Method, device, equipment and medium for observing and decomposing carbon exchange flux

Abstract

The invention relates to the cross technical field of ecology and meteorology, in particular to a method, a device, equipment and a medium for observing and decomposing carbon exchange flux. According to the method, firstly, qualified data are screened through the three indexes of turbulence exchange intensity, energy closure degree and atmospheric stability, observation errors caused by insufficient turbulence development, energy non-closure or theoretical hypothesis failure under complex terrains are effectively eliminated, and the reliability of basic data is greatly improved. Secondly, the flux contribution model is combined with high-precision vegetation partitioning, so that accurate traceability and mathematical decomposition of mixed flux are realized, and the technical bottleneck that real fluxes of different vegetation regions cannot be distinguished on heterogeneous underlying surfaces in a traditional method is broken through. Finally, a long-time sequence and spatial resolution carbon exchange process is reconstructed by establishing a response relationship between the flux of each vegetation region and a meteorological condition, so that high-precision and localizable scientific data support is provided for carbon sink function evaluation and management of forests, particularly complex terrain regions.

Description

Technical Field

[0001] This invention relates to the interdisciplinary fields of ecology and meteorology, specifically to a method, apparatus, equipment, and medium for observing and decomposing carbon exchange flux. Background Technology

[0002] Eddy correlation (EC) is a core method for observing forest carbon exchange fluxes. As an important indicator characterizing the net CO2 uptake capacity of an ecosystem, carbon exchange fluxes can be quantified through various methods such as the box method and EEC. However, the tall vegetation structure of forest ecosystems limits the application of the box method in observing forest carbon exchange fluxes. EEC, by observing high-frequency CO2 concentrations and three-dimensional wind speeds above the canopy and then calculating the covariance between vertical wind speed and CO2 concentration, quantifies forest carbon exchange fluxes. This overcomes the limitation of the box method in observing carbon exchange fluxes in tall vegetation and has become a core means for accurately observing forest carbon exchange fluxes.

[0003] However, observing forest carbon exchange fluxes using eddy covariance techniques requires ideal conditions such as sufficient turbulent exchange and atmospheric instability or neutrality. Real-world challenges, such as low turbulent exchange intensity and atmospheric stratification, lead to deviations in observed carbon exchange fluxes from actual values, necessitating quality control of the observational data. However, current eddy covariance techniques generally rely on frictional wind speed (u) for quality control. The critical value represents the turbulent exchange intensity, which means that the observed data cannot characterize the actual situation.

[0004] Furthermore, eddy covariance (EC) techniques reflect the weighted contribution of each point within the source region to the observed carbon exchange flux. However, EC assumes a homogeneous underlying surface to prevent horizontal CO2 exchange within the source region. Due to this homogeneity assumption, the carbon exchange flux values ​​at each point within the source region are consistent, allowing the weighted value of each point's contribution to the observed flux to be reflected by its individual contribution. However, in actual forest environments, the carbon exchange flux values ​​at different points within the source region vary due to different topography and species composition. Therefore, it is necessary to consider how to determine the carbon exchange flux at different points in a heterogeneous source region based on EC observations. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for observing and decomposing carbon exchange flux, in order to solve the problems of insufficient data quality control of observation data and inability to decompose carbon flux from heterogeneous source regions in the prior art.

[0006] In a first aspect, the present invention provides a method for observing and decomposing carbon exchange flux, the method comprising:

[0007] Acquire in-situ observation data of the source region at multiple times, including carbon exchange flux and environmental meteorological data; The in-situ observation data at each moment is determined by using the turbulence exchange intensity index, energy closure index and atmospheric stability index determined by the in-situ observation data to determine whether the in-situ observation data at each moment meets the index requirements, and the in-situ observation data at the corresponding moment that meets the index requirements is obtained. Based on the in-situ observation data at the corresponding time that meets the index requirements, the flux contribution of each point in the source region at the corresponding time is determined by a preset flux contribution model. Based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region, the flux contribution of each vegetation zone in the source region is statistically determined. Based on in-situ observation data at the corresponding time that meets the index requirements, the carbon exchange flux is decomposed according to the flux contribution of each vegetation zone to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements. The relationship between carbon exchange flux and environmental meteorological data in each vegetation zone was established, and the carbon exchange flux of each vegetation zone at each time point was determined by combining the environmental meteorological data at each time point.

[0008] This invention first uses a triple-index screening of turbulent exchange intensity, energy closure, and atmospheric stability to effectively eliminate observational errors caused by insufficient turbulence development, energy closure issues, or invalid theoretical assumptions in complex terrain, greatly improving the reliability of the basic data. Secondly, by utilizing a flux contribution model combined with high-precision vegetation zoning, it achieves accurate source tracing and mathematical decomposition of mixed fluxes, overcoming the technical bottleneck of traditional methods that cannot distinguish the true fluxes of different vegetation zones on heterogeneous underlying surfaces. Finally, by establishing the response relationship between fluxes and meteorological conditions in each vegetation zone, it reconstructs the long-term, spatially resolved carbon exchange process, thus providing high-precision, location-based scientific data support for the assessment and management of carbon sink functions in forests, especially in complex terrain areas.

[0009] In one optional implementation, the in-situ observation data at each moment is determined using turbulence exchange intensity, energy closure, and atmospheric stability indicators determined from in-situ observation data to determine whether the in-situ observation data meets the indicator requirements, thereby obtaining the in-situ observation data at the corresponding moment that meets the indicator requirements, including: The turbulent exchange intensity index is determined based on a pre-defined lookup table method. The energy closure index is determined based on the Bowen ratio of latent heat flux, sensible heat flux, net radiation, and soil heat flux. Atmospheric stability indices are determined based on the Monin-Obkhoff similarity theory and the Monin-Obkhoff observation height. The values ​​of turbulence exchange intensity, energy closure, and atmospheric stability were determined using in-situ observation data at each time point. Determine whether the values ​​of turbulent exchange intensity, energy closure, and atmospheric stability at each time point meet the preset requirements, and retain the in-situ observation data for the corresponding time points that meet the preset requirements.

