Sea surface methane in-situ production daily output inversion method and system based on water color remote sensing data

Abstract

The invention relates to a sea surface methane in-situ production daily output inversion method and system based on water color remote sensing, and relates to the technical field of marine environment data monitoring, and the method comprises the steps: carrying out the inversion analysis of an optical parameter and a diffusion attenuation coefficient through the sea surface remote sensing reflectivity, and then combining the underwater spectral irradiance and an environment parameter to obtain the sea surface methane in-situ production daily output. The method comprises the following steps: analyzing and integrating through an apparent quantum yield model to obtain the methane light production daily output, analyzing and fitting the relationship between the methane biological production efficiency and the concentration and characteristics of organic matters through a biological production efficiency model, integrating to obtain the methane biological production daily output, and finally calculating the methane biological production daily output based on the light production daily output and the biological production daily output. And determining the sea surface methane in-situ production daily output. Therefore, starting from the process of sea surface methane production, the influence of factors such as optical characteristics, downlink irradiance and environmental parameters on the sea surface methane production is deeply studied, and the methane light / biological production daily output is accurately inverted and analyzed.

Description

Technical Field

[0001] The present application relates to the technical field of marine environmental data monitoring, and in particular to a method and system for inverting the daily production of in-situ sea surface methane based on water color remote sensing data. Background Art

[0002] In the study of global climate change, greenhouse gas monitoring, and the ocean carbon cycle, monitoring methane (CH4) concentrations at the sea surface is crucial for understanding carbon cycle processes and assessing environmental change. Currently, most studies on sea surface methane rely on in situ measurements or laboratory analyses. These methods offer reasonable accuracy but are limited in spatial coverage, temporal resolution, and data acquisition efficiency.

[0003] Among related technologies, existing ocean methane inversion and analysis techniques primarily focus on the total distribution of methane. This includes directly measuring methane concentrations in seawater and assessing the ocean's contribution to atmospheric methane through methods such as water-air exchange models. However, the generation and consumption of methane is complex, and its production is influenced by environmental parameters. Existing technical solutions overlook key aspects of methane production, including but not limited to its specific sources, transformation pathways, and influencing factors. These approaches lack in-depth research into the specific processes of methane generation, hinder understanding the impact of environmental changes and other factors on methane production, and consequently, are unable to accurately invert sea surface methane production. Summary of the Invention

[0004] The present application provides a method and system for inverting the daily production of in situ sea surface methane based on water color remote sensing data. The method deeply analyzes the light / biological production process during methane production. By quantitatively analyzing the impact of factors such as environmental parameters on methane production efficiency, the method can accurately analyze the daily production of light generation and biological production during the methane production process, accurately analyze the impact of environmental changes on methane production, and solve the problem that the existing technology cannot accurately invert sea surface methane production due to the lack of in-depth research on the methane production process.

[0005] In a first aspect, the present application provides a method for inverting the daily production of in-situ sea surface methane based on water color remote sensing data, comprising:

[0006] Acquire water color remote sensing data and environmental remote sensing data of the target area, and obtain underwater spectral irradiance data through a preset radiation transfer model, wherein the water color remote sensing data includes sea surface remote sensing reflectivity;

[0007] Preprocessing and inversion analysis are performed based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, wherein the optical parameters include an absorption coefficient spectrum and a spectral slope of colored soluble organic matter;

[0008] Based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, light production efficiency is analyzed using a preset apparent quantum yield model, and daily integration is performed to obtain daily methane light production output;

[0009] Using the optical parameters and the environmental remote sensing data as input, the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter is analyzed and fitted using a preset bioproduction efficiency model, and the daily methane bioproduction output is obtained by integrating the daily-scale efficiency;

[0010] Based on the daily methane photoproduction and the daily methane biological production, the daily in-situ production of sea surface methane is inverted in the target area.

[0011] Optionally, preprocessing and inversion analysis are performed based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, including:

[0012] Performing preprocessing according to the sea surface remote sensing reflectivity to obtain target remote sensing reflectivity;

[0013] Performing principal component analysis and clustering processing based on the target remote sensing reflectance to obtain the target ocean color cluster to which the water color remote sensing data belongs;

[0014] According to the target ocean color cluster, the pre-trained regression coefficients are substituted into the multiple linear regression equation to obtain the absorption coefficient spectrum of the colored soluble organic matter in the water body at each wavelength;

[0015] The spectral slope of colored soluble organic matter is obtained by analyzing the target remote sensing reflectance at each wavelength;

[0016] The sea surface remote sensing reflectivity is optimized by a preset optimization algorithm, and principal component analysis and clustering are performed on the optimized sea surface remote sensing reflectivity to obtain a diffuse attenuation coefficient.

[0017] Optionally, based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, light production efficiency is analyzed using a preset apparent quantum yield model, and daily integration is performed to obtain daily methane light production, including:

[0018] Based on the environmental remote sensing data and the absorption coefficient spectrum, a spectral quantum yield spectrum is fitted using a preset apparent quantum yield model, wherein the spectral quantum yield spectrum is used to represent the efficiency of generating methane after the colored soluble organic matter absorbs photons of a specific wavelength;

[0019] Performing spectral interpolation processing on the absorption coefficient spectrum, the diffuse attenuation coefficient, the underwater spectral irradiance data, and the spectral quantum yield spectrum respectively by an interpolation method to obtain a target absorption coefficient spectrum, a target diffuse attenuation coefficient spectrum, a target underwater spectral irradiance, and a target spectral quantum yield spectrum with a complete spectral range and matching spectral resolution;

[0020] Using the exponential attenuation algorithm of the underwater light field and combining the target diffuse attenuation coefficient spectrum, the target underwater spectral irradiance at the surface layer is extended to a specified depth to obtain a spectral scalar irradiance;

[0021] Analyzing the rate at which the colored soluble organic matter absorbs light quanta according to the target absorption coefficient spectrum and the spectral scalar irradiance to obtain a photon absorption rate;

[0022] Calculate the methane photoproduction rate at a specified depth using the photon absorption rate and the target spectral quantum yield spectrum as input;

[0023] Based on the methane photoproduction rate, the daily methane photoproduction output of the mixed layer or the light-transmitting layer is analyzed by spectral range integration, vertical integration and time integration.

[0024] Optionally, the optical parameters and the environmental remote sensing data are used as inputs, a preset bioproduction efficiency model is used to analyze and fit the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter, and the daily methane bioproduction output is obtained by integrating the daily-scale efficiency, including:

[0025] Inputting the absorption coefficient spectrum and the spectrum slope into a bioproduction efficiency model, analyzing and fitting the relationship between methane bioproduction and organic matter, and analyzing the relationship between the saturation reaction rate in methane bioproduction and the characteristics of the organic matter itself to obtain the methane bioproduction efficiency;

[0026] Parameter fitting is performed based on the environmental remote sensing data and the methane biological production efficiency, and the daily methane biological production output is obtained by integrating the daily scale efficiency.

[0027] Optionally, underwater spectral irradiance data can be obtained using a preset radiation transfer model, including:

[0028] Obtaining cloud data from the water color remote sensing data or a preset atmospheric radiation model, and calculating underwater spectral scalar irradiance using a preset radiation transfer model;

[0029] The underwater spectral scalar irradiance is corrected for influence according to the cloud layer data, and is integrated according to a preset time period to obtain a daily integrated spectral irradiance as the underwater spectral irradiance data.

[0030] Optionally, before analyzing the methane photoproduction efficiency using a pre-defined apparent quantum yield model, the following is also included:

[0031] Acquiring experimental data samples from a preset sample area through a preset methane production experiment, wherein the experimental data samples include an absorption spectrum sample and an environmental parameter sample;

[0032] Based on the experimental data sample analysis, the integral data of colored soluble organic matter in different dimensions are calculated, and the apparent quantum yield of methane photoproduction is calculated by combining the temperature information in the absorption spectrum sample and the environmental parameter sample to establish an apparent quantum yield model.

[0033] Optionally, based on the analysis and calculation of the integral data of colored soluble organic matter in different dimensions of the experimental data sample, and in combination with the temperature information in the absorption spectrum sample and the environmental parameter sample, the apparent quantum yield of methane photoproduction is analyzed and calculated to establish an apparent quantum yield model, including:

[0034] The experimental data samples are analyzed using the preset radiation transfer model. Calculate the integral of the number of photons absorbed by colored soluble organic matter in different dimensions to obtain the number of photons absorbed by colored soluble organic matter in the mixed layer or photic layer of the sea surface during the day.

