Cultivated land vegetation NEP simulation and prediction method, equipment and medium

Abstract

The invention relates to the technical field of agricultural carbon neutralization, in particular to a cultivated land vegetation NEP simulation and prediction method and device and a medium, and the method comprises the steps: obtaining cultivated land vegetation observation data in a region, and calculating an enhanced vegetation index and a surface water index based on the cultivated land vegetation observation data; calculating the net primary productivity and the soil heterotrophic respiratory capacity in the vegetation growth season; nEP space-time simulation is carried out; obtaining a cultivated land vegetation NEP impact factor in the region, constructing a ridge regression model, and calculating the relative contribution of the cultivated land vegetation NEP impact factor to the NEP; and calculating a regression coefficient by adopting a ridge regression model, establishing a multiple linear regression prediction equation based on the regression coefficient, and performing NEP simulation prediction by adopting the multiple linear regression prediction equation. According to the method, the carbon absorption capacity of cultivated land vegetation in the growing season is accurately simulated by combining climatic change scenes and agricultural management practices, and the relationship among grain production, carbon emission reduction and ecological protection can be balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural carbon neutrality, and in particular to a method, device and medium for simulating and predicting NEP of cultivated land vegetation. Background Art

[0002] The relationship between carbon sources and sinks in cultivated land is complex, with many influencing factors and strong interactions. The accurate monitoring and analysis of carbon budget still faces great uncertainty. Net ecosystem productivity (NEP) is the difference between net primary productivity and heterotrophic respiration, and is an important indicator for quantitatively describing carbon exchange between terrestrial ecosystems and the atmosphere.

[0003] At present, there are many technical means for monitoring the carbon effect of cultivated land, including carbon emission / absorption accounting system method, flux observation method, model simulation method, etc. The carbon emission / absorption accounting system method mostly sets corresponding carbon emission / absorption coefficients for different carbon sources / sinks, and takes administrative units as the research object to obtain the carbon balance of regional cultivated land. This method is simple and easy to implement, but different scholars have different opinions on the sources and structural characteristics of carbon emissions, which leads to large uncertainty in the accounting results. In addition, this method is mostly based on statistical data, ignoring the spatial details of the carbon balance of cultivated land in each region. The flux observation method uses monitoring points and observation networks to directly obtain carbon exchange between the atmosphere and the underlying surface. The results are accurate and reliable, but its equipment is expensive, has poor mobility, and has high requirements for the underlying surface. It is only suitable for small-area monitoring. The model simulation method considers the input, output and storage of carbon in the ecosystem, establishes a systematic model to simulate and estimate the carbon balance of a large area. This method can effectively make up for the defects of spatial discontinuity of methods such as flux observation, but it also has disadvantages such as many input parameters and complex processes.

[0004] In summary, there is an urgent need for a method, device and medium for simulating and predicting NEP of cultivated land vegetation to solve the problems in the existing technology. Summary of the invention

[0005] The present invention aims to provide a method, device and medium for simulating and predicting NEP of cultivated land vegetation. The specific technical scheme is as follows: A method for simulating and predicting NEP of cultivated land vegetation comprises the following steps: S1, obtaining the cultivated land vegetation observation data in the region, and calculating the enhanced vegetation index and the surface moisture index based on the cultivated land vegetation observation data; S2: Call the VPM model to calculate the net primary productivity of vegetation during the growing season, and call the regression model to calculate soil heterotrophic respiration; S3: NEP spatiotemporal simulation based on net primary productivity and soil heterotrophic respiration during the vegetation growing season; S4: Obtain the influencing factors of NEP of cultivated land vegetation in the region, construct a ridge regression model, and calculate the relative contribution of each NEP influencing factor of cultivated land vegetation to NEP based on the ridge regression model and NEP spatiotemporal simulation; S5: The ridge regression model is used to calculate the regression coefficients, and a multivariate linear regression prediction equation is established based on the regression coefficients. The multivariate linear regression prediction equation is used to simulate and predict the NEP of cultivated land vegetation in the region.