[0010] In this invention, based on a pre-defined lookup table method, Bowen ratio calculation, and Moning-Obukhov similarity theory, turbulent exchange intensity, energy closure, and atmospheric stability are quantitatively evaluated, respectively. This systematically eliminates low-quality observational data caused by insufficient turbulence development, energy closure issues, or unmet theoretical conditions, significantly improving the accuracy and reliability of the dataset. This multi-dimensional, process-oriented quality control method overcomes the limitations of single-index screening, enabling a more comprehensive characterization of physical processes under complex underlying surface conditions, thereby enhancing the credibility of subsequent flux analysis and carbon sink assessment results.

[0011] In one optional implementation, based on in-situ observation data at the corresponding time that meets the index requirements, the carbon exchange flux is decomposed according to the flux contribution of each vegetation zone to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements, including: The in-situ observation data at the corresponding time that meet the index requirements are divided into two groups based on the differences in the dominant factors of carbon exchange flux: the first preset time period and the second preset time period. The in-situ observation data of each group were sorted according to the preset environmental meteorological data. The carbon exchange flux and the flux contribution of each vegetation zone were selected from the multiple adjacent in-situ observation data after sorting. The least squares method was used for fitting to obtain the carbon exchange flux of each vegetation zone in each group.

[0012] In this invention, high-quality observation data after screening are grouped according to different time periods and finely sorted according to environmental parameters, effectively separating the influence of physiological processes (photosynthesis and respiration) on flux at different time periods. At the same time, the least squares method is used to synchronously fit flux observation values ​​at multiple adjacent time periods and pre-calculated contribution weights of each vegetation zone, successfully realizing the mathematical inversion of mixed flux signals and accurately decomposing the independent carbon exchange flux of each vegetation zone within the heterogeneous source region.

[0013] In one alternative implementation, the relationship between carbon exchange flux and environmental meteorological data for each vegetation zone is constructed, including: The environmental meteorological data corresponding to each vegetation zone are determined based on the environmental meteorological data from multiple in-situ observation data selected in each group. A nonlinear fitting method was used to construct the fitting relationship between the carbon exchange flux of each vegetation zone and the corresponding environmental meteorological data of each vegetation zone.

[0014] This invention establishes a fitting relationship between carbon exchange flux in vegetation zones and meteorological conditions, enabling refined modeling and accurate inversion of carbon exchange processes in heterogeneous ecosystems. Using this constructed fitting relationship, long-term meteorological data can be input to reconstruct the theoretical flux value for each vegetation zone at any given time. This effectively fills the gaps in observational data, extending the scope from discrete, limited observation points to continuous, complete regional flux processes. It provides a high-resolution and dynamic analytical tool for assessing the spatial heterogeneity of carbon budgets in forest ecosystems and their response to climate change.

[0015] In an optional implementation, the method further includes: The carbon exchange flux at each time point is calculated based on the carbon exchange flux of each vegetation zone and the flux contribution of each point in the source region at the corresponding time point. The calculated carbon exchange flux is compared with the carbon exchange flux at the corresponding time point, and the carbon exchange flux of each vegetation zone is recalculated based on the comparison results.

[0016] In this invention, by introducing an iterative optimization process of calculation-comparison-re-decomposition, the random deviations or model incompatibility that may exist in a single decomposition are effectively avoided, ensuring that the final decomposition results not only conform to the contribution weights of each vegetation zone, but also remain consistent with the actual observation data. This provides a self-verified and continuously optimized technical guarantee for the accurate tracing and quantification of carbon flux in heterogeneous ecosystems.

[0017] In one alternative implementation, the preset flux contribution model includes a two-dimensional flux contribution region model.

[0018] In one optional implementation, the preset index requirements include a turbulent exchange intensity index value greater than a preset critical value, an energy closure index value greater than 0.5 and less than 2, and an atmospheric stability value less than -1.

[0019] In this invention, by explicitly requiring the turbulence exchange intensity index value to be greater than a preset critical value, the full development of atmospheric turbulence during the observation period is effectively guaranteed, avoiding the problem of flux underestimation due to insufficient mixing. Simultaneously, setting the energy closure degree between 0.5 and 2.0 excludes unreliable data from those with severely incomplete energy balance or potentially systematic errors in the observation system. Furthermore, specifying an atmospheric stability index less than -1 ensures that only data under unstable atmospheric conditions are retained, guaranteeing that the observation scenario conforms to the optimal applicability range of eddy covariance theory. This set of three standards works together to systematically identify and extract high-value data with clear physical meaning and reliable quality from massive amounts of raw observations, laying a solid and credible data foundation for subsequent accurate flux calculations, ecosystem carbon sink assessments, and model construction.

[0020] Secondly, the present invention provides a device for observing and decomposing carbon exchange flux, the device comprising: The data acquisition module is used to acquire in-situ observation data of the source region at multiple times, including carbon exchange flux and environmental meteorological data. The data filtering module is used to determine whether the in-situ observation data at each moment meets the index requirements by using the turbulence exchange intensity index, energy closure index and atmospheric stability index determined by the in-situ observation data, and to obtain the in-situ observation data at the corresponding moment that meets the index requirements. The contribution determination module is used to determine the flux contribution of each point in the source region at the corresponding time based on the in-situ observation data that meets the index requirements and adopts a preset flux contribution model. The regional statistics module is used to statistically determine the flux contribution of each vegetation zone in the source region based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region. The regional exchange flux determination module is used to decompose the carbon exchange flux according to the flux contribution of each vegetation zone based on the in-situ observation data at the corresponding time that meets the index requirements, so as to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements. The relationship building module is used to build the relationship between carbon exchange flux and environmental meteorological data of each vegetation zone, and to determine the carbon exchange flux of each vegetation zone at each time point by combining the environmental meteorological data at each time point.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the carbon exchange flux observation and decomposition method of the first aspect or any corresponding embodiment described above.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the carbon exchange flux observation and decomposition method of the first aspect or any corresponding embodiment described above.

