Method and system for predicting methane emission flux based on data coupling model

Abstract

The invention discloses a method and system for predicting methane emission flux based on a data coupling model, and the method comprises the steps: downloading remote sensing, soil, hydrology, observation and meteorological data of a target wetland region, and carrying out the data preprocessing; the TECO model and the Wetland-DNDC model are coupled, and hourly underground water level data output by the TECO model are transmitted to the DNDC model; preprocessing the wetland soil data, and calculating the water content change of the soil profile; operating the coupling model, and simulating methane generation, oxidation and emission; based on actually measured data, assimilating and optimizing key parameters by adopting Bayesian data, quantifying uncertainty through Monte Carlo simulation, and performing methane emission flux prediction data; and finally, carrying out precision verification on a prediction result and an actual measurement result. According to the method, high-precision prediction of the wetland methane emission flux is realized through the coupling model.

Description

Technical Field

[0001] The present invention relates to the technical field of predicting methane emission flux in wetland ecosystems, and particularly to a method and system for predicting methane emission flux based on a data coupling model. Background Art

[0002] Wetlands are one of the largest natural methane emission sources globally. As a potent greenhouse gas, accurate prediction of its emission flux is of great significance for understanding the global carbon cycle and climate change.

[0003] Traditional methane emission prediction methods mainly rely on on-site observations and single-model simulations, but these methods have the following limitations: difficult data acquisition, insufficient model accuracy, and poor real-time performance. Obtaining data requires a large amount of equipment and manpower in on-site observations and can only cover limited areas and time scales; single models, such as TECO (Terrestrial Ecosystem Carbon Model) or Wetland-DNDC (Wetland-DeNitrification-DeComposition model, which is an extended and modified version of the famous agricultural ecosystem model DNDC (DeNitrification-DeComposition). DNDC was originally designed for dryland farmland, and Wetland-DNDC was modified by introducing wetland-specific processes), often cannot comprehensively consider the interactions of multiple factors such as groundwater level, soil moisture content, and vegetation cover when simulating the complex ecological and hydrological processes of wetlands; and traditional methods are difficult to achieve large-scale, high spatio-temporal resolution prediction of methane emission flux.

[0004] In recent years, with the development of remote sensing technology and data assimilation methods, the existing methods still have the following problems: the fusion and processing process of multi-source data is relatively cumbersome, and the model parameter optimization is insufficient, resulting in low reliability of the prediction results.

[0005] In view of the above problems, the present invention proposes a method and system for predicting wetland methane emission flux based on a data coupling model. By coupling the TECO and Wetland-DNDC models and combining Bayesian data assimilation and Monte Carlo simulation, high-precision, high spatio-temporal resolution prediction of methane emission flux is achieved. Summary of the Invention

[0006] To achieve large-scale, high spatio-temporal resolution prediction of methane emission flux and improve the reliability of predicting methane emission flux, the present invention provides a method and system for predicting methane emission flux based on a data coupling model, which can solve the existing problems raised in the background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for predicting methane emission flux based on a data coupling model, which includes the following specific steps:

[0009] Step 1: Download remote sensing data, soil, hydrological, observational data, and meteorological data at daily and hourly scales of the target wetland area, and perform data preprocessing;

[0010] Step 2: Preprocess the obtained wetland soil data, and use a water balance model with an hourly time step to calculate the water content change in the top 30 cm soil profile;

[0011] Step 3: Couple the TECO model and the Wetland-DNDC model, and transfer the groundwater level data output by the TECO model to the DNDC model; Run the coupled TECO-DNDC model to simulate methane generation, oxidation, and emissions (diffusion, plant transport, ebullition);

[0012] Step 4: Based on the measured methane flux emission data by the static chamber-gas chromatograph method, use Bayesian data assimilation to optimize key parameters; Quantify the uncertainty of parameters and model structure through Monte Carlo simulation to generate a methane flux prediction interval;

[0013] Step 5: Use the improved wetland coupling model to predict the methane emission flux of the target area and verify the accuracy with the measured results.

[0014] As a method for predicting methane emission flux based on a data coupling model of the present invention, in step 1, the Sentinel-2 remote sensing image with a high spatial resolution of 10 m is downloaded, the latest soil hydrology and observational data are downloaded and used, and the meteorological data is obtained and updated in real time.