[0006] Optionally, in S1, the cultivated land vegetation observation data include surface reflectance, temperature, rainfall and incident photosynthetically active radiation data; The calculation expression of enhanced vegetation index is as follows: ; in, represents the enhanced vegetation index, , , Represent the surface reflectance in the near-infrared, red, and blue bands, respectively; The calculation expression of surface moisture index is as follows: ; in, represents the surface moisture index, Represents the surface reflectance in the shortwave infrared band.

[0007] Optionally, in S2, before calling the VPM model to calculate the net primary productivity of vegetation during the growing season, the step further includes calculating input parameters of the VPM model, wherein the input parameters include a temperature limiting factor, a water limiting factor, a leaf age limiting factor, and a maximum light energy utilization rate; Temperature Limiting Factor : ; in, , and They are the lowest, highest and most suitable temperatures for vegetation to carry out photosynthesis. Indicates temperature; Water limiting factor : ; in, is the maximum vegetation growth rate in a single pixel during the growing season. value; Leaf age limiting factor : ; in, This is the period when the leaves are fully expanded; Maximum light energy utilization : ; ; ; in, For vegetation Maximum photosynthetic utilization rate under irradiance, in ; is the chlorophyll remote sensing index, is the surface reflectance in the green band; and represents the empirical parameter; is the proportion of photosynthetically active radiation absorbed by chlorophyll.

[0008] Optionally, in S2, the calculation process of the net primary productivity of vegetation during the growing season is as follows: ; ; ; in, represents the proportionality coefficient, is the total primary productivity, represents the adjacent factor, It stands for photosynthetically active radiation.

[0009] Optionally, in S2, soil heterotrophic respiration is calculated as follows: ; in, is the monthly mean temperature; is the monthly precipitation; is the soil heterotrophic respiration.

[0010] Optionally, in S4, after obtaining the NEP impact factor of cultivated land vegetation, the NEP impact factor of cultivated land vegetation is normalized, and the expression is as follows: ; in, represents the normalized value of the NEP impact factor of cultivated land vegetation, represents the NEP impact factor of cultivated land vegetation; It represents the minimum value among the factors affecting NEP of cultivated land vegetation; Indicates the maximum value of the factors affecting NEP of cultivated land vegetation.

[0011] Optionally, in S4, the ridge regression model is expressed as follows: ; in, Represents the ridge coefficient The regression coefficient under represents the normalized NEP impact factor of cultivated land vegetation, represents the identity matrix, represents the transpose of a matrix, Indicates the NEP value; The calculation process of the relative contribution of cultivated land vegetation NEP influencing factors to NEP is as follows: ; ; in, It indicates the contribution of the factors affecting NEP of cultivated land vegetation to NEP. represents the normalized trend of the NEP influencing factors of cultivated land vegetation, represents the regression coefficient of the factors affecting NEP of cultivated land vegetation; It indicates the relative contribution of the factors affecting NEP of cultivated land vegetation to NEP.

[0012] Optionally, the expression of the multiple linear regression prediction equation is as follows: ; in, Representation variables The regression coefficient of represents a constant term, represents the NEP impact factor of cultivated land vegetation; The multivariate linear regression prediction equation was used to simulate the NEP prediction of cultivated land vegetation in the region, and the root mean square error was used to test the accuracy of the model. The calculation expression of the root mean square error is as follows: ; in, Represents the root mean square error, which is used to test the accuracy of the model; Indicates Historical NEP value of the year, Indicates The model simulation value for the year, Indicates the duration of the study.

[0013] In addition, the present invention also includes a computer device, including a memory and a processor; The memory is used to store a computer program executable on the processor; The processor is used to implement the steps of the above-mentioned method for simulating and predicting NEP of cultivated land vegetation when executing the computer program.

