Method and system for estimating methane emission flux of rice field based on remote sensing observation of vegetation fluorescence and photosynthesis regulation mechanism

Abstract

The application discloses a rice field methane emission flux estimation method and system based on vegetation fluorescence remote sensing observation and photosynthesis regulation mechanism, total primary productivity is obtained by acquiring sunlight-induced chlorophyll fluorescence observation data and simulation, combined with a GPP-CH4 flux response function, an initial estimated value of the methane emission flux is obtained; and multi-source constraint information such as methane flux observation, concentration observation and stable carbon isotope observation is introduced to correct the initial estimated value of the methane emission flux. The method realizes the organic combination of remote sensing continuous observation and the photosynthesis regulation mechanism, improves the estimation accuracy and process interpretation ability, effectively reduces the estimation uncertainty caused by changes in different growth stages, water management conditions and transmission paths, and can be applied to rice field methane emission monitoring, emission reduction evaluation and greenhouse gas inventory optimization.

Description

Technical Field

[0001] This invention relates to the field of greenhouse gas monitoring technology in agricultural ecosystems, and in particular to a method and system for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms. Background Technology

[0002] methane ( Methane is an important short-lived greenhouse gas and climate forcing factor. Although its atmospheric lifetime is significantly shorter than that of carbon dioxide, its warming effect per unit mass is stronger, and it has a significant impact on global warming on a scale of several decades. As an important source of methane emissions in agricultural ecosystems, accurate estimation of methane emission fluxes from paddy fields is of great significance for the compilation of agricultural greenhouse gas inventories, the evaluation of the effectiveness of emission reduction measures, and regional carbon cycle research.

[0003] Methane emissions from paddy fields are the result of the combined effects of methanogenesis, methane oxidation, and multiple transport pathways. Specifically, these include: methane production in the rice root system soil under anaerobic conditions; methane oxidation in the rhizosphere, topsoil, and water bodies; and methane emission through plant-mediated transport, gas bubble escape, and liquid-phase diffusion. Because this process is influenced by multiple factors such as crop photosynthesis, water management, temperature conditions, soil properties, vegetation structure, and field management practices, paddy field methane emissions exhibit significant spatiotemporal heterogeneity, making accurate estimation a considerable challenge.

[0004] Currently, methane emissions estimation in paddy fields mainly employs box sampling, eddy covariance flux observation, empirical emission factor methods, and ecosystem process model simulations. Box sampling can directly obtain methane emission flux data at the field scale through static or dynamic box sampling, but it typically suffers from discontinuous observation time, limited spatial representativeness, and difficulty in capturing emission pulses. Eddy covariance methods, based on high-frequency wind speed and concentration covariance calculations, can provide continuous flux observations at the ecosystem scale, but the cost of site construction and maintenance is high, and regional spatial coverage is limited. Empirical emission factor methods are simple in structure and easy to compile inventories, but their responsiveness to crop growth status, water management, and regional differences is insufficient. Ecosystem process models can describe the relevant biogeochemical mechanisms of methane production, oxidation, and transport, but they usually require a large number of soil, vegetation, and management parameters, resulting in significant parameter uncertainties when applied at the regional scale.

[0005] Photosynthesis in rice is a key biological process regulating methane emissions from paddy fields, and its role is manifested at two levels. At the carbon substrate supply level, photosynthetic products are imported into the rhizosphere through root exudates and litter, providing organic carbon substrates for methanogenesis and directly affecting the methane production rate. At the transport capacity regulation level, photosynthesis drives plant growth, root activity, and aerenchyma development, thereby influencing plant-mediated methane transport efficiency. Studies have shown that plant-mediated transport is the most important pathway for methane emissions from paddy fields. Canopy total primary productivity (GPP), as a core indicator characterizing the intensity of rice photosynthesis and recent photosynthetic carbon input, can effectively reflect the growth status of rice and thus has a significant characterizing effect on the plant's ability to transport methane. Therefore, constructing a quantitative estimation relationship between GPP and paddy field methane emission flux based on the photosynthetic regulation mechanism is an important way to improve the continuous estimation capability of paddy field methane emissions.

[0006] Sunlight-induced chlorophyll fluorescence (SIF) is a passive chlorophyll fluorescence signal emitted by plants under sunlight excitation, and it has a direct and close physical link with the photosynthetic process. SIF observations can be obtained from specific wavelengths, such as the red or far-red bands, or full-band integral values ​​can be obtained through spectral conversion, spectral reconstruction, or model parameterization. Existing research has shown that SIF observation data can serve as an important input for ecosystem mechanism process models, used to simulate the gross pulverization (GPP) of paddy field canopies, and further establish a technical correlation pathway between SIF, GPP, and methane emission flux.