[0015] As a method for predicting methane emission flux based on a data coupling model of the present invention, in step 2, for the preprocessing of wetland soil data, a constructed cubic spline interpolation function is used to generate hourly data points, so as to obtain the hourly input data required by the DNDC model. The data volume and data processing process are reduced.

[0016] As a method for predicting methane emission flux based on a data coupling model of the present invention, the groundwater level depth equation of the water balance model in step 2 is

[0017]

[0018] As a method for predicting methane emission flux based on a data coupling model of the present invention, the method for evaluating the inversion accuracy in step four is as follows: optimizing key parameters by Bayesian data assimilation; quantifying the uncertainty of parameters and model structure through Monte Carlo simulation.

[0019] Using Bayesian probabilistic inversion technology and based on prior knowledge of the parameter range and in-situ measurement values of CH4 emissions, the present invention estimates the posterior distribution of model parameters. The present invention randomly selects parameter sets from the posterior distribution of the parameter set for 100 predictions, randomly selects a set of randomly generated environmental variables, and uses the same set of variables for all predictions.

[0020] Bayes' theorem provides an equation for the posterior probability density function of model parameters given the observed value Z Based on the prior knowledge of the parameter distribution and the likelihood function :

[0021]

[0022] The present invention assumes that the prior knowledge of the parameter distribution p(θ) is a uniform distribution. Due to the equivalence and non-identifiable parameters when using only one observation data stream to constrain multiple parameters, the present invention only selects 3 highly sensitive parameters for data assimilation, and the prior range uses the default values of wetlands of the same latitude and type. The error between each observation data and the model simulation result independently follows a normal distribution with a mean of zero, so the likelihood function is expressed as

[0023]

[0024] The posterior probability distribution of parameter sampling is carried out using the adaptive Metropolis-Hastings (M-H) algorithm, and the Markov chain Monte Carlo technique is adopted. The parameter values of the set are randomly accepted with a probability of 0.05. After running the simulation chain, the Gelman-Rubin statistic is used to check the convergence of the sampling chain. Only the second half of the acceptable parameter values is used for posterior analysis.

[0025] Parameter quantification method: Select , , These three parameters with high sensitivity indices and strong interactions in the methane production formula. After inputting one of the parameters for data assimilation, the distributions of the other two in the parameter histogram are well constrained and the input parameter is within the confidence interval, which can reduce the uncertainty of simulation predictions.

[0026] As a method for predicting methane emission flux based on a data coupling model provided by the present invention, the method for evaluating the inversion accuracy in step five is as follows:

[0027] The root mean square error (RMSE) and the coefficient of determination R 2 are used as accuracy evaluation indicators, and the formulas are as follows:

[0028] Root mean square error (RMSE):

[0029]

[0030] In the formula, n is the total number of samples, is the model prediction value, is the true value; the ranges of both the root mean square error and the mean absolute percentage error are , and it is equal to 0 when the predicted value perfectly matches the true value, i.e., perfect prediction.

[0031] Coefficient of determination R 2 The calculation formula is as follows:

[0032]

[0033] Where:

[0034] is the sum of squared residuals, representing the total difference between the model prediction value and the actual observed value.

[0035] is the total sum of squares, representing the total difference between the actual observed value and the observed mean.

[0036] R 2 represents the proportion of the data variance that the model can explain. When R 2 is close to 1, it means the model can fit the data well; when R 2 is close to 0, it means the model cannot fit the data well.

[0037] The present invention also discloses a system for predicting methane emission flux based on a data coupling model, including:

[0038] Data acquisition module: used to download remote sensing, meteorological, soil, and hydrological data;

[0039] Preprocessing module: performs radiometric correction, cubic spline interpolation, and spatio-temporal resolution unification;

[0040] Model coupling engine: dynamically connects the TECO model and the Wetland-DNDC model to achieve hourly transfer of groundwater level data;

[0041] Parameter optimization module: optimizes key parameters and quantifies uncertainties based on Bayesian data assimilation and Monte Carlo simulation;

[0042] Verification and Output Module: Calculate RMSE and R², and generate a methane flux prediction report and visualization results.

[0043] Among them, the model coupling engine automates the data interface through Python scripts, specifically including:

[0044] Read the hourly groundwater level output file of the TECO model, convert it into the input format required by the DNDC model, and trigger the operation of the coupled model.