[0023] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the carbon exchange flux observation and decomposition method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the first process of the method for observing and decomposing carbon exchange flux according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process for observing and decomposing carbon exchange flux according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the improved quality of vorticity-related observation data according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the proportion of data removed from the total throughput observation data in different data quality control processes in the Greater Khingan Mountains region according to an embodiment of the present invention. Figure 5 This is a schematic diagram showing the contribution of various locations in the source region to the carbon exchange flux at the observation point at a typical moment according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the topographic and vegetation difference zoning map around the observation point according to an embodiment of the present invention. Figure 7 This is a schematic diagram illustrating the average contribution of different vegetation zones to the flux at the observation point according to an embodiment of the present invention. Figure 8 This is a diagram showing the correspondence between the integrated flux value (y-axis) that integrates the contribution of each vegetation zone and the flux value obtained by decomposition according to an embodiment of the present invention and the carbon flux observed at the observation point. The two sub-figures are schematic diagrams of the correspondence at different times. Figure 9 This is a structural block diagram of a carbon exchange flux observation and decomposition device according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] According to an embodiment of the present invention, a method for observing and decomposing carbon exchange flux is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a method for observing and decomposing carbon exchange flux. Figure 1 This is a flowchart of a method for observing and decomposing carbon exchange flux according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain in-situ observation data of the source region at multiple time points. The in-situ observation data includes carbon exchange flux and environmental meteorological data. Specifically, in this embodiment, the in-situ observation data obtained is observation data from a heterogeneous source region obtained using eddy covariance technology. In eddy covariance flux observation, the source region can be understood as the flux footprint or flux contribution area. Specifically, the source region refers to the underlying surface (land surface) area that significantly contributes to the vertical flux observed by the eddy covariance system within a specific time period. In this embodiment, the in-situ observation data is obtained from observations of a heterogeneous source region, such as a forest in complex terrain. The source region may include hillsides, valleys, and woodlands composed of different tree species.

[0031] The observational data obtained using eddy covariance technology includes not only carbon exchange flux but also various environmental meteorological data, such as air temperature, frictional wind speed, latent heat flux, sensible heat flux, net radiation and soil heat flux, sensible heat flux, and observation altitude. It should be noted that the carbon exchange flux obtained here represents the carbon exchange flux of the entire source region. When acquiring in-situ observational data, it can be done at preset intervals, i.e., every preset time interval (e.g., 30 minutes), thus obtaining in-situ observational data at multiple time points. Each time point's observational data includes both carbon exchange flux and environmental meteorological data.

[0032] Step S102 involves using turbulence exchange intensity, energy closure, and atmospheric stability indices determined from in-situ observation data to determine whether the in-situ observation data at each moment meets the index requirements, thus obtaining the in-situ observation data for the corresponding moment that meets the index requirements; specifically, in related technologies, u is mostly used Data quality control is based on the turbulent exchange intensity determined by the critical value. This embodiment further adds an energy closure index and an atmospheric stability index, employing three indices for data quality control. The energy closure index characterizes the balance of surface energy. If the energy closure index value determined from in-situ observation data exceeds a certain range, it indicates poor surface energy balance, suggesting potential problems with the in-situ observation data, which is then discarded. The atmospheric stability index characterizes the ease of vertical atmospheric motion. The ideal operating condition for eddy covariance technology is atmospheric instability; therefore, data indicating atmospheric instability calculated from the in-situ observation data are filtered out.

[0033] During the filtering process, it is determined whether the data at each time point meets the corresponding indicator requirements. If it does, the data at that time point is retained; otherwise, it is discarded. Thus, if the original data obtained consists of in-situ observations at 48 times a day, after filtering, only in-situ observation data at 24 times a day may remain.

[0034] Step S103: Based on the in-situ observation data at the corresponding time that meets the index requirements, a preset flux contribution model is used to determine the flux contribution of each point in the source region at the corresponding time. Specifically, the preset flux contribution model can be a two-dimensional flux contribution region model; in other embodiments, it can also be determined using a Lagrange stochastic model or large eddy simulation. During calculation, the in-situ observation data at each selected time is input into the model, and the flux contribution of each point in the source region at the corresponding time is output.

[0035] Step S104: Based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region, the flux contribution of each vegetation zone in the source region is statistically determined. The topographic and vegetation map of the source region can be directly obtained from relevant technologies; this map includes the topography and vegetation distribution of the source region. The determined flux contribution of each point in the source region is overlaid with the distribution map to determine the flux contribution of each vegetation zone. Specifically, after determining each vegetation zone based on the distribution map, the flux contribution of each point in each vegetation zone is added together to obtain the flux contribution of each vegetation zone.

[0036] Step S105: Based on the in-situ observation data at the corresponding time that meets the index requirements, the carbon exchange flux is decomposed according to the flux contribution of each vegetation zone to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements. Specifically, the weighted sum of the carbon exchange flux and flux contribution of each vegetation zone should be equal to the observed carbon exchange flux of the source region. Therefore, after obtaining the carbon exchange flux and flux contribution at multiple times, the carbon exchange flux can be decomposed using multiple weighted equations to obtain the carbon exchange flux of each vegetation zone. It should be noted that the number of weighted equations can be determined by the number of vegetation zones. For example, if there are 5 vegetation zones, the number of weighted equations can be 5 or more.

