Method and system for predicting carbon sequestration potential of wetland vegetation under future climate change

Abstract

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

Description

Technical Field

[0001] This invention relates to the field of wetland vegetation carbon sequestration potential prediction technology, and in particular to a method and system for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change. Background Technology

[0002] Wetland ecosystems play a crucial role in the global carbon cycle. Vegetation, as an important component of wetland ecosystems, is the foundation and source of carbon sequestration. Through photosynthesis, wetland vegetation can fix and store large amounts of carbon. Climate change can significantly impact the carbon sequestration capacity of wetland vegetation by affecting its growth. In the context of global climate change, quantitatively estimating the carbon sequestration potential of wetland vegetation and accurately predicting its future impact under climate change are of great significance for global carbon cycle research and wetland conservation. Traditional methods for assessing the carbon sequestration potential of wetland vegetation are mainly based on field measurements, typically relying on measured data at the sampling scale or qualitative descriptions. These methods are limited by small survey scales and high costs, and cannot accurately predict the future. With the development of remote sensing technology, combining remote sensing data with meteorological data to predict the carbon sequestration potential of wetland vegetation has become an effective approach. There is an urgent need for methods and systems that can combine remote sensing data on net primary productivity (NPP) and vegetation cover (FVC) with historical and future climate data to more accurately assess and predict the carbon sequestration potential of regional wetland vegetation under the influence of climate change. Summary of the Invention

[0003] This invention provides a method and system for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change. It can combine remote sensing data on net primary productivity and vegetation cover with historical and future climate data to more accurately assess and predict the carbon sequestration potential of regional wetland vegetation under the influence of climate change.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, comprising: S1: Obtain historical net primary productivity (NPP), vegetation cover (FVC), monthly temperature (MAP), precipitation (CO2) and carbon dioxide concentration (CO2) data for historical and future periods, and wetland distribution datasets for the two periods, and perform preprocessing. S2: The NPP and FVC are combined into monthly NPP and monthly FVC using the maximum value synthesis method, and the annual average NPP, annual average FVC and annual average FVC are calculated. S3: Calculate monthly temperature and monthly CO2, obtain annual average temperature MAT and annual average CO2, calculate monthly precipitation, obtain annual total precipitation, and then obtain annual MAT, annual CO2 and annual MAP. S4: Using the two wetland distribution datasets, extract the unchanged wetland distribution range and extract all pixels with an annual average FVC>0 as the study area range; S5: Spatial clipping is performed using the annual average NPP, annual average FVC, annual MAT, CO2 and MAP to obtain the historical annual average NPP and annual average FVC, historical and future annual MAT, CO2 and MAP for each pixel. S6: Calculate the ratio of annual average NPP to annual average FVC in historical periods to obtain the annual wetland vegetation carbon sequestration potential (CSP). S7: Based on the CSP, historical annual MAT, CO2 and MAP, a model for estimating the carbon sequestration potential of wetland vegetation is constructed using a multivariate stepwise regression analysis method. S8: Based on the wetland vegetation carbon sequestration potential estimation model, the annual MAT, CO2 and MAP of the future period are used to predict the wetland vegetation carbon sequestration potential within the study area in a future period.

[0005] Furthermore, in S1, the preprocessing includes: uniformly resampling the NPP, the FVC, the monthly temperature and precipitation data of historical and future periods, and the two-period wetland distribution datasets to the same spatial resolution, projection, and geographic coordinate system as the NPP.

[0006] Furthermore, in step S6, the CSP calculation formula is as follows: CSP(n) = NPP(n) / FVC(n) Formula (1) Where n is the year, CSP(n) represents the wetland vegetation carbon sequestration potential value in year n, NPP(n) represents the wetland net primary productivity value in year n, and FVC(n) represents the wetland vegetation cover value in year n.