[0045] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0046] The present invention provides a method and system for predicting methane emission flux based on a data coupling model. By coupling the TECO and Wetland-DNDC models, the present model directly inputs the groundwater level depth in the TECO model into the DNDC model, solves the problem of insufficient spatio-temporal continuity of the groundwater level depth, combines Bayesian data assimilation and Monte Carlo simulation, optimizes key parameters and quantifies uncertainties, and realizes high-precision prediction of wetland methane emission flux, providing a scientific basis for wetland management and response to climate change. Brief Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0048] Figure 1 It is a flowchart of the method for predicting methane emission flux based on the data coupling model of the present invention. Detailed Embodiments

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe the embodiments of the present invention in detail with reference to the drawings.

[0050] The present invention provides a method for predicting methane emission flux based on a data coupling model. For the detailed process, please refer to Figure 1 , including the following specific steps:

[0051] Step 1: Download the remote sensing data, soil, hydrological, observation data, and daily-scale and hourly-scale meteorological data of the target wetland area, and perform data preprocessing;

[0052] Download Sentinel-2 high-spatial-resolution images, obtain the soil, hydrological, observation data, and daily-scale and hourly-scale meteorological data of the site, and perform preprocessing on the remote sensing data. The preprocessing includes radiometric correction, atmospheric correction, and geometric correction to obtain accurate multi-spectral canopy reflectance data;

[0053] Radiometric correction, geometric correction, and atmospheric correction preprocessing are common remote sensing image processing methods. In this embodiment, the above processing is completed using ENVI5.6 software;

[0054] Step 2: Preprocess the obtained wetland soil data, and use a water balance model with an hourly time step to calculate the water content change in the top 30 cm soil profile;

[0055] The preprocessing is as follows: Convert the TECO daily output to DNDC hourly input through cubic spline interpolation. The operation is as follows:

[0056] Time series construction: Construct a time series from the daily output data of the TECO model, where each data point represents the average value of a day.

[0057] Cubic spline interpolation: Apply the cubic spline interpolation method to expand the daily data points into hourly data. Cubic spline interpolation is a method of approximating the functional relationship between data points by constructing piecewise cubic polynomials. It can ensure the continuity and smoothness of the interpolation function at the data points.

[0058] Boundary condition handling: When performing cubic spline interpolation, boundary conditions need to be handled. Usually, natural boundary conditions are adopted, that is, it is assumed that at both ends of the data points, the second derivative of the function is zero.

[0059] Interpolation function construction: For each adjacent pair of daily data points, construct a cubic polynomial such that the value of the polynomial at the data points is the same as the original data and satisfies the boundary conditions.

[0060] Hourly data generation: Use the constructed cubic spline interpolation function to generate hourly data points, thereby obtaining the hourly input data required by the DNDC model.

[0061] Step 3: Couple the TECO model and the Wetland-DNDC model, and transfer the groundwater level data output by the TECO model to the DNDC model; Run the coupled TECO-DNDC model to simulate methane production, oxidation, and emission (diffusion, plant transport, boiling);

[0062] The groundwater level module can calculate the water content change in the top 30 cm soil profile through a water balance model characterized by hourly water input and output:

[0063] In the unsaturated zone, use a quadratic function, the soil volumetric water content from the volumetric water content at the vegetation surface to the groundwater level position The increment is as follows:

[0064]

[0065] In the formula The constant value is 0.95, is the soil depth (mm), Expressed as

[0066]

[0067] in is the minimum volumetric water content held by peat moss at the soil surface and is set to 0.25, is the gradient of a linear decrease;

[0068]

[0069] in is the maximum inhalation interval for a given value of 100 mm; therefore, above The total amount of water in the soil profile should be:

[0070]

[0071] The first part of the formula is the water content in the saturated zone , the second part is the water content in the unsaturated zone ; If the entire section is saturated, the still water height is expressed as and The difference between

[0072] The groundwater depth equation of the water balance model is:

[0073]

[0074] Table 1 is a graph of the main parameters of methane production, oxidation, diffusion, boiling, and plant-mediated transport used in the present invention;

[0075]

[0076] Step 4: Based on the measured methane flux emission data from the static chamber-gas chromatograph method, Bayesian data assimilation is used to optimize key parameters. The uncertainty of the parameters and model structure is quantified through Monte Carlo simulation to generate methane flux prediction intervals.

