A method and system for estimating transverse carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling

Abstract

The present application belongs to the technical field of ecological environment remote sensing and carbon cycle quantification, and particularly relates to a kind of based on water power-remote sensing coupling's coastal wetland horizontal carbon output estimation method and system.The method obtains multi-source remote sensing image, terrain elevation, tidal boundary, runoff boundary and measured carbon concentration data, constructs DOC, POC inversion model and total carbon concentration field;Combined with two-dimensional unsteady flow hydrodynamic model, water depth field and flow velocity field are obtained;According to the coupling relationship between water depth gradient and flow velocity direction, the active section of carbon exchange is automatically identified;The carbon concentration field is coupled with the normal flow velocity, water depth and flux correction coefficient, the instantaneous carbon flux is calculated, and the net horizontal carbon output total amount is obtained through the decomposition of rising tide-falling tide and adaptive time integration;Then, the measured data is used for parameter iteration update.The present application can improve the continuity, dynamics, accuracy and comparability of the estimation of coastal wetland horizontal carbon output.

Description

Technical Field

[0001] This invention belongs to the field of ecological environment remote sensing and carbon cycle quantification technology, and in particular relates to a method and system for estimating the lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling. Background Technology

[0002] Coastal wetland ecosystems, encompassing various types such as mangroves, salt marshes, seagrass beds, and tidal flats, are typical blue carbon ecosystems, playing a crucial role in global carbon cycling and climate change regulation. Compared to terrestrial forest ecosystems, coastal wetlands can form high-intensity carbon sinks through processes like vegetation fixation and sedimentation. Furthermore, they undergo significant lateral carbon migration and export processes driven by tides, hydrodynamic exchange, runoff input, and suspended particulate transport. Recent studies have shown that DOC, POC, and other forms of organic carbon are frequently exchanged between wetlands and adjacent estuaries and nearshore waters during tidal cycles. This lateral exchange has become an indispensable component in evaluating the true carbon budget of coastal wetlands. Moreover, tidal-driven lateral exchange exhibits significant seasonal and tidal cycle variations, making it difficult to accurately characterize its full scope using only static monitoring points.

[0003] Existing technical approaches for carbon accounting in coastal wetlands can be broadly categorized into three types: first, field observation methods based on point sampling and experimental analysis; second, regional-scale spatial estimation methods based on remote sensing inversion; and third, numerical simulation methods based on hydrodynamic or ecological process models. While field observation methods can directly acquire data on organic carbon concentration, suspended solids concentration, and cross-sectional flow velocity in water bodies, they typically rely on fixed stations, manual sampling, and limited frequency monitoring, making them susceptible to constraints such as tidal changes, weather conditions, and labor costs, and hindering the acquisition of large-scale, continuous, and long-term data. Numerical simulation methods, while capable of characterizing tidal currents, runoff, sediment transport, and some material exchange processes, often rely on extrapolation from a small number of points if a spatially continuous carbon concentration input field is lacking, resulting in insufficient regional representativeness. Remote sensing inversion methods offer advantages such as large-scale synchronous observation, short repetition cycles, and strong spatiotemporal coverage, making them suitable for constructing the spatial distribution of water carbon components such as DOC and POC. However, if they only focus on concentration inversion, they are difficult to directly translate into lateral carbon fluxes with boundary exchange significance.

[0004] Among the prior art most relevant to this invention, Chinese patent CN107064068A proposes a method to address the problem of weak optical activity of POC in Class II turbid water bodies, making direct inversion difficult. This method introduces suspended solids concentration (TSM), suspended solids particle size distribution (PSD), and on-site remote sensing reflectance data to establish a statistical relationship between POC concentration and suspended solids concentration and particle size distribution. This relationship is then combined with remote sensing images of the study area to achieve remote sensing inversion of surface POC. The advantage of this technique is that it enhances the physical meaning of POC inversion, improves the accuracy of POC concentration inversion under turbid water conditions, and has positive implications for spatial mapping of particulate organic carbon in water bodies. However, the technical focus of this paper remains primarily on the concentration inversion of single carbon components. Its core objective is to improve the accuracy of POC remote sensing inversion, but it fails to address several more critical issues in estimating the transverse carbon output of coastal wetlands: First, this paper focuses on POC concentration inversion without simultaneously constructing a unified total carbon concentration field for both DOC and POC, making it difficult to fully characterize the main forms of organic carbon output from wetland water bodies. Second, this paper is essentially still a concentration inversion technique and is not coupled with a tidal-driven two-dimensional or three-dimensional hydrodynamic model, thus failing to convert concentration distribution into cross-sectional flux. Third, this paper does not establish an automatic identification and flux integration mechanism for wetland boundaries or tidal channel cross-sections, nor does it consider the directional differences between high tide input and low tide output. Fourth, this paper lacks time-series integration of carbon output results, construction of standardized indicators, and a dynamic correction closed loop based on measured cross-sectional data. Therefore, while this paper provides a valuable technical foundation for the concentration input term in the transverse carbon output of coastal wetlands, it cannot yet independently complete the systematic accounting of transverse carbon output in wetlands.

[0005] Chinese patent CN111881407A discloses a method for coupled estimation of surface water, heat and carbon flux based on remote sensing information. This paper focuses on the carbon, water and heat exchange process between terrestrial ecosystems and the atmosphere, and estimates carbon flux, latent heat flux and sensible heat flux by combining ground observation, remote sensing information, meteorological data and model calibration. The technical value of this paper is that it reflects the overall idea of ​​remote sensing information + ground observation + model parameter calibration + multi-element coupled estimation, which shows that remote sensing information can not only be used for surface state characterization, but also participate in the construction and parameter optimization of carbon flux models, and has certain inspirational significance in regional scale flux inversion. However, this paper focuses on surface carbon flux in the sense of vertical exchange at the land-atmosphere interface, and its carbon flux calculation object is fundamentally different from the horizontal carbon output between coastal wetlands and adjacent water bodies in this invention. First, the carbon flux in the paper [2] mainly serves vegetation photosynthesis, transpiration, soil evaporation and energy balance analysis, and its flux direction is mainly around surface-atmosphere exchange, rather than water transport around tidal channels, wetland boundaries or estuary sections. Secondly, although reference [2] involves coupled estimation, it does not establish the water transport integral relationship based on the velocity field, water depth field and cross-sectional normal velocity, and cannot be used to solve the lateral output of DOC / POC carried by water under tidal conditions. Thirdly, the coastal wetland system is affected by the tidal reciprocating motion, and there are obvious phenomena of high tide input, low tide output, local stagnation and turbulent mixing. Reference [2] does not propose solutions to problems such as tidal period decomposition, adaptive time step integration, mixing effect correction and residence time correction, so it cannot be directly applied to the estimation of the lateral output of the blue carbon system.

[0006] Further analysis of current academic research reveals that existing literature indicates the main methods for lateral carbon exchange in coastal wetlands fall into three categories: mathematical models, field observations, and remote sensing inversion. However, these methods often exhibit disconnect: mathematical models are primarily based on measured cross-sectional carbon concentrations and flows, lacking sufficient spatial continuity; while remote sensing inversion provides continuous concentration distributions, it often remains at the level of surface concentration estimation, lacking stable coupling with tidal flow fields; and simply multiplying carbon concentration by flow over a given period fails to reflect the strong intra-diurnal and seasonal variations driven by tides. Related research also indicates that long-term series modeling methods combining remote sensing satellites and numerical simulations hold promise for more accurate assessment of organic carbon transport fluxes than simplified concentration × flow estimations used for single or monthly measurements. However, when applied to coastal wetlands, these methods still face challenges such as subjective cross-sectional selection, insufficient simultaneous estimation of DOC / POC, inadequate refinement of time-series dynamic integration, and weak comparability of results across regions.

[0007] Furthermore, coastal wetlands differ from ordinary rivers or lakes in that their underlying surfaces exhibit significant complexity. Tidal channel networks, shoals, vegetation communities, bottom sediment undulations, and tidal range variations all collectively influence the flow field structure, resulting in strong spatial heterogeneity in flow velocity, water depth, and exchange intensity within the same area. If the traditional method of manually selecting cross-sections is still used, it can easily lead to insufficient cross-sectional representativeness, causing flux estimation results to be highly dependent on operator experience and making it difficult to establish a unified and repeatable technical process. On the other hand, organic carbon transport in wetland waters is not an ideal flat-push process; local turbulence, backwater retention, vegetation blockage, and increased bottom friction all alter carbon transport efficiency. If only a simple... or If basic calculations are performed without considering mixing, diffusion, and retention effects, the resulting transverse carbon output may still deviate from the actual process.