[0035] Establish the apparent quantum yield model and use the number of photons For input, according to Calculate the daily production of methane light in the ocean surface mixed layer or euphotic layer;

[0036] according to Analyze the relationship between methane photoproduction and the absorption coefficient spectrum of colored soluble organic matter, and combine different temperature information to Analyze the changes in methane photoproduction with temperature and adjust the parameters of the apparent quantum yield model;

[0037] Among them, a g is the absorption coefficient spectrum of colored soluble organic matter, K d is the diffuse attenuation coefficient of the sea surface layer, ΔCH4 is the output of methane light production, is the apparent quantum yield of methane photoproduction, and x are model parameters based on the experimental data sample fitting, b is the temperature correction factor, and T is the temperature.

[0038] Optionally, before analyzing and fitting the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter using a preset bioproduction efficiency model, the following steps may be included:

[0039] Based on the experimental data samples, the changes in methane concentration are analyzed using differential equation modeling, the methane oxidation rate constant is determined, and modeling is performed to construct a methane photoproduction oxidation model;

[0040] The methane oxidation rate constant is used to estimate the methane oxidation consumption, quantitatively analyze the methane biological production rate, and obtain the methane biological production efficiency;

[0041] Based on the methane bioproduction efficiency, a bioproduction efficiency model is constructed, and by analyzing the relationship between the methane bioproduction efficiency, organic matter characteristics and environmental parameters, the parameters of the bioproduction efficiency model are regulated.

[0042] Optionally, based on the methane bioproduction efficiency, a bioproduction efficiency model is constructed, and by analyzing the relationship between the methane bioproduction efficiency, organic matter characteristics and environmental parameters, the parameters of the bioproduction efficiency model are adjusted, including:

[0043] The changes in methane concentration were modeled using partial differential equations, and a biological production efficiency model was constructed. The methane oxidation rate constant was used as input. Analysis and calculation of methane biological production efficiency P bio ;

[0044] Establish parameter control model according to Analyzing the relationship between the efficiency of methane bioproduction and the concentration of organic matter, and analyzing the relationship between the saturation reaction rate and the characteristics of the organic matter itself, and regulating the bioproduction efficiency model;

[0045] according to The relationship between the efficiency of methane biological production and temperature is analyzed, and the biological production efficiency model is regulated.

[0046] In a second aspect, the present application provides a system for in-situ daily production inversion of sea surface methane based on water color remote sensing, comprising:

[0047] A data acquisition module is used to acquire water color remote sensing data and environmental remote sensing data of the target area, and underwater spectral irradiance data through a preset radiation transfer model, wherein the water color remote sensing data includes sea surface remote sensing reflectivity;

[0048] A preprocessing and inversion analysis module, configured to perform preprocessing and inversion analysis based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, wherein the optical parameters include an absorption coefficient spectrum and a spectral slope of colored soluble organic matter;

[0049] a light production daily yield analysis module, configured to analyze light production efficiency using a preset apparent quantum yield model based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, and to perform daily-scale integration to obtain the daily methane light production yield;

[0050] A bioproduction daily yield analysis module is configured to use the optical parameters and the environmental remote sensing data as inputs, analyze and fit the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter through a preset bioproduction efficiency model, and obtain the methane bioproduction daily yield by integrating the daily scale efficiency;

[0051] The daily production inversion module is used to invert the daily production of sea surface methane in situ in the target area based on the daily production of methane produced by light and the daily production of methane produced by biological means.

[0052] In summary, the embodiment of the present application utilizes the inversion analysis of optical parameters and diffuse attenuation coefficients of sea surface remote sensing reflectivity, and then combines the underwater spectral irradiance and environmental parameters to first analyze the daily methane light production rate through the apparent quantum yield model, and integrate to obtain the daily methane light production output. Then, the relationship between the methane bioproduction efficiency and the concentration and characteristics of organic matter is analyzed and fitted through the biological production efficiency model, and the daily methane bioproduction output is obtained by integration. Finally, based on the daily light production output and the daily biological production output, the daily in-situ production output of sea surface methane is determined. Therefore, the present application starts with the process of sea surface methane production, and conducts in-depth research on the influence of factors such as optical properties, downward irradiance, and environmental parameters on sea surface methane production, so as to achieve accurate inversion and analysis of the daily methane light / biological production output. The present application provides a comprehensive and accurate inversion model for the light production of marine methane, which solves the problem that the existing technology cannot accurately invert the sea surface methane production due to the lack of in-depth research on the methane production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0055] Figure 1 A schematic flow chart of a method for inverting daily production of in-situ sea surface methane based on water color remote sensing provided in an embodiment of the present application;

[0056] Figure 2This is a schematic flow chart of the steps of a method for inverting daily production of in-situ sea surface methane based on water color remote sensing, provided in an optional embodiment of the present application;

[0057] Figure 3 This is a flowchart of a method for inverting the daily production of in-situ sea surface methane based on water color remote sensing, provided as an optional example of this application;

[0058] Figure 4 A structural block diagram of a system for in-situ daily production inversion of sea surface methane based on water color remote sensing provided in an embodiment of the present application;

[0059] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] To facilitate understanding of the embodiments of the present application, further explanation will be given below in conjunction with the drawings and specific embodiments. The embodiments do not constitute a limitation on the embodiments of the present application.

[0062] Figure 1 The present invention provides a flow chart of a method for inverting the daily production of sea surface methane in situ production based on water color remote sensing. Figure 1 As shown, the method for inverting the daily production of sea surface methane in situ production based on water color remote sensing provided in the embodiment of the present application may specifically include the following steps:

[0063] Step 110: Acquire water color remote sensing data and environmental remote sensing data of the target area and acquire underwater spectral irradiance data through a preset radiation transfer model.

[0064] The water color remote sensing data includes sea surface remote sensing reflectivity.

[0065] In this embodiment, the water color remote sensing data is water color satellite data. The remote sensing data of a specified area can be obtained through satellites and used as a benchmark for inverting the daily methane production in the area. The water color remote sensing data mainly includes the sea surface remote sensing reflectance (RSR) rs); Environmental remote sensing data mainly include temperature parameters (SeaSurfaceTemperature); underwater spectral irradiance data, also known as underwater spectral scalar irradiance, is mainly calculated through radiation transfer model.

[0066] Step 120 : Preprocessing and inversion analysis are performed based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients.

[0067] Wherein, the optical parameters include the absorption coefficient spectrum and spectrum slope of the colored soluble organic matter.

[0068] In practice, satellite remote sensing data acquired by different satellites may have different wavelengths. Therefore, this embodiment first preprocesses the sea surface remote sensing reflectance, converting it to a preset wavelength. The preprocessed sea surface remote sensing reflectance is then inverted and analyzed to calculate the absorption coefficient spectrum and spectral slope of colored soluble organic matter (CDOM). Furthermore, the diffuse attenuation coefficient algorithm is used to analyze the sea surface remote sensing reflectance to obtain the diffuse attenuation coefficient.

[0069] Step 130 , based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data and the diffuse attenuation coefficient, analyze the light production efficiency through a preset apparent quantum yield model, and perform daily scale integration to obtain the daily methane light production output.

[0070] In this embodiment, the apparent quantum yield model is based on experimental data and is established by analyzing key parameters involved in the photoproduction process of surface methane. In this embodiment, the absorption coefficient spectrum of CDOM, underwater spectral irradiance data, environmental remote sensing data, and diffuse attenuation coefficient are input into the apparent quantum yield model. The model first analyzes the apparent quantum yield (AQY) based on the input parameter data. The AQY is used to quantitatively describe the photoproduction process, that is, the number of methane molecules produced per unit photon, which is a "rate" relative to the number of photons. The model can then combine the various parameter data and the apparent quantum yield to calculate the actual daily photoproduction rate under the current parameter conditions. The time integration based on the daily photoproduction rate is used to obtain the daily methane photoproduction rate.

[0071] The daily production rate refers to the methane production per unit time (such as per day), which is a "rate" (its unit can be nmol / m2). -2 day -1 ).

[0072] Daily production refers to the total amount of methane produced on a specific day, which is an absolute value (the unit can be nmolm -2 )

[0073] Step 140, using the optical parameters and the environmental remote sensing data as input, analyzing and fitting the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter through a preset bioproduction efficiency model, and obtaining the daily methane bioproduction output by integrating the daily scale efficiency.

[0074] In this embodiment, the biological production efficiency model is established based on experimental data and combined with analysis of key parameters involved in the production process of sea surface methane, including processes such as oxygen / oxygen photoproduction and aerobic biological production.