[0077] Methane emission data are the only observational dataset available for data assimilation. The most sensitive parameters are selected for data assimilation because in data assimilation, an initial sensitivity test is performed when a parameter can be constrained by that variable; the sensitivity of a parameter is determined by the sensitivity index defined as:

[0078]

[0079] Among them, y0 is the model output of the initial value x0 of the independent variable. The model output is methane emission. The independent variable values are ±Δx, and the corresponding dependent variable values are y2 and y1. Δx is set to 0.25 times the initial value;

[0080] The emission data of methane flux is measured by the static chamber-gas chromatograph method. The target area for predicting methane flux emission is selected. The formula for gas emission flux on the ground is:

[0081]

[0082] Among them is the gas flux. A positive value indicates emission, and a negative value indicates absorption; 、 、 are the standard atmospheric pressure, gas molar volume, and absolute temperature under standard conditions respectively; dc / dt is the linear slope of the sampled gas concentration changing with time; is the molar mass of the gas to be measured; 、 are the actual atmospheric pressure and temperature at the sampling point; is the height of the sampling chamber.

[0083] The formula for gas emission flux on the water surface is:

[0084]

[0085] Among them is the greenhouse gas diffusion flux;

[0086] Slop: The slope in the time-concentration relationship diagram, ppm·s⁻¹;

[0087] V: The air volume in the static chamber, m³; S: The water surface area covered by the chamber, m²;

[0088] F1: The conversion coefficient from the standard temperature and pressure of the gas in the air from ppm to mg·m⁻³. The conversion relationship is as follows:

[0089]

[0090] M: Gas molar mass, g·mole⁻¹;

[0091] P: The on-site atmospheric pressure during monitoring, kpa;

[0092] T: The temperature inside the chamber during monitoring, ℃;

[0093] F2: Is the conversion coefficient between seconds and days, 86400s·d⁻¹.

[0094] Based on the above formula, the methane flux emissions can be measured and calculated.

[0095] Using Bayesian probability inversion technology, based on the prior knowledge of the parameter range (Table 1) and the field measurement values of CH4 emissions, the present invention estimates the posterior distribution of the model parameters. The present invention randomly selects parameter sets from the posterior distribution of the parameter set for 100 predictions, and randomly selects a set of randomly generated environmental variables, and uses the same set of variables for all predictions.

[0096] Bayes' theorem provides an equation in which the posterior probability density function of the model parameters given the observations Z Based on the parameter distribution of the prior knowledge and the likelihood function :

[0097]

[0098] Assume that the prior knowledge of the parameter distribution p(θ) is uniformly distributed. Due to the equivalence and unidentifiable parameters when using only one observation data stream to constrain multiple parameters, in this regard, 3 highly sensitive parameters can be selected for data assimilation, and the prior range uses the default values of wetlands of the same type and latitude. The error between each observation data and the model simulation results independently follows a normal distribution with a mean of zero. Therefore, the likelihood function is expressed as

[0099]

[0100] In the formula is the only observation stream at time t, is the simulated corresponding variable, is the standard deviation of the observation set. The posterior probability distribution of parameter sampling is performed using the adaptive Metropolis-Hastings (M-H) algorithm, and the Markov chain Monte Carlo technique is used. The parameter values of the set are randomly accepted with a probability of 0.05. After running the simulation chain, the Gelman-Rubin statistic is used to check the convergence of the sampling chain. Only the second half of the acceptable parameter values is used for posterior analysis.

[0101] Parameter quantification method: Select , , These three parameters with high sensitivity indices and strong interactions in the methane production formula. r_me is the potential proportion of anaerobic mineralized carbon released as methane, is the temperature coefficient of methane production, is the optimal temperature of methane production. After inputting one of the parameters for data assimilation, the distributions of the other two in the parameter histogram are well constrained and the input parameters are within the confidence interval, which can reduce the uncertainty of the simulation prediction.

[0102] The parameter values used in this process are shown in Table 1.

[0103] Step 5: Use the improved wetland coupling model to predict the methane emission flux in the target area and verify its accuracy with the measured results.

[0104] Among them, the method for evaluating the inversion accuracy in step 5 is:

[0105] The root mean square error (RMSE) and the coefficient of determination R 2 As an accuracy evaluation indicator, the formula is as follows:

[0106] Root Mean Square Error (RMSE):

[0107]

[0108] Where n is the total number of samples, is the model prediction value, is the true value; the range of root mean square error and mean absolute percentage error is , which is equal to 0 when the predicted value is exactly the same as the true value, i.e. perfect prediction.