[0014] In addition, the present invention also includes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for simulating and predicting the NEP of cultivated land vegetation are implemented.

[0015] The application of the technical solution of the present invention has the following beneficial effects: The present invention proposes a method for simulating and predicting the NEP of cultivated land vegetation. The method of the present invention combines climate change scenarios with agricultural management practices to accurately simulate the carbon absorption capacity of cultivated land vegetation during the growing season, which helps to balance the relationship between food production, carbon emission reduction and ecological protection, and helps to formulate more targeted agricultural production and carbon emission reduction strategies.

[0016] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flowchart of the steps of the method for simulating and predicting NEP of cultivated land vegetation in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Glossary: NEP: Net ecosystem productivity, an important indicator for quantitative evaluation of carbon sources / sinks in terrestrial ecosystems.

[0021] VPM model: Vegetation Photosynthesis Model, is a model used to guide vegetation photosynthesis. It simulates the gross primary productivity (GPP) of vegetation based on remote sensing data (such as MODIS data) and meteorological data.

[0022] like Figure 1As shown, this embodiment provides a method for simulating and predicting NEP of cultivated land vegetation, comprising the following steps: S1, obtaining the cultivated land vegetation observation data in the region, and calculating the enhanced vegetation index and the surface moisture index based on the cultivated land vegetation observation data; S2: Call the VPM model to calculate the net primary productivity of vegetation during the growing season, and call the regression model to calculate soil heterotrophic respiration; S3: NEP spatiotemporal simulation based on net primary productivity and soil heterotrophic respiration during the vegetation growing season; S4: Obtain all the NEP influencing factors of cultivated land vegetation in the region, construct a ridge regression model, and calculate the relative contribution of the NEP influencing factors of cultivated land vegetation to NEP based on the ridge regression model and NEP spatiotemporal simulation; S5: The ridge regression model is used to calculate the regression coefficients, and a multivariate linear regression prediction equation is established based on the regression coefficients. The multivariate linear regression prediction equation is used to simulate and predict the NEP of cultivated land vegetation in the region.

[0023] Optionally, in S1, the cultivated land vegetation observation data include surface reflectance, temperature, rainfall and incident photosynthetically active radiation data ( ); The calculation expression of enhanced vegetation index is as follows: ; in, represents the enhanced vegetation index, , , Respectively represent the surface reflectance of the near-infrared, red, and blue bands; It should be noted that, for cloud holes, this embodiment uses the mean value of the same period of adjacent years to fill, and performs SG filtering and smoothing to eliminate the influence of clouds. For the convenience of statistics and analysis, this embodiment also converts all data into Albers projection, and uses the resampling method to unify the data to 1km and 1 year.

[0024] The calculation expression of surface moisture index is as follows: ; in, represents the surface moisture index, Represents the surface reflectance in the shortwave infrared band.

[0025] Optionally, in S2, before calling the VPM model to calculate the net primary productivity of vegetation during the growing season, the step further includes calculating input parameters of the VPM model, wherein the input parameters include a temperature limiting factor, a water limiting factor, a leaf age limiting factor, and a maximum light energy utilization rate; Temperature Limiting Factor , the calculation expression is as follows: ; in, , and They are the lowest, highest and most suitable temperatures for vegetation to carry out photosynthesis. is temperature; for example, for rice, the lowest temperature is -3°C, the highest temperature is 42°C, and the optimum temperature is 16°C. or hour, is 0.

[0026] Water limiting factor , the calculation expression is as follows: ; in, is the maximum vegetation growth rate in a single pixel during the growing season. value; Leaf age limiting factor , characterizes the limiting effect of leaf phenology on light energy utilization, and the calculation expression is as follows: ; in, The leaf phenology is the period when the leaves are fully expanded. For farmland ecosystems, crops are planted and new leaves grow continuously during the growing season. The impact of leaf phenology on photosynthesis is very limited. is 1.