[0037] Step S106: Construct the relationship between carbon exchange flux and environmental meteorological data for each vegetation zone, and determine the carbon exchange flux of each vegetation zone at each time point by combining the environmental meteorological data at each time point. Specifically, after obtaining the carbon exchange flux of each vegetation zone, it is fitted with the corresponding environmental meteorological data to obtain the relationship between the carbon exchange flux of each vegetation zone and the environmental meteorological data. Based on this relationship, when environmental meteorological data is obtained, it is substituted into this relationship to obtain the carbon exchange flux of the vegetation zone.

[0038] This embodiment provides a method for observing and decomposing carbon exchange flux, which includes the following steps: Step S201: Obtain in-situ observation data of the source region at multiple time points, including carbon exchange flux and environmental meteorological data; for details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0039] Step S202: Use the turbulence exchange intensity index, energy closure index and atmospheric stability index determined by the in-situ observation data to determine whether the in-situ observation data at each time moment meets the index requirements, and obtain the in-situ observation data at the corresponding time moment that meets the index requirements.

[0040] Specifically, step S202 includes: Step S2021: Determine the turbulence exchange intensity index based on a pre-defined lookup table method. Specifically, the turbulence exchange intensity index uses the critical value of frictional wind speed as the standard. In this embodiment, the critical value of frictional wind speed is determined by a lookup table, that is, the critical value of frictional wind speed and the corresponding influencing factor data are pre-set in a table. Each row in the table represents the numerical value of the influencing factor data and the corresponding critical value of frictional wind speed. When determining the turbulence exchange intensity index subsequently, the numerical value of the influencing factor data is substituted into this table to obtain the corresponding critical value of frictional wind speed. The obtained critical value of frictional wind speed can then be used as the preset required range value of the turbulence exchange intensity index.

[0041] Step S2022: The energy closure index is determined based on the Bowen ratio of latent heat flux (LE), sensible heat flux (Hs), net radiation (Rn), and soil heat flux (G). Specifically, energy closure characterizes the surface energy balance, and whether the surface energy is balanced is determined by whether the solar radiation energy reaching the surface is equal to the sum of the energy used by the surface to heat the atmosphere (sensible heat flux), the energy used for water evaporation (latent heat flux), and the energy used to heat the soil (soil heat flux). Therefore, in this embodiment, the energy closure index is quantified using (LE+Hs) / (Rn-G).

[0042] Step S2023: Determine the atmospheric stability index based on the observed altitude and the Monin-Obkhoff similarity theory-determined Monin-Obkhoff length; specifically, the Monin-Obkhoff similarity theory-determined Monin-Obkhoff length is expressed by the following formula:

[0043] In the formula, u The wind speed is represented by friction, T represents the absolute surface temperature, g represents the acceleration due to gravity, and k represents the von Kármán constant. The turbulent fluctuation component representing temperature. The turbulent pulsation component representing the vertical velocity.

[0044] In this study, after determining the length of the Moning-Obhall, the ratio of the observation height z to the length L of the Moning-Obhall was used as an index of atmospheric stability.

[0045] Step S2024 involves determining the turbulence exchange intensity index, energy closure index, and atmospheric stability index values ​​at each time point using in-situ observation data. Specifically, after determining the specific calculation method for each index, the observed index values ​​for each time point are determined based on the acquired in-situ observation data. For the turbulence exchange intensity index, influencing factors such as air temperature and observed carbon exchange flux at each time point are first substituted into a pre-set table to obtain the corresponding critical value for frictional wind speed. Then, the frictional wind speed in the environmental meteorological data acquired at that time point is compared with this critical value to determine whether the index requirements are met. For the energy closure index, the corresponding parameters from the observed environmental meteorological data can be directly substituted into the above formula to obtain the index value for each time point. For atmospheric stability, the Moning-Obhall length at each time point needs to be calculated first, and then the index value is determined based on z / L.

[0046] Step S2025, determine whether the turbulent exchange intensity index value, energy closure index value, and atmospheric stability index value at each moment meet the preset index requirements, and retain the in-situ observation data at the corresponding moments that meet the preset index requirements. Specifically, the preset index requirement for the turbulent exchange intensity index is greater than the critical value of the friction velocity. Although, based on the surface energy balance, an energy closure of 1 is the ideal situation, considering the lack of some energy budget terms (such as vegetation heat storage), when using the energy closure to conduct quality control on the observation data, the observation data with 0.5 < BRR < 2 is regarded as meeting the preset index requirements. In addition, when determining the atmospheric stability according to z / L, z / L < -1 is regarded as the atmospheric unstable condition; -1 < z / L < 1 is regarded as the atmospheric neutral condition; z / L > 1 is regarded as the atmospheric stable condition. And when the atmosphere is unstable, it indicates sufficient carbon exchange, so z / L < -1 is taken as the preset index requirement for atmospheric stability.

[0047] After determining the index values at each moment, compare them with the corresponding preset index requirements respectively. If all three index requirements are met, it means that the in-situ observation data at this moment is available and can be retained for subsequent analysis. When any one index value does not meet the preset index requirements, or all index values do not meet the preset index requirements, the in-situ observation data at this moment is unavailable and needs to be excluded.

[0048] Step S203, based on the in-situ observation data at the corresponding moments that meet the index requirements, use the preset flux contribution model to determine the flux contribution of each point in the source area at the corresponding moments; specifically, in this embodiment, the preset flux contribution model is taken as an example of a two-dimensional flux contribution area model to illustrate the determination method of the flux contribution. Among them, the two-dimensional flux contribution area model (Flux Footprint Prediction, FFP), also known as the flux footprint model, its core goal is to answer a question: For the carbon flux measured by the observation tower, which places within what range around the tower contribute? Based on this, the model calculates a function through mathematical methods, called the flux footprint function f(x,y), and the value of f(x,y) directly reflects the contribution degree of the point (x,y) to the observed flux. The larger the value, the greater the contribution. When calculating this model, variables such as the wind speed, wind direction, friction wind speed, observation height, and canopy height of the eddy covariance observation are obtained, and parameters such as the zero-plane displacement (taking 0.7 times the canopy height) and the atmospheric boundary layer height h of this area are calculated in combination with empirical formulas and used as input parameters to input into the model, and the flux contribution of each point in the source area is output.

