Method and device for determining total primary productivity, electronic equipment and storage medium

Abstract

The invention provides a method and device for determining total primary productivity, electronic equipment and a storage medium. According to the method, a target evaluation model is coupled based on a water potential difference model and a salinity stress model of a sample salt marsh wetland. The target evaluation model evaluates the total primary productivity of the target salt marsh wetland based on the environment driving data of the area where the target salt marsh wetland is located and the physiological data of the vegetation. Wherein the water potential difference model is obtained based on the soil salinity, the average temperature of the latitude zone where the water potential difference model is located, the salinity-chlorine degree conversion coefficient and the vegetation physiological characteristic parameters, and the water potential difference model is used for describing the deviation between the vegetation water potential and the soil water potential. The method fully considers the influence of the soil salinity on the photosynthesis of vegetation on the salt marsh wetland by changing the soil water potential. In addition, the water potential difference model in the method considers the direct influence of soil salinity on the photosynthesis of vegetation on the salt marsh wetland. Therefore, the method can accurately evaluate the total primary productivity of the target salt marsh wetland.

Description

Technical Field

[0001] The present application relates to the technical field of wetland ecohydrology, and more specifically, to a method, device, electronic device and storage medium for determining total primary productivity in the technical field of wetland ecohydrology. Background Art

[0002] As an important coastal "blue carbon" ecosystem, salt marsh wetlands have a high carbon fixation capacity and can efficiently absorb and store carbon dioxide in the air. Gross Primary Productivity (GPP) refers to the amount of organic carbon fixed by vegetation through photosynthesis per unit area, which characterizes the carbon fixation capacity of vegetation. Therefore, it is very important to evaluate the GPP of salt marsh wetlands.

[0003] At present, the existing technology uses the mangrove GPP model to evaluate the GPP of salt marsh wetlands. However, the mangrove GPP model is a process model established based on the coastal tropical and subtropical environment where mangroves grow, and it mainly targets tree and shrub vegetation. The main vegetation in salt marsh wetlands is herbaceous vegetation and gramineous vegetation, and the tolerance of the two types of vegetation to temperature and salinity are very different. Therefore, if the mangrove GPP model is applied to the evaluation of the GPP of salt marsh wetlands, errors will occur in the evaluated GPP.

[0004] Therefore, a method to determine total primary productivity is urgently needed to improve the accuracy of assessing total primary productivity of salt marsh wetlands. Summary of the invention

[0005] The present application provides a method, device, electronic device and storage medium for determining total primary productivity, which can improve the accuracy of evaluating the total primary productivity of salt marsh wetlands.

[0006] In a first aspect, a method for determining total primary productivity is provided, the method comprising: constructing a water potential difference model corresponding to the sample salt marsh wetland based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone in which it is located, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of vegetation, the water potential difference model being used to describe the deviation between vegetation water potential and soil water potential; constructing a salinity stress model corresponding to the sample salt marsh wetland based on the soil salinity, the salinity stress model being used to describe the degree of influence of soil salinity on vegetation; coupling a target evaluation model based on the water potential difference model and the salinity stress model, the target evaluation model being used to evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data of the area where the target salt marsh wetland is located and the physiological data of the vegetation.

[0007] In the above technical solution, based on the water potential difference model and the corresponding salinity stress model corresponding to the sample salt marsh wetland, a target evaluation model is coupled. The target evaluation model can evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data and the physiological data of the vegetation in the area where the target salt marsh wetland is located. Among them, the water potential difference model is obtained based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of the vegetation, and the water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential. Therefore, the water potential difference model in this method fully considers the indirect effect of soil salinity on the total primary productivity, that is, it considers the influence of soil salinity, the average temperature of the latitude zone and the salinity-chlorinity conversion coefficient on the photosynthesis of vegetation on the salt marsh wetland by directly changing the soil water potential. In addition, the salinity stress model is used to describe the degree of influence of soil salinity on vegetation, that is, the salinity stress model in this method considers the direct effect of soil salinity on the photosynthesis of vegetation on the salt marsh wetland. Therefore, in this method, based on the water potential difference model and the salinity stress model, the coupled target assessment model can accurately assess the total primary productivity of the target salt marsh wetland, and can improve the accuracy of the assessment of the total primary productivity of the salt marsh wetland.

[0008] In combination with the first aspect, in some possible implementations, based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of the vegetation, a water potential difference model corresponding to the sample salt marsh wetland is constructed, including: determining the water potential difference model based on the following formula;

[0009] F WPD =PWP mim -SWP;

[0010] Among them, F WPD is the water potential difference model, PWP min is the physiological characteristic parameter of the vegetation, specifically the plant water potential of the vegetation on the sample salt marsh wetland, SWP is the soil water potential of the sample salt marsh wetland, and the soil water potential can be determined by the following formula;

[0011]

[0012] Among them, ρ is the density of seawater, s is the salinity of the soil, k Cl is the conversion coefficient of salinity-chlorinity, M Cl is the molar mass of chloride ions, R is the Boltzmann constant, and T is the average temperature of the latitude zone where the sample salt marsh is located.