[0008] Furthermore, existing technologies also have shortcomings in terms of result expression. Many studies and existing solutions often provide flux values ​​for a specific cross-section, a specific moment, or a specific time period, but lack a unified standardized indicator system, making it difficult to directly compare carbon output results under different regions, areas, and background concentrations. For blue carbon resource management, ecological restoration effect assessment, and cross-regional policy formulation, absolute flux values ​​alone are often insufficient; it is also necessary to construct evaluation indicators that reflect output intensity and flux stability per unit area and per unit background concentration. However, existing technologies have not developed a complete integrated technical solution for the application scenario of cross-regional carbon output in coastal wetlands, encompassing concentration inversion, hydrodynamic simulation, cross-section identification, flux coupling, time-series integration, indicator standardization, and dynamic correction.

[0009] In summary, while existing technologies have laid the foundation for remote sensing inversion of carbon emissions (POC) in water bodies, coupled estimation of surface carbon flux, and research on carbon exchange mechanisms in coastal wetlands, they still have the following shortcomings: First, there is a lack of a mechanism for constructing a spatially continuous carbon concentration field that simultaneously considers both DOC and POC; second, there is a lack of a deep coupling mechanism between the remote sensing concentration field and the hydrodynamic flow field for the tidal environment of coastal wetlands; third, there is a lack of an automatic cross-section identification mechanism for active carbon exchange zones; fourth, there is a lack of a dynamic flux integration mechanism that simultaneously considers mixing, retention, and bidirectional exchange during tidal cycles; and fifth, there is a lack of a standardized output system that can support cross-regional comparisons and model closed-loop correction. Therefore, it is necessary to propose a new method and system for estimating lateral carbon output in coastal wetlands based on hydrodynamic-remote sensing coupling, in order to achieve spatially continuous, dynamically responsive, automatically identified cross-sections, process-correctable, and comparable quantitative estimation of lateral carbon output. Summary of the Invention

[0010] The purpose of this invention is to provide a method and system for estimating the lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling. By integrating the carbon concentration field obtained from the inversion of multi-source remote sensing data with the flow velocity and water depth fields obtained from hydrodynamic model simulation, a mechanism for automatic cross-section identification, carbon flux coupling calculation, time-series dynamic integration, and model correction oriented towards tidal processes is established. This enables the spatial continuity, dynamism, and standardization of lateral carbon output estimation in coastal wetlands, thereby improving the accuracy and comparability of carbon budget accounting for blue carbon ecosystems.

[0011] Firstly, in order to achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0012] A method for estimating lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling includes the following steps:

[0013] S1. Acquire multi-source remote sensing images, topographic elevation, tidal boundary, runoff boundary, and cross-sectional measured carbon concentration data of the study area during the study period. Perform radiometric calibration, atmospheric correction, geometric registration, water body extraction, and time matching on the multi-source remote sensing images to construct remote sensing feature vectors and extract the boundaries of the study area.

[0014] S2. Based on remote sensing feature vectors and measured water sample data, DOC inversion models and POC inversion models are established to obtain DOC concentration fields, POC concentration fields and total carbon concentration fields.

[0015] S3, perform spatial consistency constraint optimization on the total carbon concentration field to obtain the optimized carbon concentration field;

[0016] S4. A two-dimensional unsteady hydrodynamic model was constructed based on topographic elevation data. The water depth field and velocity field of the study area were obtained by simulating the tidal boundary and runoff boundary.

[0017] S5 automatically identifies active carbon exchange sections based on the coupling relationship between water depth gradient and flow velocity direction;

[0018] S6. The optimized carbon concentration field is coupled with the normal velocity, water depth and flux correction coefficient of each cross section to calculate the instantaneous carbon flux of each cross section at each time.

[0019] S7. The instantaneous carbon flux of each section is integrated over time using an adaptive time step to obtain the total net transverse carbon output.

[0020] S8 uses the error between the measured carbon concentration data of the cross section and the calculation results of the model to iteratively update the parameters of the DOC inversion model and the POC inversion model until the error meets the preset threshold, and outputs the estimation results of the transverse carbon output of the coastal wetland.

[0021] Preferably, in step S1, the remote sensing feature vector is:

[0022] ;

[0023] in, This is a remote sensing feature vector; wavelength Remote sensing reflectance at the location; The number of spectral bands selected; Normalized water index; To improve the normalized water index; Turbidity index; Chlorophyll Concentration agent quantity; For the first The center wavelength of each band .

[0024] Preferably, in step S2, the DOC inversion model and the POC inversion model respectively satisfy:

[0025] ;

[0026] ;

[0027] in, , , , , These are the regression coefficients of the DOC inversion model; , , These are the regression coefficients of the POC inversion model; This is the turbidity adjustment coefficient; It is a characterization measure of turbidity in water bodies; This is the DOC inversion error term; This is the error term for POC inversion;

[0028] The total carbon concentration field satisfies:

[0029] ;

[0030] in, For position At any moment Total carbon concentration; For position At any moment The concentration of dissolved organic carbon; For position At any moment The concentration of particulate organic carbon; , Spatial coordinates; For time.

[0031] Preferably, in step S3, the spatial consistency constraint optimization satisfies:

[0032] ;

[0033] in, The optimized carbon concentration field; It is the spatial smoothing coefficient; is the Laplace operator value of the carbon concentration field, used to characterize the intensity of the second-order spatial variation;

[0034] And / or, in step S5, the active carbon exchange section satisfies:

[0035] ;

[0036] in, For the first One carbon flux calculation section; For position At any moment The water depth gradient; For position At any moment The velocity vector; The threshold for cross-section identification; This is the section number.

[0037] Preferably, in step S4, the continuity equation of the two-dimensional unsteady hydrodynamic model satisfies:

[0038] ;

[0039] in, The rate of change of water depth with respect to time; It is a divergence operator; A unit width flow vector;

[0040] Furthermore, the tidal boundary satisfies:

[0041] ;

[0042] in, For a moment Boundary tide level; Tidal range; The tidal angular frequency; This is the initial phase;

[0043] And / or, in step S4, a bottom friction term and a vegetation resistance term are introduced into the hydrodynamic model to obtain the water depth distribution and velocity distribution at each time point; the bottom friction term and the vegetation resistance term together form the total resistance term, satisfying:

[0044] ;

[0045] in, For position At any moment Total resistance term; The coefficient of friction is the lowest. The magnitude of the flow velocity vector; This is the vegetation resistance coefficient; For position At any moment Leaf area index.

[0046] Preferably, in step S6, the instantaneous carbon flux satisfies:

[0047] ;

[0048] in, For the first Each cross-section at time Instantaneous carbon flux; For position At any moment Relative to cross-section Normal flow velocity; For position At any moment The water depth; For position At any moment flux correction factor; The length of the cross-sectional micro-element;

[0049] And / or, in step S6, the flux correction coefficient satisfies:

[0050] ;

[0051] in,

[0052] ;

[0053] ;

[0054] in, For mixed correction coefficients; This is the retention correction factor; This is the velocity gradient correction coefficient; For the flow velocity along Gradient of direction; This is the dwell time decay coefficient; For position At any moment The time the water remains in the body.

[0055] Preferably, in step S7, the total net transverse carbon output satisfies:

[0056] ;

[0057] in, The total net transverse carbon output during the study period; This represents the total number of discrete time steps. The total number of cross-sections; For the first The moment corresponding to each time step; For the first One time step;

[0058] And / or, in step S7, the study period is divided into a high tide phase and a low tide phase, and the high tide input carbon flux and low tide output carbon flux are calculated respectively. The net transverse carbon output satisfies:

[0059] ;

[0060] in, Net transverse carbon output; This represents the cumulative carbon flux during the low tide phase. This represents the cumulative carbon flux during the high tide phase.

[0061] Furthermore, the adaptive time step satisfies:

[0062] ;

[0063] in, For the first One time step; For the first The characteristic flow velocity modulus at each time step; This is a time step mapping function that decreases as the flow velocity increases.

[0064] Secondly, the present invention also provides a coastal wetland lateral carbon output estimation system based on hydrodynamic-remote sensing coupling, the system being used to implement the method described above, comprising:

[0065] The data management module is used to acquire and manage multi-source remote sensing images, topographic elevation data, tidal boundary data, runoff boundary data, and cross-sectional measured carbon concentration data.