[0109] Coefficient of determination (R) 2 The calculation formula is as follows:

[0110]

[0111] in:

[0112] is the residual sum of squares, which represents the sum of the differences between the model's predicted values and the actual observed values.

[0113] is the total sum of squares, which represents the sum of the differences between the actual observations and the observed mean.

[0114] R 2 Indicates the proportion of data variance that the model can explain. 2 When it is close to 1, it means that the model can fit the data well; when R 2 When it is close to 0, it means that the model does not fit the data well.

[0115] In this regard, the present invention also discloses a system for the above method, comprising:

[0116] Data acquisition module: used to download remote sensing, meteorological, soil and hydrological data;

[0117] Preprocessing module: performs radiation correction, cubic spline interpolation and spatial and temporal resolution unification;

[0118] Model coupling engine: Dynamically connect the TECO model and the Wetland-DNDC model to achieve hourly transfer of groundwater level data;

[0119] Parameter optimization module: Optimize key parameters and quantify uncertainties based on Bayesian data assimilation and Monte Carlo simulation;

[0120] Verification and output module: Calculate RMSE and R², and generate methane flux prediction reports and visualization results.

[0121] Among them, the model coupling engine automatically realizes the data interface through Python scripts, specifically including: reading the hourly groundwater level output file of the TECO model, converting it into the input format required by the DNDC model, and triggering the operation of the coupled model.

[0122] In summary, the present invention couples the TECO and Wetland-DNDC models, directly inputs the groundwater level depth in the TECO model into the DNDC model, solves the problem of insufficient spatio-temporal continuity of the groundwater level depth, combines Bayesian data assimilation and Monte Carlo simulation, optimizes key parameters and quantifies uncertainties, realizes high-precision prediction of wetland methane emission flux, and provides a scientific basis for wetland management and response to climate change.

Claims

1. A method for predicting methane emission flux based on a data coupling model, characterized in that, It includes the following steps: Step 1: Obtain the remote sensing data, soil data, hydrological data, observation data, and meteorological data at daily and hourly scales of the target wetland area, and perform data preprocessing; Step 2: Preprocess the wetland soil data, convert the daily output data of the TECO model to hourly data using the cubic spline interpolation method, and calculate the water content change in the top 30 cm soil profile based on the water balance model; Step 3: Couple the TECO model with the Wetland-DNDC model, input the hourly groundwater level data output by the TECO model into the DNDC model; run the coupled TECO-DNDC model to simulate the methane generation, oxidation, and emission processes; Step 4: Based on the measured methane flux emission data by the static chamber-gas chromatograph method, optimize the key parameters using Bayesian data assimilation; and quantify the uncertainty of the parameters and model structure through Monte Carlo simulation to generate the methane flux prediction interval; Step 5: Use the improved wetland coupling model to predict the methane emission flux in the target area and verify the accuracy with the measured results; use the root mean square error RMSE and the coefficient of determination R 2 as the accuracy evaluation indicators.

2. The method for predicting methane emission flux based on a data coupling model according to claim 1, wherein In the data preprocessing in Step 1, the remote sensing data is processed through radiometric correction, geometric correction, and atmospheric correction to invert the vegetation coverage and soil water content; the spatio-temporal resolutions of the meteorological data, hydrological data, and soil data are unified to the hourly scale.

3. A method for predicting methane emission flux based on a data coupling model according to claim 1, characterized in that The preprocessing of the wetland soil data in Step 2 includes converting the TECO daily output to the DNDC hourly input through cubic spline interpolation, and the operation is as follows: Time series construction: Construct a time series from the daily output data of the TECO model, and each data point represents the average value of one day; Cubic spline interpolation: Apply the cubic spline interpolation method to expand the daily data points to hourly data; Boundary condition processing: When performing cubic spline interpolation, boundary conditions need to be processed, and natural boundary conditions are adopted, that is, it is assumed that the second derivative of the function is zero at both ends of the data points; Interpolation function construction: For each adjacent daily data point, construct a cubic polynomial so that the value of the polynomial at the data point is the same as the original data and satisfies the boundary conditions; Hourly data generation: Use the constructed cubic spline interpolation function to generate hourly data points, so as to obtain the hourly input data required by the DNDC model.