[0007] While existing technologies exist for retrieving GPP from SIF (Self-Induced Irradiance), there is a lack of technical solutions that combine GPP-driven relationships with multi-source process constraint information to estimate methane emission fluxes from paddy fields. Specifically, in the technical approach of using SIF observation data as input, simulating GPP using ecosystem mechanistic process models, and further using GPP as a driving variable for constructing the estimation relationship of methane emission fluxes from paddy fields, existing technologies lack effective integration of multi-source process constraint information. This leads to significant uncertainties in the estimation results when the growth stages, water management conditions, and transport pathways change.

[0008] The aforementioned multi-source process constraints include methane flux observation data, methane concentration observation data, methane stable carbon isotope observation data, water state parameters, and soil redox state parameters. This information provides crucial process-level constraints for emission flux estimation from different stages of methane generation, oxidation, and transport. Combining GPP driving relationships with the aforementioned multi-source process constraints can effectively reduce estimation biases caused by changes in growth stage, water management practices, and transport pathways, thereby significantly improving the robustness and accuracy of the rice paddy methane emission flux estimation method.

[0009] In summary, existing methods for estimating methane emissions from paddy fields have limitations in terms of spatiotemporal continuity, regional applicability, representation of photosynthetic regulation mechanisms, process constraints, and estimation accuracy. In particular, while existing technologies have established the SIF-GPP inversion relationship, they have not fully utilized the synergistic effect of this photosynthetic regulation mechanism and multi-source process constraint information to establish a continuous estimation method for paddy field methane emission flux based on vegetation fluorescence remote sensing.

[0010] Therefore, there is an urgent need to develop a method and system for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism, in order to solve the problems of insufficient spatiotemporal continuity, insufficient expression of photosynthetic regulation mechanism, limited regional application capacity, insufficient process constraints and high estimation uncertainty in the existing paddy field methane emission estimation. Summary of the Invention

[0011] To achieve the above objectives, the present invention provides the following technical solution: A method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms includes the following steps: Step S1: Obtain vegetation fluorescence remote sensing observation data of the paddy field area, and obtain the total primary productivity (GPP) of the paddy field area based on the vegetation fluorescence remote sensing observation data; Step S2: Obtain environmental and management factors affecting methane emissions from paddy fields; Step S3: Based on GPP, environmental and management factors, construct... Flux response function H; Step S4, according to The flux response function H yields the initial estimate of methane emission flux from paddy fields; Step S5: Obtain constraint information, which includes one or more of the following: methane emission flux observation, methane concentration observation, methane stable carbon isotope observation, isotope characteristics of methane emission sources in paddy fields, plant-mediated emission end-member information, non-plant-mediated emission end-member information, water status observation, temperature condition observation, soil redox status observation, or other paddy field methane process observation information. Step S6: Based on the constraint information, the initial estimate of methane emission flux is corrected to obtain the final estimate of methane emission flux from paddy fields.

[0012] Preferably, the vegetation fluorescence remote sensing observation data includes sunlight-induced chlorophyll fluorescence observation values.

[0013] Preferably, the observed values ​​of sunlight-induced chlorophyll fluorescence include one or more of single-band sunlight-induced chlorophyll fluorescence, narrow-band sunlight-induced chlorophyll fluorescence, or integrated sunlight-induced chlorophyll fluorescence.

[0014] Preferably, the environmental and management factors include one or more of the following: water status, temperature conditions, vegetation structure, rice growth stage, and field management measures.

[0015] Preferably, the initial estimate of the methane emission flux is the methane emission flux on an hourly, daily, fertile period, or annual scale.

[0016] As a preferred option, it also includes: The initial estimate of methane emission flux was corrected to obtain the final estimate of methane emission flux from paddy fields, expressed as follows: ; in, Based on The initial estimate of methane emission flux obtained from the flux response function H. The constraint correction function is determined by the constraint information. This is the final estimate of methane emission flux after correction.

[0017] As a preferred approach, when the constraint information includes stable carbon isotope observation data, an isotope constraint correction function is constructed by combining a plant-mediated structure correction function, an isotope bias correction function, and environmental factor regulation terms.