[0066] The carbon concentration inversion module is used to construct DOC inversion models and POC inversion models based on the multi-source remote sensing images and measured water sample data, generate the total carbon concentration field, and perform spatial consistency constraint optimization.

[0067] The hydrodynamic simulation module is used to construct a two-dimensional unsteady flow hydrodynamic model and output the water depth field and velocity field of the study area.

[0068] The cross-section recognition module is used to automatically identify active carbon exchange cross sections based on the coupling relationship between water depth gradient and flow velocity direction.

[0069] The carbon flux calculation module is used to couple the optimized carbon concentration field, water depth field, flow velocity field and flux correction coefficient to calculate the instantaneous carbon flux at each cross section.

[0070] The time-series integration module is used to adaptively integrate the instantaneous carbon flux of each section according to the high tide and low tide stages to obtain the total net transverse carbon output.

[0071] The model calibration module is used to perform closed-loop iterative updates on the model parameters in the DOC inversion model and the POC inversion model based on the error between the measured carbon concentration data of the cross section and the carbon concentration calculated by the model.

[0072] The results output module is used to output the total transverse carbon output, the time series of cross-sectional carbon flux, and / or standardized evaluation indicators.

[0073] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method described above.

[0074] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described thereon.

[0075] This invention constructs a lateral carbon output estimation technique for coastal wetland tidal exchange processes by uniformly coupling the DOC and POC carbon concentration fields obtained from multi-source remote sensing inversion with the velocity and depth fields obtained from a two-dimensional unsteady hydrodynamic model. Compared with existing techniques that rely on point sampling, static cross-section extrapolation, or independent remote sensing and hydrodynamic methods, this invention significantly improves the spatial continuity, temporal dynamics, and reliability of lateral carbon output estimation. Furthermore, by automatically identifying active carbon exchange cross-sections, this invention reduces subjective errors caused by manual cross-section selection. By introducing mixing and retention effects into flux calculations, it improves the physical accuracy of carbon transport characterization under complex tidal channels, shoals, and vegetation-blocked conditions. Finally, this invention further enhances the estimation of carbon output through the analysis of high and low tides. Integrating across different tidal phases allows for a more accurate distinction between carbon input and output processes, improving the precision of net lateral carbon output discrimination. Furthermore, an adaptive time-step integration mechanism enhances the computational granularity during highly dynamic tidal phases. In addition, this invention constructs a standardized index system, including a normalized lateral carbon output index, a stability index, and a system coupling efficiency index, enabling better comparability of carbon output results for coastal wetlands across different regions, scales, and ecological types. By introducing a dynamic correction loop based on measured data feedback, remote sensing inversion errors and model propagation errors are reduced. Ultimately, this achieves high-precision, dynamic, and standardized quantitative estimation of lateral carbon output in coastal wetlands, providing reliable technical support for blue carbon ecosystem carbon budget accounting, ecological restoration effectiveness assessment, and climate change response research. Attached Figure Description

[0076] Figure 1 This is a schematic diagram of the overall process of a method for estimating the lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling according to the present invention.

[0077] Figure 2 This is a schematic diagram of cross-section identification and carbon flux calculation according to the present invention.

[0078] Figure 3 This is a schematic diagram of the hydrodynamic-remote sensing coupled computing mechanism of the present invention.

[0079] Figure 4 This is a schematic diagram of the spatial distribution of the DOC concentration field, POC concentration field, and total carbon concentration field of the present invention.

[0080] Figure 5 This is a curve comparing the instantaneous carbon flux of the cross section within one tidal cycle between the method of the present invention and the comparative method.

[0081] Figure 6 This is a comparison diagram of the technical effects of the method of the present invention and the comparative method. Detailed Implementation

[0082] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0083] I. Terminology Explanation

[0084] To facilitate understanding of the technical solution of this invention, the main terms used herein will be explained first:

[0085] Coastal wetlands: refers to nearshore wetland ecosystems affected by tides, seawater intrusion, or river-sea interaction, including mangroves, salt marshes, seagrass beds, tidal flats, and tidal channel networks.

[0086] Lateral carbon output: refers to the process by which carbon components in coastal wetland ecosystems are transported to adjacent estuaries, bays or nearshore waters through boundary sections as water flows.

[0087] DOC: Dissolved Organic Carbon, refers to the organic carbon components dissolved in water.

[0088] POC: Particulate Organic Carbon, refers to organic carbon components that are attached to suspended particles or exist in water in particulate form.

[0089] Carbon concentration field: refers to the spatial distribution of total carbon concentration at various locations and times within the study area, usually obtained by superimposing DOC and POC.

[0090] Hydrodynamic model: refers to a two-dimensional unsteady flow model used to simulate the changes in water flow, water depth, tidal level and their spatiotemporal distribution within a research area.

[0091] Cross-section: refers to the calculation boundary selected on wetland boundaries, tidal channels, or areas of active water exchange for the integration calculation of water and carbon flux.

[0092] Normal velocity: refers to the component of the velocity vector in the direction normal to the cross section, used to characterize the actual exchange intensity of water crossing the cross section.

[0093] Tidal cycle decomposition: This refers to dividing a time series according to the rising tide and falling tide processes, and calculating the input carbon flux and output carbon flux separately.

[0094] Standardized indicators: These are normalized evaluation indicators used to characterize the intensity, stability, and coupling efficiency of lateral carbon output.

[0095] II. System Structure

[0096] like Figure 1 As shown, this invention provides a system for estimating the lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling. The system is preferably deployed on a server, workstation, or cloud computing platform and can consist of one or more processors, memory, and program modules. Each module can be implemented in software or collaboratively with sensing devices, remote sensing data interfaces, databases, and display terminals.

[0097] The system includes at least the following modules:

[0098] 1. Data Management Module

[0099] The data management module is used to uniformly manage relevant basic data for the study area, including remote sensing imagery, topographic data, tidal boundary data, runoff boundary data, vegetation parameter data, and cross-sectional measured carbon concentration data. Specifically, the remote sensing data import unit is responsible for importing Sentinel-2, Landsat 8 / 9, UAV imagery, or other available multi-source water remote sensing data; the measured data management unit is used to manage cross-sectional sampling, tidal observation, flow velocity measurements, water quality testing, and historical calibration data.

[0100] 2. Carbon Concentration Inversion Module

[0101] The carbon concentration inversion module is used to establish inversion models of DOC and POC based on remote sensing images and measured samples, and outputs the carbon concentration distribution in the temporal and spatial dimensions within the study area. The carbon concentration inversion module includes a DOC / POC inversion unit and a concentration field optimization unit.

[0102] 3. Hydrodynamic Simulation Module

[0103] The hydrodynamic simulation module is used to construct a two-dimensional unsteady flow hydrodynamic model of the study area and output the velocity field and water depth field at each time step. The hydrodynamic simulation module includes a velocity calculation unit and a water depth simulation unit.

[0104] 4. Cross-section recognition module

[0105] The cross-section identification module is used to automatically identify active carbon exchange cross-sections based on flow velocity, water depth, and boundary exchange characteristics. This module does not rely on technicians manually specifying fixed cross-sections; instead, it identifies representative computational cross-sections within the study boundary and tidal channel network by coupling the water depth gradient with the flow velocity direction.

[0106] 5. Carbon flux calculation module

[0107] The carbon flux calculation module is used to perform cross-sectional coupled calculations of the carbon concentration field and the hydrodynamic field to obtain the instantaneous carbon flux at each cross-section and at each time point. This includes a flux correction unit for introducing mixing and retention corrections.

[0108] 6. Time Series Analysis Module

[0109] The time-series analysis module is used to perform discrete integration and tidal cycle decomposition on the instantaneous cross-sectional carbon flux over time, calculating the high tide input, low tide output, and net carbon output during the study period. This module includes a time integration unit 14 and a total output calculation unit 15.

[0110] 7. Indicator Calculation Module

[0111] The index calculation module is used to construct the normalized horizontal carbon output index, carbon output stability index, and system coupling efficiency index to achieve comparability of results in different regions and at different time scales.

[0112] 8. Results Visualization Module

[0113] The results visualization module is used to output concentration distribution maps, flow field maps, cross-sectional flux change maps, total volume bar charts, and standardized index charts.