[0007] Furthermore, S7 includes: for each pixel, using historical annual MAT, CO2, and MAP as independent variables, and annual wetland vegetation carbon sequestration potential (CSP) as the dependent variable, constructing a wetland vegetation carbon sequestration potential estimation model under the influence of climate change through multiple stepwise regression analysis, and selecting the model with the highest goodness of fit as the final model, as shown in the following formula: CSP = d + r1m1 + r2m2 + r3m3 Formula (2) Wherein, CSP is the annual wetland vegetation carbon sequestration potential value, d is a constant term, r1 and r2 are regression coefficients, and m1, m2 and m3 are the annual MAT, CO2 and MAP values ​​during the study period, respectively.

[0008] This invention also provides a system for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, comprising: Acquisition module: used to acquire historical net primary productivity (NPP) of vegetation, vegetation cover (FVC), monthly temperature, precipitation (MAP), carbon dioxide concentration (CO2) for historical and future periods, and wetland distribution datasets for the two periods covering the study area, and to perform preprocessing. First calculation module: used to combine the NPP and FVC into monthly NPP and monthly FVC using the maximum value synthesis method, and to calculate annual average NPP, annual average FVC, and annual average FVC; The second calculation module is used to calculate monthly temperature and monthly CO2, obtain annual average temperature (MAT) and annual average CO2, calculate monthly precipitation, obtain annual total precipitation, and then obtain annual MAT, annual CO2 and annual MAP. Extraction module: used to extract the unchanged wetland distribution range using the two wetland distribution datasets, and extract all pixels with annual average FVC>0 as the study area range; Cropping module: used to perform spatial cropping using the annual average NPP, annual average FVC, annual MAT, CO2 and MAP to obtain the historical annual average NPP and annual average FVC, historical and future annual MAT, CO2 and MAP per pixel; The third calculation module is used to calculate the ratio of annual average NPP to annual average FVC in historical periods, and to obtain the annual wetland vegetation carbon sequestration potential (CSP). Construction module: used to construct a wetland vegetation carbon sequestration potential estimation model based on the CSP, historical annual MAT, CO2 and MAP, using a multivariate stepwise regression analysis method; Prediction module: Used to predict the carbon sequestration potential of wetland vegetation in the study area in a future period based on the wetland vegetation carbon sequestration potential estimation model and by using the annual MAT, CO2 and MAP of the future period.

[0009] Compared with the prior art, the technical solution disclosed in this invention has the following beneficial effects: This invention, based on NPP and FVC remote sensing datasets, historical and future monthly temperature, precipitation, and carbon dioxide concentration datasets, and two-period wetland distribution datasets, constructs a wetland vegetation carbon sequestration potential estimation model to estimate the wetland vegetation carbon sequestration potential in wetland vegetation distribution areas and predict the future wetland vegetation carbon sequestration potential under the influence of climate change. Compared with existing technologies, it has the advantage of fully utilizing the temporal and spatial continuity of remote sensing and climate data, realizing the estimation and future prediction of wetland vegetation carbon sequestration potential, and overcoming the problem that traditional methods are difficult to estimate and predict wetland vegetation carbon sequestration potential on a large scale. By conducting pixel-scale analysis in stable wetland distribution areas, it effectively reduces the uncertainty of results caused by human activities and improves the stability and reliability of wetland vegetation carbon sequestration potential estimation. At the same time, this invention combines vegetation net primary productivity with vegetation cover, and by constructing an index of wetland vegetation carbon sequestration potential, i.e., the carbon sequestration capacity per unit of wetland vegetation cover, it can more accurately reflect the differences in wetland vegetation carbon sequestration potential under different climatic conditions. Furthermore, by introducing future climate scenarios, it achieves the advantage of predicting the future wetland vegetation carbon sequestration potential. It has significant scientific and practical value for wetland carbon sequestration assessment, climate change response analysis, and wetland protection and management decision-making. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the principle of the method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, as provided in an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] This invention provides a method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change. It can combine remote sensing data on net primary productivity and vegetation cover with historical and future climate data to more accurately assess and predict the carbon sequestration potential of regional wetland vegetation under the influence of climate change.

[0015] like Figures 1-2 As shown, this embodiment of the invention provides a method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, including: S1: Obtain historical net primary productivity (NPP), vegetation cover (FVC), monthly temperature (MAP), precipitation (CO2), and wetland distribution datasets for the historical and future periods covering the study area, and perform preprocessing.