[0075] This embodiment combines optical parameters, environmental remote sensing data and other parameters to quantitatively analyze the methane bioproduction rate, analyze the methane bioproduction efficiency / rate per unit time, and then obtain the daily methane bioproduction output by integrating the daily-scale efficiency.

[0076] Step 150: Invert the daily in-situ production of sea surface methane in the target area based on the daily methane production from light and the daily methane production from biological production.

[0077] In a specific implementation, after obtaining the daily methane production from light and biomass, this embodiment can accurately calculate the daily in-situ production of sea surface methane.

[0078] Among them, the in-situ production daily output includes the light production daily output and the biological production daily output.

[0079] It can be seen that the embodiment of the present application uses the optical parameters and diffuse attenuation coefficients of the sea surface remote sensing reflectivity inversion analysis, combined with the underwater spectral irradiance and environmental parameters as input, and respectively analyzes, fits and performs time integration through the apparent quantum yield model and the biological production efficiency model to obtain the daily methane light production and biological production daily production, and finally inverts the daily production of in-situ methane production at the sea surface. This embodiment comprehensively considers factors such as optical properties, downward irradiance, and environmental parameters. On the one hand, it accurately inverts and calculates the methane light / biological production efficiency to obtain the light / biological production daily production through time integration, and then determines the in-situ production daily production, realizing the automated inversion of methane light / biological production daily production in a large area, breaking through the limitations of low spatial and temporal resolution in traditional sea surface methane monitoring methods, and providing a global, large-scale, continuous, and real-time monitoring capability for methane in-situ production daily production.

[0080] Compared to traditional laboratory analysis methods, this embodiment significantly improves monitoring efficiency and expands data coverage, reducing reliance on and costs for manual measurements. This provides a comprehensive and accurate inversion model for photo- and bio-production of marine methane, significantly improving the stability and reliability of the inversion of total daily methane production. This addresses the existing inability to accurately invert sea surface methane production due to a lack of in-depth research into the methane production process.

[0081] Reference Figure 2 , shows a schematic flow chart of the steps of a method for inverting the daily production of in-situ methane on the sea surface based on water color remote sensing, provided in an optional embodiment of the present application. The method for inverting the daily production of in-situ methane on the sea surface based on water color remote sensing may specifically include the following steps:

[0082] Step 210: Acquire water color remote sensing data and environmental remote sensing data of the target area and acquire underwater spectral irradiance data through a preset radiation transfer model.

[0083] The water color remote sensing data includes sea surface remote sensing reflectivity.

[0084] In related technologies, existing sea surface methane monitoring methods mostly rely on in-situ measurements. Due to the limitations of spatial coverage and temporal resolution, this technical solution cannot meet the needs of large-scale, real-time monitoring.

[0085] To solve this technical problem, this embodiment fully utilizes the advantages of water color remote sensing data to achieve global monitoring of daily methane photo / biological production, making up for the spatial limitations of in-situ measurements and providing more extensive temporal and spatial data support.

[0086] Reference Figure 3 In order to realize the inversion analysis of methane production efficiency in the light / biological production stage based on water color remote sensing data and determine the daily methane production, this embodiment introduces spectral scalar irradiance as underwater spectral irradiance data, and combines it with environmental parameters to assist in the inversion analysis of the daily production of sea surface methane.

[0087] Optionally, this embodiment obtains underwater spectral irradiance data through a preset radiation transfer model, which may include: obtaining cloud data from the water color remote sensing data or a preset atmospheric radiation model, and calculating the underwater spectral scalar irradiance through a preset radiation transfer model; performing influence correction on the underwater spectral scalar irradiance according to the cloud data, and integrating it according to a preset time period to obtain daily integrated spectral irradiance as the underwater spectral irradiance data.

[0088] In this embodiment, the radiation transfer model may be a STAR model (System for Transfer of Atmospheric Radiation), and the underwater spectral scalar irradiance may be calculated using the irradiance transfer model.

[0089] In actual implementation, factors such as cloud cover and atmospheric ozone content may affect the spectral scalar irradiance. Therefore, this embodiment can correct for the effects of cloud cover when calculating the underwater spectral scalar irradiance. The corrected spectral scalar irradiance is then integrated over a certain period of time (e.g., 24 hours) to obtain the daily integrated spectral irradiance. This serves as the underwater spectral irradiance data for subsequent integration processing in the calculation of the daily methane production rate.

[0090] The cloud layer data may be obtained through satellite remote sensing data (such as SeaWiFS) or an atmospheric radiation model (such as TOMS), which is not limited in this embodiment.

[0091] Step 220 : Preprocessing and inversion analysis are performed based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients.

[0092] Wherein, the optical parameters include the absorption coefficient spectrum and spectrum slope of the colored soluble organic matter.

[0093] In the specific implementation, refer to Figure 3 Acquired water color remote sensing data is a type of satellite data and typically includes corresponding data bands. To improve inversion efficiency, this embodiment performs a unified preprocessing of the sea surface remote sensing reflectance. This preprocessing process includes, but is not limited to, data band conversion and data band normalization. Principal component analysis and classification of the preprocessed sea surface remote sensing reflectance can then be performed to invert and analyze the CDOM absorption coefficient spectrum and spectral slope.

[0094] Optionally, this embodiment performs preprocessing and inversion analysis based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, which may include the following sub-steps:

[0095] Sub-step 2201 , performing preprocessing based on the sea surface remote sensing reflectivity to obtain target remote sensing reflectivity.

[0096] In a specific implementation, this embodiment converts the sea surface remote sensing reflectivity into a preset standard band, such as the SeaWiFS standard band, which mainly includes six bands: 412 nanometers (nm), 443 nm, 490 nm, 510 nm, 555 nm, and 670 nm. The sea surface remote sensing reflectivity of different satellite data bands can also be unified into this standard band. The input sea surface remote sensing reflectivity of each band can then be logarithmically transformed and normalized to obtain the target remote sensing reflectivity.

[0097] Sub-step 2202: performing principal component analysis and clustering processing based on the target remote sensing reflectance to obtain the target ocean color cluster to which the water color remote sensing data belongs.

[0098] In this embodiment, when performing principal component analysis on the target remote sensing reflectivity, an eigenvector matrix, such as a PCA eigenvector matrix, can be used to analyze the first three principal components (PCs for short) in the target remote sensing reflectivity to obtain the corresponding three principal component values. Then, clustering processing is performed based on the values ​​of the three principal components. Through a pre-trained clustering model, the ocean color cluster corresponding to the input remote sensing reflectivity is analyzed and determined to obtain the cluster number to which the remote sensing reflectivity belongs.

[0099] In a specific implementation, this embodiment can pre-train a clustering model, which is trained with different ocean color cluster samples as training data to obtain an ocean color cluster classifier for automatically analyzing the input principal component values ​​to determine the cluster number corresponding to the remote sensing reflectance.

[0100] Among them, the ocean color cluster samples contain at least 9 ocean color clusters. Analyzing the input principal component values ​​can determine which of the 9 ocean color clusters the remote sensing reflectance belongs to.

[0101] Sub-step 2203, based on the target ocean color cluster, using the pre-trained regression coefficients, substitute them into the multiple linear regression equation to obtain the absorption coefficient spectrum of the colored soluble organic matter in the water body corresponding to each wavelength.

[0102] In this embodiment, a multiple linear regression equation can be pre-trained. The multiple linear regression equation takes the wavelength corresponding to the remote sensing reflectance, the regression coefficient, and the principal component value as input to analyze and calculate the absorption coefficient spectrum.

[0103] In actual implementation, the regression coefficient is determined according to the ocean color cluster number. In this embodiment, the corresponding regression coefficient can be pre-trained for each cluster number. After determining the target ocean color cluster, the regression coefficient is selected according to the cluster number.

[0104] In the specific implementation, considering the six wavelengths corresponding to the remote sensing reflectance data, this embodiment analyzes and calculates each wavelength (located at 275-450nm) separately, and finally obtains a complete CDOM absorption coefficient spectrum. The absorption coefficient spectrum corresponds to the range of: 275-450nm

[0105] For example, to invert and analyze the absorption coefficient spectrum from the sea surface remote sensing reflectivity, this example provides a multivariate linear regression equation to invert and analyze the absorption coefficient spectrum. The formula can be referred to as follows:

[0106] ln(a g (λ))=β0(λ)+β1(λ)*PC1 i +β2(λ)*PC2 i +β3(λ)*PC3 i

[0107] Among them, a g is the CDOM absorption coefficient spectrum, β0, β1, β2 and β3 are regression coefficients determined according to the cluster number, λ is the corresponding input wavelength, and i is the cluster number.