[0114] 9. Model calibration module

[0115] The model calibration module is used to compare the measured carbon concentration or measured carbon flux at the cross section with the model results to identify errors, and to dynamically update the DOC / POC inversion parameters and / or coupling parameters through the error inversion unit and the parameter update unit.

[0116] Thus, this invention forms a complete closed-loop system: from data import to carbon concentration inversion, and then to hydrodynamic simulation, cross-section identification, carbon flux coupling, time integration, index calculation and error correction, the modules in the whole chain are interconnected and do not operate in isolation, thereby ensuring the dynamism, continuity and reliability of the horizontal carbon output results.

[0117] III. Specific Technical Route for Implementing the Method of the Invention

[0118] The core objective of this invention is to obtain the total net lateral carbon flux output from wetland boundaries within a given study area and time period, and to further develop a standardized index. This total can be expressed as the sum of carbon fluxes at all representative cross sections and at all discrete moments during the study period.

[0119] In this invention, the total transverse carbon output during the study period can be expressed as:

[0120] ;

[0121] in: This represents the total net transverse carbon output during the study period; This represents the total number of discrete time steps. Indicates the total number of cross sections involved in the calculation; Indicates the first The cross-section at the first Each time step corresponds to a time. Instantaneous carbon flux; Indicates the first Each time step.

[0122] Based on this, the present invention further subdivides the technical route into steps S1 to S8, which are described in detail below.

[0123] (I) Step S1: Multi-source data acquisition, preprocessing and unified management

[0124] Step S1 is a fundamental supporting step of this invention, and its main objective is to construct the data foundation required for subsequent calculations. Although this step has a relatively low direct contribution to the inventive step, it determines the input quality of subsequent inversion, simulation, and coupling, and therefore still needs to be fully explained.

[0125] 1. Data types

[0126] The data required for this invention preferably include: multi-source remote sensing image data; DEM topographic elevation data; tidal boundary data; river runoff boundary data; vegetation parameter data, such as leaf area index (LAI); cross-sectional measured water sample data, including DOC, POC, turbidity, suspended particles, chlorophyll, etc.; and cross-sectional measured flow velocity and water depth data.

[0127] 2. Remote sensing data preprocessing

[0128] The following processing steps are preferred for remote sensing images: radiometric calibration; atmospheric correction; geometric correction and multi-temporal registration; water mask extraction; cloud and shadow removal; and matching with measured sampling time.

[0129] In a preferred embodiment, Sentinel-2 10m to 20m resolution multispectral data can be used as the primary data source, supplemented by Landsat 8 / 9 data, and UAV high-resolution data can be used as local calibration data.

[0130] 3. Database organization

[0131] Preferably, the data is stored uniformly in a spatiotemporal database, and a unified coordinate system and time label are established for each type of data.

[0132] For example, all raster data can be projected onto the same plane coordinate system and a unified time reference, such as UTC time or local standard time, can be set to ensure that the remote sensing time phase, hydrodynamic boundary and cross section observation time can correspond one by one.

[0133] The result of this step is the creation of a data management platform that can be used for both carbon concentration inversion and hydrodynamic simulation, thereby avoiding the disconnect between isolated processing of remote sensing data and separate processing of hydrodynamic data in traditional technologies.

[0134] (ii) Step S2: DOC / POC inversion and construction of total carbon concentration field

[0135] Step S2 is one of the key preliminary steps of this invention, and its technical focus is on: establishing a joint inversion model of DOC and POC through multi-source remote sensing feature vectors and measured samples, thereby forming a spatially continuous total carbon concentration field with temporal attributes. Compared with traditional processing methods that only target a single component or a single time profile, this invention incorporates DOC and POC into a unified framework simultaneously, providing a complete concentration input for subsequent lateral carbon output estimation.

[0136] 1. Construction of remote sensing feature vectors

[0137] The present invention preferably constructs the following remote sensing feature vector:

[0138] ;

[0139] in: Represents remote sensing feature vectors; Indicates wavelength Remote sensing reflectance at the location; Indicates the number of spectral bands selected; Indicates the normalized water index; This indicates an improved normalized water index; Indicates the turbidity index; Indicates chlorophyll Concentration agent quantity.

[0140] By introducing reflectance, water index, turbidity proxy, and chlorophyll proxy, the optical properties of complex coastal wetland water bodies can be more fully characterized. Especially under turbid water conditions, it is difficult to stably invert DOC and POC by relying solely on single-band reflectance. This invention significantly enhances the robustness of the model through multi-dimensional feature combination.

[0141] 2. DOC Inversion Model

[0142] The preferred method for establishing the DOC inversion model is regression.

[0143] ;

[0144] in: Indicates position At any moment DOC concentration; For constant terms; , , , These are the regression coefficients; This is the DOC inversion error term.

[0145] In implementation, measured DOC samples can be paired with remote sensing pixel features from the same time and location, and calibration can be performed using least squares, partial least squares, random forest regression, or other statistical learning methods suitable for remote sensing inversion. To comply with the claims, this embodiment preferably employs a regression model with clearly defined parameters.

[0146] 3. POC Inversion Model

[0147] The preferred form for the POC inversion model is as follows:

[0148] ;

[0149] in: Indicates position At any moment POC concentration; For constant terms; , These are the regression coefficients; This is the turbidity adjustment coefficient; Indicates a measure of turbidity in water bodies; This is the error term for POC inversion.

[0150] Compared with the method of directly inverting POC based solely on reflectance, this invention introduces a turbidity adjustment term, making the POC more sensitive to fluctuations in suspended particles and more suitable for highly turbid tidal channels, estuaries, and tidal flats.

[0151] 4. Construction of the total carbon concentration field

[0152] After obtaining DOC and POC respectively, a total carbon concentration field is formed:

[0153] ;

[0154] in: Indicates position At any moment Total carbon concentration.

[0155] This invention unifies DOC and POC into the same spatiotemporal grid, forming a unique concentration input for subsequent flux integration. Compared to traditional methods that only use point concentration or the concentration of a single component, this step can more comprehensively reflect the overall level of mobile carbon in water bodies.

[0156] The technical effects of this step are: improving the stability of DOC / POC inversion through multi-source remote sensing features; forming a complete total carbon concentration input through unified modeling of DOC and POC; and expanding the discrete sampled concentration into a spatially continuous concentration field, providing conditions for subsequent boundary integration.

[0157] (III) Step S3: Optimization of spatial consistency constraints in carbon concentration field

[0158] Step S3 is the transition step from remote sensing inversion results to a physically usable concentration field. Remote sensing data is easily affected by sensor noise, thin clouds, interference from neighboring pixels, and complex background reflections, leading to unreasonable abrupt changes in local pixels. If the unprocessed concentration map is directly input into the carbon flux calculation module, it can easily cause instability in the cross-sectional area integration results. Therefore, this invention introduces spatial consistency constraints.

[0159] The optimized carbon concentration field is expressed as follows:

[0160] ;

[0161] in: This represents the optimized carbon concentration field; Represents the original total carbon concentration field; Indicates the spatial smoothing coefficient; The value of the Laplace operator representing the carbon concentration field is used to characterize the intensity of the second-order spatial variation.

[0162] In practical implementation, the following methods can be adopted:

[0163] 1) Perform local window convolution on the original total carbon concentration field;

[0164] 2) Calculate the second-order difference of each pixel in its top, bottom, left, right, and diagonal neighborhoods;

[0165] 3) According to the preset smoothing coefficient Smoothly adjust abnormal pixels;

[0166] 4) Preserve the true gradient in regions with significant boundary changes and avoid over-smoothing.

[0167] This step is not a simple image filtering process, but rather a physical consistency optimization that serves subsequent flux calculations. The optimized concentration field better reflects the continuous transport characteristics of water bodies, making it particularly suitable for cross-sectional infinitesimal integration.

[0168] (iv) Step S4: Construction of two-dimensional unsteady hydrodynamic field

[0169] Step S4 is another key foundational step of the present invention. Its core objective is to calculate the water depth and flow velocity at different locations and times within the study area in order to form the flow field input required for subsequent lateral carbon output estimation.

[0170] 1. Model Basics

[0171] This invention preferably employs a two-dimensional unsteady flow model, whose continuity equation can be expressed as:

[0172] ;

[0173] in: Indicates position At any moment The water depth; This represents the rate of change of water depth with respect to time; Indicates position At any moment The velocity vector; This represents the divergence operator.