[0016] In this embodiment, historical net primary productivity (NPP) datasets and vegetation cover (FVC) datasets covering the study area are acquired, along with monthly temperature, precipitation, and atmospheric carbon dioxide concentration datasets for historical and future periods, and two-period wetland distribution datasets for the start and end years of the historical periods. The above data are then preprocessed. Preprocessing includes uniformly resampling the NPP, FVC, monthly temperature and precipitation datasets for historical and future periods, and the two-period wetland distribution datasets to the same spatial resolution, projection, and geographic coordinate system as the NPP datasets.

[0017] S2: The NPP and FVC are combined into monthly NPP and monthly FVC using the maximum value synthesis method, and the annual average NPP, annual average FVC and annual average FVC are calculated. In the embodiments of this specification, the maximum value synthesis method is used to combine the NPP dataset and FVC dataset in S1 into monthly NPP and FVC datasets, and the arithmetic mean method is used to calculate the annual average NPP, annual average FVC, and multi-year average FVC corresponding to the historical period. S3: Calculate monthly temperature and monthly CO2, obtain annual average temperature MAT and annual average CO2, calculate monthly precipitation, obtain annual total precipitation, and then obtain annual MAT, annual CO2 and annual MAP. In the embodiments of this specification, the arithmetic mean method is used to calculate the monthly temperature and carbon dioxide concentration data of historical and future periods to obtain the annual average temperature (MAT) and carbon dioxide concentration (CO2) of historical and future periods. The cumulative method is used to calculate the monthly precipitation data of historical and future periods to obtain the annual total precipitation (MAP) of historical and future periods, thereby obtaining the annual MAT, CO2 and MAP datasets of historical and future periods.

[0018] S4: Using the two wetland distribution datasets, extract the unchanged wetland distribution range and extract all pixels with an annual average FVC>0 as the study area range.

[0019] In the embodiments of this specification, the unchanged wetland distribution range is extracted using two wetland distribution datasets from the start and end years of the historical period. Within the unchanged wetland distribution range, all pixels with a multi-year average annual FVC value > 0 are extracted to obtain the wetland vegetation pixel distribution as the study area range.

[0020] S5: Spatial clipping is performed using the annual average NPP, annual average FVC, annual MAT, CO2 and MAP to obtain the annual average NPP and annual average FVC for each pixel in the historical period, and the annual MAT, CO2 and MAP for the historical period and the future period.

[0021] In the embodiments of this specification, the annual average NPP dataset and annual average FVC dataset obtained in S2, as well as the annual MAT, CO2 and MAP datasets obtained in S3, are spatially clipped using the above-mentioned study area range to obtain the annual average NPP value, annual average FVC value, historical period annual average MAT value, CO2 value and MAP value for each pixel within the study area.

[0022] S6: Calculate the ratio of annual average NPP to annual average FVC for historical periods to obtain the annual wetland vegetation carbon sequestration potential (CSP).

[0023] In the embodiments of this specification, the ratio of the annual average NPP value and the annual average FVC value of each pixel in the study area during the historical period is calculated to obtain the annual wetland vegetation carbon sequestration potential (CSP) value of each pixel during the historical period.

[0024] S7: Based on the CSP, historical annual MAT, CO2 and MAP, a model for estimating the carbon sequestration potential of wetland vegetation is constructed using a multivariate stepwise regression analysis method.

[0025] In the embodiments of this specification, based on the calculated annual wetland vegetation carbon sequestration potential (CSP) value per pixel and the annual MAT, CO2, and MAP values ​​obtained in S5, a multivariate stepwise regression analysis method is used to construct an estimation model for the annual wetland vegetation carbon sequestration potential under the influence of climate change.

[0026] S8: Based on the wetland vegetation carbon sequestration potential estimation model, the annual MAT, CO2 and MAP of the future period are used to predict the wetland vegetation carbon sequestration potential within the study area in a future period.