[0013] In the above technical solution, the water potential difference model corresponding to the sample salt marsh wetland can be obtained based on the deviation between the plant water potential of the vegetation on the sample salt marsh wetland and the soil water potential of the sample salt marsh wetland. Among them, the plant water potential is a physiological characteristic parameter of vegetation, which is used to characterize the physiological characteristics of vegetation. Soil salinity can affect the water and nutrient absorption of plants by reducing soil water potential, inhibit chlorophyll synthesis and the occurrence of various enzymes in the photosynthetic apparatus, affect photosynthesis, and thus inhibit plant growth. Therefore, the soil water potential of the sample salt marsh wetland is related to soil salinity.

[0014] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, based on the water potential difference model and the salinity stress model, a target evaluation model is coupled, including: based on the water potential difference model and the salinity stress model, a canopy stomatal conductance model of the vegetation on the sample salt marsh wetland is constructed; based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland and the concentration of carbon dioxide in the cells of the vegetation, a first photosynthetic rate model for being limited by carbon dioxide is determined; based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light, the target evaluation model is coupled.

[0015] In the above technical scheme, the water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential, which refers to the driving force of the water movement inside and outside the vegetation body, which affects the opening and closing state of the stomata of the vegetation, thereby affecting the stomatal conductance parameters. The salinity stress model is used to describe the degree of influence of soil salinity on vegetation. Under the condition of salinity stress, the vegetation reduces the osmotic potential and the leaf water potential before dawn by increasing the plant hormone abscisic acid, thereby adjusting the stomatal conductance parameters. Therefore, the method constructs a canopy stomatal conductance model of the vegetation on the sample salt marsh wetland based on the water potential difference model and the salinity stress model. The increase in stomatal conductance enables the vegetation leaves to absorb carbon dioxide quickly to help the vegetation to photosynthesize. The reduction in the concentration of carbon dioxide in the atmosphere will make it impossible for plant leaves to effectively use carbon dioxide in the atmosphere to produce organic matter, thereby inhibiting photosynthesis. The increase in the concentration of carbon dioxide in the cell will promote photosynthesis, but if the concentration is too high, it may cause enzyme inactivation, thereby inhibiting photosynthesis. Furthermore, illumination is the energy source for photosynthesis, therefore, the method is based on a second photosynthetic rate model for being limited by illumination and the first photosynthetic rate model, coupled with a target evaluation model, to obtain an accurate GPP evaluation model.

[0016] In combination with the first aspect and the above implementations, in some possible implementations, based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in cells of vegetation, determining a first photosynthetic rate model limited by carbon dioxide includes: determining the first photosynthetic rate model based on the following formula;

[0017] P C =g c ×(C a -C i );

[0018] Among them, P C is the first photosynthetic rate model, g c is the canopy stomatal conductance model, C a is the concentration of carbon dioxide in the atmosphere, C i is the concentration of carbon dioxide in the cell, and the concentration of carbon dioxide in the cell can be determined by the following formula;

[0019]

[0020] A=(C a +qp) 2 -4×(C a ×qp×C comp );

[0021] q=C half -C comp ;

[0022] p=P N ×F T / g c ;

[0023] Among them, C half is the carbon dioxide concentration in the vegetation canopy when photosynthesis limited by carbon dioxide reaches half of its maximum photosynthetic rate, C comp The carbon dioxide compensation point is used to describe the concentration of carbon dioxide in the cells of vegetation when photosynthesis and respiration are balanced. N is the third photosynthetic rate model limited by leaf nitrogen content and leaf area index of vegetation, g c is the canopy stomatal conductance model, F T Used to describe the impact of atmospheric temperature on vegetation. T It can be determined by the following formula;

[0024]

[0025] in, is the daily average temperature, T min , T max , T opt They are respectively used to describe the minimum temperature, maximum temperature and optimum temperature for vegetation to carry out photosynthesis.

[0026] In the above technical solution, the stomatal conductance of the vegetation canopy, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in the cells of the vegetation will all affect the photosynthesis of the vegetation on the sample salt marsh wetland. Therefore, the first photosynthetic rate model for being limited by carbon dioxide can be determined based on the stomatal conductance model of the canopy, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in the cells of the vegetation. In addition, the concentration of carbon dioxide in the cells is related to the daily average temperature, the concentration of carbon dioxide in the atmosphere, and the minimum temperature, maximum temperature, and optimum temperature when the vegetation performs photosynthesis. Therefore, this method can accurately determine the first photosynthetic rate model for being limited by carbon dioxide through the above multiple parameters.

[0027] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light, the target evaluation model is coupled, including: based on the first photosynthetic rate model and the second photosynthetic rate model, a candidate evaluation model is coupled; the test environment driving data of the area where each test salt marsh wetland is located and the test physiological data of the vegetation in the multiple test salt marsh wetlands are respectively input into the candidate evaluation model to obtain the predicted total primary productivity of each test salt marsh wetland; based on the predicted total primary productivity and the corresponding test total primary productivity, an evaluation index for evaluating the candidate evaluation model is constructed, and based on the evaluation index, the candidate evaluation model is determined as the target evaluation model.

[0028] In the above technical solution, based on the first photosynthetic rate model and the second photosynthetic rate model, a candidate evaluation model is coupled, and based on the candidate evaluation model, the test environment driving data and the test physiological data of the vegetation in the area where each test salt marsh wetland is located in a plurality of test salt marsh wetlands are processed to predict the total primary productivity of each test salt marsh wetland, that is, to predict the total primary productivity. Furthermore, the method measures the prediction ability of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity, and then determines the target evaluation model based on the prediction ability. Therefore, the method can obtain a GPP evaluation model with the best evaluation performance. In actual application, the total primary productivity of the target salt marsh wetland can be accurately evaluated based on the target evaluation model.