[0049] Specifically, the atmospheric boundary layer height is calculated and determined through the friction wind speed, Coriolis parameter f and the Monin-Obukhov length. Among them, based on different Monin-Obukhov lengths L, the calculation method of the atmospheric boundary layer height is different. When L is less than 0, the atmospheric boundary layer height is calculated using the following formula:

[0050] When L is greater than 0, the atmospheric boundary layer height is calculated using the following formula:

[0051] Coriolis parameters f Determined by geographical latitude (lat), that is:

[0052] f This demonstrates the influence of the Earth's rotation on atmospheric motion.

[0053] After obtaining the required parameters, the actual physical parameters are converted into dimensionless numbers, thereby giving the model universality. In this embodiment, the following seven dimensional groups are set to measure the instrument's observation height (…). Z m Atmospheric boundary layer height ( h ), wind speed at observation altitude ( u Zm Frictional wind speed ( u ), standard deviation of crosswind distance ( σ y ), and the standard deviation of lateral wind speed ( σ v ) and other parameters are dimensionless:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] Therefore, based on the above 7 dimensional groups, the crosswind integral footprint ( = 1 3 -1 4) Upwind distance ( = 2 3 4 -1 ), standard deviation of crosswind distance ( = 6 7 -1 ) and crosswind distance ( = 5 7 -1 ) Each dimension is converted into its corresponding dimensionless form.

[0061] The model then uses an empirical fitting function to correlate the dimensionless variables. The flux contribution can be decomposed into the crosswind integral footprint using the flux footprint function. f y ( x,z ) and crosswind diffusion function D y ( x,y The product of ) . The crosswind integral footprint is fitted using the following formula:

[0062] In the formula, a, b, c, and d are empirical parameters obtained by fitting a large amount of experimental data. This formula represents the total contribution of a line along the entire y-direction to the flux at an upwind distance x, without considering the crosswind direction y.

[0063] The crosswind diffusion function is fitted using the following formula:

[0064] In the formula, a c ,b c ,c c This is another set of empirically fitted parameters. The formula represents how the contribution is distributed along the crosswind direction y at a certain upwind distance x (usually assumed to be a Gaussian distribution).

[0065] The crosswind integral footprint and crosswind diffusion function obtained by fitting the above formula are dimensionless, and are then converted back to dimensional using dimensional relationships. and crosswind standard deviation Based on the crosswind standard deviation, the crosswind diffusion function is represented by the following Gaussian probability density function:

[0066] Therefore, the flux footprint function can be expressed as:

[0067] The flux footprint function represents the contribution of a point (x,y) to the flux, which is equal to the total contribution intensity fy(x) at a distance x upwind of that point, multiplied by the probability distribution Dy(x,y) of that contribution in the crosswind direction y. Therefore, based on the above steps, for any observation time, as long as the wind speed, wind direction, turbulence intensity, stability, and other parameters are input, the FFP model can output a distribution map representing the flux contribution of each point in the source region.

[0068] Step S204: Based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region, statistically determine the flux contribution of each vegetation zone in the source region. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0069] Step S205: Based on the in-situ observation data at the corresponding time that meets the index requirements, the carbon exchange flux is decomposed according to the flux contribution of each vegetation zone to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements.

[0070] Specifically, step S205 includes: Step S2051 involves dividing the in-situ observation data at the corresponding time that meets the index requirements into two groups based on the differences in the dominant factors of carbon exchange flux: a first preset time period and a second preset time period. Specifically, the carbon exchange mode differs depending on whether the source region is during the day or night. During the day, photosynthesis dominates, while at night, respiration dominates. Therefore, in this embodiment, before decomposing the carbon exchange flux, the in-situ observation data is first divided according to different time periods. Specifically, during the division, the specific time periods for daytime and nighttime are first determined based on the actual situation, and then the division is based on the time period to which the in-situ observation data was acquired, thereby dividing the in-situ observation data into two groups: daytime and nighttime.

[0071] Step S2052: Sort the in-situ observation data of each group according to the preset environmental meteorological data, select the carbon exchange flux and the flux contribution of each vegetation zone from multiple adjacent in-situ observation data after sorting, and use the least squares method to fit to obtain the carbon exchange flux of each vegetation zone in each group.

[0072] Specifically, when decomposing carbon exchange fluxes within each group, it is usually not necessary to use the carbon exchange fluxes at all times within that group. Therefore, it is necessary to filter a portion of the carbon exchange fluxes. In this embodiment, the in-situ observation data is sorted based on preset environmental meteorological data in the in-situ observation data, and then the carbon exchange fluxes in adjacent in-situ observation data after sorting are filtered for decomposition.

[0073] During the sorting process, temperature data from the preset environmental meteorological data is prioritized for sorting. If temperature data are equal, photosynthetically active radiation (PAR) is further used for sorting. The sorting can proceed from smallest to largest. When filtering carbon exchange fluxes based on the sorting results, the number of carbon exchange fluxes can be determined according to the number of vegetation zones. The number of carbon exchange fluxes can be greater than or equal to the number of vegetation zones to accurately decompose the carbon exchange fluxes.