[0108] Sub-step 2204 , analyzing the target remote sensing reflectance at each wavelength to obtain the spectral slope of the colored soluble organic matter.

[0109] In the specific implementation, the spectral slope of CDOM is analyzed, mainly taking the remote sensing reflectance of different wavelengths as input, and analyzing the spectral slope value of interest.

[0110] For example, to calculate the spectral slope of CDOM based on remote sensing reflectance analysis at different wavelengths, this embodiment can be calculated using the following formula:

[0111] ln(S 275-295 )=α+β*ln[R rs (443)]+γ*ln[R rs (488)]+δ*ln[R rs (531)]+ε

[0112] *ln[R rs (555)]+ζ*ln[R rs (667)]

[0113] Among them, S 275-295 is the spectral slope value of interest, and α, β, γ, δ, ε, and ζ are all calculated and measured parameters. Preferably, α = -3.0230, β = 0.3101, γ = 0.0732, δ = -0.4528, ε = 0.2078, and ζ = -0.0309.

[0114] Sub-step 2205 , optimizing the sea surface remote sensing reflectivity using a preset optimization algorithm, and performing principal component analysis and clustering on the optimized sea surface remote sensing reflectivity to obtain a diffuse attenuation coefficient.

[0115] In its implementation, this embodiment uses remotely sensed sea surface reflectance as input. By optimizing the SeaUV algorithm, the natural logarithm is extracted from the remotely sensed reflectance. After logarithmic conversion, the data is standardized (e.g., normalized). Principal component analysis (PCA) is then performed on the standardized data. When analyzing the PCA, the eigenvectors of the SeaUV algorithm are combined to calculate the target PCA values, which can include four PCA values.

[0116] Cluster analysis is performed on the first two of the four principal component values ​​(e.g., using the SeaUV clustering version) to determine the ocean optical clusters. By clustering and distinguishing the sea surface remote sensing reflectivity, the prediction accuracy can be improved, making it more suitable for areas with complex optical properties.

[0117] Finally, the diffuse attenuation coefficients at different wavelengths were calculated for the four principal component values ​​using the multivariate linear regression model optimized with the SeaUV algorithm, and the true diffuse attenuation coefficient Kd was obtained based on the exponential calculation results.

[0118] As an example, this embodiment provides a formula for calculating the diffuse attenuation coefficient Kd(λ) of each principal component value, as follows:

[0119] ln[K d (λ)]=α+βPC1+γPC2+βPC3+∈PC4

[0120] In the above formula, α, β, γ, δ and ∈ are regression coefficients, PC i are the corresponding four principal component values, and λ is the band.

[0121] For the four principal component values ​​obtained, the true Kd value can be obtained by taking the exponent using the following formula:

[0122]

[0123] Among them, based on the pre-trained regression equation, the wavelength of the Kd value that can be obtained can be 320nm, 340nm, 380nm, 412nm, 443nm or 490nm.

[0124] Therefore, this embodiment achieves the inversion and analysis of the optical parameters and diffuse attenuation coefficient of CDOM from remote sensing reflectance through preprocessing, principal component analysis, color clustering, etc., which serves as the input for subsequent analysis of methane production rate. In addition, combined with the above calculation processes, the optical parameters related to methane inversion in each band can be accurately calculated, including the absorption coefficient spectrum a g (continuous spectrum), spectral slope value S 275-295 (spectral characteristic values), and diffuse attenuation coefficient Kd (covering key wavelengths in the ultraviolet and blue light bands).

[0125] Step 230 : fitting a spectral quantum yield spectrum using a preset apparent quantum yield model based on the environmental remote sensing data and the absorption coefficient spectrum.

[0126] Among them, the spectral quantum yield spectrum is used to indicate the efficiency of colored soluble organic matter in producing methane after absorbing photons of a specific wavelength.

[0127] In specific implementation, this embodiment inverts and calculates the daily output of sea surface methane photoproduction, mainly involving three core elements, namely, underwater spectral scalar irradiance, the ocean surface spectral diffuse attenuation coefficient and CDOM absorption coefficient spectrum in optical parameters, and the spectral quantum yield spectrum of methane photoproduction.

[0128] The spectral quantum yield spectrum is obtained by a pre-constructed apparent quantum yield model. This embodiment uses underwater spectral scalar irradiance and absorption coefficient spectrum as inputs, and calculates and analyzes the spectral quantum yield spectrum through the apparent quantum yield model.

[0129] The apparent quantum yield model and the spectral quantum yield spectrum are obtained by conducting a series of experiments related to methane photoproduction and modeling and measuring based on the experimental results data, which will not be described in detail in this embodiment.

[0130] Step 240, performing spectral interpolation processing on the absorption coefficient spectrum, the diffuse attenuation coefficient, the underwater spectral irradiance data, and the spectral quantum yield spectrum respectively by an interpolation method to obtain a target absorption coefficient spectrum, a target diffuse attenuation coefficient spectrum, a target underwater spectral irradiance, and a target spectral quantum yield spectrum with a complete spectral range and matching spectral resolution.

[0131] In practice, the data obtained above is typically discrete wavelength values, while sea surface remote sensing reflectance corresponds to six wavelengths. To achieve coverage of the entire effective wavelength range, interpolation methods (such as cubic spline interpolation) can be used to process the absorption coefficient spectrum, diffuse attenuation coefficient, underwater spectral irradiance data, and spectral quantum yield spectrum to obtain complete data within the spectral range.

[0132] Step 250 , using an exponential attenuation algorithm of the underwater light field and combining the target diffuse attenuation coefficient spectrum, the target underwater spectral irradiance at the surface layer is extended to a specified depth to obtain a spectral scalar irradiance.

[0133] Step 260 , analyzing the rate at which the colored soluble organic matter absorbs light quanta according to the target absorption coefficient spectrum and the spectral scalar irradiance to obtain a photon absorption rate.

[0134] Step 270 , using the photon absorption rate and the target spectral quantum yield spectrum as input, calculates the methane photoproduction rate at a specified depth.

[0135] Step 280 : Analyze the daily methane photoproduction output of the mixed layer or the light-transmitting layer based on the methane photoproduction rate by spectral range integration, vertical integration, and time integration.

[0136] A unified description of steps 250 to 280 is provided:

[0137] In practice, different ocean depths correspond to different underwater light fields. Deeper areas experience exponential decay in the underwater light field, which also affects the spectral scalar irradiance. Therefore, when using spectral scalar irradiance as one of the input parameters to invert the methane photoproduction rate, the attenuation of spectral scalar irradiance needs to be considered when determining the daily methane photoproduction rate, thereby improving the accuracy of the inversion.

[0138] Specifically, using an exponential decay algorithm for underwater light fields, the surface underwater spectral irradiance is extended to a specified depth, generating the spectral scalar irradiance at that depth. Then, using the target absorption coefficient spectrum and the spectral scalar irradiance at the specified depth as a benchmark, the photon absorption rate of CDOM at the specified depth is analyzed to obtain the photon absorption rate. Combining the photon absorption rate with the spectral quantum yield spectrum, the methane photoproduction rate is analyzed.

[0139] Considering that the effective wavelength range is 290-490nm, it is used as the spectral range and integrated along the spectral range. The vertical integration is used to analyze the daily methane photoproduction rate of the mixed layer or the translucent layer. Finally, the time integration (i.e., daily scale integration) is used to analyze the daily methane photoproduction rate of the mixed layer or the translucent layer.

[0140] Therefore, this embodiment rationally inverts and analyzes the daily methane photoproduction rate from various dimensions, such as spectral range and vertical depth, enabling accurate calculation of the methane production rate and, consequently, the daily methane photoproduction yield. In practical implementation, the photoproduction yield inversion method provided in this embodiment can be used to calculate the daily methane photoproduction yield distribution on a global or regional spatial gridded dataset, thereby determining the daily methane photoproduction yield and calculating the corresponding global or regional total methane photoproduction budget.

[0141] Furthermore, in this embodiment, the methane photoproduction efficiency is related to the AQY value. As one of the key parameters for inverting the methane photoproduction efficiency, this embodiment uses the apparent quantum yield model to analyze and calculate the AQY value. Before modeling a series of experimental data to construct an apparent quantum yield model, the model is modeled.

[0142] In an optional embodiment, before analyzing the methane photoproduction efficiency through a preset apparent quantum yield model, this embodiment also includes: obtaining experimental data samples from a preset sample area through a preset methane production experiment, the experimental data samples including absorption spectrum samples and environmental parameter samples; analyzing and calculating the integral data of colored soluble organic matter in different dimensions based on the experimental data samples, and analyzing and calculating the apparent quantum yield of methane photoproduction in combination with the temperature information in the absorption spectrum samples and the environmental parameter samples, and establishing an apparent quantum yield model.