[0027] Maximum light energy utilization , which is usually set to a corresponding constant according to the vegetation type, is the main source of uncertainty in ecosystem productivity modeling and is calculated as follows: ; ; ; in, For vegetation Maximum photosynthetic utilization rate under irradiance, in ; is the chlorophyll remote sensing index, is the surface reflectance in the green band; and Represents empirical parameters, for arable crops, and The values ​​are 0.371 and -0.361 respectively; is the proportion of photosynthetically active radiation absorbed by chlorophyll. In the VPM model, .

[0028] Optionally, in S2, the calculation process of the net primary productivity of vegetation during the growing season is as follows: ; ; ; in, represents the proportionality coefficient, is the total primary productivity, represents the adjacent factor, It stands for photosynthetically active radiation.

[0029] Optionally, in S2, soil heterotrophic respiration is calculated as follows: ; in, is the monthly mean temperature; is the monthly precipitation; is the soil heterotrophic respiration.

[0030] It should be noted that in S3, the calculation expression of space-time simulation is: .when When , it means that the carbon fixed by vegetation is greater than the carbon emitted by soil microbial respiration, and it behaves as a carbon sink. Otherwise, it behaves as a carbon source.

[0031] Optionally, in S4, after obtaining the NEP impact factor of cultivated land vegetation, the NEP impact factor of cultivated land vegetation is normalized, and the expression is as follows: ; in, represents the normalized value of the NEP impact factor of cultivated land vegetation, represents the NEP impact factor of cultivated land vegetation; It represents the minimum value among the factors affecting NEP of cultivated land vegetation; Indicates the maximum value of the factors affecting NEP of cultivated land vegetation.

[0032] It should be noted that the preferred NEP influencing factors of cultivated land vegetation in this embodiment include LAI, NDVI, nitrogen fertilizer application, soil erosion, temperature, rainfall and downward shortwave radiation data of cultivated land in the study area from 2001 to 2020. In this embodiment, for the convenience of statistics and analysis, all data are converted to Albers projection, and the resampling method is used to unify the data to 1 km and 1 year.

[0033] Optionally, in S4, the ridge regression model is expressed as follows: ; in, Represents the ridge coefficient The regression coefficient under represents the normalized NEP impact factor of cultivated land vegetation, represents the identity matrix, represents the transpose of a matrix, Indicates the NEP value; The calculation process of the relative contribution of cultivated land vegetation NEP influencing factors to NEP is as follows: ; ; in, It indicates the contribution of the factors affecting NEP of cultivated land vegetation to NEP. represents the normalized trend of the NEP influencing factors of cultivated land vegetation, represents the regression coefficient of the factors affecting NEP of cultivated land vegetation; It indicates the relative contribution of the factors affecting NEP of cultivated land vegetation to NEP.

[0034] Optionally, the expression of the multiple linear regression prediction equation is as follows: ; in, Representation variables The regression coefficient of represents a constant term, represents the NEP impact factor of cultivated land vegetation; The multivariate linear regression prediction equation was used to simulate the NEP prediction of cultivated land vegetation in the region, and the root mean square error was used to test the accuracy of the model. The calculation expression of the root mean square error is as follows: ; in, Represents the root mean square error, which is used to test the accuracy of the model; Indicates Historical NEP value of the year, Indicates The model simulation value for the year, Indicates the duration of the study.

[0035] The process of using the method of this embodiment to perform NEP simulation prediction is as follows: The first step is scenario design: 1) Natural development scenario: without considering external interference (such as climate change, human activities, etc.), based on the data of the past 20 years, assuming that the change pattern of various variables (temperature, rainfall, downward shortwave radiation, soil erosion modulus, nitrogen fertilizer application, NDVI, LAI) will continue, predicting the development status in 2035.