[0074] For example, in this embodiment, the number of vegetation zones is 5, and the decomposition formula of the following formula is constructed by obtaining 8 carbon exchange fluxes:

[0075] In the formula, F i Let F1, F2, F3, F4, and F5 represent the carbon exchange flux at time i, and let W represent the carbon exchange flux of each vegetation zone to be determined. 1i W 2i W 3i W 4i W 5i This represents the flux contribution of each vegetation zone at time i (also known as the weight value). To determine the carbon exchange flux of each vegetation zone in each group, the carbon exchange flux and weight values ​​at 8 observation times are combined, and the flux values ​​(F1, F2, F3, F4, F5) of each vegetation zone in the above decomposition formula are solved using the least squares fitting function lsqcurvefit with constraints in Matlab software.

[0076] Step S206: Calculate the carbon exchange flux at the corresponding time based on the carbon exchange flux of each vegetation zone and the flux contribution of each point in the source area at the corresponding time; compare the calculated carbon exchange flux with the obtained carbon exchange flux at the corresponding time, and determine whether to re-decompose the carbon exchange flux of each vegetation zone based on the comparison results.

[0077] Specifically, after decomposing the carbon exchange fluxes of each vegetation zone, the decomposed values ​​are verified to determine their appropriateness. During verification, the carbon exchange fluxes of each vegetation zone are weighted and calculated using their corresponding weight values ​​to obtain the carbon exchange flux at that given moment. The calculated carbon exchange flux is then compared with the observed carbon exchange flux at the corresponding moment. For example, it is determined whether the difference between the two is within a certain range. If it is within a certain range, it indicates good consistency between the two. If it is not within a certain range, a new carbon exchange flux can be selected for decomposition and determination.

[0078] Step S207: Construct the relationship between carbon exchange flux and environmental meteorological data for each vegetation zone, and determine the carbon exchange flux for each vegetation zone at each time point by combining the environmental meteorological data at each time point.

[0079] Specifically, step S207 includes: Step S2071: Determine the environmental meteorological data corresponding to each vegetation zone based on the environmental meteorological data from multiple in-situ observation data selected in each group. Specifically, since the carbon exchange flux of each vegetation zone is obtained by decomposing the carbon exchange flux at multiple times, it is necessary to first determine the environmental meteorological data corresponding to the carbon exchange flux of each vegetation zone when constructing the relationship between the carbon exchange flux and the environmental meteorological data. Specifically, in this embodiment, the environmental meteorological data at the corresponding times are averaged to obtain the corresponding environmental meteorological data. For example, if data from 8 times are selected in each group for decomposition, the environmental meteorological data from these 8 times are directly averaged to obtain the environmental meteorological data corresponding to the carbon exchange flux of each vegetation zone.

[0080] Step S2072 involves using a nonlinear fitting method to construct a fitting relationship between the carbon exchange flux of each vegetation zone and the corresponding environmental meteorological data for each vegetation zone. Specifically, after determining the carbon exchange flux and corresponding environmental meteorological data for each vegetation zone, a nonlinear fitting method is used to construct a fitting relationship between the carbon exchange flux and meteorological conditions for each vegetation zone. Based on this fitting relationship, when environmental meteorological data for a certain moment is obtained, it can be directly input into the fitting relationship to directly determine the carbon exchange flux of each vegetation zone. For example, in step S202 above, some in-situ observation data for certain moments were removed, making it impossible to obtain the flux contribution at these moments through subsequent steps. In this case, the environmental meteorological data for these moments can be directly input into the constructed fitting relationship to obtain the corresponding carbon exchange flux for each vegetation zone.

[0081] As one or more specific application embodiments of the present invention, such as Figure 2 As shown, taking the Greater Khingan Mountains forest region as an example, the method for observing and decomposing this carbon exchange flux is explained: 1. Utilize eddy covariance techniques to obtain 30-minute-scale carbon exchange flux (hereinafter referred to as carbon flux) and related meteorological auxiliary data (i.e., environmental meteorological data) observed at typical forest stations. For example... Figure 3 As shown, the quality control of the observed data is achieved through a combination of three aspects: turbulent exchange intensity, energy closure, and atmospheric stability. Among these, the turbulent exchange intensity is selected as u... The critical value is determined; the energy closure is determined by the ratio of energy flux (LE+H) to available energy, and an energy closure greater than 0.5 and less than 2.0 is considered to be of high quality; atmospheric stability is defined by the ratio of observation height (z) to Morningkow similarity length (L), and a value less than -1 is considered unstable, while a value between -1 and 1 is considered atmospheric neutral.

[0082] Analysis using eddy covariance flux observation data from typical ecosystems in the Greater Khingan Mountains region shows that different data quality control processes have varying effects on data quality, exhibiting the same trend during both day and night. For example... Figure 4 As shown, u characterizes the intensity of turbulent exchange. Elimination plays a crucial role in flux data quality control and is a key process for controlling the quality of flux data observations. After u After removal, the effect of atmospheric stability removal on flux data quality control is significantly weakened. Compared to atmospheric stability removal, the effect of Bowen ratio removal on data quality control is significantly increased, resulting in a u-shaped relationship between the strength of different data quality control processes in flux data quality control. The elimination rate, from bowen ratio elimination to atmospheric stability elimination, confirms that the combination of these three methods can further control the quality of observational data. It should be noted that the effectiveness of different data quality control methods is determined by the elimination ratio, for example, u If the proportion of data removed is greater than the proportion of data removed by Bowen ratio, then the effect of turbulent exchange intensity on data quality control is greater than that of Bowen ratio removal.

[0083] 2. The contribution of different vegetation zones in the source region to the observed carbon exchange flux is quantified by using high-precision topographic and vegetation data and a two-dimensional flux contribution zone model. Specifically, based on high-precision topographic and vegetation data, the flux source region can be divided into several vegetation zones covering different topographic (slope, aspect, altitude) and ecosystem types. Based on the flux contribution zone model (FFP model) and combined with meteorological elements (wind speed, wind direction, canopy height, observation height, etc.) at each observation time, the contribution of each vegetation zone to the observed carbon exchange flux at different times can be calculated.