[0143] For example, for the inversion analysis of methane photo-biological production efficiency, this embodiment constructs a related mechanism experiment, aiming to quantify the photoproduction and biological production processes of sea surface methane through the experimental design of methane photo-biological production, and construct a related mechanism model, namely the apparent quantum yield model, to provide key input parameters for the remote sensing inversion model.

[0144] The methane production experiment mainly consists of three parts:

[0145] ① Anoxic photoproduction: Study the effects of photon wavelength, photon number, environmental factors, and organic matter properties on methane photoproduction, and quantitatively describe the photoproduction process through apparent quantum yield (AQY) spectroscopy;

[0146] ② Aerobic photoproduction: Introducing the oxidation process at the same time as photoproduction, establishing a partial differential equation to analyze the oxidation rate of methane, and combining experimental data to solve the oxidation rate constant k ox [CH4] (related to biological methane production).

[0147] ③ Aerobic biological production: Under no light conditions, only the biological production and oxidation process are studied, combined with the above-mentioned oxidation rate constant k ox [CH4] Analysis of methane biological production rate P bio .

[0148] This experiment collected water samples from 12 stations in the northern waters of the X Sea and 41 stations in the Beibu Gulf. The core parameters of the samples served as experimental data samples, including: sea surface methane concentration (mainly measured using PicarroG2301); remote sensing reflectance (mainly measured using optical sensors such as Triplet AOP or EasyAOP); CDOM absorption spectrum (mainly measured using a dual-path UV-visible spectrophotometer); and environmental parameters (including but not limited to: temperature, salinity, dissolved oxygen, pH, etc., which can be collected using YSI).

[0149] In the specific implementation, this embodiment mainly measures the apparent quantum yield spectrum of methane production by seawater CDOM under light. To quantitatively estimate the methane photoproduction yield, a radiometer was used to measure the spectral irradiance of the solar simulator light source under the filter. The amount of light absorbed by the organic matter in the experimental sample was calculated using a water radiation transfer model and relevant measurement data. Based on the experimentally measured methane production, the apparent quantum yield of methane photoproduction was calculated, thereby establishing an apparent quantum yield model.

[0150] Optionally, the above-mentioned analysis and calculation of the integral data of colored soluble organic matter in different dimensions based on the experimental data sample, and the analysis and calculation of the apparent quantum yield of methane light production in combination with the temperature information in the absorption spectrum sample and the environmental parameter sample, and the establishment of an apparent quantum yield model may include: using a preset radiation transfer model to analyze the experimental data sample, according to Calculate the integral of the number of photons absorbed by colored soluble organic matter in different dimensions to obtain the number of photons absorbed by colored soluble organic matter in the mixed layer or photic layer of the sea surface during the day. Establish the apparent quantum yield model and use the number of photons For input, according to Calculate the daily production of methane light in the mixed layer or euphotic layer of the ocean surface; Analyze the relationship between methane photoproduction and the absorption coefficient spectrum of colored soluble organic matter, and combine different temperature information to Analyze the change of methane light production with temperature and adjust the parameters of the apparent quantum yield model; g is the absorption coefficient spectrum of colored soluble organic matter, K d is the diffuse attenuation coefficient of the sea surface layer, ΔCH4 is the output of methane light production, is the apparent quantum yield of methane photoproduction, and x are model parameters based on the experimental data sample fitting, b is the temperature correction factor, and T is the temperature.

[0151] In this embodiment, ΔCH4 is actually the measured increase in methane concentration, in mol / L. The unit can be nm -1 , is the number of light quanta absorbed by CDOM in the quartz tube, unit: mol -L nm -1 .

[0152] The number of photons absorbed by CDOM is obtained by integrating the photons absorbed by CDOM at a specific time and location calculated based on the radiation transfer model in two dimensions: time and water depth, i.e., the integral of different dimensions E abs (z,t,λ).

[0153] In specific implementation, when constructing the apparent quantum yield model, the effects of temperature, pH value and CDOM characteristics on methane photoproduction can be additionally considered. Specifically, the temperature can be set to: 5°C, 20°C, and 35°C to analyze the temperature dependence of photoproduction; pH: adjust the pH value of the water body through buffer solution to evaluate the impact of pH; CDOM characteristics: by using samples from different sea areas, analyze the impact of CDOM absorption characteristics on methane photoproduction.

[0154] In actual implementation, methane photoproduction originates from the photodegradation of organic matter. There is usually a certain relationship between its photoproduction and the CDOM absorption coefficient spectrum. Temperature also has an impact on the methane photoproduction process. Therefore, to improve the accuracy of the inversion analysis of methane photoproduction efficiency, this embodiment can adjust the parameters of the apparent quantum yield model based on the analyzed influences related to methane photoproduction.

[0155] The CDOM absorption coefficient spectrum characterizes the substrate concentration of the biogeochemical process of methane bioproduction. The equation governing the reaction rate with substrate concentration conforms to the Michaelis-Menten equation: as substrate concentration increases, the reaction rate first rises slowly, then accelerates, then rises slowly again, ultimately reaching saturation. The spectral slope characterizes the type of organic matter. Different types of organic matter have different reaction rates at saturation concentrations. This function, multiplied by the Michaelis-Menten equation, demonstrates the nonlinear relationship between the saturation rate and organic matter type. The final parameter is water temperature. Water temperature regulates the reaction rate according to the Arrhenius equation, and both methane photo- and bioproduction follow the Arrhenius equation as a function of temperature.

[0156] Step 290: Input the absorption coefficient spectrum and the spectrum slope into the bioproduction efficiency model, analyze and fit the relationship between methane bioproduction and organic matter, and analyze the relationship between the saturation reaction rate in methane bioproduction and the characteristics of the organic matter itself to obtain the methane bioproduction efficiency.

[0157] Step 300: performing parameter fitting based on the environmental remote sensing data and the methane bioproduction efficiency, and obtaining the daily methane bioproduction output by integrating the daily scale efficiency.

[0158] A unified description of steps 290 to 300 is provided:

[0159] Reference Figure 3In its implementation, this embodiment considers the impact of the CDOM absorption coefficient spectrum, spectral slope, and environmental parameters on bioproduction. Using these absorption coefficient spectrum and spectral slope as input, this embodiment utilizes a bioproduction efficiency model to analyze the relationship between methane bioproduction and organic matter, as well as the relationship between the saturation reaction rate and the characteristics of the organic matter itself. This model then calculates the methane bioproduction efficiency, or daily methane bioproduction rate. The model then fits the methane bioproduction efficiency using temperature parameters to obtain the model's output of methane bioproduction efficiency per unit time. By integrating the daily-scale efficiency, the daily methane bioproduction yield is accurately calculated.

[0160] In an optional embodiment, before analyzing and fitting the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter through a preset bioproduction efficiency model, it may also include: based on the experimental data sample, using differential equation modeling to analyze the change in methane concentration, determining the methane oxidation rate constant, and performing modeling processing to construct a methane photoproduction oxidation model; using the methane oxidation rate constant to estimate the methane oxidation consumption, quantitatively analyze the methane bioproduction rate, and obtain the methane bioproduction efficiency; based on the methane bioproduction efficiency, construct a bioproduction efficiency model, and by analyzing the relationship between methane bioproduction efficiency, organic matter characteristics and environmental parameters, perform parameter control on the bioproduction efficiency model.

[0161] In a specific implementation, in order to quantitatively analyze the oxidation process in methane photoproduction, this embodiment constructs a methane photoproduction oxidation model and solves the methane oxidation rate constant. By using partial differential equation modeling, a methane photoproduction oxidation model is constructed, and the methane photoproduction efficiency is used as input, and the oxidation rate constant is calculated in combination with the analyzed apparent quantum yield value. Then, the influence of the photochemical process is removed and the methane biological production process is analyzed. Using partial differential equation modeling, a biological production efficiency model is constructed, and changes in methane concentration are analyzed. Combined with the methane oxidation rate constant measured in the aerobic photoproduction experiment, the methane oxidation consumption is estimated, and the methane biological production rate is quantitatively analyzed as the methane biological production efficiency.

