An evaluation method for coastal wetland greenhouse gas exchange based on multi-source data fusion

Abstract

This invention relates to the field of ecological environment monitoring and assessment technology, specifically to a method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion, comprising the following steps: S1: Input multi-source observation data of the coastal wetlands, identify whether the exchange attribution boundary has been rewritten, and output the corresponding boundary rewriting unit; S2: Input the boundary rewriting unit, perform spatial attribution redistribution on the affected observation fluxes, and output the corresponding redistribution exchange unit; S3: Input the redistribution exchange unit, fuse the stable observation fluxes that have not undergone boundary rewriting, and generate the greenhouse gas exchange assessment result for the target area. This invention, by identifying the dynamic rewriting of the exchange attribution boundary of coastal wetlands and performing spatial attribution redistribution on the affected observation fluxes before fusing them with stable fluxes for assessment, thereby eliminating attribution bias caused by boundary changes while improving the accuracy and stability of greenhouse gas exchange results.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and assessment technology, and in particular to a method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion. Background Technology

[0002] Coastal wetlands, as an important transitional zone between land and sea, are vital and active areas for global greenhouse gas exchange. Their carbon source / sink functions are influenced by a variety of factors, including tidal changes, vegetation succession, and dynamic migration of the land-water boundary. Current assessment methods for greenhouse gas exchange in coastal wetlands mainly rely on multi-source observation information, such as flux observation tower data, remote sensing inversion data, and meteorological monitoring data. By spatially partitioning and summarizing the observed fluxes and performing time-scale statistics, the regional-scale greenhouse gas exchange can be obtained. To ensure the comparability of data from different sources, it is usually necessary to perform time alignment and spatial registration processing on the multi-source observation data, and to perform flux attribution statistics within a unified spatial partitioning framework, thereby forming the greenhouse gas exchange assessment results for the target area.

[0003] However, in practical applications, existing technologies typically assign observed fluxes based on static surface zoning or fixed exchange attribution boundaries, failing to fully consider the dynamic migration characteristics of land-water boundaries, tidal channel boundaries, and vegetation boundaries in coastal wetlands at different time scales. This leads to the situation where, even with significant boundary shifts, observed fluxes are still statistically analyzed according to their original attribution zones, resulting in distorted exchange attribution. Furthermore, existing methods often directly perform uniform statistics or simple spatial interpolation on all observed fluxes, lacking a mechanism to distinguish between fluxes affected by boundary changes and unaffected stable fluxes. This makes the assessment results susceptible to interference from local boundary changes, failing to accurately reflect the spatiotemporal dynamics of wetland exchange processes. Therefore, it is necessary to propose a multi-source data fusion-based method for assessing greenhouse gas exchange in coastal wetlands to address the insufficient accuracy and inaccurate spatial attribution issues in existing technologies. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion.

[0005] A method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion includes the following steps: S1: Input multi-source observation data of coastal wetlands, identify whether the exchange ownership boundary of each observation location has been rewritten in the current time period, mark the observation flux that has not been rewritten as stable observation flux, and output the corresponding boundary rewriting unit. S2: Input the boundary rewriting unit, perform spatial attribution reallocation on the affected observation flux, and output the corresponding reallocation exchange unit; S3: Input the redistribution exchange unit, merge the stable observation fluxes that have not undergone boundary rewriting, and generate the greenhouse gas exchange assessment results for the target area.

[0006] Optionally, S1 specifically includes: Input multi-source observation data of coastal wetlands, including flux observation data, tidal level change data, surface boundary distribution data, vegetation cover data, and meteorological observation data; Perform unified time reference alignment and unified spatial coordinate registration on multi-source observation data to form a spatiotemporally aligned dataset that corresponds one-to-one with each observation location; Based on the spatiotemporal alignment dataset, the boundary state information corresponding to each observation location in the current time period is extracted and used as the input for the boundary rewriting recognition of the exchange.

[0007] Optionally, identifying whether the exchange attribution boundary of each observation location has been rewritten in the current time period specifically includes: Extract the boundary coordinates of each observation location under the reference time period and the boundary coordinates under the current time period, and calculate the spatial offset of the corresponding boundary for each observation location; The spatial offset is compared with a preset boundary rewriting threshold. When the spatial offset is greater than or equal to the preset boundary rewriting threshold, it is determined that the corresponding observation position has undergone a change of ownership boundary rewriting. When the spatial offset is less than the preset boundary rewriting threshold, it is determined that no boundary rewriting has occurred at the corresponding observation location, and the corresponding observation flux is marked as a stable observation flux.