[0174] In practical implementation, a computational grid can be constructed based on the DEM. For areas with dense tidal channels, narrow passages, or areas sensitive to boundary exchanges, a locally refined grid is preferred to enhance the ability to simulate water flow details.

[0175] 2. Tidal boundary and runoff boundary

[0176] The present invention preferably introduces a time-varying tidal level boundary:

[0177] ;

[0178] in: Indicates time Boundary tide level; Indicates tidal amplitude; Indicates the tidal angular frequency; Indicates the initial phase.

[0179] In practical applications, the river inflow boundary can be superimposed on the landside boundary in the form of a flow time series. Through the combined effects of seaside tidal changes and landside runoff input, the two-way exchange process between the coastal wet Mediterranean Sea and the land can be reconstructed more realistically.

[0180] 3. Bottom friction and vegetation resistance

[0181] To better suit the complex topography and vegetation environment of coastal wetlands, this invention introduces a bottom friction term and a vegetation resistance term, with the total resistance term preferably expressed as follows:

[0182] ;

[0183] in: Indicates position At any moment Total resistance term; Indicates the bottom friction coefficient; This represents the magnitude of the flow velocity vector; Indicates the vegetation resistance coefficient; Indicates position At any moment Leaf area index.

[0184] In implementation, It can be estimated from remote sensing vegetation indices or assigned values ​​from ground vegetation surveys. This resistance term can reflect the weakening effect of reeds, salt marsh vegetation, mangrove roots, etc., on local flow velocity, making the flow field results closer to the actual situation.

[0185] The technical benefits of this step are: outputting water depth and velocity fields that change over time; providing a dynamic basis for cross-section identification; providing velocity and water depth inputs for cross-section normal flux calculation; and explicitly incorporating topography and vegetation resistance into the flow field calculation, thereby improving the realism of simulations in complex wetlands.

[0186] (v) Step S5: Automatic identification of active carbon exchange sections

[0187] Step S5 is one of the core innovative steps of this invention and a key technical feature that distinguishes it from the traditional scheme of manually selecting cross-sections and static integration. Traditional methods usually rely on researchers to manually select one or more cross-sections at the boundary or tidal channel mouth based on experience. However, due to the complexity of the tidal channel network in coastal wetlands, the constant switching of tidal directions, and the changing flow path with water level, manually selecting cross-sections often suffers from strong subjectivity, poor stability, and a high dependence on the operator for the results.

[0188] This invention automatically identifies the most active boundary regions for carbon exchange by utilizing the coupling relationship between water depth gradient and flow velocity direction, forming a set of cross-sections that can be used for integration. Preferred identification conditions are as follows:

[0189] ;

[0190] in: Indicates the first One carbon flux calculation section; Indicates position At any moment The water depth gradient; Indicates position At any moment The velocity vector; This indicates the threshold for cross-section recognition.

[0191] 1. Recognition Principle

[0192] The water depth gradient reflects the trend of local water body elevation changes, while the velocity vector reflects the direction and intensity of water movement. When and A large dot product indicates that there is not only significant water level / depth driving force at that location, but also a strong exchange flow. This typically corresponds to tidal channel mouths, boundary narrow passages, main exchange channels, or local acceleration zones. Identifying these areas as priority sections can significantly improve the representativeness of the sections to actual carbon exchange processes.

[0193] 2. Implementation method

[0194] The preferred implementation process is as follows:

[0195] 1) Extract candidate cross-sectional units from the boundary of the study area and the main tidal channel network;

[0196] 2) Calculate the time steps of each candidate unit over a consecutive number of time steps. value;

[0197] 3) Determine the threshold based on statistical distribution. For example, taking the upper quantile of the candidate value;

[0198] 4) Define the region that meets the conditions multiple times within a continuous period as a stable exchange active region;

[0199] 5) Perform connectivity analysis on adjacent candidate units and merge them into a single representative cross-section;

[0200] 6) Generate a normal vector for each cross section for subsequent normal velocity extraction.

[0201] Compared to traditional cross-section selection methods, this step has the following advantages:

[0202] 1) The selection of cross-sections is driven by flow characteristics, not by human experience;

[0203] 2) It can dynamically identify active exchange zones according to different water level conditions;

[0204] 3) It helps improve the consistency of results among different researchers and different regions;

[0205] 4) A standardized cross-section determination mechanism was established for estimating lateral carbon output.

[0206] like Figure 2 As shown, cross-sections 1 and 2 are not fixed presets, but are obtained by screening from the boundary and tidal channel network through the above mechanism, thus demonstrating the innovation of this invention in automatic cross-section identification.

[0207] (vi) Step S6: Cross-sectional coupling calculation of carbon concentration field and hydrodynamic field

[0208] Step S6 is one of the most crucial and inventive steps in this invention. Its essence is to couple the spatially continuous carbon concentration field and the spatiotemporally varying hydrodynamic field at the cross-sectional micro-element scale to obtain the instantaneous carbon flux at each cross-section at any given time. Unlike the traditional estimation method of point concentration × cross-sectional average flow, this invention adopts an integral form oriented towards cross-sectional micro-elements and further introduces a mixing-retention joint correction mechanism, making the carbon flux results closer to the actual physical process.

[0209] 1. Basic cross-sectional flux

[0210] The basic instantaneous carbon flux is expressed as:

[0211] ;

[0212] in: Indicates the first Each cross-section at time Instantaneous carbon flux; Indicates position At any moment carbon concentration; Relative to cross section Normal flow velocity; Indicates position At any moment The water depth; This indicates the length of the cross-sectional micro-element.

[0213] This formula embodies the core physical logic of the present invention: the amount of carbon transported on any cross-sectional micro-element is equal to the carbon concentration per unit volume multiplied by the volumetric flow rate through that micro-element, and the volumetric flow rate can be determined by the normal flow velocity and the local water depth.

[0214] 2. Normal velocity extraction

[0215] In order to obtain The present invention preferably uses the projection relationship between the cross-sectional normal direction and the local velocity vector for calculation:

[0216] ;

[0217] in: Indicates the first The unit normal vector of each cross section; Indicates position At any moment The velocity vector.

[0218] This normal velocity extraction method ensures that only the exchange flow that actually crosses the cross section is counted, and that the tangential flow along the cross section direction is not mistakenly included in the lateral output.

[0219] 3. Hybrid-Retention Joint Correction

[0220] In coastal wetland environments, carbon transport is not entirely equivalent to ideal convective transport. Tidal channel diffusion, local backflow, vegetation blockage, and stagnant water in tidal flats can all alter the intensity of carbon output under the same flow conditions. Therefore, this invention introduces a flux correction coefficient into the basic flux formula. ,get:

[0221] ;

[0222] in: Indicates position At any moment The flux correction factor.

[0223] Furthermore, the correction coefficient satisfies:

[0224] ;

[0225] in: Indicates the mixed correction factor; This represents the retention correction factor.

[0226] The preferred expression for the mixed correction coefficient is:

[0227] ;

[0228] in: This represents the velocity gradient correction coefficient; Indicates flow velocity along Gradient of direction.

[0229] The preferred expression for the retention correction factor is:

[0230] ;

[0231] in: Indicates the dwell time decay coefficient; Indicates position At any moment The time the water remains in the body.

[0232] 4. Obtaining the length of stay

[0233] Duration of stay It can be derived from hydrodynamic models, for example, through particle tracing, exchange time statistics, grid outflow time estimation, or tidal cycle mean residence time estimation. For a given grid cell, the longer the residence time, the more likely carbon is to undergo sedimentation, redistribution, decomposition, or local retention at that location. Therefore, its effectiveness in participating in boundary output is relatively reduced, hence an exponential decay method is used for correction.

[0234] 5. Numerical Integration Implementation

[0235] In practical implementation, each cross-section can be discretized into several micro-segments, and each micro-segment can be summed to approximate the cross-section.

[0236] ;

[0237] in: Indicates the first The number of micro-segments into which each cross-section is divided; Indicates the first Each micro-segment at time carbon concentration; Indicates the first Normal velocity of each micro-segment; Indicates the first The water depth of a micro-segment; Indicates the first Correction coefficients for each micro-segment; Indicates the first The length of a micro-segment.

[0238] The innovative effects of this step are mainly reflected in:

[0239] 1) Instead of using the coarse method of single-point concentration × total flow rate, cross-sectional infinitesimal integral is used;

[0240] 2) Accurately couple remote sensing concentration information with hydrodynamic information at the same cross section and at the same time;

[0241] 3) By using hybrid correction and retention correction, the results are made more consistent with the complex transport mechanism of coastal wetlands;

[0242] 4) Provides high-quality instantaneous flux input for subsequent tidal cycle decomposition and total integral.