[0027] In the embodiments of this specification, based on the pixel wetland vegetation carbon sequestration potential estimation model under the influence of climate change, the annual MAT value, CO2 value and MAP value obtained in S5 are used to predict the wetland vegetation carbon sequestration potential in the study area in a future period.

[0028] In step S6, the CSP calculation formula is as follows: CSP(n) = NPP(n) / FVC(n) Formula (1) Where n is the year, CSP(n) represents the wetland vegetation carbon sequestration potential value in year n, NPP(n) represents the wetland net primary productivity value in year n, and FVC(n) represents the wetland vegetation cover value in year n.

[0029] S7 includes: for each pixel, using historical annual MAT, CO2, and MAP as independent variables, and annual wetland vegetation carbon sequestration potential (CSP) as the dependent variable, constructing a wetland vegetation carbon sequestration potential estimation model under the influence of climate change through multiple stepwise regression analysis, and selecting the model with the highest goodness of fit as the final model, as shown in the following formula: CSP = d + r1m1 + r2m2 + r3m3 Formula (2) Wherein, CSP is the annual wetland vegetation carbon sequestration potential value, d is a constant term, r1 and r2 are regression coefficients, and m1, m2 and m3 are the annual MAT, CO2 and MAP values ​​during the study period, respectively.

[0030] Based on the same idea, this invention also provides a system for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, including: Acquisition module: used to acquire historical net primary productivity (NPP) of vegetation, vegetation cover (FVC), monthly temperature, precipitation (MAP), carbon dioxide concentration (CO2) for historical and future periods, and wetland distribution datasets for the two periods covering the study area, and to perform preprocessing. First calculation module: used to combine the NPP and FVC into monthly NPP and monthly FVC using the maximum value synthesis method, and to calculate annual average NPP, annual average FVC, and annual average FVC; The second calculation module is used to calculate monthly temperature and monthly CO2, obtain annual average temperature (MAT) and annual average CO2, calculate monthly precipitation, obtain annual total precipitation, and then obtain annual MAT, annual CO2 and annual MAP. Extraction module: used to extract the unchanged wetland distribution range using the two wetland distribution datasets, and extract all pixels with annual average FVC>0 as the study area range; Cropping module: used to perform spatial cropping using the annual average NPP, annual average FVC, annual MAT, CO2 and MAP to obtain the historical annual average NPP and annual average FVC, historical and future annual MAT, CO2 and MAP per pixel; The third calculation module is used to calculate the ratio of annual average NPP to annual average FVC in historical periods, and to obtain the annual wetland vegetation carbon sequestration potential (CSP). Construction module: used to construct a wetland vegetation carbon sequestration potential estimation model based on the CSP, historical annual MAT, CO2 and MAP, using a multivariate stepwise regression analysis method; Prediction module: Used to predict the carbon sequestration potential of wetland vegetation in the study area in a future period based on the wetland vegetation carbon sequestration potential estimation model and by using the annual MAT, CO2 and MAP of the future period.

[0031] Specifically, the Sanjiang Plain is selected as the study area. The specific implementation of this specification will take the period from 2001 to 2020 as an example, and will be explained in conjunction with the following implementation steps: The steps of a method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change are as follows... Figure 1 As shown.