[0008] Optionally, the boundary rewriting unit specifically includes an observation location identifier, a boundary type identifier, a boundary offset direction, a boundary offset amount, a corresponding time period identifier, and affected flux marking information.

[0009] Optionally, the affected observation flux in S2 specifically includes: Read the observation location identifier, boundary offset, and corresponding time period identifier from the boundary rewriting unit; Centered on the observation location where the boundary rewriting of the exchange occurred, a boundary rewriting influence zone was constructed according to the preset influence bandwidth. Within the current time period, the observed fluxes whose spatial locations fall within the boundary rewriting influence area are selected from all observed fluxes and identified as the affected observed fluxes.

[0010] Optionally, S2 involves performing a spatial attribution reallocation on the affected observation fluxes, specifically including: Based on the boundary positions of each swapped home region after boundary rewriting, calculate the vertical distance from the observation position corresponding to each affected observation flux to the boundary of each candidate swapped home region. The candidate swap assignment region with the smallest vertical distance is determined as the swap assignment region after the affected observation flux is updated. The affected observation fluxes are migrated from the original exchange home region to the updated exchange home region, thus completing the spatial home reassignment.

[0011] Optionally, the redistribution switching unit includes the original switching home area identifier, the updated switching home area identifier, the redistribution throughput value, the redistribution basis identifier, the corresponding time period identifier, and spatial location index information.

[0012] Optionally, the fusion of stable observation fluxes that have not undergone boundary rewriting in S3 specifically includes: The observation fluxes that are not marked by boundary rewriting units and do not fall within the boundary rewriting influence area are extracted from multi-source observation data and used as stable observation fluxes. The stable fluxes are sorted according to the observation location identifier and the corresponding time period identifier to form a stable flux set; The stable flux set and the redistribution exchange unit are used together as input data for the assessment of greenhouse gas exchange in the target area.

[0013] Optionally, the greenhouse gas exchange assessment results for the target area generated in step S3 specifically include: Based on the exchange affiliation zones within the target area, the redistributed flux values ​​in the redistributed exchange units and the stable observed fluxes in the stable flux sets are summarized by partition; The results of the regional aggregation of each exchange area are accumulated over time according to the preset time scale to generate the greenhouse gas exchange volume of the target area in the corresponding time period. Based on the partition summary results of each swap home zone, the system outputs the partition swap strength and swap timing results.

[0014] Optionally, before generating the greenhouse gas exchange assessment results for the target area, the following steps are also included: Perform time continuity checks on the redistribution exchange units and stable flux sets, and remove data whose time interval between adjacent observations exceeds a preset time gap threshold; Perform spatial coverage integrity verification on the redistribution switching units and stable throughput sets, and retain only data with spatial coverage greater than or equal to a preset coverage threshold. Abnormal flux removal is performed on the redistribution exchange units and stable flux sets, deleting abnormal observed fluxes that deviate from the current time period mean by more than three standard deviations.

[0015] The beneficial effects of this invention are: This invention introduces an exchange attribution boundary rewriting and identification mechanism. Based on a unified spatiotemporal benchmark of multi-source observation data, it quantitatively determines the dynamic migration of boundaries in coastal wetlands caused by tides, water-land interaction, and vegetation changes. This makes the observed flux no longer dependent on fixed spatial partitions for attribution statistics, thereby truly reflecting the impact of boundary changes on the greenhouse gas exchange process and improving the consistency between exchange assessment results and actual surface conditions.

[0016] This invention significantly improves the accuracy and stability of greenhouse gas exchange assessment results in coastal wetlands by performing spatial reassignment of observed fluxes affected by boundary rewriting and merging them with unaffected stable observed fluxes for evaluation. This allows the evaluation process to take into account the data characteristics of both dynamically changing and stable regions, avoiding the error amplification problem caused by uniform processing of all observed fluxes. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the coastal wetland greenhouse gas exchange assessment method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the execution space ownership reallocation process according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0021] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figures 1-2 As shown, a method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion includes the following steps: S1: Input multi-source observation data of coastal wetlands, identify whether the exchange ownership boundary of each observation location has been rewritten in the current time period, mark the observation flux that has not been rewritten as stable observation flux, and output the corresponding boundary rewriting unit. S1 specifically includes: Input multi-source observation data of coastal wetlands, including flux observation data, tidal level change data, surface boundary distribution data, vegetation cover data, and meteorological observation data; Perform unified time reference alignment and unified spatial coordinate registration on multi-source observation data to form a spatiotemporally aligned dataset that corresponds one-to-one with each observation location; Based on the spatiotemporal alignment dataset, the boundary state information corresponding to each observation location in the current time period is extracted and used as the input for the boundary rewriting recognition of the exchange.