[0018] This invention also proposes a system for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms, comprising the following modules: (1) Vegetation fluorescence remote sensing observation data acquisition and GPP simulation module, used to acquire vegetation fluorescence remote sensing observation data of paddy field area, and obtain the total primary productivity (GPP) of paddy field area based on the vegetation fluorescence remote sensing observation data; (2) Environmental and management factor acquisition module, used to acquire environmental and management factors that affect the methane emission process in paddy fields; (3) The flux response function construction module is used to construct functions based on GPP and environmental and management factors. Flux response function H; (4) Initial methane flux estimation module, used to estimate based on The flux response function H yields the initial estimate of methane emission flux from paddy fields; (5) Constraint correction module, used to correct the initial estimate of methane emission flux based on one or more of the following: methane emission flux observation, methane concentration observation, methane stable carbon isotope observation, isotope characteristics of methane emission sources in paddy fields, plant-mediated emission end-member information, non-plant-mediated emission end-member information, water status observation, temperature condition observation, soil redox status observation, or other paddy field methane process observation information, to obtain the final estimate of methane emission flux in paddy fields; (6) Results output module, used to output the final estimated value, time series, spatial distribution, cumulative emissions or uncertainty range of methane emission flux in paddy fields.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: First, this invention introduces vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms into the rice paddy methane emission flux estimation system. With rice paddy methane emission flux estimation as the objective, it uses sunlight-induced chlorophyll fluorescence (SIF) remote sensing data as input to an ecosystem mechanism model to simulate rice paddy GPP. Furthermore, GPP is used as the driving variable for constructing the rice paddy methane emission flux estimation relationship, replacing traditional empirical emission factors or single process parameters. This achieves an organic combination of continuous remote sensing observation capabilities and photosynthetic carbon input regulation mechanisms. Simultaneously, a GPP-environment-management factor coupled response architecture is adopted, incorporating water status, temperature conditions, vegetation structure, rice growth stages, and field management measures into a unified estimation framework. This effectively solves the problem of the separation between "remote sensing observation" and "emission mechanism" in existing methods, significantly improving the spatiotemporal continuity and mechanistic explanation capability of rice paddy methane emission flux estimation, demonstrating a sound mechanistic foundation and scalability.

[0020] Secondly, build The flux response function H integrates photosynthetic-driven carbon substrate supply and plant-mediated transport capacity changes into the methane emission estimation system. It is used to express the comprehensive impact of GPP, water state, temperature conditions, vegetation structure, growth stage, and field management practices on methane emission flux in paddy fields. By quantitatively linking photosynthetic carbon input and methane emission intensity through GPP and emission response coefficients, it achieves explicit coupling between photosynthetic regulation mechanisms and emission flux estimation, avoiding simple reliance on fixed empirical emission factors. Furthermore, it introduces dynamic modulation of environmental and management factors into the paddy field methane emission estimation. By setting quantitative attributions of environmental-management changes on emission flux estimation under different conditions, it achieves mechanistic constraints on changes in methanogenic substrate supply and emission intensity, providing a key means for analyzing the coupled process of photosynthesis-crop growth-methane emission.

[0021] Furthermore, a multi-source process constraint correction module is explicitly constructed, incorporating constraint information such as methane flux observation, methane concentration observation, stable carbon isotope observation, and water status or soil redox status to correct the constraints. The flux response function H, emission response coefficient, function parameters, or initial estimates are corrected. Through hierarchical coupling of plant-mediated structure correction, isotope bias correction, and dynamic regulation of environmental factors, the comprehensive impact of plant-mediated emission ratio deviation, source-end isotope structure deviation, and changes in environmental management conditions on the estimation results are calculated. This achieves adaptive correction under conditions of insufficient and sufficient constraint information, effectively solving the problem of the separation between "flux estimation" and "isotope analysis" in traditional models. It significantly improves the explanatory power and comprehensive diagnostic accuracy of the intrinsic mechanism of methane emissions from paddy fields, and provides an independent and verifiable technical means for quantifying the uncertainty of estimation results and improving accuracy.

[0022] Furthermore, a multi-scale application interface and verification bridge were established to support the joint input of site observations, tower-based observations, and satellite remote sensing observations, enabling continuous estimation at the field, site, and regional scales. The time series, spatial distribution, and cumulative emissions of methane emission flux output by the model were displayed and compared. The simulation accuracy was quantified through statistical indicators, and a verification bridge between the remote sensing-driven model and ground observations was established. This significantly enhanced the model's verifiability, data adaptability, and regional scale extension capabilities, and improved its ability to characterize the dynamic changes of methane in paddy fields.

[0023] Finally, this invention expands the application boundaries of the method, forming a system based on vegetation fluorescence remote sensing observation as input, ecosystem mechanism process model simulation of GPP, and... The method system for estimating methane emission flux from paddy fields, which is based on the flux response function and combined with process observation information for constraint correction, can be widely applied to scenarios such as monitoring methane emissions from paddy fields, evaluating agricultural methane emission reduction measures, optimizing greenhouse gas inventories, validating carbon cycle models, and estimating methane emissions from wetlands or other anaerobic ecosystems with vegetation cover. It forms a complete technical chain from vegetation fluorescence remote sensing observation to photosynthesis mechanism simulation, and then to methane emission flux estimation and process constraint correction, providing a scientific basis for the formulation of methane emission reduction strategies from paddy fields and the precise management of agricultural greenhouse gases. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a flowchart of the overall process for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism of the present invention. Figure 2 A flowchart illustrating the process for obtaining an initial estimate of methane emission flux from paddy fields; Figure 3 A schematic diagram illustrating the process of constraining and correcting the initial estimate of methane emission flux from paddy fields; Figure 4 This is a flowchart illustrating the application of the present invention at the site and regional scales. Figure 5 A schematic diagram of the observation deployment at the Jurong rice paddy monitoring station; Figure 6 This is a schematic diagram comparing the estimated methane emission flux from paddy fields with the measured methane emission flux, as presented in this invention. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort, such as equivalent substitutions, parameter adjustments, changes in function form, or expansions in application scale, are within the scope of protection of this application.