[0032] A method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change is carried out according to the following steps: (i) Obtain the following datasets covering the Sanjiang Plain region: MOD17A3HGF net primary productivity (NPP) dataset from 2001 to 2020, GLASS vegetation cover (FVC) dataset, CRU-TS-4.09 monthly temperature and precipitation dataset from 2001 to 2020, GCXCO2 monthly carbon dioxide concentration dataset from 2001 to 2020, monthly temperature, precipitation, and carbon dioxide concentration datasets under the SSP126 scenario of CMIP6 in 2050, and wetland distribution datasets for the Sanjiang Plain in 2001 and 2020. Then, perform format conversion and reprojection processing on the above data to unify them with the same projection and coordinate system as the NPP dataset. (ii) The maximum value synthesis method is used to combine the 8-day MOD17A3HGF NPP dataset and the 8-day GLASS FVC dataset into monthly NPP and FVC datasets, and the arithmetic mean method is used to calculate the annual average NPP and FVC datasets from 2001 to 2020, as well as the multi-year average annual FVC. (III) The arithmetic mean method was used to process the monthly temperature data of CRU-TS-4.09 from 2001 to 2020 and the monthly temperature data under the SSP126 scenario of CMIP6 in 2050 to obtain the historical and future annual average temperature (MAT); the arithmetic mean method was used to process the monthly atmospheric carbon dioxide concentration data of GCXCO2 from 2001 to 2020 and the monthly atmospheric carbon dioxide concentration data under the SSP126 scenario of CMIP6 in 2050 to obtain the historical and future annual average atmospheric carbon dioxide concentration (CO2); the cumulative method was used to process the monthly precipitation data of CRU-TS-4.09 from 2001 to 2020 and the monthly precipitation data under the SSP126 scenario of CMIP6 in 2050 to obtain the historical and future annual total precipitation (MAP). (iv) Using the wetland distribution datasets from 2001 and 2020, the unchanged wetland distribution range in the Sanjiang Plain area was extracted. Within the unchanged wetland distribution range, all pixels with a multi-year average annual FVC value > 0 were extracted to obtain the wetland vegetation pixel distribution in the Sanjiang Plain as the study area. (v) Using the above-mentioned study area, the annual average NPP dataset and annual average FVC dataset obtained in step (ii), as well as the annual MAT, CO2 and MAP dataset obtained in step (iii), are spatially clipped to obtain the annual average NPP value and annual average FVC value per pixel in the study area from 2001 to 2020, and the MAT value, CO2 value and MAP value under the SSP126 scenario of CMIP6 from 2001 to 2020 and 2050. (vi) Calculate the ratio of the annual average NPP value to the annual average FVC value for each pixel within the study area obtained in step (v) to obtain the annual wetland vegetation carbon sequestration potential (CSP) value for each pixel. The calculation formula is as follows: CSP(10) = NPP(n) / FVC(n) Formula (1) Where n is the year, CSP(n) represents the wetland vegetation carbon sequestration potential value per pixel in year n, NPP(n) represents the wetland net primary productivity value per pixel in year n, and FVC(n) represents the wetland vegetation cover value per pixel in year n.

[0033] (VII) Based on the annual wetland vegetation carbon sequestration potential values ​​per pixel within the study area and the corresponding annual MAT, CO2, and MAP values, a multivariate stepwise regression analysis method was used to construct a pixel-by-pixel wetland vegetation carbon sequestration potential estimation model under the influence of climate change in the Sanjiang Plain. The expression is as follows: CSP = d + r1m1 + r2m2 + r3m3 Formula (2) Wherein, CSP is the annual wetland vegetation carbon sequestration potential value per pixel, d is a constant term, r1 and r2 are regression coefficients, and m1, m2 and m3 are the annual MAT value, CO2 value and MAP value per pixel during the study period, respectively.

[0034] (viii) Based on the pixel wetland vegetation carbon sequestration potential estimation model under the influence of climate change in the Sanjiang Plain, the MAT, CO2 and MAP values ​​obtained in step (v) under the SSP126 scenario of CMIP6 in 2050 are used to predict the wetland vegetation carbon sequestration potential in the study area under the SSP126 scenario of CMIP6 in 2050.