[0023] The core of the aforementioned unified time reference alignment lies in mapping data from different observation devices and sampling frequencies onto the same time axis, thereby ensuring the comparability of various observation data at the same moment. This is achieved by first selecting a standard time series as a reference time axis, which is divided according to fixed time intervals; then matching the original timestamps of various observation data with the reference time axis; for time points that do not completely overlap, interpolation is used to calculate the corresponding data values, thus forming a data series with a unified time scale. Specifically: the target time on the unified time axis is set as... For any observation data sequence, select the two closest time points from the original time series. and And satisfy: Then the corresponding time The observed values ​​were calculated using linear interpolation: ,in, To align to a unified time base at time Observed values; For time points in the original data The corresponding observations; For time points in the original data The corresponding observations; The target time point under a unified time base; Neighboring from the original time series Two time points; a unified time interval is recommended to be set as follows: Through the time alignment process described above, observation data from different sources and with different sampling frequencies can form a consistent temporal representation under the same time reference, thus providing a synchronous input basis for subsequent boundary rewriting and identification.

[0024] The core of the aforementioned unified spatial coordinate registration lies in transforming spatial location data from different observation sources to the same coordinate reference system, thus giving all observation locations a unified spatial representation. This is achieved by first selecting a unified spatial coordinate system as the target coordinate system; then performing coordinate transformation on the original spatial coordinates of each data source; and finally, using coordinate mapping or interpolation, expressing various types of data on a unified spatial grid or unified coordinate points. Specifically: for any observation point, its original coordinates are... Mapping coordinates to a unified coordinate system through affine transformation The calculation is as follows: ; ;in, To unify the coordinates of observation points in a spatial coordinate system; These are the coordinates of the observation point in the original coordinate system; The coordinate transformation parameters are obtained from the registration of known control points. Furthermore, to achieve the fusion of multi-source data under a unified spatial representation, a gridded mapping is performed on the spatial data, assuming the center coordinates of the grid cells are... Then the observed values ​​are mapped as follows: ,in, The fused observations are located at the center of the grid cells; For the first Observed values ​​at each observation point; For the first The distance from each observation point to the center of the grid; This represents the number of observation points involved in the mapping. By using unified spatial coordinate registration, observation data from different sources can be expressed within the same spatial reference frame, ensuring that subsequent boundary rewriting identification and spatial attribution redistribution are performed based on consistent spatial location relationships.

[0025] Identify whether the exchange attribution boundaries for each observation location have been rewritten in the current time period, specifically including: Extract the boundary coordinates of each observation location under the reference time period and the boundary coordinates under the current time period, and calculate the spatial offset of the corresponding boundary for each observation location; The spatial offset is compared with a preset boundary rewriting threshold. When the spatial offset is greater than or equal to the preset boundary rewriting threshold, it is determined that the corresponding observation position has undergone boundary rewriting with an exchange of ownership. When the spatial offset is less than the preset boundary rewriting threshold, it is determined that no exchange of ownership boundary rewriting has occurred at the corresponding observation location, and the corresponding observation flux is marked as a stable observation flux. The spatial offset is calculated using the following formula: ,in, Indicates the first Spatial offset of the boundary corresponding to each observation position; and They represent the first The x and y coordinates of each observation location at the current time period corresponding to the boundary; and They represent the first The x and y coordinates of each observation location at the corresponding boundary during the reference time period; the aforementioned preset boundary rewriting threshold. Take 2.0m.

[0026] The boundary rewriting unit specifically includes observation location identifier, boundary type identifier, boundary offset direction, boundary offset amount, corresponding time period identifier, and affected flux marking information; specifically: Boundary type identifiers are used to characterize the category to which the rewritten exchange ownership boundary belongs; The boundary offset direction is used to characterize the migration direction of the boundary from the reference time period to the current time period; The affected flux labeling information is used to characterize whether the observed flux at the corresponding observation location enters the subsequent spatial attribution redistribution process.