[0162] In addition, in order to obtain methane bioproduction efficiency more accurately, parameter fitting and regulation of methane bioproduction efficiency can also be performed. Specifically, by analyzing the relationship between methane bioproduction efficiency and organic matter characteristics and environmental parameters, a parameter regulation model is established. This parameter regulation model is modified based on the Michaelis-Mentenequation. The efficiency of bioproduction is first related to the concentration of organic matter (to a certain extent, the CDOM absorption coefficient spectrum can be used). g The saturation reaction rate should be consistent with the characteristics of the organic matter itself (the spectral slope S of the CDOM absorption coefficient spectrum can be used here). 275-295The modeling of temperature effect is similar to that of light production, and the parameters are also fitted based on the Arrhenius equation.

[0163] Therefore, this embodiment analyzes multiple influencing factors to regulate the bioproduction efficiency model, thereby obtaining accurate methane bioproduction efficiency.

[0164] Optionally, the above-mentioned bio-production efficiency model is constructed based on the methane bio-production efficiency, and the relationship between the methane bio-production efficiency, organic matter characteristics and environmental parameters is analyzed to adjust the parameters of the bio-production efficiency model, including: using partial differential equation modeling to model the change of methane concentration, constructing a bio-production efficiency model, and taking the methane oxidation rate constant as input, according to Analysis and calculation of methane biological production efficiency P bio ; Establish parameter control model, according to

[0165] Analyze the relationship between the efficiency of methane bioproduction and the concentration of organic matter, as well as the relationship between the saturation reaction rate and the characteristics of the organic matter itself, and regulate the bioproduction efficiency model; The relationship between the efficiency of methane biological production and temperature is analyzed, and the biological production efficiency model is regulated.

[0166] Among them, P bio (a g ,S 275-295 ,20℃) is the biological production efficiency of methane at a temperature of 20℃, S 275-295 is the spectral slope.

[0167] Therefore, this embodiment analyzes various key factors in the methane bioproduction process, constructs a bioproduction efficiency model, and realizes the quantitative analysis of methane bioproduction efficiency. Secondly, this embodiment combines various influencing factors such as ambient temperature to perform parameter control, optimizes the calculation of methane bioproduction efficiency, and can accurately analyze and calculate the daily methane bioproduction rate, and perform daily-scale integration to calculate the daily methane bioproduction output.

[0168] Step 310: Invert the daily production of sea surface methane in situ in the target area based on the daily methane production from light and the daily methane production from biological production.

[0169] Therefore, this example establishes an independent daily methane production calculation model for in situ production and proposes a systematic inversion framework for methane photo- and bio-production. Based on experimentally measured parameters, the accuracy of the calculations is improved, and combined with remote sensing data for large-scale monitoring, in-depth research on the ocean methane cycle is conducted. This addresses the problem of inaccurate methane production estimates caused by existing technologies focusing more on methane stocks and not on the specific processes of methane formation and consumption. By studying methane photo- and bio-production, the specific sources, transformation pathways, and influencing factors of methane are revealed, thereby improving the accuracy of estimates of sea surface methane production.

[0170] In summary, the embodiment of the present application uses the sea surface remote sensing reflectivity to accurately invert and analyze the various optical parameters of CDOM, as well as the diffuse attenuation coefficient, and combines environmental parameters and underwater spectral irradiance to accurately analyze and calculate the daily methane light production and biological production. Specifically, the efficiency of CDOM absorbing photons to generate methane is quantitatively analyzed by combining the various parameters obtained and the spectral quantum yield spectrum output by the apparent quantum yield model. Then, the underwater spectral irradiance is combined to analyze and calculate from different dimensions to obtain an accurate methane light production daily yield, and by integrating the time scale, the methane light production daily yield is obtained; when analyzing methane biological production, the relationship between the methane biological production efficiency and the concentration and characteristics of organic matter is analyzed by combining the various parameters obtained to obtain the methane biological production daily efficiency, and the methane biological production daily yield is obtained by integrating the daily scale efficiency. Finally, based on the light production daily yield and the biological production daily yield, the in-situ production daily yield of sea surface methane is inverted. Therefore, this application starts from the process of sea surface methane production, and conducts in-depth research on the impact of factors such as optical properties, downward irradiance, and environmental parameters on sea surface methane production, so as to accurately calculate the methane photo / biological production efficiency, and provide a comprehensive and accurate inversion model for ocean methane production, solving the problem that the existing technology cannot accurately invert the sea surface methane production due to the lack of in-depth research on the methane production process.

[0171] Furthermore, this proposal focuses on methane photo-biological production yields and optimizes existing methods for calculating water-gas exchange fluxes. Photo- and bio-methane production processes are important factors influencing methane exchange between the ocean and atmosphere. In-depth research on these processes will help improve the accuracy of water-gas exchange flux calculations and provide a solid foundation for further research into in situ methane production.

[0172] In addition, this embodiment also provides a scientific basis for environmental protection and policy making. By fully understanding how methane production is affected by environmental changes, it provides strong technical support for global carbon cycle research, climate change assessment and marine ecological protection. It has important application value and prospects, and will help to formulate more effective climate policies and environmental protection measures.

[0173] Furthermore, existing techniques use a carbon monoxide proxy method to estimate methane production efficiency. However, this solution does not incorporate sea surface temperature into the calculation of the photoproduction yield and yield. This example demonstrates that using temperature as a secondary correction for the apparent quantum yield of methane photoproduction can increase yield by 1.5 times between 5°C and 35°C. Temperature also influences methane bioproduction yield. Therefore, existing techniques that do not incorporate sea surface temperature into modeling for secondary corrections will result in significant errors.

[0174] It should be noted that, for the purpose of simple description, the method embodiments are expressed as a series of action combinations, but those skilled in the art should know that the embodiments of the present application are not limited to the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously.

[0175] like Figure 4 As shown, the embodiment of the present application further provides a system 400 for inverting daily production of sea surface methane in situ production based on water color remote sensing, comprising:

[0176] Data acquisition module 410, used to acquire water color remote sensing data and environmental remote sensing data of the target area, and underwater spectral irradiance data through a preset radiation transfer model, wherein the water color remote sensing data includes sea surface remote sensing reflectivity;

[0177] A preprocessing and inversion analysis module 420 is configured to perform preprocessing and inversion analysis based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, wherein the optical parameters include an absorption coefficient spectrum and a spectral slope of colored soluble organic matter;

[0178] a light production daily yield analysis module 430 for analyzing light production efficiency using a preset apparent quantum yield model based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, and performing daily-scale integration to obtain the daily methane light production yield;

[0179] The bioproduction daily yield analysis module 440 is configured to use the optical parameters and the environmental remote sensing data as inputs, analyze and fit the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter using a preset bioproduction efficiency model, and obtain the methane bioproduction daily yield by integrating the daily-scale efficiency;

[0180] The daily production inversion module 450 is configured to invert the daily production of in-situ sea surface methane in the target area based on the daily methane production from light and the daily methane production from biological sources.

[0181] Optionally, the pre-processing and inversion analysis module 420 includes:

[0182] A preprocessing submodule, configured to perform preprocessing based on the sea surface remote sensing reflectivity to obtain a target remote sensing reflectivity;

[0183] An analysis and aggregation submodule, configured to perform principal component analysis and clustering processing based on the target remote sensing reflectance to obtain the target ocean color cluster to which the water color remote sensing data belongs;

[0184] The absorption coefficient spectrum analysis submodule is used to obtain the absorption coefficient spectrum of the colored soluble organic matter in the water body at each wavelength by substituting the pre-trained regression coefficient into the multiple linear regression equation according to the target ocean color cluster;

[0185] The spectral slope analysis submodule is used to analyze the target remote sensing reflectance at each wavelength to obtain the spectral slope of colored soluble organic matter;

[0186] The optimization processing submodule is used to optimize the sea surface remote sensing reflectivity through a preset optimization algorithm, and perform principal component analysis and clustering on the optimized sea surface remote sensing reflectivity to obtain a diffuse attenuation coefficient.