[0026] Combined with appendix Figure 1-6 This invention provides a method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms. The specific steps are as follows: Step S1: Obtain vegetation fluorescence remote sensing observation data of the paddy field area, and obtain the total primary productivity (GPP) of the paddy field area based on the vegetation fluorescence remote sensing observation data; The vegetation fluorescence remote sensing observation data includes observations of sunlight-induced chlorophyll fluorescence (SIF).

[0027] Using the SIF observation data and its associated photosynthetic and environmental parameters as input, the total primary productivity (GPP) of the paddy field area was simulated using an ecosystem mechanism process model.

[0028] The vegetation fluorescence remote sensing observations can come from one or more of the following: ground-based observations, tower-based observations, UAV observations, aerial observations, or satellite remote sensing observations.

[0029] The SIF observations include one or more of single-band SIF, narrow-band SIF, or integrated SIF; the SIF observations can be specific band or narrow-band fluorescence signals, such as those in the red band. or far-infrared band It can also be the target band or full band integral obtained through spectral conversion, spectral reconstruction, or model parameterization. .

[0030] The ecosystem mechanism process model may include one or more of the following: MLR light reaction mechanism model, photosynthesis process model, canopy-scale ecosystem model, or mechanism-data fusion model.

[0031] The GPP is used to characterize one or more of the following: canopy photosynthetic intensity, recent photosynthetic product input, rhizosphere organic carbon substrate supply potential, or plant growth status in paddy fields. It can also serve as an indirect characterization variable reflecting changes in plant growth status and plant-mediated emission potential.

[0032] The MLR photoreaction mechanism model is only a preferred embodiment used to illustrate that SIF observation data can participate in GPP simulation through photoreaction process constraints, and does not limit the present invention to only using this model.

[0033] Step S2: Obtain environmental and management factors affecting methane emissions from paddy fields; Among them, combined with the appendix Figure 1 The environmental and management factors include one or more of the following: water status, temperature conditions, vegetation structure, rice growth stage, and field management practices.

[0034] The moisture status may include one or more of the following: water level, groundwater level, soil moisture content, and soil redox status; The temperature conditions may include one or more of the following: air temperature, soil temperature, leaf temperature, water temperature, or accumulated temperature. The vegetation structure may include one or more of the following: leaf area index, vegetation index, plant height, canopy conductance, crop coverage, biomass, root indicators, or aerenchyma development status. The rice growth stages may include one or more of the following: greening stage, tillering stage, jointing and booting stage, heading and flowering stage, grain filling stage, maturity stage, or harvest stage; The field management measures may include one or more of the following: continuous flooding, intermittent irrigation, mid-term field drying, pre-harvest drainage, fertilization, straw return to the field, rice-duck co-cultivation, or other paddy field management measures.

[0035] Step S3: Based on GPP, environmental and management factors, construct... Flux response function H; Combined with appendix Figure 2 By combining Gross Primary Productivity (GPP) with environmental management factors, we can jointly construct... Flux response function H.

[0036] The The flux response function H is used to characterize the combined effects of photosynthesis and its related environmental regulatory factors on methane emission flux from paddy fields. Furthermore, the response function H can be parameterized according to site observations, regional data, or model application requirements, and can be an empirical function, a semi-empirical function, a piecewise function, a linear function, a nonlinear function, a multiplicative function, a logarithmic function, an exponential function, a Bayesian function, a machine learning parameterized function, or a combination thereof.

[0037] Furthermore, the response function H can be expressed as: ; in, The initial estimate of methane emission flux from paddy fields is given by , GPP is the total primary productivity of the paddy field area, W represents the water state, T represents the temperature condition, V represents the vegetation structure, P represents the rice growth stage, M represents the field management measures, and θ represents the set of parameters to be calibrated.

[0038] In another preferred embodiment, H is represented as: ; in, The methane emission response coefficient is used to characterize the methane emission intensity per unit GPP under different water states, temperature conditions, vegetation structures, rice growth stages, and field management practices. The dimensions of are from The units are jointly determined with GPP; in addition, It can be determined by site observation data, regional observation data, empirical relationships, semi-empirical relationships, process models, machine learning models, or a combination thereof.

[0039] Furthermore, it may include a time response correction step, which adjusts the methane emission flux estimation results to an hourly, daily, or fertile period scale based on the time response relationship between GPP and methane emission flux.