[0027] S2: Input boundary rewriting unit, performs spatial attribution redistribution on the affected observation flux, and outputs the corresponding redistribution exchange unit; The affected observation fluxes in S2 specifically include: Read the observation location identifier, boundary offset, and corresponding time period identifier from the boundary rewriting unit; Centered on the observation location where the boundary rewriting of the exchange occurred, a boundary rewriting influence zone was constructed according to the preset influence bandwidth. Within the current time period, the observed fluxes whose spatial locations fall within the boundary rewriting influence area are selected from all observed fluxes and identified as the affected observed fluxes.

[0028] The purpose of establishing the boundary rewriting influence zone is to first clarify which observation range will be affected by the boundary rewriting. This is achieved by first reading the observation location within the boundary rewriting unit where the exchange of ownership boundary rewriting has already been determined, and then using this observation location as the center of influence propagation. Finally, the influence zone is expanded outwards from this center location according to a preset influence bandwidth, forming the corresponding boundary rewriting influence zone. Specifically: Let the first observation location be the boundary rewriting unit where the exchange of ownership boundary rewriting occurs... The coordinates of each observation location are Then, the corresponding boundary rewriting influence area is represented as: ,in, Indicates the first The boundary rewriting influence area corresponding to each boundary rewriting observation location; Represents the coordinates of any spatial location to be determined; Indicates the first The coordinates of the observation location where the boundary of ownership was rewritten during the exchange; This indicates the preset impact bandwidth, with a specific value of [value to be filled in]. In other words, a spatial region with a radius of 5.0m centered on the observation location where boundary rewriting occurs is defined as the boundary rewriting influence zone corresponding to that observation location. By extending the influence range of boundary rewriting from a single point to a spatial influence zone, the determination of subsequent affected observation fluxes has a clear spatial boundary, thereby improving the targeting and accuracy of subsequent redistribution processing.

[0029] After constructing the boundary rewriting influence zone, the next step is to identify which observation fluxes, within the current time period, have their spatial locations falling within this influence zone. Any observation flux whose spatial location falls within the boundary rewriting influence zone and whose temporal location belongs to the current time period is considered potentially affected by boundary rewriting and is therefore identified as an affected observation flux. This process actually involves two constraints: a temporal constraint, processing only observation fluxes within the current time period; and a spatial constraint, retaining only observation fluxes whose spatial location falls within the boundary rewriting influence zone. In other words, the rule for determining affected observation fluxes is essentially a dual selection rule based on both temporal co-location and spatial inclusion within the influence zone. Specifically: Let the current time period be the first The coordinates of the observation location corresponding to each observed flux are: The corresponding sampling time is Then first calculate the observed flux up to the [number]th [number]. Each boundary rewrites the spatial distance of the observation location: ,in, Indicates the first The observed flux location to the first Each boundary rewrites the spatial distance of the observation location; Indicates the first Each boundary rewrites the coordinates of the observation location.

[0030] Then construct the spatial filtering conditions: ;in, Indicates the first The observed flux is relative to the first observation flux. The spatial filtering markers of the observation location are rewritten by the boundary; when When, it indicates the first The spatial location of the observed flux falls into the first A boundary rewriting effect area; when When, it indicates the first The spatial location of the observed flux did not fall within the first... The boundary rewriting influence area. Then, the time filtering conditions are reconstructed; let the start time of the current time period be... The end time is ,but: ,in, Indicates the first A time-filtering marker indicating whether an observed flux belongs to the current time period; Indicates the first The sampling time of each observed flux; Indicates the start time of the current time period; This indicates the end time of the current time period.

[0031] Finally, by combining temporal and spatial conditions, the affected observation flux markers are determined: ;in, Indicates the first The affected markers of the observed flux; when When, it indicates the first Each observation flux belongs to the affected observation flux; when When, it indicates the first The observed flux is not among the affected observed fluxes; This indicates the number of observation locations where the attribution boundary has been rewritten within the current time period; This indicates that the observation flux falls within at least one boundary rewriting influence zone. By jointly filtering the current time period constraint and the boundary rewriting influence zone constraint, the observation flux affected by boundary rewriting can be accurately extracted from all observation fluxes, avoiding erroneous reallocation of unaffected stable observation fluxes. This ensures that subsequent spatial assignment reallocation is performed only on data truly affected by boundary rewriting.