[0187] Optionally, the light production daily output analysis module 430 includes:

[0188] A spectral quantum yield spectrum fitting submodule, configured to fit a spectral quantum yield spectrum using a preset apparent quantum yield model based on the environmental remote sensing data and the absorption coefficient spectrum, wherein the spectral quantum yield spectrum is used to represent the efficiency of colored soluble organic matter in generating methane after absorbing photons of a specific wavelength;

[0189] a spectral interpolation processing submodule, configured to perform spectral interpolation processing on the absorption coefficient spectrum, the diffuse attenuation coefficient, the underwater spectral irradiance data, and the spectral quantum yield spectrum, respectively, by an interpolation method, to obtain a target absorption coefficient spectrum, a target diffuse attenuation coefficient spectrum, a target underwater spectral irradiance, and a target spectral quantum yield spectrum with a complete spectral range and matching spectral resolution;

[0190] A spectral scalar irradiance analysis submodule is used to extend the target underwater spectral irradiance at a specified depth using an exponential attenuation algorithm of the underwater light field in combination with the target diffuse attenuation coefficient spectrum to obtain a spectral scalar irradiance;

[0191] A photon absorption rate analysis submodule, configured to analyze the rate at which the colored soluble organic matter absorbs light quanta based on the target absorption coefficient spectrum and the spectral scalar irradiance to obtain a photon absorption rate;

[0192] a methane photoproduction rate calculation submodule, configured to calculate the methane photoproduction rate at a specified depth by taking the photon absorption rate and the target spectral quantum yield spectrum as input;

[0193] The light production integration submodule is used to analyze the daily methane light production output of the mixed layer or the light-transmitting layer through spectral range integration, vertical integration and time integration based on the methane light production rate.

[0194] Optionally, the biological production daily output analysis module 440 includes:

[0195] a bioproduction efficiency analysis submodule, configured to input the absorption coefficient spectrum and the spectral slope into a bioproduction efficiency model, analyze and fit the relationship between methane bioproduction and organic matter, and analyze the relationship between the saturation reaction rate in methane bioproduction and the characteristics of the organic matter itself to obtain methane bioproduction efficiency;

[0196] The bioproduction daily output analysis submodule is used to perform parameter fitting based on the environmental remote sensing data and the methane bioproduction efficiency, and obtain the methane bioproduction daily output by integrating the daily scale efficiency.

[0197] Optionally, the data acquisition module 410 includes:

[0198] A scalar irradiance calculation submodule is used to obtain cloud data from the water color remote sensing data or a preset atmospheric radiation model, and calculate underwater spectral scalar irradiance using a preset radiation transfer model;

[0199] The correction integration submodule is used to perform an impact correction on the underwater spectral scalar irradiance according to the cloud data, and perform integration processing according to a preset time period to obtain a daily integrated spectral irradiance as the underwater spectral irradiance data.

[0200] Optionally, the sea surface methane in-situ production daily production inversion system 400 based on water color remote sensing also includes:

[0201] An experimental sample acquisition module is used to acquire experimental data samples from a preset sample area through a preset methane production experiment. The experimental data samples include sea surface methane concentration, remote sensing reflectance samples, absorption spectrum samples, and environmental parameter samples;

[0202] An apparent quantum yield model construction module is used to analyze and calculate the integral data of colored soluble organic matter in different dimensions based on the experimental data samples, and analyze and calculate the apparent quantum yield of methane photoproduction in combination with the temperature information in the absorption spectrum samples and the environmental parameter samples, to establish an apparent quantum yield model;

[0203] A methane photoproduction oxidation model construction module is used to analyze the change in methane concentration based on the experimental data sample using differential equation modeling, determine the methane oxidation rate constant, and perform modeling processing to construct a methane photoproduction oxidation model;

[0204] A biological production rate quantitative analysis module is used to estimate methane oxidation consumption using the methane oxidation rate constant, quantitatively analyze the methane biological production rate, and obtain the methane biological production efficiency;

[0205] The bioproduction efficiency model construction module is used to construct a bioproduction efficiency model based on the methane bioproduction efficiency, and to adjust the parameters of the bioproduction efficiency model by analyzing the relationship between the methane bioproduction efficiency, organic matter characteristics and environmental parameters.

[0206] Optionally, the apparent quantum yield model building module is specifically used to: analyze the experimental data sample using a preset radiation transfer model, and Calculate the integral of the number of photons absorbed by colored soluble organic matter in different dimensions to obtain the number of photons absorbed by colored soluble organic matter in the mixed layer or photic layer of the sea surface during the day. Establish the apparent quantum yield model and use the number of photons For input, according to Calculate the daily production of methane light in the mixed layer or euphotic layer of the ocean surface; Analyze the relationship between methane photoproduction and the absorption coefficient spectrum of colored soluble organic matter, and combine different temperature information to Analyze the change of methane light production with temperature and adjust the parameters of the apparent quantum yield model; g is the absorption coefficient spectrum of colored soluble organic matter, K d is the diffuse attenuation coefficient of the sea surface layer, ΔCH4 is the output of methane light production, is the apparent quantum yield of methane photoproduction, and x are model parameters based on the experimental data sample fitting, b is the temperature correction factor, and T is the temperature.

[0207] Optional, biological production efficiency model building module, specifically used to: use partial differential equation modeling to model the change of methane concentration, build a biological production efficiency model, and use the methane oxidation rate constant as input, according to Analysis and calculation of methane biological production efficiency P bio ; Establish parameter control model, according to Analyze the relationship between the efficiency of methane bioproduction and the concentration of organic matter, as well as the relationship between the saturation reaction rate and the characteristics of the organic matter itself, and regulate the bioproduction efficiency model; The relationship between the efficiency of methane biological production and temperature is analyzed, and the biological production efficiency model is regulated.

[0208] In summary, to address the existing issues of sea surface methane production inversion, which rely heavily on manual and inefficient processes and lack automated calculations, resulting in slow data processing and difficulty meeting the needs of large-scale, long-term monitoring, this embodiment provides a system for inverting daily sea surface methane production based on water color remote sensing. This system integrates remote sensing data download, preprocessing, model calculation, and result output, greatly improving data processing efficiency and operability.

[0209] It should be noted that the sea surface methane in situ daily production inversion system based on water color remote sensing provided in the embodiments of the present application can execute the sea surface methane in situ daily production inversion method based on water color remote sensing provided in any embodiment of the present application, and has the corresponding functions and beneficial effects of the execution method.

[0210] In a specific implementation, the above-mentioned sea surface methane in-situ production daily output inversion system based on water color remote sensing can be integrated into a device, so that the device can use the sea surface remote sensing reflectivity in the water color remote sensing data as a benchmark to invert and analyze the daily yield of methane light / biological production, and then determine the daily output of sea surface methane light / biological production. As an electronic device, starting from the process of sea surface methane production, in-depth research is carried out on the influence of factors such as optical properties, downward irradiance, and environmental parameters on sea surface methane production, and accurate inversion and analysis of methane light / biological production daily output is achieved. The electronic device can be composed of two or more physical entities, or it can be composed of one physical entity. For example, the electronic device can be a personal computer (PC), a computer, a server, etc., and the embodiment of the present application does not impose specific restrictions on this.

[0211] like Figure 5As shown, an embodiment of the present application provides an electronic device, including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other via the communication bus 114; the memory 113 is used to store computer programs; the processor 111 is used to implement the steps of the inversion method for daily production of sea surface methane in situ based on water color remote sensing data provided by any of the aforementioned method embodiments when executing the program stored in the memory 113. Exemplarily, the steps of the inversion method for daily production of sea surface methane in situ based on water color remote sensing data may include the following steps: obtaining water color remote sensing data and environmental remote sensing data of the target area and obtaining underwater spectral irradiance data through a preset radiation transmission model, wherein the water color remote sensing data includes sea surface remote sensing reflectivity; performing preprocessing and inversion analysis based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, wherein the optical parameters include the absorption coefficient spectrum and spectral slope of colored soluble organic matter; and obtaining the underwater spectral irradiance data based on the absorption coefficient spectrum and the underwater spectral irradiance data. According to the optical parameters, the environmental remote sensing data and the diffuse attenuation coefficient, the light production efficiency is analyzed by a preset apparent quantum yield model, and the daily methane light production is obtained by daily integration; with the optical parameters and the environmental remote sensing data as input, the relationship between the methane bioproduction efficiency and the concentration and characteristics of organic matter is analyzed and fitted by a preset biological production efficiency model, and the daily methane bioproduction is obtained by integrating the daily-scale efficiency; based on the daily methane light production and the daily methane bioproduction, the daily in situ production of sea surface methane is inverted in the target area.

[0212] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for inverting the daily production of in-situ sea surface methane based on water color remote sensing data as provided in any of the aforementioned method embodiments are implemented.

[0213] 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 entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0214] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A method for inverting the daily production of in-situ sea surface methane based on water color remote sensing data, characterized in that: include: Acquire water color remote sensing data and environmental remote sensing data of the target area, and obtain underwater spectral irradiance data through a preset radiation transfer model, wherein the water color remote sensing data includes sea surface remote sensing reflectivity; Preprocessing and inversion analysis are performed based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, wherein the optical parameters include an absorption coefficient spectrum and a spectral slope of colored soluble organic matter; Based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, light production efficiency is analyzed using a preset apparent quantum yield model, and daily integration is performed to obtain daily methane light production output; Using the optical parameters and the environmental remote sensing data as input, the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter is analyzed and fitted using a preset bioproduction efficiency model, and the daily methane bioproduction output is obtained by integrating the daily-scale efficiency; Based on the daily methane photoproduction and the daily methane biological production, the daily in-situ production of sea surface methane is inverted in the target area.