[0029] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, based on the predicted total primary productivity and the corresponding test total primary productivity, an evaluation index for evaluating the candidate evaluation model is constructed, including any one of the following: based on the predicted total primary productivity and the corresponding test total primary productivity, the model efficiency of the candidate evaluation model is constructed; based on the predicted total primary productivity and the corresponding test total primary productivity, the root mean square error of the candidate evaluation model is constructed; based on the predicted total primary productivity and the corresponding test total primary productivity, the determination coefficient of the candidate evaluation model is constructed.

[0030] In the above technical solution, the model efficiency and / or the root mean square error and / or the determination coefficient are determined as the evaluation indicators for evaluating the candidate evaluation model, which can measure the prediction ability of the candidate evaluation model from a variety of different perspectives. In this way, it is helpful to obtain the GPP evaluation model with the best evaluation performance.

[0031] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, based on the evaluation index, the candidate evaluation model is determined as the target evaluation model, including: when the model efficiency is greater than the preset model efficiency, the candidate evaluation model is determined as the target evaluation model; and / or, when the root mean square error is less than the preset root mean square error, the candidate evaluation model is determined as the target evaluation model; and / or, when the determination coefficient is greater than the preset determination coefficient, the candidate evaluation model is determined as the target evaluation model.

[0032] In the above technical solution, when the model efficiency is greater than the preset model efficiency, and / or when the root mean square error is less than the preset root mean square error, and / or when the determination coefficient is greater than the preset determination coefficient, the candidate evaluation model is determined as the target evaluation model. The above process evaluates the candidate evaluation model from a variety of different perspectives, and can obtain a GPP evaluation model with the best evaluation performance to accurately evaluate the total primary productivity of the target salt marsh wetland.

[0033] In a second aspect, a device for determining total primary productivity is provided, the device comprising: a construction module, for constructing a water potential difference model corresponding to the sample salt marsh wetland based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of the vegetation, the water potential difference model being used to describe the deviation between the vegetation water potential and the soil water potential; constructing a salinity stress model corresponding to the sample salt marsh wetland based on the soil salinity, the salinity stress model being used to describe the degree of influence of soil salinity on vegetation; a coupling module, for coupling a target evaluation model based on the water potential difference model and the salinity stress model, the target evaluation model being used to evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data of the area where the target salt marsh wetland is located and the physiological data of the vegetation.

[0034] In conjunction with the second aspect, in some possible implementations, the building module is specifically used to: determine the water potential difference model based on the following formula;

[0035] F WPD =PWP min -SWP;

[0036] Among them, F WPD is the water potential difference model, PWP min is the physiological characteristic parameter of the vegetation, specifically the plant water potential of the vegetation on the sample salt marsh wetland, SWP is the soil water potential of the sample salt marsh wetland, and the soil water potential can be determined by the following formula;

[0037]

[0038] Among them, ρ is the density of seawater, s is the salinity of the soil, k Cl is the conversion coefficient of salinity-chlorinity, M Cl is the molar mass of chloride ions, R is the Boltzmann constant, and T is the average temperature of the latitude zone where the sample salt marsh is located.

[0039] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the construction module is specifically used to construct a canopy stomatal conductance model of the vegetation on the sample salt marsh wetland based on the water potential difference model and the salinity stress model; the device also includes: a determination module, used to determine a first photosynthetic rate model for being limited by carbon dioxide based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of intracellular carbon dioxide in the vegetation; the coupling module is specifically used to couple the target evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light.

[0040] In combination with the second aspect and the above implementation, in some possible implementations, the determination module is specifically used to determine the first photosynthetic rate model based on the following formula;

[0041] P C =g c ×(C a -C i );

[0042] Among them, P C is the first photosynthetic rate model, g c is the canopy stomatal conductance model, C a is the concentration of carbon dioxide in the atmosphere, C i is the concentration of carbon dioxide in the cell, and the concentration of carbon dioxide in the cell can be determined by the following formula;

[0043]

[0044] A=(C a +qp) 2 -4×(C a ×qp×C comp );

[0045] q=C half -C comp ;

[0046] p=P N ×F T / g c ;

[0047] Among them, C half is the carbon dioxide concentration in the vegetation canopy when photosynthesis limited by carbon dioxide reaches half of its maximum photosynthetic rate, C comp The carbon dioxide compensation point is used to describe the concentration of carbon dioxide in the cells of vegetation when photosynthesis and respiration are balanced. N is the third photosynthetic rate model limited by leaf nitrogen content and leaf area index of vegetation, g c is the canopy stomatal conductance model, F T Used to describe the impact of atmospheric temperature on vegetation. T It can be determined by the following formula;

[0048]

[0049] in, is the daily average temperature, T min , T max , T opt They are respectively used to describe the minimum temperature, maximum temperature and optimum temperature for vegetation to carry out photosynthesis.