[0040] In a preferred embodiment, the initial estimate of the methane emission flux, after introducing the time response parameter τ, is expressed as follows: ; Where τ is the time response parameter and t is time.

[0041] Step S4, according to The flux response function H yields the initial estimate of methane emission flux from paddy fields; The initial estimate of the methane emission flux is the methane emission flux on an hourly, daily, fertile period, or annual scale.

[0042] Step S5: Obtain constraint information, which includes one or more of the following: methane emission flux observation, methane concentration observation, methane stable carbon isotope observation, isotope characteristics of methane emission sources in paddy fields, plant-mediated emission end-member information, non-plant-mediated emission end-member information, water status observation, temperature condition observation, soil redox status observation, or other paddy field methane process observation information. The methane emission flux observation may include eddy covariance methane flux observation, dynamic box methane flux observation, or static box methane flux observation; The methane concentration observation may include methane concentration observation at multiple altitude gradients or canopy methane concentration observation; The methane stable carbon isotope observations may include atmospheric methane isotope observations. The isotopes are stable isotopes.

[0043] When the constraint information includes observations of stable carbon isotopes of methane, the isotopic characteristics of methane emission sources from paddy fields can be obtained through observational data such as observed methane concentrations and atmospheric methane isotope values. .

[0044] In one implementation method This can be obtained using the Keeling plot method: ; in, These are atmospheric methane isotope observations, and the isotopes are stable isotopes. For methane concentration observation, 'a' represents the regression slope.

[0045] Furthermore, It can also be obtained through the Miller-Tans method, mass balance method, or other isotope source determination methods.

[0046] When constraint information includes , and At that time, flux-weighted isotope mixing relationships can be constructed: ; in, Contribution of plant-mediated emissions For plants to mediate the emission of isotope end-members, It is a non-plant-mediated emission of isotope end-members.

[0047] Furthermore, when there are observation errors, external source influences, or processes not explicitly expressed, residual terms or uncertainty terms can be introduced for correction.

[0048] when , and When the contribution of plant-mediated emissions is known or has a priori scope, Estimate using the following methods: ; in, The range of values ​​is constrained to 0 ≤ ≤ 1, Contribution of non-plant-mediated emissions for .

[0049] Step S6: Based on the constraint information, the initial estimate of methane emission flux is corrected to obtain the final estimate of methane emission flux from paddy fields; The correction of the initial estimate of methane emission flux can include adjustments made during the process of obtaining the initial estimate of methane emission flux. Flux response function H, methane emission response coefficient , function parameter θ or initial estimate of methane emission flux It can be corrected in various ways, including through multiplicative correction, additive correction, or parameter updates.

[0050] Furthermore, in conjunction with the appendix Figure 3 The initial estimate of methane emission flux was corrected to obtain the final estimate of methane emission flux from paddy fields, expressed as follows: ; in, Based on The initial estimate of methane emission flux obtained from the flux response function H. The constraint correction function is determined by the constraint information. This is the final estimate of methane emission flux after correction.

[0051] Furthermore, when the constraint information includes stable carbon isotope observation data, an isotope constraint correction function can be constructed based on the isotope source apportionment results obtained in step S5. Specifically, an isotope-constrained correction function is constructed by combining plant-mediated structure correction functions, isotope bias correction functions, and environmental factor regulatory terms.

[0052] Plant-mediated structure correction function Isotope bias correction function and environmental factor regulation items Couple the components and construct the isotope-constrained correction function. Its expression is: ; in, Used to characterize the bias correction of plant-mediated emission proportions relative to the prior structure of the GPP-driven model. Used to correct the consistency between model-predicted source-end isotope structures and observationally resolved source-end isotope characteristics. This is used to characterize the dynamic regulatory effects of environmental conditions and field management practices on methane production, oxidation, and transport processes. Therefore, the aforementioned It is not a fixed empirical correction coefficient, but a dynamic correction function determined by the proportion of plant-mediated emissions, source-end isotope bias, and environmental management factors.

[0053] Step S61: Construct the plant-mediated structure correction function ; Furthermore, it can be expressed as: ; in, This represents the proportion of plant-mediated emissions obtained from the inversion in step S5, which is also the proportion of plant contribution calculated from actual observations. This represents the implicit prior proportion of plant-mediated emissions in the GPP-driven model, which is also the proportion of plant contribution assumed by the model.

[0054] Wherein, α is the plant aerenchyma response coefficient, which can be determined using a least-squares error optimization method based on observational data. It is used to characterize the nonlinear regulatory strength of plant-mediated transport capacity in response to methane emissions. Its expression is: ; in, This represents the measured methane emission flux obtained from the i-th observation. This represents the initial estimate at the corresponding time point, while n represents the number of observations (sample size).

[0055] This represents the isotope constraint correction function that includes the α parameter, i.e.: ; ; in, This represents the implicit plant-mediated emission prior proportion in the GPP-driven model.