[0036] 2) Climate change scenarios. A series of scenarios combining the Shared Socioeconomic Pathway (SSPS) and the Representative Condensed Pathway (RCPS) proposed in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations are used as future climate change scenarios, including SSP119, SSP245, and SSP585.

[0037] 3) Stable production and grain security scenario: Based on the grain production setting, the grain production in 2035 is preset based on the regional grain production target.

[0038] The second step is model parameter configuration: 1) Natural development scenario. Based on the changing trends of various factors from 2001 to 2019, the development status in 2020 is predicted and compared with the actual values ​​in 2020. On this basis, the values ​​of various variables in 2035 are obtained.

[0039] 2) Climate change scenarios. Based on the data of the sixth phase of the International Coupled Model Intercomparison Program (CMIP6) established by the Intergovernmental Panel on Climate Change (IPCC), basic data for climate change scenario simulations were selected, focusing on the analysis of two variables: temperature and rainfall.

[0040] 3) Scenario of stable production and grain security. Assuming that grain output will continue to remain at 30.75 million tons in 2035, a scenario of stable production and grain security is set, focusing on the LAI factor. LAI, NDVI, nitrogen fertilizer application and grain output are used to establish a multiple linear regression model. Assuming that NDVI and nitrogen fertilizer application continue the historical trend of change from 2001 to 2020, the forecast values ​​of the two in 2035 are obtained. On this basis, according to the multiple linear regression model, under the condition that the grain output target in 2035 is 30.75 million tons, the corresponding LAI value is calculated by reverse calculation. Combined with the spatial distribution and mean of LAI under the natural development scenario in 2035, the conversion coefficient is obtained, and the spatial distribution of LAI under the scenario of stable production and grain security in 2035 is further deduced.

[0041] The third step is to simulate the NEP of cultivated land in multiple scenarios: The factors under different scenarios in 2035 are input into the multivariate linear regression equation established in the analysis of influencing factors to obtain the NEP of cultivated land under the corresponding scenarios and realize NEP simulation prediction.

[0042] In addition, this embodiment also provides a computer device, including a memory and a processor; The memory is used to store a computer program executable on the processor; The processor is used to implement the steps of the above-mentioned cultivated land vegetation NEP simulation prediction method when executing the computer program.

[0043] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0044] The computer device may be a computing device such as a mobile phone, a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory. For example, the computer device may also include an input / output device, a network access device, a bus, etc.

[0045] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0046] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0047] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0048] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for simulating and predicting NEP of cultivated land vegetation are implemented.

[0049] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for simulating and predicting NEP of cultivated land vegetation, characterized in that: The following steps are involved: S1, obtaining the cultivated land vegetation observation data in the region, and calculating the enhanced vegetation index and the surface moisture index based on the cultivated land vegetation observation data; S2: Call the VPM model to calculate the net primary productivity of vegetation during the growing season, and call the regression model to calculate soil heterotrophic respiration; S3: NEP spatiotemporal simulation based on net primary productivity and soil heterotrophic respiration during the vegetation growing season; S4: Obtain the influencing factors of NEP of cultivated land vegetation in the region, construct a ridge regression model, and calculate the relative contribution of the influencing factors of NEP of cultivated land vegetation to NEP based on the ridge regression model and NEP spatiotemporal simulation; S5: The ridge regression model is used to calculate the regression coefficients, and a multivariate linear regression prediction equation is established based on the regression coefficients. The multivariate linear regression prediction equation is used to simulate and predict the NEP of cultivated land vegetation in the region.

2. The method for simulating and predicting NEP of cultivated land vegetation according to claim 1, characterized in that: In S1, the cultivated land vegetation observation data include surface reflectance, temperature, rainfall and incident photosynthetically active radiation data; The calculation expression of enhanced vegetation index is as follows: ； in, represents the enhanced vegetation index, , , Represent the surface reflectance in the near-infrared, red, and blue bands, respectively; The calculation expression of surface moisture index is as follows: ； in, represents the surface moisture index, Represents the surface reflectance in the shortwave infrared band.