[0243] like Figure 3 As shown, the coupling of this invention is not to first obtain the concentration map, then obtain the flow field map separately, and finally simply multiply them, but to complete the construction of cross-sectional flux within a unified space-time framework through cross-section identification, normal velocity extraction and process correction.

[0244] (vii) Step S7: Tidal period decomposition and adaptive time step integration

[0245] Step S7 is another important inventive step of this invention. Because coastal wetlands are highly dynamic systems controlled by tides, carbon flux exhibits a clear directional variation: during high tide, offshore or estuarine waters may input carbon into the wetland; during low tide, the wetland waters may output carbon to the outside. Therefore, if high and low tides are not distinguished and only the absolute values ​​are summed, it is impossible to accurately determine whether the study area exhibits net carbon output or net carbon input during the study period.

[0246] 1. Tidal Period Decomposition

[0247] This invention preferably decomposes the research period into a high tide phase and a low tide phase, and integrates them separately. The net transverse carbon output is expressed as:

[0248] ;

[0249] in: Indicates net lateral carbon output; This indicates the cumulative carbon flux during the low tide phase; This represents the cumulative carbon flux during the high tide phase.

[0250] In practice, the first derivative of the tide level time series or the sign of the main current direction at the cross section can be used to determine whether a certain period of time is a rising tide or a falling tide. For example, when the boundary tide level is continuously rising, it can be determined as a rising tide phase; when the boundary tide level is continuously falling, it can be determined as a falling tide phase.

[0251] 2. Adaptive time step

[0252] Considering the dramatic velocity changes during the high-high-high pressure phase or the main exchange period, using a fixed, large time step would smooth out peak flux, thus underestimating the short-duration, dramatic exchange process. This invention employs an adaptive time step:

[0253] ;

[0254] in: Indicates the first One time step; Indicates the first The characteristic flow velocity modulus at each time step; This represents a time step mapping function that decreases as the flow velocity increases.

[0255] For example, in implementation, it can be set that: when A larger time step is used; when When using a medium time step; A smaller time step is used. This reduces computational cost during periods of low change and improves integration accuracy during periods of high change.

[0256] 3. Total Integral Amount

[0257] After obtaining the instantaneous carbon flux at each cross section and time step, the total transverse carbon output is calculated as follows:

[0258] ;

[0259] If it is necessary to distinguish between high tide and low tide, the corresponding time index sets can be summed separately.

[0260] (viii) Step S8: Error Feedback and Closed-Loop Dynamic Correction

[0261] Step S8 is the crucial final step in this invention, transitioning from an estimation method to a sustainable optimization system. Traditional methods often output results directly after model construction, lacking continuous feedback from field measurements. This leads to errors potentially accumulating gradually throughout the entire process of concentration inversion, flow field calculation, cross-sectional area integration, and time integration. This invention forms a closed loop through a dynamic correction mechanism.

[0262] 1. Error Definition

[0263] Preferably, the error between the measured carbon concentration and the model carbon concentration is expressed as:

[0264] ;

[0265] in: Indicates error; This indicates the measured carbon concentration; This indicates that the model calculates the carbon concentration.

[0266] (ii) Parameter update

[0267] The following method is preferred for parameter updating:

[0268] ;

[0269] in: This represents the updated set of model parameters; This represents the set of model parameters before the update. Indicates the adjustment factor; Indicates error.

[0270] here, It may include one or more of the regression coefficients in the DOC inversion model, the regression coefficients in the POC inversion model, the turbidity adjustment coefficient, the smoothing coefficient, the mixing correction coefficient, or the retention correction coefficient.

[0271] 3. Closed-loop mechanism

[0272] The updated parameters are re-entered into steps S2 to S7 to recalculate the concentration field, flow field coupling results, and carbon output results. When the error meets the preset threshold, the iteration stops and the final result is output.

[0273] Thus, this invention constructs a complete closed loop: data input → inversion → simulation → coupling → integration → comparison → update → recalculation.

[0274] (ix) Construction of standardized indicators and output of results

[0275] After completing the total calculation, the present invention also preferably outputs several standardized indicators to improve the ability to compare across regions.

[0276] 1. Normalized lateral carbon output index

[0277] The preferred option is expressed as:

[0278] ;

[0279] in: This represents the normalized horizontal carbon output index; This represents the total cross-sectional carbon output during the study period; Indicates the area of ​​the study region; This indicates the average carbon concentration in the study area during the study period.

[0280] 2. Carbon output stability index

[0281] The preferred option is expressed as:

[0282] ;

[0283] in: Indicates the carbon output stability index; The standard deviation of an instantaneous carbon flux time series; This represents the average value of the instantaneous carbon flux time series.

[0284] 3. System Coupling Efficiency Index

[0285] The preferred option is expressed as:

[0286] ;

[0287] in: Indicates the system coupling efficiency index; Indicates soil organic carbon storage; This indicates biomass carbon storage.

[0288] Through these indicators, this invention not only provides the amount of carbon output, but also evaluates whether the output intensity is high, whether the output process is stable, and the relative efficiency of the system's internal carbon reserves in outputting to the outside, thus making the results more scientifically explanatory and valuable for management applications.

[0289] IV. Specific Application Examples

[0290] (I) Application Example Design Ideas

[0291] To verify the technical effectiveness of the present invention's method and system for estimating lateral carbon output in coastal wetlands based on hydrodynamic-remote sensing coupling, a typical coastal tidal wetland was selected as a test area for demonstrative application. The test area includes the main tidal channel, secondary tidal channels, tidal flats, and vegetation cover, exhibiting distinct tidal processes, making it suitable for lateral carbon output estimation.

[0292] (II) Test Area and Test Conditions

[0293] 1. Overview of the Experimental Zone

[0294] The experimental area is located in a coastal wetland at a river estuary, covering an area of ​​approximately The area includes mangrove patches, salt marsh vegetation, exposed tidal flats, and two main tidal channels. The study boundary connects to the open sea and is controlled by semi-diurnal tides, with an average tidal range of approximately [missing information]. The tidal current velocity changes significantly during both rise and fall.

[0295] 2. Data Sources and Sampling Deployment

[0296] (1) Remote sensing data: Sentinel-2 multispectral images with a spatial resolution of 10m to 20m were used, and 10 valid images were selected from 5 consecutive monitoring days;

[0297] (2) Elevation data: The DEM was mapped using UAVs, with a horizontal resolution of 2m;

[0298] (3) Tide level data: One tide level monitoring point is set up at the outer sea boundary, and the monitoring time interval is 10 minutes;

[0299] (4) Velocity data: ADCP velocity profilers were installed at the outlet of the main tidal channel, the outlet of the secondary tidal channel, and the south exchange port.

[0300] (5) Water sample data: Eighteen sampling points were set up in the experimental area. On each monitoring day, samples were taken once at mid-high tide, high tide, mid-low tide, and low tide to measure DOC, POC, turbidity, and chlorophyll. ;

[0301] (6) Validation data: The average carbon concentration and flow rate of the synchronous cross section were obtained at the three boundary cross sections for result comparison.

[0302] 3. Comparison Method Settings

[0303] To demonstrate the technical effects of the present invention, the following comparative group was set up:

[0304] Comparative Example 1: Fixed-point extrapolation method

[0305] Carbon flux is estimated by multiplying the measured DOC+POC concentration at a single point on the cross section by the average flow rate of the cross section, without constructing a spatially continuous concentration field or performing automatic cross section identification.

[0306] Comparative Example 2: Remote Sensing Concentration + Artificial Cross-Section Method

[0307] Carbon concentration was retrieved using remote sensing, but the cross-section was manually specified by researchers based on experience, and the flux was calculated according to the basic formula. The calculation does not introduce mixed corrections or retention corrections.

[0308] Comparative Example 3: Remote sensing concentration + hydrodynamic coupling method (without dynamic correction)

[0309] The remote sensing concentration field and hydrodynamic field are coupled, but no cross-sectional measured error feedback update is performed.

[0310] Example: Method of the Invention

[0311] use Figures 1-3The complete technical process shown includes multi-source remote sensing inversion, spatial consistency optimization, hydrodynamic simulation, automatic cross-section identification, hybrid-retention correction, adaptive time integration, and dynamic correction.