[0084] In this embodiment, the contribution of each point in the source region to the observed carbon exchange flux is first determined using a flux contribution region model, such as... Figure 5 As shown, there are significant differences in the contribution of each point, which is significantly affected by wind direction and speed. The contribution of the flux near the observation point is the highest, and it shows a clear decreasing trend with increasing distance from the observation point. Obtaining the flux contribution at various points in the source region provides data support for quantifying the flux contribution intensity of heterogeneous source regions.

[0085] In addition, based on the contribution of each point in the source region, such as Figure 6As shown, by combining topographic and vegetation distribution, the flux contribution of each vegetation zone at each observation time can be statistically obtained. Different numbers represent different vegetation types or topography, and the blue pentagram indicates the location of the observation tower. The results show that, as... Figure 7 As shown, there are significant differences in the contribution rate of different regions to the observation flux of the observation tower. The type area with the same vegetation distribution as the observation tower in the northeast direction contributed 22.28% of the observation tower flux value, while the vegetation type area (2) with a smaller area in the north contributed only 4.04% to the observation flux of the observation tower. The vegetation in the southeast direction contributed 25.74% to the observation flux, while the vegetation in the northwest direction had the highest contribution rate, reaching 36.84%, and the contribution rate in the southwest direction was only 11.10%.

[0086] 3. The carbon exchange flux intensity of each vegetation zone and its corresponding meteorological conditions were obtained by fitting the carbon exchange flux observations at multiple observation times and the contribution of each vegetation zone in the source region to the observed carbon exchange flux. Specifically, for carbon exchange flux observations that have undergone data quality control, similar meteorological conditions (0.5℃ temperature interval, 100 μmol / m³) were selected. - 2 s -1 Multiple observations under the photosynthetically active radiation interval (PARI) and the contribution of each vegetation zone to the observed carbon exchange flux were used to fit the carbon exchange flux of each vegetation zone using the least squares fitting method. The mean value of the meteorological conditions at each time of the observed values ​​was calculated simultaneously to characterize the meteorological conditions of the decomposed carbon exchange flux.

[0087] After decomposition, the rationality of the flux decomposition at the observation points is verified based on the flux values ​​of each vegetation zone obtained from the decomposition and their corresponding weight values. Specifically, for example... Figure 8 As shown, based on the flux values ​​of each vegetation zone obtained by decomposition and their corresponding weight values, the integrated flux value is obtained by accumulating the decomposition equation. The integrated flux value is then compared with the flux value observed at the observation point. It is found that the two show good consistency, which confirms that the flux observation results of heterogeneous source regions can be decomposed based on the contribution rate of different vegetation zones in the flux source region combined with the observed carbon flux value.

[0088] 4. The carbon exchange flux intensity of each vegetation zone in the source region is decomposed by combining the carbon exchange flux intensity of each vegetation zone with the corresponding meteorological conditions and a nonlinear fitting method. Specifically, during the day and night, a nonlinear fitting method is used to construct the fitting relationship between the carbon exchange flux of each vegetation zone and the meteorological conditions. Combined with the meteorological conditions (meteorological factors) at all times during the observation period, the carbon exchange flux of each vegetation zone is obtained. This decomposes the single carbon exchange flux observation result under complex terrain into the different carbon exchange flux intensities of different vegetation zones in a heterogeneous source region, thereby obtaining the complete time series carbon exchange flux of different vegetation zones.

[0089] This invention proposes a specific method for observing forest carbon exchange fluxes using eddy covariance (ECC) technology in complex terrain. This method includes strengthening data quality control through multiple approaches and decomposing single carbon exchange flux observations into carbon exchange fluxes in different vegetation zones of a heterogeneous source region. Without adding additional observation items, this method accurately obtains carbon exchange fluxes in different vegetation zones of a heterogeneous source region using only items observed using ECC technology (wind speed, wind direction, carbon exchange flux, etc.). The decomposed carbon exchange fluxes of each vegetation zone, weighted by their contribution, yield the total carbon exchange flux of the source region, which shows significant consistency with the actual observed values, demonstrating a good decomposition effect. This invention is both simple to operate and exhibits excellent decomposition results, providing a methodological basis for accurately obtaining carbon exchange fluxes in complex terrain and data support for ground verification of high-precision remote sensing data.

[0090] This embodiment also provides a device for observing and decomposing carbon exchange flux, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0091] This embodiment provides a device for observing and decomposing carbon exchange flux, such as... Figure 9 As shown, it includes: The data acquisition module 91 is used to acquire in-situ observation data of the source region at multiple times, including carbon exchange flux and environmental meteorological data. The data filtering module 92 is used to determine whether the in-situ observation data at each moment meets the index requirements by using the turbulence exchange intensity index, energy closure index and atmospheric stability index determined by the in-situ observation data, and to obtain the in-situ observation data at the corresponding moment that meets the index requirements. The contribution determination module 93 is used to determine the flux contribution of each point in the source region at the corresponding time based on the in-situ observation data at the corresponding time that meets the index requirements, using a preset flux contribution model. The regional statistics module 94 is used to statistically determine the flux contribution of each vegetation zone in the source region based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region. The regional exchange flux determination module 95 is used to decompose the carbon exchange flux according to the flux contribution of each vegetation zone based on the in-situ observation data at the corresponding time that meets the index requirements, so as to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements. The relationship building module 96 is used to build the relationship between carbon exchange flux and environmental meteorological data of each vegetation zone, and to determine the carbon exchange flux of each vegetation zone at each time by combining the environmental meteorological data at each time.