[0050] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the coupling module is specifically used to: couple the candidate evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model; the determination module is specifically used to: input the test environment driving data and the test physiological data of the vegetation in the area where each test salt marsh wetland is located in a plurality of test salt marsh wetlands into the candidate evaluation model respectively to obtain the predicted total primary productivity of each test salt marsh wetland; based on the predicted total primary productivity and the corresponding test total primary productivity, construct an evaluation index for evaluating the candidate evaluation model, and based on the evaluation index, determine the candidate evaluation model as the target evaluation model.

[0051] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the construction module is specifically also used for any one of the following: constructing the model efficiency of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity; constructing the root mean square error of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity; constructing the determination coefficient of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity.

[0052] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, the determination module is specifically used to: when the model efficiency is greater than the preset model efficiency, determine the candidate evaluation model as the target evaluation model; and / or, when the root mean square error is less than the preset root mean square error, determine the candidate evaluation model as the target evaluation model; and / or, when the determination coefficient is greater than the preset determination coefficient, determine the candidate evaluation model as the target evaluation model.

[0053] In a third aspect, an electronic device is provided, comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the electronic device executes the method in the first aspect or any possible implementation of the first aspect.

[0054] In a fourth aspect, a computer-readable storage medium is provided, which stores an executable program code. When the executable program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic diagram of using an electronic device provided in an embodiment of the present application;

[0056] Figure 2 is a schematic flow chart of a method for determining total primary productivity provided in an embodiment of the present application;

[0057] Figure 3 is a schematic diagram of determining the conditions under which different vegetation is subjected to high temperature stress provided by an embodiment of the present application;

[0058] Figure 4 is a schematic diagram of a candidate evaluation model based on a determination coefficient provided in an embodiment of the present application;

[0059] Figure 5 is a schematic diagram of the structure of a device for determining total primary productivity provided in an embodiment of the present application;

[0060] Figure 6It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0062] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0063] Figure 1 It is a schematic diagram of using an electronic device provided in an embodiment of the present application.

[0064] For example, Figure 1 As shown, usually, the gross primary productivity (GPP) of salt marsh wetlands is evaluated by electronic device A based on the mangrove GPP model. However, the mangrove GPP model is a process model established based on the coastal tropical and subtropical environment in which mangroves grow, and it mainly targets tree and shrub vegetation. The main vegetation in salt marsh wetlands is herbaceous vegetation and gramineous vegetation, and the tolerance of the two types of vegetation to temperature and salinity are very different. Therefore, if the mangrove GPP model is applied to the evaluation of the GPP of salt marsh wetlands, errors will occur in the evaluated GPP of salt marsh wetlands.

[0065] In addition, the GPP of salt marsh wetlands can also be evaluated by the Salt Marsh Model based on Processes for the Yangtze Estuary (SMM-YE) model of electronic device A. However, in the process of evaluating the GPP of salt marsh wetlands, the SMM-YE model does not consider the impact of the salinity characteristics of salt marsh wetlands on the photosynthesis of vegetation, and does not consider the impact mechanism of the physiological and ecological characteristics of different salt marsh vegetation (Spartina alterniflora, reed, fox grass, three-cornered grass, etc.) on photosynthetic capacity. At the same time, the SMM-YE model is based on a very complex set of driving data, and each vegetation requires 10 to 20 parameters, which is not conducive to the promotion of GPP evaluation in a wide range of salt marsh wetland areas.

[0066] Therefore, this application proposes a method for determining the total primary productivity, which takes into account the physiological characteristics of vegetation on salt marsh wetlands and the impact of soil salinity on the GPP assessment, and can improve the accuracy of the assessment of the total primary productivity of salt marsh wetlands. For details, please refer to the following Figure 2 .

[0067] Figure 2 It is a schematic flow chart of a method for determining total primary productivity provided in an embodiment of the present application.

[0068] It should be understood that a method for determining total primary productivity in the present application is applied to an electronic device. In some embodiments, the electronic device includes but is not limited to: a personal computer, a tablet computer, a computing device or other processing device connected to a wireless modem, etc. The electronic device can also be a server.

[0069] For example, Figure 2 As shown, the method 200 includes:

[0070] In step 201, the electronic device constructs a water potential difference model corresponding to the sample salt marsh wetland based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of the vegetation. The water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential.

[0071] It should be understood that the "sample salt marsh wetland" in the above step 201 includes a plurality of salt marsh wetlands with different soil salinities. In some embodiments, the sample salt marsh wetland is a coastal salt marsh wetland.

[0072] It should also be understood that the "salt marsh wetland" in the above scheme refers to areas where the surface is too wet or seasonally waterlogged, the soil is salinized, and there are halophytic vegetation. Salt marsh wetlands are characterized by alkaline surface water, high salt content in the soil, and accumulation of soluble salts in the surface layer. Salt marsh wetlands are widely distributed on the coast, estuaries, or salt lakes or low wetlands in arid or semi-arid grasslands and deserts.

[0073] It should also be understood that, generally speaking, the soil salt content in salt marsh wetlands is relatively high. However, the temperature of the latitude zone where the salt marsh wetlands are located will affect the distribution and variation of soil salinity. Temperature may affect the evaporation rate of water and the migration of salt. Therefore, in the above step 201, when constructing the water potential difference model corresponding to the sample salt marsh wetland, the average temperature of the latitude zone where the sample salt marsh wetland is located is introduced, so that the influence of the average temperature on the soil salinity and then the influence on the water potential difference model can be considered.