4. A method for predicting methane emission flux based on a data coupling model according to claim 1, characterized in that, In Step 3, the groundwater level module calculates the water content change in the top 30 cm soil profile through a water balance model characterized by hourly water input and output; In the unsaturated zone, a quadratic function is used for the incremental change of soil volumetric water content from the volumetric water content at the vegetation surface to the position of the groundwater table ( ) as follows: Among them The constant value is 0.95, is the soil depth, with the unit of mm, is expressed as Among them is the minimum volume water content held by Sphagnum on the soil surface and is set to 0.25, is the linearly decreasing gradient; Among them is the maximum inspiration interval of the given value of 100 mm; therefore, the total water volume in the soil profile higher than should be: where the first part is the water content in the above-mentioned saturated zone , and the second part is the water content in the unsaturated zone ; if the entire profile is in a saturated state, the hydrostatic head is represented by the difference between and ; the final equation for the depth of the groundwater table is 。 5. The method for predicting methane emission flux based on a data coupling model according to claim 1, wherein In Step 4: Data assimilation method: Use Bayesian probability inversion technology, based on the prior knowledge of the parameter range and the on-site measurement values of CH4 emissions, estimate the posterior distribution of the model parameters, randomly select parameter sets from the posterior distribution of the parameter set for 100 predictions, and randomly select a set of randomly generated environmental variables, and use the same set of variables for all predictions; According to the equation in Bayes' theorem, where the posterior probability density function of the model parameters given the observations is Based on the prior knowledge of the parameter distribution is: Assume that the prior knowledge of the parameter distribution is uniformly distributed. Due to the equivalence and non-identifiable parameters when using only one observation data stream to constrain multiple parameters, three highly sensitive parameters are selected for data assimilation. The prior range uses the default values of wetlands of the same latitude and type. The error between each observation data and the model simulation results independently follows a normal distribution with a mean of zero. Therefore, the likelihood function is expressed as: wherein is the only observed stream at time t, is the corresponding simulated variable, is the standard deviation of the observation set. The posterior probability distribution for parameter sampling is carried out using the adaptive Metropolis-Hastings algorithm. The Markov chain Monte Carlo technique is adopted. The parameter values of the set are randomly accepted with a probability of 0.

05. After running the simulation chain, the Gelman-Rubin statistic is used to check the convergence of the sampling chain. Only the second half of the acceptable parameter values is used for posterior analysis; Parameter quantification method: Select the potential proportion of anaerobic mineralized carbon released as methane , temperature coefficient of methane production , optimal temperature for methane production These three parameters with high sensitivity indices and strong interactions in the methane production calculation formula. After inputting one of the parameters in data assimilation, the distributions of the other two are well constrained in the parameter histogram and the input parameter is within the confidence interval, which is used to reduce the uncertainty of simulation prediction.

6. The method for predicting methane emission flux based on a data coupling model according to claim 1, wherein The method for evaluating the inversion accuracy in Step 5 is: Using the root mean square error RMSE and the coefficient of determination R 2 as the accuracy evaluation index, where: Root mean square error RMSE: where n is the total number of samples, is the predicted value of the model, is the true value; the ranges of both the root mean square error and the mean absolute percentage error are , and it is equal to 0 when the predicted value perfectly matches the true value, i.e., perfect prediction; Coefficient of determination R 2 The calculation formula is as follows: Wherein: is the sum of squared residuals, representing the total difference between the model's predicted values and the actual observed values; is the total sum of squares, representing the total difference between the actual observations and the observed mean; R 2 represents the proportion of the data variance that the model can explain; when R 2 is close to 1, it indicates that the model can fit the data; when R 2 is close to 0, it indicates that the model cannot fit the data.

7. A system for predicting methane emission flux based on a data coupling model, which is used to implement the method for predicting methane emission flux based on the data coupling model according to any one of claims 1-6, characterized in that It includes: Data acquisition module: Used to download remote sensing, meteorological, soil, and hydrological data; Preprocessing module: Perform radiometric correction, cubic spline interpolation, and spatio-temporal resolution unification; Model coupling engine: Dynamically connect the TECO model and the Wetland-DNDC model to achieve the hourly transfer of groundwater level data; Parameter optimization module: Optimize key parameters and quantify uncertainties based on Bayesian data assimilation and Monte Carlo simulation; Verification and output module: Calculate RMSE and R², generate methane flux prediction reports and visualization results.

8. The system for predicting methane emission flux based on the data coupling model according to claim 7, wherein The model coupling engine automates the data interface through Python scripts, specifically including: Read the hourly groundwater level output file of the TECO model, convert it to the input format required by the DNDC model, and trigger the operation of the coupled model.

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