[0312] (III) Specific Operation Process of the Embodiments

[0313] Example 1: Estimation of lateral carbon output from coastal wetlands based on the method of the present invention

[0314] Step S1: Multi-source data preprocessing

[0315] First, atmospheric correction, geometric correction, water mask extraction, and time registration were performed on the Sentinel-2 remote sensing imagery to obtain the effective reflectance data of the water body during the study period.

[0316] Meanwhile, the measured DOC, POC, turbidity, and chlorophyll levels at 18 sampling points were also analyzed. The data underwent quality control, and after removing outliers, 342 valid samples were retained.

[0317] A hydrodynamic grid for the study area was established using a DEM, comprising 18,462 two-dimensional unstructured units, with local densification in the main and secondary tidal channels.

[0318] Step S2: Construct DOC and POC inversion models

[0319] Constructing remote sensing feature vectors:

[0320] ;

[0321] in, , , , These correspond to reflectance in the blue, green, red, and near-infrared bands, respectively.

[0322] A regression model for DOC and POC was established based on 342 sets of samples. After calibration, the following exemplary model was obtained:

[0323] ;

[0324] in, The unit is ; Reflectivity at 560nm wavelength; Reflectivity at 665nm wavelength; Normalized water index; Turbidity index; Chlorophyll Concentration agent quantity.

[0325] ;

[0326] in, The unit is ; Turbidity is a characterization measure, and the unit is NTU.

[0327] Then construct the total carbon concentration field:

[0328] ;

[0329] in, For position At any moment Total carbon concentration, in units of .

[0330] Step S3: Spatial Consistency Constraints

[0331] Optimize the total carbon concentration field using spatial consistency constraints:

[0332] ;

[0333] in, The optimized total carbon concentration field; As a smoothing coefficient, this embodiment takes... ; This is the value of the Laplace operator.

[0334] After optimization, the number of local abnormal spots was significantly reduced. Figure 4 The total carbon concentration can form a continuous and smooth banded distribution, especially a high carbon concentration band near the outlet of the main tidal channel, which is consistent with the tidal transport pattern.

[0335] Step S4: Hydrodynamic Simulation

[0336] Establish a two-dimensional unsteady hydrodynamic model:

[0337] ;

[0338] in, Water depth, in meters (m). This is the velocity vector, in units of... .

[0339] The seaside boundary tide level is set as follows:

[0340] ;

[0341] in, ; ; .

[0342] Simultaneously, a total resistance term is introduced:

[0343] ;

[0344] in, ; ; Leaf area index.

[0345] After the model was run, water depth and velocity fields were obtained with a time resolution of 10 minutes. Validation results show that the correlation coefficient between the simulated and measured tidal levels is 0.982, and the root mean square error of the velocity is [missing value]. This indicates that the hydrodynamic field simulation is reliable.

[0346] Step S5: Automatic Cross-Section Recognition

[0347] Automatically filter carbon exchange active sections based on the following section identification criteria:

[0348] ;

[0349] in, Take all candidate boundary cells The 75th percentile of the distribution.

[0350] Three main cross-sections were ultimately identified:

[0351] Section 1: Main tidal channel outlet section;

[0352] Section 2: Secondary tidal channel outlet section;

[0353] Section 3: South side exchange port section.

[0354] Automatic recognition results and Figure 2 The cross-sections shown are in the same location, with cross-section 1 showing the strongest exchange, followed by cross-section 2, and cross-section 3 showing the weakest exchange.

[0355] Step S6: Instantaneous carbon flux coupling calculation

[0356] Calculate the instantaneous carbon flux for each cross section:

[0357] ;

[0358] in, The unit is ; Normal velocity; For water depth; This is a correction factor.

[0359] Further definition:

[0360] ;

[0361] ;

[0362] ;

[0363] in, ; ; This refers to the water retention time, expressed in minutes.

[0364] Step S7: Tidal decomposition and time integration

[0365] The time series was divided into high tide and low tide phases based on tidal level changes, and an adaptive time step integration was used. The total flux is:

[0366] ;

[0367] Net transverse carbon output is:

[0368] ;

[0369] in, The cumulative carbon flux output during the low tide phase; This represents the cumulative carbon flux input during the high tide phase.

[0370] Step S8: Dynamic calibration

[0371] Dynamic correction was performed using real-time cross-sectional carbon concentration measurements.

[0372] ;

[0373] ;

[0374] in, After three iterations, the inversion model and flux calculation results tend to stabilize.

[0375] (iv) Test data and proof of technical effectiveness

[0376] 1. Verification of the accuracy of DOC and POC inversion

[0377] Table 1. Verification results of DOC and POC inversion models in the embodiments of the present invention.

[0378]

[0379] As shown in Table 1, both the DOC and POC inversion models constructed in this invention have high accuracy and can meet the input requirements for a spatially continuous carbon concentration field in the estimation of lateral carbon output from coastal wetlands. In particular, the DOC model... The result of 0.874 indicates that DOC estimation using multi-source remote sensing feature vectors has strong stability; the POC model, by introducing a turbidity adjustment term, can better reflect the changes in particulate organic carbon in turbid tidal channels.

[0380] 2. Cross-section identification and flux calculation results

[0381] Table 2 Geometric and dynamic parameters of automatically identified cross-sections

[0382]

[0383] As shown in Table 2, sections 1, 2, and 3, automatically identified by this invention, are all located in the actual active exchange region. Among them, section 1 has the longest cross-sectional length, the largest average water depth, and the highest normal flow velocity, thus it is the main lateral carbon export channel. This is consistent with... Figure 2 The cross-section identification diagram shown is consistent with the diagram, proving that the automatic identification mechanism can effectively identify representative cross-sections.

[0384] (III) Dynamic changes in carbon flux during tidal cycles

[0385] Table 3. Cumulative carbon flux during high and low tides at various cross sections within a complete tidal cycle.

[0386]

[0387] Table 3 shows that during this complete tidal cycle, the total tidal input carbon flux in the experimental area was 730.7 kgC, the total tidal output carbon flux was 1180.5 kgC, and the net lateral carbon output was 449.8 kgC, indicating that the experimental area exhibited a net carbon output state during this monitoring period. Without tidal cycle decomposition, simply averaging the total absolute flux would fail to accurately distinguish the wetland's net source / sink attributes in the carbon cycle. This invention significantly improves the accuracy of carbon source / sink identification through tidal cycle decomposition.

[0388] 4. Accuracy comparison with the comparative example

[0389] Table 4 Comparison of estimation results of net transverse carbon output of different methods.

[0390]

[0391] Table 4 shows that the estimation results of the method of the present invention are closest to the measured reference values, with a deviation of only -1.6%, which is significantly better than Comparative Examples 1, 2, and 3. Among them, Comparative Example 1, which only uses point concentration extrapolation, cannot reflect the spatial differences and tidal dynamics within the cross section, and has the largest error; Comparative Example 2, although it introduces a remote sensing concentration field, still has a significant deviation because the cross section is manually specified and the mixing and retention effects are not considered; Comparative Example 3 shows that remote sensing-hydrodynamic coupling itself can improve accuracy, but without dynamic correction, model parameter drift will still accumulate errors; The present invention achieves the best accuracy and the lowest fluctuation through automatic cross section identification, mixing-retention correction, and dynamic correction.

[0392] 5. Verification of dynamic correction effect

[0393] Table 5. Changes in errors of carbon concentration and carbon flux before and after dynamic correction.

[0394]

[0395] As shown in Table 5, the dynamic correction mechanism of this invention can effectively reduce concentration inversion error and final flux error. After three iterations, the RMSE of DOC decreased from 0.88 mg / L to 0.71 mg / L, the RMSE of POC decreased from 0.72 mg / L to 0.58 mg / L, and the net output deviation gradually converged from -6.8% to -1.6%. This indicates that this invention can not only perform initial estimation but also continuously correct the results using experimental feedback, demonstrating closed-loop optimization capability.

[0396] 6. Standardized indicator verification

[0397] Table 6 Comparison of Standardized Evaluation Indicators for Each Method

[0398]

[0399] in:

[0400] ;

[0401] ;

[0402] ;

[0403] As shown in Table 6, the method of this invention outperforms the comparative methods in terms of standardized transverse carbon output intensity, output stability, and system coupling efficiency. In particular, the stability index... The result of 0.728 indicates that the time series carbon flux fluctuations obtained by this invention are more consistent with the actual tidal process, rather than spurious fluctuations caused by method noise or improper section selection.