2. The method according to claim 1, characterized in that Preprocessing and inversion analysis are performed based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, including: Performing preprocessing according to the sea surface remote sensing reflectivity to obtain target remote sensing reflectivity; Performing principal component analysis and clustering processing based on the target remote sensing reflectance to obtain the target ocean color cluster to which the water color remote sensing data belongs; According to the target ocean color cluster, the pre-trained regression coefficients are substituted into the multiple linear regression equation to obtain the absorption coefficient spectrum of the colored soluble organic matter in the water body at each wavelength; The spectral slope of colored soluble organic matter is obtained by analyzing the target remote sensing reflectance at each wavelength; The sea surface remote sensing reflectivity is optimized by a preset optimization algorithm, and principal component analysis and clustering are performed on the optimized sea surface remote sensing reflectivity to obtain a diffuse attenuation coefficient.

3. The method according to claim 1, characterized in that Based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, the light production efficiency is analyzed using a preset apparent quantum yield model, and daily integration is performed to obtain the daily methane light production output, including: Based on the environmental remote sensing data and the absorption coefficient spectrum, a spectral quantum yield spectrum is fitted using a preset apparent quantum yield model, wherein the spectral quantum yield spectrum is used to represent the efficiency of generating methane after the colored soluble organic matter absorbs photons of a specific wavelength; Performing spectral interpolation processing on the absorption coefficient spectrum, the diffuse attenuation coefficient, the underwater spectral irradiance data, and the spectral quantum yield spectrum respectively by an interpolation method to obtain a target absorption coefficient spectrum, a target diffuse attenuation coefficient spectrum, a target underwater spectral irradiance, and a target spectral quantum yield spectrum with a complete spectral range and matching spectral resolution; Using the exponential attenuation algorithm of the underwater light field and combining the target diffuse attenuation coefficient spectrum, the target underwater spectral irradiance at the surface layer is extended to a specified depth to obtain a spectral scalar irradiance; Analyzing the rate at which the colored soluble organic matter absorbs light quanta according to the target absorption coefficient spectrum and the spectral scalar irradiance to obtain a photon absorption rate; Calculate the methane photoproduction rate at a specified depth using the photon absorption rate and the target spectral quantum yield spectrum as input; Based on the methane photoproduction rate, the daily methane photoproduction output of the mixed layer or the light-transmitting layer is analyzed by spectral range integration, vertical integration and time integration.

4. The method according to claim 1, wherein The optical parameters and the environmental remote sensing data are used as inputs, and the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter is analyzed and fitted through a preset bioproduction efficiency model, and the daily methane bioproduction output is obtained by integrating the daily scale efficiency, including: Inputting the absorption coefficient spectrum and the spectrum slope into a bioproduction efficiency model, analyzing and fitting the relationship between methane bioproduction and organic matter, and analyzing the relationship between the saturation reaction rate in methane bioproduction and the characteristics of the organic matter itself to obtain the methane bioproduction efficiency; Parameter fitting is performed based on the environmental remote sensing data and the methane biological production efficiency, and the daily methane biological production output is obtained by integrating the daily scale efficiency.

5. The method according to claim 1, wherein Underwater spectral irradiance data is obtained through a preset radiation transfer model, including: Obtaining cloud data from the water color remote sensing data or a preset atmospheric radiation model, and calculating underwater spectral scalar irradiance using a preset radiation transfer model; The underwater spectral scalar irradiance is corrected for influence according to the cloud layer data, and is integrated according to a preset time period to obtain a daily integrated spectral irradiance as the underwater spectral irradiance data.

6. The method according to claim 1, wherein Before analyzing the methane photoproduction efficiency through the preset apparent quantum yield model, it also includes: Acquiring experimental data samples from a preset sample area through a preset methane production experiment, wherein the experimental data samples include an absorption spectrum sample and an environmental parameter sample; Based on the experimental data sample analysis, the integral data of colored soluble organic matter in different dimensions are calculated, and the apparent quantum yield of methane photoproduction is calculated by combining the temperature information in the absorption spectrum sample and the environmental parameter sample to establish an apparent quantum yield model.

7. The method according to claim 6, characterized in that Based on the experimental data sample, the integral data of colored soluble organic matter in different dimensions are analyzed and calculated, and the apparent quantum yield of methane photoproduction is analyzed and calculated in combination with the temperature information in the absorption spectrum sample and the environmental parameter sample, and an apparent quantum yield model is established, including: The experimental data samples are analyzed using the preset radiation transfer model. Calculate the integral of the number of photons absorbed by colored soluble organic matter in different dimensions to obtain the number of photons absorbed by colored soluble organic matter in the mixed layer or photic layer of the sea surface during the day. Establish an apparent quantum yield model based on the number of photons For input, according to Calculate the daily production of methane light in the ocean surface mixed layer or euphotic layer; according to Analyze the relationship between methane photoproduction and the absorption coefficient spectrum of colored soluble organic matter, and combine different temperature information to Analyze the changes in methane photoproduction with temperature and adjust the parameters of the apparent quantum yield model; Among them, a g is the absorption coefficient spectrum of colored soluble organic matter, K d is the diffuse attenuation coefficient of the sea surface layer, ΔCH4 is the output of methane light production, is the apparent quantum yield of methane photoproduction, and x are model parameters based on the experimental data sample fitting, b is the temperature correction factor, and T is the temperature.

8. The method according to claim 7, characterized in that Before fitting the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter through the preset bioproduction efficiency model, it also includes: Based on the experimental data samples, the changes in methane concentration are analyzed using differential equation modeling, the methane oxidation rate constant is determined, and modeling is performed to construct a methane photoproduction oxidation model; The methane oxidation rate constant is used to estimate the methane oxidation consumption, quantitatively analyze the methane biological production rate, and obtain the methane biological production efficiency; Based on the methane bioproduction efficiency, a bioproduction efficiency model is constructed, and by analyzing the relationship between the methane bioproduction efficiency, organic matter characteristics and environmental parameters, the parameters of the bioproduction efficiency model are regulated.

9. The method according to claim 8, characterized in that Based on the methane bioproduction efficiency, a bioproduction efficiency model is constructed, and by analyzing the relationship between the methane bioproduction efficiency, organic matter characteristics and environmental parameters, the parameters of the bioproduction efficiency model are adjusted, including: The changes in methane concentration were modeled using partial differential equations, and a biological production efficiency model was constructed. The methane oxidation rate constant was used as input. Analysis and calculation of methane biological production efficiency P bio ; Establish parameter control model according to Analyzing the relationship between the efficiency of methane bioproduction and the concentration of organic matter, and analyzing the relationship between the saturation reaction rate and the characteristics of the organic matter itself, and regulating the bioproduction efficiency model; according to The relationship between the efficiency of methane biological production and temperature is analyzed, and the biological production efficiency model is regulated.

10. A sea surface methane in-situ production daily production inversion system based on water color remote sensing, characterized by: include: A data acquisition module is used to acquire water color remote sensing data and environmental remote sensing data of the target area, and underwater spectral irradiance data through a preset radiation transfer model, wherein the water color remote sensing data includes sea surface remote sensing reflectivity; A preprocessing and inversion analysis module, configured to perform preprocessing and inversion analysis based on the sea surface remote sensing reflectivity to obtain optical parameters and diffuse attenuation coefficients, wherein the optical parameters include an absorption coefficient spectrum and a spectral slope of colored soluble organic matter; a light production daily yield analysis module, configured to analyze light production efficiency using a preset apparent quantum yield model based on the absorption coefficient spectrum, the underwater spectral irradiance data, the environmental remote sensing data, and the diffuse attenuation coefficient, and to perform daily-scale integration to obtain the daily methane light production yield; A bioproduction daily yield analysis module is configured to use the optical parameters and the environmental remote sensing data as inputs, analyze and fit the relationship between methane bioproduction efficiency and the concentration and characteristics of organic matter through a preset bioproduction efficiency model, and obtain the methane bioproduction daily yield by integrating the daily scale efficiency; The daily production inversion module is used to invert the daily production of sea surface methane in situ in the target area based on the daily production of methane produced by light and the daily production of methane produced by biological means.

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