[0074] It should also be understood that salt marsh wetlands are special ecosystems in which the water contains a high concentration of salt, which is mainly present in the water in a dissolved state. Salinity and chlorinity are two important parameters that describe the salinity and alkalinity of water quality. The salinity-chlorinity conversion coefficient reflects the proportional relationship between the chloride ion concentration and the total salinity in water under specific conditions. Therefore, the salinity-chlorinity conversion coefficient is closely related to the soil salinity. Therefore, in the above step 201, when constructing the water potential difference model corresponding to the sample salt marsh wetland, the salinity-chlorinity conversion coefficient is introduced.

[0075] It should also be understood that the GPP of salt marsh wetlands refers to the total amount of organic carbon fixed by vegetation through photosynthesis within a certain period of time within a unit area, reflecting the total amount of carbon fixed by vegetation through photosynthesis within a certain period of time. The salt content in the soil (soil salinity) has an important impact on the photosynthesis of vegetation in salt marsh wetlands. Under conditions of higher soil salinity, vegetation may face difficulties in absorbing water and malnutrition, which will reduce the chlorophyll content and affect the efficiency of photosynthesis. In addition, higher soil salinity may also directly inhibit the light reaction stage of photosynthesis and reduce the production of adenosine triphosphate and reduced coenzymes. Among them, adenosine triphosphate provides energy for the reduction of three-carbon compounds, and reduced coenzymes act as enzymes. Reduced coenzymes are formed in the light reaction stage of photosynthesis, and enter the carbon reaction together with adenosine triphosphate to participate in the fixation of carbon dioxide. Long-term salt stress may also lead to the obstruction of the synthesis of photosynthetic pigments in vegetation, stomata closure, and further reduce the photosynthetic rate. Therefore, soil salinity will affect the total amount of organic carbon fixed by vegetation through photosynthesis, that is, GPP.

[0076] It should also be understood that vegetation physiological characteristic parameters include plant water potential. Water is an important component of photosynthesis, and water affects the opening and closing of stomata in vegetation leaves, thereby affecting the progress of photosynthesis. For example, insufficient water can cause stomatal closure, reduce the intake of carbon dioxide, and thus inhibit photosynthesis. In addition, water stress may also indirectly affect photosynthesis through other mechanisms, such as reducing the activity of photosynthetic enzymes or damaging photosynthetic organs. Therefore, vegetation physiological characteristic parameters will affect the total amount of organic carbon fixed by vegetation through photosynthesis, i.e., GPP. Therefore, the method 200 takes into account the effects of soil salinity and vegetation physiological characteristic parameters on GPP in the process of coupling the target evaluation model for evaluating GPP.

[0077] It should also be understood that the "water potential difference model" in the above step 201 can be specifically understood as a function. The water potential difference model is used to quantify the impact of soil salinity on the photosynthesis of vegetation on salt marsh wetlands by directly changing the soil water potential.

[0078] In a possible implementation, the electronic device in step 201 constructs a water potential difference model corresponding to the sample salt marsh wetland based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of the vegetation, including: the electronic device determines the water potential difference model based on the following formula (1);

[0079] F WPD =PWP min -SWP formula(1)

[0080] Among them, F WPD is the water potential difference model, PWP min is the physiological characteristic parameter of the vegetation, specifically the plant water potential of the vegetation on the sample salt marsh wetland, SWP is the soil water potential of the sample salt marsh wetland, and the soil water potential can be determined by the following formula (2);

[0081]

[0082] Among them, ρ is the density of seawater, s is the salinity of the soil, k Cl is the conversion coefficient of salinity-chlorinity, M Cl is the molar mass of chloride ions, R is the Boltzmann constant, and T is the average temperature of the latitude zone where the sample salt marsh is located.

[0083] It should be understood that the "plant water potential of vegetation on the sample salt marsh wetland" in the above scheme can be regarded as the minimum leaf water potential of vegetation, which can be obtained by field measurement. The "soil water potential of the sample salt marsh wetland" in the above scheme includes matrix potential and osmotic potential. Among them, the matrix potential is the potential energy generated by the adsorption of water by soil solid particles using soil solid particles as a water-absorbing matrix. After the soil solid particles adsorb water, the free movement ability of water is reduced. Therefore, the matrix potential is usually a negative value. Considering that the soil in the salt marsh wetland is in a saturated state for a long time, it is assumed that the matrix potential is 0. Osmotic potential, also known as solute potential, is the potential energy generated by the solute ions in the soil solution absorbing water, causing the soil water to lose some of its free movement ability. Its value is equal to the osmotic pressure of the soil solution, which is usually a negative value. This osmotic potential reflects the effect of soil salinity on soil water potential. Assuming that sodium chloride is the main component of soil salt, based on the pressure formula of ideal gas, the soil water potential can be expressed by the formula of osmotic pressure, that is, formula (2). Among them, "2" in formula (2) represents sodium ions (Na + ) and chloride ions (Cl - ) is the sum of the concentrations of the two. This is because sodium chloride generates sodium ions and chloride ions after ionization. The ion concentrations of the two are the same, and the ion concentration of the total aqueous solution is multiplied by 2. From formula (2), it can be concluded that the soil water potential is linearly related to the soil salinity.