[0056] By adjusting the parameter α, the product of the initial estimate and the isotope constraint correction function is made as close as possible to the observed methane emission flux, i.e., the sum of squared residuals between the predicted and observed values ​​is minimized.

[0057] Based on the obtained α parameter, a plant-mediated structure correction function can be constructed. ; In addition, α can be segmented or functionalized according to the rice growth stage to reflect the differences in the regulation of methane emission process by the degree of development of plant aerenchyma at different growth stages.

[0058] Step S62: Construct the isotope bias correction function ; Specifically, define the source-end isotope relative deviation index. This is used to characterize the normalized deviation between the source isotopic characteristics of methane emissions from paddy fields obtained from methane concentration observations and atmospheric methane isotope observations and the source-end isotopic characteristics predicted by the model. Its expression is: ; in, This represents the isotopic characteristics of the methane emission source from paddy fields obtained by analyzing methane concentration observations and atmospheric methane isotope observations based on step S5. The model-predicted source-end isotope characteristics, calculated from the model's prior plant-mediated proportion and plant-mediated emission isotope endmembers, are expressed as follows: ; in, This represents the implicit prior proportion of plant-mediated emissions in the GPP-driven initial model.

[0059] based on Constructing an isotope bias correction function Its expression is: ; in, is the isotope deviation sensitivity coefficient, used to characterize the effect of the degree of source-end isotope deviation on the flux correction intensity. When When the value is close to 0, it indicates that the model's predicted source-end isotopic structure is largely consistent with the observed source-end characteristics. Approaching 1; when When the value increases, it indicates an increased deviation between the model's source-end structure and the observed source-end features. This is subsequently reduced to mitigate the direct amplification effect on flux estimation results under conditions of inconsistent source-end isotope structures.

[0060] in, This is a dimensionless parameter. A larger value indicates a stronger relative deviation between the measured isotopic signal and the source resolution result; a smaller value indicates a higher degree of consistency between the measured isotopic signal and the source resolution result. This index is not used to recalculate the source-end isotopic composition, but rather to measure the consistency between the source resolution result and the measured isotopic constraints, and serves as an input variable for the isotopic bias correction function.

[0061] Step S63: Construct environmental factor regulation terms ; Furthermore, considering the comprehensive regulatory effects of water status, rice growth stage, and field management practices on methane production, oxidation, and emission pathways in paddy fields, an environmental factor regulation term was constructed. The environmental factor regulation term is used to characterize the dynamic changes in plant-mediated transport processes, non-plant-mediated emission processes, and source-end isotope bias correction intensity under different environmental and management conditions. Its expression is: ; Where W represents the water status, P represents the rice growth stage, and M represents the field management measures; This represents the water state regulation function, which is used to characterize the effects of changes in water conditions, such as continuous flooding, intermittent irrigation, mid-term field drying, or pre-harvest drainage, on the anaerobic environment, methane production process, and methane oxidation process in paddy fields. This represents the regulatory function for different growth stages of rice, used to characterize changes in root activity, aerenchyma development, and plant-mediated transport capacity at different growth stages (such as tillering, heading, and maturity). This represents the field management regulation function, used to characterize the impact of fertilization, straw return to the field, irrigation, field drying, or other management measures on the supply of methane production substrates, redox environment, and emission pathway structure.

[0062] in, , and It can be determined based on site observation data, field management records, prior parameters from literature, or model calibration results.

[0063] The above corrections can combine the GPP-driven positive flux estimation results with the stable carbon isotope source apportionment results, so that the final flux estimation results are simultaneously constrained by photosynthesis, plant-mediated emission ratio, source-end isotope consistency, and environmental management conditions. This distinguishes it from the ordinary fixed-coefficient multiplication correction method and improves the process interpretability and dynamic adaptability of paddy field methane emission flux estimation.

[0064] Furthermore, the correction function is not limited to stable carbon isotope constraints; it can also be determined by methane flux observations, methane concentration observations, water level observations, soil redox status, temperature observations, vegetation structure observations, field management information, or combinations thereof, and used to correct the initial estimate of methane emission flux.

[0065] Step S7: Output the final estimated value, time series, spatial distribution map, cumulative emissions or uncertainty range of methane emission flux from paddy fields.

[0066] The final estimated value of methane emission flux from paddy fields can be time series of methane emission flux at hourly, daily, growing season, or annual scales. Combined with appendix Figure 4 The paper presents flowcharts for the above methods to perform site monitoring, regional estimation, or inventory optimization at the site and regional scales.

[0067] Furthermore, in conjunction with the appendix Figure 5 Taking the Jurong paddy field observation station as an example, the observation deployment of the paddy field station can be obtained. Through the deployment of tower-based SIF observation, eddy covariance methane flux observation, methane concentration / isotope observation, meteorological observation, water level observation, etc., SIF observation data, supporting photosynthetic parameters and environmental parameters, meteorological data, water management data, vegetation structure data, methane emission flux observation data, and methane stable carbon isotope observation data during the paddy field growing season can be obtained.