[0092] The carbon exchange flux observation and decomposition device provided in this embodiment of the invention can execute the carbon exchange flux observation and decomposition method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0093] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0094] The following is a detailed reference. Figure 10 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 11, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 12 or a program loaded from memory 18 into random access memory (RAM) 13. The RAM 13 also stores various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] Typically, the following devices can be connected to I / O interface 15: input devices 16 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 17 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 18 including, for example, magnetic tapes, hard disks, etc.; and communication devices 19. Communication device 19 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 10 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0096] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 19, or installed from a memory 18, or installed from a ROM 12. When the computer program is executed by the processor 11, it performs the functions defined in the carbon exchange flux observation and decomposition method of the embodiments of the present invention.

[0097] Figure 10The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0098] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the carbon exchange flux observation and decomposition method shown in the above embodiments is implemented.

[0099] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0100] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for observing and decomposing carbon exchange flux, characterized in that, The method includes: Acquire in-situ observation data of the source region at multiple times, including carbon exchange flux and environmental meteorological data; The in-situ observation data at each moment is determined by using the turbulence exchange intensity index, energy closure index and atmospheric stability index determined by the in-situ observation data to determine whether the in-situ observation data at each moment meets the index requirements, and the in-situ observation data at the corresponding moment that meets the index requirements is obtained. Based on the in-situ observation data at the corresponding time that meets the index requirements, the flux contribution of each point in the source region at the corresponding time is determined by a preset flux contribution model. Based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region, the flux contribution of each vegetation zone in the source region is statistically determined. Based on in-situ observation data at the corresponding time that meets the index requirements, the carbon exchange flux is decomposed according to the flux contribution of each vegetation zone to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements. The relationship between carbon exchange flux and environmental meteorological data in each vegetation zone was established, and the carbon exchange flux of each vegetation zone at each time point was determined by combining the environmental meteorological data at each time point.

2. The method according to claim 1, characterized in that, The in-situ observation data at each time point are determined using turbulence exchange intensity, energy closure, and atmospheric stability indices based on in-situ observation data. This yields the in-situ observation data for the corresponding time points that meet the indices, including: The turbulent exchange intensity index is determined based on a pre-defined lookup table method. The energy closure index is determined based on the Bowen ratio of latent heat flux, sensible heat flux, net radiation, and soil heat flux. Atmospheric stability indices are determined based on the Monin-Obkhoff similarity theory and the Monin-Obkhoff observation height. The values ​​of turbulence exchange intensity, energy closure, and atmospheric stability were determined using in-situ observation data at each time point. Determine whether the values ​​of turbulent exchange intensity, energy closure, and atmospheric stability at each time point meet the preset requirements, and retain the in-situ observation data for the corresponding time points that meet the preset requirements.

3. The method according to claim 1, characterized in that, Based on in-situ observation data at the corresponding time points that meet the index requirements, the carbon exchange flux is decomposed according to the flux contribution of each vegetation zone to obtain the carbon exchange flux of each vegetation zone at the corresponding time points that meet the index requirements, including: The in-situ observation data at the corresponding time that meet the index requirements are divided into two groups based on the differences in the dominant factors of carbon exchange flux: the first preset time period and the second preset time period. The in-situ observation data of each group were sorted according to the preset environmental meteorological data. The carbon exchange flux and the flux contribution of each vegetation zone were selected from the multiple adjacent in-situ observation data after sorting. The least squares method was used for fitting to obtain the carbon exchange flux of each vegetation zone in each group.

4. The method according to claim 3, characterized in that, The relationship between carbon exchange fluxes and environmental meteorological data in each vegetation zone was established, including: The environmental meteorological data corresponding to each vegetation zone are determined based on the environmental meteorological data from multiple in-situ observation data selected in each group. A nonlinear fitting method was used to construct the fitting relationship between the carbon exchange flux of each vegetation zone and the corresponding environmental meteorological data of each vegetation zone.

5. The method according to claim 1, characterized in that, The method further includes: The carbon exchange flux at each time point is calculated based on the carbon exchange flux of each vegetation zone and the flux contribution of each point in the source region at the corresponding time point. The calculated carbon exchange flux is compared with the carbon exchange flux at the corresponding time point, and the carbon exchange flux of each vegetation zone is recalculated based on the comparison results.

6. The method according to claim 1, characterized in that, The preset flux contribution model includes a two-dimensional flux contribution region model.

7. The method according to claim 2, characterized in that, The preset requirements include a turbulent exchange intensity value greater than a preset critical value, an energy closure index value greater than 0.5 and less than 2, and an atmospheric stability value less than -1.

8. A device for observing and decomposing carbon exchange flux, characterized in that, The device includes: The data acquisition module is used to acquire in-situ observation data of the source region at multiple times, including carbon exchange flux and environmental meteorological data. The data filtering module is used to determine whether the in-situ observation data at each moment meets the index requirements by using the turbulence exchange intensity index, energy closure index and atmospheric stability index determined by the in-situ observation data, and to obtain the in-situ observation data at the corresponding moment that meets the index requirements. The contribution determination module is used to determine the flux contribution of each point in the source region at the corresponding time based on the in-situ observation data that meets the index requirements and adopts a preset flux contribution model. The regional statistics module is used to statistically determine the flux contribution of each vegetation zone in the source region based on the flux contribution of each point in the source region and the topographic and vegetation map of the source region. The regional exchange flux determination module is used to decompose the carbon exchange flux according to the flux contribution of each vegetation zone based on the in-situ observation data at the corresponding time that meets the index requirements, so as to obtain the carbon exchange flux of each vegetation zone at the corresponding time that meets the index requirements. The relationship building module is used to build the relationship between carbon exchange flux and environmental meteorological data of each vegetation zone, and to determine the carbon exchange flux of each vegetation zone at each time point by combining the environmental meteorological data at each time point.

9. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for observing and decomposing carbon exchange flux as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for observing and decomposing carbon exchange flux as described in any one of claims 1 to 7.

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