[0084] It should also be understood that the salt marsh is a coastal salt marsh. Therefore, the soil water potential is related to the seawater density. In addition, the Boltzmann constant, also known as the ideal gas constant, R does not refer to a specific gas, but is a physical constant used to describe the properties of an ideal gas. The value of the ideal gas constant has nothing to do with the type of gas, but only with its unit, and the commonly used unit is J / (mol*K).

[0085] It should also be understood that F WPD 、PWP min The unit of and SWP is MPa, the unit of T is K, and the unit of s is ppt. Sodium ion (Na + ) and chloride ions (Cl - ) concentration, its unit is mol / cm 3 .

[0086] In the above technical solution, the water potential difference model corresponding to the sample salt marsh wetland can be obtained based on the deviation between the plant water potential of the vegetation on the sample salt marsh wetland and the soil water potential of the sample salt marsh wetland. Among them, the plant water potential is a physiological characteristic parameter of vegetation, which is used to characterize the physiological characteristics of vegetation. Soil salinity can affect the water and nutrient absorption of plants by reducing soil water potential, inhibit chlorophyll synthesis and the occurrence of various enzymes in the photosynthetic apparatus, affect photosynthesis, and thus inhibit plant growth. Therefore, the soil water potential of the sample salt marsh wetland is related to soil salinity.

[0087] In some embodiments, the density of seawater is 1.025×10 -3 kg / cm 3 , k Cl is 1.80655, M Cl It is 35.5 g / mol and R is 8.314 J / (mol*K).

[0088] Step 202: The electronic device constructs a salinity stress model corresponding to the sample salt marsh wetland based on the soil salinity. The salinity stress model is used to describe the impact of soil salinity on vegetation.

[0089] It should be understood that the "salinity stress model" in the above step 202 can be specifically understood as a function. The salinity stress model is used to quantify the degree of direct impact of soil salinity on the photosynthesis of vegetation on salt marsh wetlands.

[0090] In some embodiments, the electronic device in step 202 constructs a salinity stress model corresponding to the sample salt marsh wetland based on the soil salinity, including: the electronic device determines the salinity stress model based on the following formula (3);

[0091]

[0092] Among them, F S is the salinity stress model, m is the salinity threshold, and its unit is ppt.

[0093] It should be understood that the salinity threshold in the above formula (3) can be obtained by calibrating parameters of multiple salt marsh wetlands corresponding to different salinities (i.e., the stations corresponding to salt marsh wetlands refer to scientific research monitoring stations established at specific geographical locations, which are used for long-term observation, data collection and scientific research of the ecological environment of salt marsh wetlands). Soil salinity (salinity stress) plays an important regulatory role in physiological processes such as photosynthesis of vegetation on salt marsh wetlands. Considering that salt marsh wetlands have been affected by salt for a long time and the soil has been flooded for a long time, the soil water potential suitable for salt marsh wetlands (i.e., formula (2)) is introduced into the water potential difference model, and the influence function of the physiological response of salt marsh wetlands to salinity stress (i.e., salinity stress model) is established. In other words, the influence of soil salinity is reflected in both the water potential difference model (specifically in soil water potential) and the salinity stress model.

[0094] Step 203, the electronic device couples a target evaluation model based on the water potential difference model and the salinity stress model, and the target evaluation model is used to evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data and the physiological data of the vegetation in the area where the target salt marsh wetland is located.

[0095] It should be understood that the "target evaluation model" in the above step 203 can be specifically understood as a function. The target evaluation model is the final evaluation model trained based on data from sample salt marsh wetlands (i.e., multiple salt marsh wetlands with different soil salinities) (including but not limited to soil salinity, vegetation physiological characteristic parameters, daily average temperature, day and night temperature difference, leaf area index, annual average atmospheric carbon dioxide concentration, etc.), and the target evaluation model has passed the testing process.

[0096] It should also be understood that the difference between the "sample salt marsh wetland" in the above step 201 and the "target salt marsh wetland" in the above step 203 is that the sample salt marsh wetland is the salt marsh wetland used to train the target evaluation model, while the target salt marsh wetland is any salt marsh wetland (referred to as the target salt marsh wetland in this application) that uses the target evaluation model to evaluate GPP during actual use.

[0097] It should also be understood that the "environmental driving data" in the above step 203 is used to describe the state and changes of the natural environment. The environmental driving data includes driving variables such as the average daily temperature, the temperature difference between day and night, the incident shortwave radiation, the leaf area index and the soil salinity, as well as driving constants such as the annual average atmospheric carbon dioxide concentration, the leaf nitrogen content of the vegetation and the latitude of the target salt marsh wetland. Among them, the leaf area index refers to the ratio between the total leaf area on the vertical projection surface of the unit ground and the surface area of ​​the same area. The leaf area index is an evaluation indicator of plant growth status and density. The "physiological data of vegetation" in the above step 203 includes the minimum leaf water potential.

[0098] In one possible implementation, the electronic device in step 203 couples the target evaluation model based on the water potential difference model and the salinity stress model, including: the electronic device constructs a canopy stomatal conductance model of the vegetation on the sample salt marsh wetland based on the water potential difference model and the salinity stress model; the electronic device determines a first photosynthetic rate model for being limited by carbon dioxide based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in the cells of the vegetation; the electronic device couples the target evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light.