[0068] The SIF observation data may include SIF observations in a specific band or a narrow band, or may include target band or full-band integrals obtained by spectral conversion or model reconstruction of SIF in a specific band. In this embodiment, SIF observation data and associated photosynthetic parameters and environmental parameters are used as inputs, and the GPP of the paddy field area is simulated using an ecosystem mechanism process model.

[0069] Subsequently, GPP can be used as a key variable reflecting the process of photosynthesis regulating methane production and emissions in paddy fields. Combined with variables such as water level, soil temperature, air temperature, LAI (leaf area index), VPD (vapor pressure difference), rice growth stage, and field management practices, a system can be constructed. The flux response function H is derived, and an initial estimate of the methane emission flux is obtained: ; Further, methane emission flux observation data can be used to analyze H or Parameter calibration is then performed. If methane concentration observations and atmospheric methane isotope observations are available, the isotopic characteristics of methane emission sources can be further obtained. Based on the isotopic characteristics of methane emission sources, isotope endmembers of plant-mediated emissions, and isotope endmembers of non-plant-mediated emissions, isotope constraint relationships can be constructed to correct the initial estimates of H, methane emission response coefficients, or methane emission flux, thus obtaining the final estimate of methane emission flux.

[0070] In this embodiment, the initial estimate of methane emission flux is further corrected using stable carbon isotope observation data, combined with methane concentration observation data, to obtain the final estimate of methane emission flux. (See attached...) Figure 6 It can be seen that the final estimated value of methane emission flux after correction is close to the observed value of methane flux.

[0071] Furthermore, it also includes estimating the methane emission flux from paddy fields based on remote sensing data of the region, and obtaining data on the distribution of paddy fields in the study area, vegetation fluorescence remote sensing observation data, meteorological reanalysis data, vegetation index products, crop phenology data, and water management information.

[0072] First, SIF observation data and environmental management factors were resampled to a uniform spatiotemporal resolution and quality controlled. Then, SIF observation data and their associated photosynthetic and environmental parameters were used as inputs to simulate GPP using an ecosystem mechanism process model. Subsequently, based on GPP and... The flux response function H is used to calculate the initial estimate of methane emission flux from paddy fields in the region.

[0073] If the study area has prior information such as methane flux observations, atmospheric methane isotope observations, methane emission source isotope characteristics, water management data, or other constraint information, further constraint corrections can be made to obtain the spatial distribution and cumulative emissions of methane emission flux from paddy fields in the region.

[0074] Furthermore, this also includes processes based on observation constraints. Flux response function optimization was performed. At paddy field sites with continuous methane emission flux observations, the initial estimates of methane emission flux estimated by the model were validated using eddy covariance or box-type methane flux observations. The flux response function H is optimized for parameters.

[0075] If methane concentration observations, atmospheric methane isotope observations, water level, soil temperature, or soil redox state observations are simultaneously available, then these process observation information can be further used to correct the initial estimates of H, methane emission response coefficients, or methane emission fluxes. By simultaneously constraining changes in methane flux and environmental processes, the model's explanatory power for methane emission variations under different growth stages and water management conditions can be improved.

[0076] Furthermore, it also includes simplified applications under conditions of insufficient constraint information. In areas lacking methane flux or stable carbon isotope observations, it is possible to establish data on different paddy field types, different growth stages, and different water management conditions based on existing site observations, historical observations, literature data, or experience from similar areas. Prior range of flux response function parameters. If constraint information is completely unavailable, the uncorrected range is used. The flux response function H is used for initial estimation of methane emission flux, and the constraint term is evaluated as a source of uncertainty. This simplified application method is suitable for estimating methane emissions from paddy fields over large areas or historical periods.

[0077] This invention also proposes a system for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms, comprising the following modules: (1) Vegetation fluorescence remote sensing observation data acquisition and GPP simulation module, used to acquire vegetation fluorescence remote sensing observation data of paddy field area, and obtain the total primary productivity (GPP) of paddy field area based on the vegetation fluorescence remote sensing observation data; (2) Environmental and management factor acquisition module, used to acquire environmental and management factors that affect the methane emission process in paddy fields; The environmental and management factors include one or more of the following: water status, temperature conditions, vegetation structure, rice growth stage, and field management practices. (3) The flux response function construction module is used to construct functions based on GPP and environmental and management factors. Flux response function H; (4) Initial methane flux estimation module, used to estimate based on The flux response function H yields the initial estimate of methane emission flux from paddy fields; (5) Constraint correction module, used to correct the initial estimate of methane emission flux based on one or more of the following: methane emission flux observation, methane concentration observation, methane stable carbon isotope observation, isotope characteristics of methane emission sources in paddy fields, plant-mediated emission end-member information, non-plant-mediated emission end-member information, water status observation, temperature condition observation, soil redox status observation, or other paddy field methane process observation information, to obtain the final estimate of methane emission flux in paddy fields; (6) Results output module, used to output the final estimated value, time series, spatial distribution, cumulative emissions or uncertainty range of methane emission flux in paddy fields.