[0035] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0036] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0037] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0038] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0039] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0040] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, characterized in that, include: S1: Obtain historical net primary productivity (NPP), vegetation cover (FVC), monthly temperature (MAP), precipitation (CO2) and carbon dioxide concentration (CO2) data for historical and future periods, and wetland distribution datasets for the two periods, and perform preprocessing. S2: The NPP and FVC are combined into monthly NPP and monthly FVC using the maximum value synthesis method, and the annual average NPP, annual average FVC, and annual average FVC are calculated. S3: Calculate monthly temperature and monthly CO2, obtain annual average temperature MAT and annual average CO2, calculate monthly precipitation, obtain annual total precipitation, and then obtain annual MAT, annual CO2 and annual MAP. S4: Using the two wetland distribution datasets, extract the unchanged wetland distribution range and extract all pixels with an annual average FVC>0 as the study area range; S5: Spatial clipping is performed using the annual average NPP, annual average FVC, annual MAT, CO2 and MAP to obtain the historical annual average NPP and annual average FVC, historical and future annual MAT, CO2 and MAP for each pixel. S6: Calculate the ratio of annual average NPP to annual average FVC in historical periods to obtain the annual wetland vegetation carbon sequestration potential (CSP). S7: Based on the CSP, historical annual MAT, CO2 and MAP, a model for estimating the carbon sequestration potential of wetland vegetation is constructed using a multivariate stepwise regression analysis method. S8: Based on the wetland vegetation carbon sequestration potential estimation model, the annual MAT, CO2 and MAP of the future period are used to predict the wetland vegetation carbon sequestration potential within the study area in a future period.

2. The method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change as described in claim 1, characterized in that, In step S1, the preprocessing includes: uniformly resampling the NPP, the FVC, the monthly temperature and precipitation data of historical and future periods, and the two-period wetland distribution datasets to the same spatial resolution, projection, and geographic coordinate system as the NPP.

3. The method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change as described in claim 1, characterized in that, In step S6, the CSP calculation formula is as follows: CSP(n) = NPP(n) / FVC(n) Formula (1) Where n is the year, CSP(n) represents the wetland vegetation carbon sequestration potential value in year n, NPP(n) represents the wetland net primary productivity value in year n, and FVC(n) represents the wetland vegetation cover value in year n.

4. The method for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change as described in claim 1, characterized in that, S7 includes: for each pixel, using historical annual MAT, CO2, and MAP as independent variables, and annual wetland vegetation carbon sequestration potential (CSP) as the dependent variable, constructing a wetland vegetation carbon sequestration potential estimation model under the influence of climate change through multiple stepwise regression analysis, and selecting the model with the highest goodness of fit as the final model, as shown in the following formula: CSP = d + r1m1 + r2m2 + r3m3 Formula (2) Wherein, CSP is the annual wetland vegetation carbon sequestration potential value, d is a constant term, r1 and r2 are regression coefficients, and m1, m2 and m3 are the annual MAT, CO2 and MAP values ​​during the study period, respectively.

5. A system for predicting the carbon sequestration potential of wetland vegetation under the influence of future climate change, characterized in that, include: Acquisition module: used to acquire historical net primary productivity (NPP) of vegetation, vegetation cover (FVC), monthly temperature, precipitation (MAP), carbon dioxide concentration (CO2) for historical and future periods, and wetland distribution datasets for the two periods covering the study area, and to perform preprocessing. First calculation module: used to combine the NPP and FVC into monthly NPP and monthly FVC using the maximum value synthesis method, and to calculate annual average NPP, annual average FVC, and annual average FVC; The second calculation module is used to calculate monthly temperature and monthly CO2, obtain annual average temperature (MAT) and annual average CO2, calculate monthly precipitation, obtain annual total precipitation, and then obtain annual MAT, annual CO2 and annual MAP. Extraction module: used to extract the unchanged wetland distribution range using the two wetland distribution datasets, and extract all pixels with an annual average FVC>0 as the study area range; Cropping module: used to perform spatial cropping using the annual average NPP, annual average FVC, annual MAT, CO2 and MAP to obtain the historical annual average NPP and annual average FVC, historical and future annual MAT, CO2 and MAP per pixel; The third calculation module is used to calculate the ratio of annual average NPP to annual average FVC in historical periods, and to obtain the annual wetland vegetation carbon sequestration potential (CSP). Construction module: used to construct a wetland vegetation carbon sequestration potential estimation model based on the CSP, historical annual MAT, CO2 and MAP, using a multivariate stepwise regression analysis method; Prediction module: Used to predict the carbon sequestration potential of wetland vegetation in the study area in a future period based on the wetland vegetation carbon sequestration potential estimation model and by using the annual MAT, CO2 and MAP of the future period.

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