[0099] It should be understood that the "canopy stomatal conductance model" in the above scheme is used to describe the canopy stomatal conductance of vegetation, which refers to the conductivity of carbon dioxide, water vapor or heat between the canopy of vegetation and the atmosphere, and is used to indicate the strength of photosynthesis and transpiration of vegetation. Among them, the canopy of vegetation refers to the dense top layer of branches and leaves in vegetation. In ecology, the canopy structure of vegetation refers to the composition of the top space of the vegetation community, which includes the leaves, branches and other parts of the vegetation, and is an important organ for vegetation to interact with the outside world.

[0100] It should also be understood that the "canopy stomatal conductance model", "first photosynthetic rate model" and "second photosynthetic rate model" in the above scheme can all be specifically understood as functions. Among them, the first photosynthetic rate model is used to quantify the photosynthetic rate after being limited by carbon dioxide, and the second photosynthetic rate model is used to quantify the photosynthetic rate after being limited by light.

[0101] In the above technical scheme, the water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential, which refers to the driving force of the water movement inside and outside the vegetation body, which affects the opening and closing state of the stomata of the vegetation, thereby affecting the stomatal conductance parameters. The salinity stress model is used to describe the degree of influence of soil salinity on vegetation. Under the condition of salinity stress, the vegetation reduces the osmotic potential and the leaf water potential before dawn by increasing the plant hormone abscisic acid, thereby adjusting the stomatal conductance parameters. Therefore, the method constructs a canopy stomatal conductance model of the vegetation on the sample salt marsh wetland based on the water potential difference model and the salinity stress model. The increase in stomatal conductance enables the vegetation leaves to absorb carbon dioxide quickly to help the vegetation to photosynthesize. The reduction in the concentration of carbon dioxide in the atmosphere will make it impossible for plant leaves to effectively use carbon dioxide in the atmosphere to produce organic matter, thereby inhibiting photosynthesis. The increase in the concentration of carbon dioxide in the cell will promote photosynthesis, but if the concentration is too high, it may cause enzyme inactivation, thereby inhibiting photosynthesis. Furthermore, illumination is the energy source for photosynthesis, therefore, the method is based on a second photosynthetic rate model for being limited by illumination and the first photosynthetic rate model, coupled with a target evaluation model, to obtain an accurate GPP evaluation model.

[0102] In some embodiments, the electronic device constructs a canopy stomatal conductance model of vegetation on the sample salt marsh wetland based on the water potential difference model and the salinity stress model, including: the electronic device determines the canopy stomatal conductance model based on the following formula (4);

[0103]

[0104] Among them, g c is the canopy stomatal conductance model, c1 is the calibrated temperature influence coefficient, and its unit is °C. is the daily average temperature, in °C, D T It is half of the temperature difference between day and night, and its unit is °C. It can be determined by formula (5);

[0105]

[0106] Among them, T Dmax is the maximum temperature of the day, T Dmin The lowest temperature of the day.

[0107] In some embodiments, the temperature influence coefficient is -0.1.

[0108] In a possible implementation, the electronic device determines a first photosynthetic rate model limited by carbon dioxide based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in cells of vegetation, including: the electronic device determines the first photosynthetic rate model based on the following formula (6);

[0109] P C =g c ×(C a -C i ) Formula (6)

[0110] Among them, P C is the first photosynthetic rate model, and its unit is gC / (m 2 d), g c is the canopy stomatal conductance model, C a is the concentration of carbon dioxide in the atmosphere, C i is the concentration of carbon dioxide in the cell, and the concentration of carbon dioxide in the cell can be determined by the following formula (7) to formula (10);

[0111]

[0112] A=(C a +qp) 2 -4×(C a ×qp×C comp ) Formula (8)

[0113] q=C half -C comp Formula (9)

[0114] p=P N ×F T / g c Formula (10)

[0115] Among them, C half is the carbon dioxide concentration in the vegetation canopy when photosynthesis limited by carbon dioxide reaches half of its maximum photosynthetic rate, C comp The carbon dioxide compensation point is used to describe the concentration of carbon dioxide in the cells of vegetation when photosynthesis and respiration are balanced. N is the third photosynthetic rate model for vegetation limited by leaf nitrogen content and leaf area index, with the unit of gC / (m 2 d), g c is the canopy stomatal conductance model, F T Used to describe the impact of atmospheric temperature on vegetation. T It can be determined by the following formula (11):

[0116]

[0117] in, is the daily average temperature, T min , T max , T optThey are respectively used to describe the minimum temperature, maximum temperature and optimum temperature for vegetation to carry out photosynthesis.

[0118] It should be understood that formula (6) uses the diffusion equation to construct the photosynthetic rate of carbon dioxide diffusion to the carbon fixation point. The "intracellular carbon dioxide concentration C i "Depends on the internal metabolic capacity of the mesophyll cells in the vegetation. Among them, C a and C i The unit is ppm, C half and C comp The unit is ppm, C half and C comp The empirical values ​​taken from previous calibrations are 576.7ppm and 32.6ppm respectively. It should also be understood that the F T is different. It should also be understood that F T The main vegetation types (C3 and C4) in salt marsh wetlands were distinguished by using different T min , T max , T opt Distinguishing between C3 vegetation and C4 vegetation can reflect the different responses of photosynthesis of the two types of vegetation to temperature. In some embodiments, C3 vegetation includes Spartina alterniflora, Spartina vulgaris, Spartina sphagnum and salt grass, and C4 vegetation includes Phragmites australis and Tripterygium wilfordii.