[0078] It is applied to the estimation of methane emission flux in paddy fields, optimization of regional greenhouse gas inventories, evaluation of agricultural methane emission reduction measures, validation of paddy field carbon cycle models, and estimation of methane emissions from wetlands or other anaerobic ecosystems with vegetation cover.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of this application.

[0080] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

Claims

1. A method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism, characterized in that... Includes the following steps: Step S1: Obtain vegetation fluorescence remote sensing observation data of the paddy field area, and obtain the total primary productivity (GPP) of the paddy field area based on the vegetation fluorescence remote sensing observation data; Step S2: Obtain environmental and management factors affecting methane emissions from paddy fields; Step S3: Based on GPP, environmental and management factors, construct... Flux response function H; Step S4, according to The flux response function H yields the initial estimate of methane emission flux from paddy fields; Step S5: Obtain constraint information, wherein the constraint information is stable carbon isotope observation data; Step S6: Based on the constraint information, construct an isotope constraint correction function to correct the initial estimate of methane emission flux, and obtain the final estimate of methane emission flux from paddy fields: ； in, Based on The initial estimate of methane emission flux obtained from the flux response function H. This is the isotope constraint correction function. This is the final estimate of methane emission flux after correction; Among them, the plant-mediated structure correction function Isotope bias correction function and environmental factor regulation items Couple the components and construct the isotope-constrained correction function. : ； in, α is the plant aerenchyma structure response coefficient, used to characterize the nonlinear regulatory strength of plant-mediated transport capacity in response to methane emissions. Contribution of plant-mediated emissions This indicates the implicit prior proportion of plant-mediated emissions in the GPP-driven model; ； in, For plants to mediate the emission of isotope end-members, For non-plant-mediated emission of isotope end-members, To obtain the isotopic characteristics of methane emission sources from paddy fields using observed methane concentration and atmospheric methane isotope values; in, , This is the isotope bias sensitivity coefficient. This is an index of the relative deviation of source-end isotopes. , The model-predicted source-end isotope characteristics are calculated from the plant-mediated emission prior proportion, plant-mediated emission isotope endmembers, and non-plant-mediated emission isotope endmembers. in, W represents moisture status, P represents the rice growth stage, and M represents field management measures. This represents the water state regulation function. This represents the regulatory function for the reproductive stage. This represents the control function for field management measures.

2. The method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism as described in claim 1, characterized in that, The vegetation fluorescence remote sensing data includes observations of sunlight-induced chlorophyll fluorescence.

3. The method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism according to claim 1, characterized in that, The observed values ​​of sunlight-induced chlorophyll fluorescence include one or more of the following: single-band sunlight-induced chlorophyll fluorescence, narrow-band sunlight-induced chlorophyll fluorescence, or integrated sunlight-induced chlorophyll fluorescence.

4. The method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism as described in claim 1, characterized in that, The environmental and management factors include one or more of the following: water status, temperature conditions, vegetation structure, rice growth stage, and field management practices.

5. The method for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanism according to claim 1, characterized in that, The initial estimates of methane emission flux are methane emission fluxes on hourly, daily, fertile period, or annual scales.

6. A system for estimating methane emission flux from paddy fields based on vegetation fluorescence remote sensing observation and photosynthetic regulation mechanisms, used to implement the method described in any one of claims 1-5, characterized in that, Includes the following modules: (1) Vegetation fluorescence remote sensing observation data acquisition and GPP simulation module, used to acquire vegetation fluorescence remote sensing observation data of paddy field area, and obtain the total primary productivity (GPP) of paddy field area based on the vegetation fluorescence remote sensing observation data; (2) Environmental and management factor acquisition module, used to acquire environmental and management factors that affect the methane emission process in paddy fields; (3) The flux response function construction module is used to construct functions based on GPP and environmental and management factors. Flux response function H; (4) Initial methane flux estimation module, used to estimate based on The flux response function H yields the initial estimate of methane emission flux from paddy fields; (5) Constraint correction module, used to correct the initial estimate of methane emission flux to obtain the final estimate of methane emission flux in paddy fields; (6) Results output module, used to output the final estimated value, time series, spatial distribution, cumulative emissions or uncertainty range of methane emission flux in paddy fields; Among them, the constraint information is stable carbon isotope observation data, and the isotope constraint correction function is constructed by the plant-mediated structure correction function, the isotope bias correction function, and the environmental factor regulation term.

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