[0032] S2 performs spatial attribution reallocation on the affected observation fluxes, specifically including: Based on the boundary positions of each exchange-of-origin region after boundary rewriting, the vertical distance from the observation position corresponding to each affected observation flux to the boundary of each candidate exchange-of-origin region is calculated; specifically, let the... The coordinates of the observation locations corresponding to the affected observation flux are: , No. The local boundary equations of the candidate exchange attribution zones within the vicinity of the observation location are expressed as: Then the first The affected observation flux corresponds to the observation location to the first Vertical distance between the boundaries of each candidate swap home zone for: ,in, Indicates the first The affected observation flux corresponds to the observation location to the first The vertical distance between the boundaries of each candidate swap home zone; Indicates the first The coefficients of the local linear equations of the boundaries of the candidate exchange home zones; Indicates the candidate exchange home area number.

[0033] The candidate swap region with the smallest vertical distance is determined as the swap region after the affected observation flux is updated; its expression is: ,in, Indicates the first The exchanged home region number after the redistribution of the affected observation flux; Indicates the first The affected observation flux corresponds to the observation location to the first The vertical distance between the boundaries of each candidate swap home zone, in units of Indicates the candidate exchange home area number.

[0034] The affected observation fluxes are migrated from the original exchange home region to the updated exchange home region, thus completing the spatial home reassignment.

[0035] The redistribution exchange unit includes the original exchange home area identifier, the updated exchange home area identifier, the redistribution flux value, the redistribution basis identifier, the corresponding time period identifier, and the spatial location index information; among them, the redistribution basis identifier is used to record the boundary rewriting unit identifier corresponding to the spatial home redistribution, and the spatial location index information is used to record the observation location corresponding to the redistribution flux value.

[0036] S3: Input the redistribution exchange unit, merge the stable observed fluxes that have not undergone boundary rewriting, and generate the greenhouse gas exchange assessment results for the target region; S3 incorporates stable observation fluxes that have not undergone boundary rewriting, specifically including: The observation fluxes that are not marked by boundary rewriting units and do not fall within the boundary rewriting influence area are extracted from multi-source observation data and used as stable observation fluxes. The stable fluxes are sorted according to the observation location and corresponding time period to form a stable flux set; The stable flux set and the redistribution exchange unit are used together as input data for the assessment of greenhouse gas exchange in the target area.

[0037] The greenhouse gas exchange assessment results generated in S3 for the target area specifically include: Based on the exchange affiliation zones within the target area, the redistributed flux values ​​in the redistributed exchange units and the stable observed fluxes in the stable flux sets are summarized by partition; The results of the regional aggregation of each exchange area are accumulated over time according to the preset time scale to generate the greenhouse gas exchange volume of the target area in the corresponding time period. Based on the partition summary results of each swap home zone, the system outputs the partition swap strength and swap timing results.

[0038] Among them, the target area is in the time period The greenhouse gas exchange rate is calculated using the following formula: ,in, Indicates the target area during the time period Greenhouse gas exchange rate; Indicates time period Next Individual redistribution of exchange throughput value; Indicates time period Next One stable observed flux value; Indicates the amount of redistributed switching throughput; This indicates the number of stable observed fluxes.

[0039] The aforementioned preset time scale is determined jointly based on the sampling time interval of the observation data and the tidal variation cycle, ensuring that each time window covers at least one-quarter of the tidal cycle while meeting the minimum number of sampling points; specifically, the preset time scale is calculated using the following formula: ,in, For the preset time scale; The sampling time interval for the original observation data; The minimum number of sampling points is set to a coefficient of 6. It is the tidal cycle; The tidal coverage coefficient is set to 0.25. By summing the exchange flux in segments using this time scale, the periodic variation characteristics of the coastal wetland exchange process can be reflected while ensuring data integrity, thereby improving the reliability of the assessment results.

[0040] Before generating the greenhouse gas exchange assessment results for the target region, the following steps are also included: Perform time continuity checks on the redistribution exchange units and stable flux sets, and remove data whose time interval between adjacent observations exceeds a preset time gap threshold; Perform spatial coverage integrity verification on the redistribution switching units and stable throughput sets, and retain only data with spatial coverage greater than or equal to a preset coverage threshold. Abnormal flux removal is performed on the redistribution exchange unit and the stable flux set, deleting abnormal observation fluxes that deviate from the current time period mean by more than three standard deviations; specifically, the preset time gap threshold is 30 min, and the preset coverage threshold is 85%.

[0041] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0042] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion, characterized in that, Includes the following steps: S1: Input multi-source observation data of coastal wetlands, identify whether the exchange ownership boundary of each observation location has been rewritten in the current time period, mark the observation flux that has not been rewritten as stable observation flux, and output the corresponding boundary rewriting unit. S2: Input the boundary rewriting unit, perform spatial attribution reallocation on the affected observation flux, and output the corresponding reallocation exchange unit; S3: Input the redistribution exchange unit, merge the stable observation fluxes that have not undergone boundary rewriting, and generate the greenhouse gas exchange assessment results for the target area.

2. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 1, characterized in that, S1 specifically includes: Input multi-source observation data of coastal wetlands, including flux observation data, tidal level change data, surface boundary distribution data, vegetation cover data, and meteorological observation data; Perform unified time reference alignment and unified spatial coordinate registration on multi-source observation data to form a spatiotemporally aligned dataset that corresponds one-to-one with each observation location; Based on the spatiotemporal alignment dataset, the boundary state information corresponding to each observation location in the current time period is extracted and used as the input for the boundary rewriting recognition of the exchange.

3. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 2, characterized in that, The step of identifying whether the exchange attribution boundary of each observation location has been rewritten in the current time period specifically includes: Extract the boundary coordinates of each observation location under the reference time period and the boundary coordinates under the current time period, and calculate the spatial offset of the corresponding boundary for each observation location; The spatial offset is compared with a preset boundary rewriting threshold. When the spatial offset is greater than or equal to the preset boundary rewriting threshold, it is determined that the corresponding observation position has undergone a change of ownership boundary rewriting. When the spatial offset is less than the preset boundary rewriting threshold, it is determined that no boundary rewriting has occurred at the corresponding observation location, and the corresponding observation flux is marked as a stable observation flux.

4. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 3, characterized in that, The boundary rewriting unit specifically includes observation location identifier, boundary type identifier, boundary offset direction, boundary offset amount, corresponding time period identifier, and affected flux marking information.

5. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 4, characterized in that, The affected observation fluxes in S2 specifically include: Read the observation location identifier, boundary offset, and corresponding time period identifier from the boundary rewriting unit; Centered on the observation location where the boundary rewriting of the exchange occurred, a boundary rewriting influence zone was constructed according to the preset influence bandwidth. Within the current time period, the observed fluxes whose spatial locations fall within the boundary rewriting influence area are selected from all observed fluxes and identified as the affected observed fluxes.

6. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 5, characterized in that, The spatial attribution reallocation of affected observation fluxes in S2 specifically includes: Based on the boundary positions of each swapped home region after boundary rewriting, calculate the vertical distance from the observation position corresponding to each affected observation flux to the boundary of each candidate swapped home region. The candidate swap assignment region with the smallest vertical distance is determined as the swap assignment region after the affected observation flux is updated. The affected observation fluxes are migrated from the original exchange home region to the updated exchange home region, thus completing the spatial home reassignment.

7. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 6, characterized in that, The redistribution switching unit includes the original switching home area identifier, the updated switching home area identifier, the redistribution throughput value, the redistribution basis identifier, the corresponding time period identifier, and spatial location index information.

8. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 7, characterized in that, The stable observation fluxes that have not undergone boundary rewriting in S3 specifically include: The observation fluxes that are not marked by boundary rewriting units and do not fall within the boundary rewriting influence area are extracted from multi-source observation data and used as stable observation fluxes. The stable fluxes are sorted according to the observation location identifier and the corresponding time period identifier to form a stable flux set; The stable flux set and the redistribution exchange unit are used together as input data for the assessment of greenhouse gas exchange in the target area.

9. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 8, characterized in that, The greenhouse gas exchange assessment results for the target area generated in S3 specifically include: Based on the exchange affiliation zones within the target area, the redistributed flux values ​​in the redistributed exchange units and the stable observed fluxes in the stable flux sets are summarized by partition; The results of the regional aggregation of each exchange area are accumulated over time according to the preset time scale to generate the greenhouse gas exchange volume of the target area in the corresponding time period. Based on the partition summary results of each swap home zone, the system outputs the partition swap strength and swap timing results.

10. The method for assessing greenhouse gas exchange in coastal wetlands based on multi-source data fusion according to claim 9, characterized in that, Before generating the greenhouse gas exchange assessment results for the target region, the following steps are also included: Perform time continuity checks on the redistribution exchange units and stable flux sets, and remove data whose time interval between adjacent observations exceeds a preset time gap threshold; Perform spatial coverage integrity verification on the redistribution switching units and stable throughput sets, and retain only data with spatial coverage greater than or equal to a preset coverage threshold. Abnormal flux removal is performed on the redistribution exchange units and stable flux sets, deleting abnormal observed fluxes that deviate from the current time period mean by more than three standard deviations.

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