3. The method for simulating and predicting NEP of cultivated land vegetation according to claim 2, characterized in that: In S2, before calling the VPM model to calculate the net primary productivity of vegetation in the growing season, the method also includes calculating the input parameters of the VPM model, wherein the input parameters include a temperature limiting factor, a water limiting factor, a leaf age limiting factor, and a maximum light energy utilization rate; Temperature Limiting Factor : ； in, , and They are the lowest, highest and most suitable temperatures for vegetation to carry out photosynthesis. Indicates temperature; Water limiting factor : ； in, is the maximum vegetation growth rate in a single pixel during the growing season. value; Leaf age limiting factor : ； in, This is the period when the leaves are fully expanded; Maximum light energy utilization : ； ； ； in, For vegetation in Maximum photosynthetic utilization rate under irradiance, in ; is the chlorophyll remote sensing index, is the surface reflectance in the green band; and represents the empirical parameter; is the proportion of photosynthetically active radiation absorbed by chlorophyll.

4. The method for simulating and predicting NEP of cultivated land vegetation according to claim 3, characterized in that: In S2, the calculation process of net primary productivity during the vegetation growing season is as follows: ； ； ； in, represents the proportionality coefficient, is the total primary productivity, represents the adjacent factor, It stands for photosynthetically active radiation.

5. The method for simulating and predicting NEP of cultivated land vegetation according to claim 4, characterized in that: In S2, the calculation process of soil heterotrophic respiration is as follows: ； in, is the monthly mean temperature; is the monthly precipitation; is the soil heterotrophic respiration.

6. The method for simulating and predicting NEP of cultivated land vegetation according to claim 5, characterized in that: In S4, after obtaining the NEP impact factor of cultivated land vegetation, the NEP impact factor of cultivated land vegetation is normalized, and the expression is as follows: ； in, represents the normalized value of the NEP impact factor of cultivated land vegetation, represents the NEP impact factor of cultivated land vegetation; It represents the minimum value among the factors affecting NEP of cultivated land vegetation; Indicates the maximum value of the factors affecting NEP of cultivated land vegetation.

7. The method for simulating and predicting NEP of cultivated land vegetation according to claim 6, characterized in that: In S4, the expression of the ridge regression model is as follows: ； in, Represents the ridge coefficient The regression coefficient under represents the normalized NEP impact factor of cultivated land vegetation, represents the identity matrix, represents the transpose of a matrix, Indicates the NEP value; The calculation process of the relative contribution of cultivated land vegetation NEP influencing factors to NEP is as follows: ； ； in, It indicates the contribution of the factors affecting NEP of cultivated land vegetation to NEP. represents the normalized trend of the NEP influencing factors of cultivated land vegetation, represents the regression coefficient of the factors affecting NEP of cultivated land vegetation; It indicates the relative contribution of the factors affecting NEP of cultivated land vegetation to NEP.

8. The method for simulating and predicting NEP of cultivated land vegetation according to claim 7, characterized in that: The expression of the multiple linear regression prediction equation is as follows: ； in, Representation variables The regression coefficient of represents a constant term, represents the NEP impact factor of cultivated land vegetation; The multivariate linear regression prediction equation was used to simulate and predict the NEP of cultivated land vegetation in the region, and the root mean square error was used to test the accuracy of the model. The calculation expression of the root mean square error is as follows: ； in, Represents the root mean square error, which is used to test the accuracy of the model; Indicates Historical NEP value of the year, Indicates The model simulation value for the year, Indicates the duration of the study.

9. A computer device, characterized in that: including memory and processor; The memory is used to store a computer program executable on the processor; The processor is used to implement the steps of the cultivated land vegetation NEP simulation prediction method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for simulating and predicting NEP of cultivated land vegetation according to any one of claims 1 to 8 are implemented.

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