[0404] Figure 4The differences in carbon concentration fields before and after optimization in the embodiments can be shown to demonstrate that spatial consistency constraints effectively reduce noise speckles; Figure 5 The cross-sectional instantaneous carbon flux curves of the present invention and the comparative example within a tidal cycle can be displayed to demonstrate that the present invention can more accurately capture peak flux in the high flow rate stage. Figure 6 The core results in Tables 4 to 6 can be displayed to visually demonstrate the advantages of this invention in terms of estimation error, stability, and normalization index.

[0405] (vi) Conclusions of Application Examples

[0406] Through the above embodiments and comparative experiments, it can be demonstrated that: (1) the present invention can stably invert DOC and POC based on multi-source remote sensing data and construct a spatially continuous total carbon concentration field; (2) the present invention can combine a two-dimensional non-steady hydrodynamic model to obtain the water depth field and velocity field that change with tides; (3) the present invention can effectively reduce the subjectivity of manual selection of cross sections by automatically identifying active carbon exchange cross sections; (4) the present invention can improve the physical authenticity of cross section carbon flux calculation in complex tidal channel environments by using hybrid correction and retention correction; (5) the present invention can accurately obtain net lateral carbon output by using high tide-low tide decomposition and adaptive time step integration; (6) the present invention can significantly reduce the final estimation error by using dynamic correction closed loop.

[0407] Therefore, compared with existing technical solutions that rely on point extrapolation, artificial cross-section selection, or lack dynamic correction, this invention can significantly improve the accuracy, stability, dynamism, and standardization of cross-sectional carbon output estimation in coastal wetlands, and is applicable to carbon budget accounting of blue carbon ecosystems, ecological restoration effectiveness assessment, and regional carbon cycle research.

[0408] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

[0409] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0410] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0411] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0412] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0413] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0414] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0415] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

Claims

1. A method for estimating lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling, characterized in that, Includes the following steps: S1. Acquire multi-source remote sensing images, topographic elevation, tidal boundary, runoff boundary, and cross-sectional measured carbon concentration data of the study area during the study period. Perform radiometric calibration, atmospheric correction, geometric registration, water body extraction, and time matching on the multi-source remote sensing images to construct remote sensing feature vectors and extract the boundaries of the study area. S2. Based on remote sensing feature vectors and measured water sample data, DOC inversion models and POC inversion models are established to obtain DOC concentration fields, POC concentration fields and total carbon concentration fields. S3, perform spatial consistency constraint optimization on the total carbon concentration field to obtain the optimized carbon concentration field; S4. A two-dimensional unsteady hydrodynamic model was constructed based on topographic elevation data. The water depth field and velocity field of the study area were obtained by simulating the tidal boundary and runoff boundary. S5 automatically identifies active carbon exchange sections based on the coupling relationship between water depth gradient and flow velocity direction; S6. The optimized carbon concentration field is coupled with the normal velocity, water depth and flux correction coefficient of each cross section to calculate the instantaneous carbon flux of each cross section at each time. S7. The instantaneous carbon flux of each section is integrated over time using an adaptive time step to obtain the total net transverse carbon output. S8 uses the error between the measured carbon concentration data of the cross section and the calculation results of the model to iteratively update the parameters of the DOC inversion model and the POC inversion model until the error meets the preset threshold, and outputs the estimation results of the transverse carbon output of the coastal wetland.

2. The method according to claim 1, characterized in that, In step S1, the remote sensing feature vector is: ； in, This is a remote sensing feature vector; wavelength Remote sensing reflectance at the location; The number of spectral bands selected; Normalized water index; To improve the normalized water index; Turbidity index; Chlorophyll Concentration agent quantity; For the first The center wavelength of each band .

3. The method according to claim 1, characterized in that, In step S2, the DOC inversion model and the POC inversion model respectively satisfy: ； ； in, , , , , These are the regression coefficients of the DOC inversion model; , , These are the regression coefficients of the POC inversion model; This is the turbidity adjustment coefficient; It is a characterization measure of turbidity in water bodies; This is the DOC inversion error term; This is the error term for POC inversion; The total carbon concentration field satisfies: ； in, For position At any moment Total carbon concentration; For position At any moment The concentration of dissolved organic carbon; For position At any moment The concentration of particulate organic carbon; , Spatial coordinates; For time.

4. The method according to claim 1, characterized in that, In step S3, the spatial consistency constraint optimization satisfies: ； in, The optimized carbon concentration field; It is the spatial smoothing coefficient; is the Laplace operator value of the carbon concentration field, used to characterize the intensity of the second-order spatial variation; And / or, in step S5, the active carbon exchange section satisfies: ； in, For the first One carbon flux calculation section; For position At any moment The water depth gradient; For position At any moment The velocity vector; The threshold for cross-section identification; This is the section number.

5. The method according to claim 1, characterized in that, In step S4, the continuity equations of the two-dimensional unsteady hydrodynamic model satisfy: ； in, The rate of change of water depth with respect to time; It is a divergence operator; A unit width flow vector; Furthermore, the tidal boundary satisfies: ； in, For a moment Boundary tide level; Tidal range; The tidal angular frequency; This is the initial phase; And / or, in step S4, a bottom friction term and a vegetation resistance term are introduced into the hydrodynamic model to obtain the water depth distribution and velocity distribution at each time point; the bottom friction term and the vegetation resistance term together form the total resistance term, satisfying: ； in, For position At any moment Total resistance term; The coefficient of friction is the lowest. The magnitude of the flow velocity vector; This is the vegetation resistance coefficient; For position At any moment Leaf area index.

6. The method according to claim 1, characterized in that, In step S6, the instantaneous carbon flux satisfies: ； in, For the first Each cross-section at time Instantaneous carbon flux; For position At any moment Relative to cross-section Normal flow velocity; For position At any moment The water depth; For position At any moment flux correction factor; The length of the cross-sectional micro-element; And / or, in step S6, the flux correction coefficient satisfies: ； in, ； ； in, For mixed correction coefficients; This is the retention correction factor; This is the velocity gradient correction coefficient; For the flow velocity along Gradient of direction; This is the dwell time decay coefficient; For position At any moment The time the water remains in the body.

7. The method according to claim 1, characterized in that, In step S7, the total net transverse carbon output satisfies: ； in, The total net transverse carbon output during the study period; This represents the total number of discrete time steps. The total number of cross-sections; For the first The moment corresponding to each time step; For the first One time step; And / or, in step S7, the study period is divided into a high tide phase and a low tide phase, and the high tide input carbon flux and low tide output carbon flux are calculated respectively. The net transverse carbon output satisfies: ； in, Net transverse carbon output; This represents the cumulative carbon flux during the low tide phase. This represents the cumulative carbon flux during the high tide phase. Furthermore, the adaptive time step satisfies: ； in, For the first One time step; For the first The characteristic flow velocity modulus at each time step; This is a time step mapping function that decreases as the flow velocity increases.

8. A system for estimating the lateral carbon output of coastal wetlands based on hydrodynamic-remote sensing coupling, characterized in that, The system is used to implement the method according to any one of claims 1-7, comprising: The data management module is used to acquire and manage multi-source remote sensing images, topographic elevation data, tidal boundary data, runoff boundary data, and cross-sectional measured carbon concentration data. The carbon concentration inversion module is used to construct DOC inversion models and POC inversion models based on the multi-source remote sensing images and measured water sample data, generate the total carbon concentration field, and perform spatial consistency constraint optimization. The hydrodynamic simulation module is used to construct a two-dimensional unsteady flow hydrodynamic model and output the water depth field and velocity field of the study area. The cross-section recognition module is used to automatically identify active carbon exchange cross sections based on the coupling relationship between water depth gradient and flow velocity direction. The carbon flux calculation module is used to couple the optimized carbon concentration field, water depth field, flow velocity field and flux correction coefficient to calculate the instantaneous carbon flux at each cross section. The time-series integration module is used to adaptively integrate the instantaneous carbon flux of each section according to the high tide and low tide stages to obtain the total net transverse carbon output. The model calibration module is used to perform closed-loop iterative updates on the model parameters in the DOC inversion model and the POC inversion model based on the error between the measured carbon concentration data of the cross section and the carbon concentration calculated by the model. The results output module is used to output the total transverse carbon output, the time series of cross-sectional carbon flux, and / or standardized evaluation indicators.

9. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7.

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