，...，c J }；
Environment covariate dataset D= { D 1 ，...，d j ，...，d J }；
Wherein d is j Representing the set of values of all environment covariates at j, but not the jth environment covariate, d j And a j 、b j C j In space-timeAre mutually corresponding, namely, the observation values of three sensors on the same day and same grid; a is due to the different degree of deletion of OCO-2, OCO-3 and GOSAT data j 、b j C j There is a possibility that missing values are present, but not at the same time;
and (3) outputting: fusion dataset e= { E 1 ，...，e j ，...，e J }；
The method comprises the following steps:
(1) fusion A and B is O:
wherein o= { O 1 ，...，o j ，...，o J }，o j ＝f 1 (a j ，b j ) Wherein a is j ，b j At least one of which is non-empty;
f 1 is a fusion function:
(2) linear transformation of GOSAT dataset C:
S＝{j|o j non-empty and c j Non-null };
M＝{c s |s∈S}，N＝{o s |s∈S}；
establishing a linear model f 3 : N≡M; establishing a linear model by taking a GOSAT monitoring value as an independent variable and an OCO monitoring value as a dependent variable;
based on f 3 Transformation c j ：
J is from 1 to J:
when c j Non-empty and
c′ j ＝f 3 (c j )
the transformed dataset was C '= { C' 1 ，...，c′ j ，...，c′ J }
(3) Taking the union of the data set O and the data set C' to obtain the final XCO 2 Fusing the data set E;
using XCO 2 Fusing data as dependent variables, taking environment covariates as independent variables, and training to obtain a machine learning model based on an XGBoost algorithm;
inputting environment covariate data into the XGBoost model, and calculating to obtain XCO 2 Is used for reconstructing the data set.
2. The method for calculating the concentration of dry air column of atmospheric carbon dioxide according to claim 1, wherein XCO is used 2 The steps of establishing the XGBoost model by fusing the data and the environment covariates are as follows:
XCO is to be XCO 2 And (5) taking the fusion data as a dependent variable, taking an environment covariate as an independent variable, and training to obtain an XGBoost model.
3. The method for calculating the concentration of the atmospheric carbon dioxide dry air column according to claim 1, which is characterized by comprising the following steps of:
carrying out space division on a research area to obtain a space grid with 1km resolution;
and (3) adopting a spatial resampling method to the environment covariate data, and assigning values to a predefined grid.
4. An atmospheric carbon dioxide dry air column concentration calculation system, characterized by comprising:
the data acquisition module is used for collecting XCO (X-ray computer operation) monitored by three remote sensing in the research area 2 Data sets and environment covariate data; the XCO 2 The data set comprises OCO-2, OCO-3 and GOSAT;
data preprocessing module for XCO of three remote sensing monitoring in research area 2 The data set and the environment covariate data are subjected to preprocessing such as meshing, space-time matching and the like, and a preprocessed data set is obtained;
the data fusion module is used for XCO (X-ray computer operation) for three remote sensing monitoring 2 Data fusion is carried out on the data set to obtain XCO 2 Fusing the data sets, comprising: input:
XCO of OCO-2 2 Monitor data a= { a 1 ，...，a j ，...，a J }；
XCO of OCO-3 2 Monitor data b= { B 1 ，...，b j ，...，b J }；
XCO of GOSAT 2 Monitor data c= { C 1 ，...，c j ，...，c J }；
Environment covariate dataset D= { D 1 ，...，d j ，...，d J }；
Wherein d is j Representing the set of values of all environment covariates at j, but not the jth environment covariate, d j And a j 、b j C j The three sensors are corresponding to each other in time and space, namely, the three sensors observe the same grid on the same day; a is due to the different degree of deletion of OCO-2, OCO-3 and GOSAT data j 、b j C j There is a possibility that missing values are present, but not at the same time;
and (3) outputting: fusion dataset e= { E 1 ，...，e j ，...，e J }；
The method comprises the following steps:
(1) fusion A and B is O:
wherein o= { O 1 ，...，o j ，...，o J }，o j ＝f 1 (a j ，b j ) Wherein a is j ，b j At least one of which is non-empty;
f 1 is a fusion function:
(2) linear transformation of GOSAT dataset C:
S＝{j|o j non-empty and c j Non-null };
M＝{c s |s∈S}，N＝{o s |s∈S}；
establishing a linear model f 3 : N≡M; establishing a linear model by taking a GOSAT monitoring value as an independent variable and an OCO monitoring value as a dependent variable;
based on f 3 Transformation c j ：
J is from 1 to J:
when c j Non-empty and
c′ j ＝f 3 (c j )
the transformed dataset was C '= { C' 1 ，...，c′ j ，...，c′ J }
(3) Taking the union of the data set O and the data set C' to obtain the final XCO 2 Fusing the data set E;
XGBoost model building module for XCO-based 2 Training the fusion data and the environment covariate data to obtain an XGBoost model;
the concentration calculation module is used for inputting the environment covariate data into the XGBoost model to calculate and obtain XCO 2 Is used for reconstructing the data set.
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