[0119] It should also be understood that the "third photosynthetic rate model" in the above scheme can be specifically understood as a function, and the third photosynthetic rate model is used to quantify the photosynthetic rate after being restricted by the leaf nitrogen content and leaf area index of the vegetation.

[0120] In the above technical solution, the stomatal conductance of the vegetation canopy, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in the cells of the vegetation will all affect the photosynthesis of the vegetation on the sample salt marsh wetland. Therefore, the first photosynthetic rate model for being limited by carbon dioxide can be determined based on the stomatal conductance model of the canopy, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in the cells of the vegetation. In addition, the concentration of carbon dioxide in the cells is related to the daily average temperature, the concentration of carbon dioxide in the atmosphere, and the minimum temperature, maximum temperature, and optimum temperature when the vegetation performs photosynthesis. Therefore, this method can accurately determine the first photosynthetic rate model for being limited by carbon dioxide through the above multiple parameters.

[0121] In some embodiments, the sample salt marsh wetland may have both C3 vegetation and C4 vegetation. There are theoretical differences between the responses of C3 vegetation and C4 vegetation to temperature. T Different temperature ranges were introduced to simulate C3 vegetation and C4 vegetation respectively. For C3 vegetation, T min , Tmax , T opt are 0℃, 45℃ and 25℃ respectively; for C4 vegetation, T min , T max , T opt They are 0℃, 50℃ and 30℃ respectively.

[0122] Figure 3 This is a schematic diagram of determining the conditions under which different vegetation is subjected to high temperature stress, provided in an embodiment of the present application.

[0123] For example, Figure 3 As shown in Figure 2, salt marsh wetlands have both C3 and C4 vegetation. For C3 vegetation, T min , T max , T opt 0℃, 45℃ and 25℃ respectively; for C4 vegetation, T min , T max , T opt Taking 0℃, 50℃ and 30℃ as examples, the conditions of C3 vegetation and C4 vegetation under high temperature stress are described.

[0124] Specifically, through Figure 3 It can be concluded that C4 vegetation is more resistant to high temperature stress than C3 vegetation.

[0125] In some embodiments, the method for determining the third photosynthetic rate model includes: the electronic device determines the third photosynthetic rate model based on the following formula (12);

[0126] P N =a1×N×LAI Formula (12)

[0127] Among them, a1 is the metabolic efficiency coefficient, N is the nitrogen content of vegetation leaves, and its unit is (gN / m 2 ), LAI is the leaf area index of vegetation, and its unit is (m 2 m -2 ).

[0128] In some embodiments, the metabolic efficiency factor is 4.5.

[0129] In one possible implementation, the electronic device couples the target evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light, including: the electronic device couples the candidate evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model; the electronic device inputs the test environment driving data of the area where each test salt marsh wetland is located and the test physiological data of the vegetation into the candidate evaluation model respectively to obtain the predicted total primary productivity of each test salt marsh wetland; the electronic device constructs an evaluation index for evaluating the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity, and determines the candidate evaluation model as the target evaluation model based on the evaluation index.

[0130] It should be understood that the "candidate evaluation model" in the above scheme can be specifically understood as a function, and the candidate evaluation model can be understood as an evaluation model that has been trained with training data (data of sample salt marsh wetlands) but has not passed the test process. After the candidate evaluation model passes the test process (trained with data of multiple test salt marsh wetlands), it can be used as the target evaluation model.

[0131] It should also be understood that the "candidate evaluation model" and "target evaluation model" in the above scheme are regression models used to describe the relationship between two or more variables. The indicators used to evaluate the predictive ability of the regression model include the coefficient of determination, root mean square error, mean square error, mean absolute error and model efficiency. Therefore, in some embodiments, the evaluation index includes at least one of the coefficient of determination, root mean square error, mean square error, mean absolute error and model efficiency. Among them, the coefficient of determination is also called goodness of fit or determination coefficient, which is used to measure the ability of the candidate evaluation model to fit the data. The coefficient of determination is a value between 0 and 1. When the coefficient of determination is 1, it indicates that the candidate evaluation model can fit the data perfectly, and all data points fall on the regression line without any random errors. The root mean square error, the mean square error and the mean absolute error are used to measure the difference between the predicted total primary productivity and the corresponding tested total primary productivity from different accuracies.

[0132] In the above technical solution, based on the first photosynthetic rate model and the second photosynthetic rate model, a candidate evaluation model is coupled, and based on the candidate evaluation model, the test environment driving data and the test physiological data of the vegetation in the area where each test salt marsh wetland is located in a plurality of test salt marsh wetlands are processed to predict the total primary productivity of each test salt marsh wetland, that is, to predict the total primary productivity. Furthermore, the method measures the prediction ability of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity, and then determines the target evaluation model based on the prediction ability. Therefore, the method can obtain a GPP evaluation model with the best evaluation performance. In actual application, the total primary productivity of the target salt marsh wetland can be accurately evaluated based on the target evaluation model.

[0133] In some embodiments, the plurality of test salt marsh wetlands include a plurality of test salt marsh wetlands at different salinities.

[0134] In some embodiments, the plurality of test salt marsh wetlands include a test salt marsh wetland at a first salinity, a test salt marsh wetland at a second salinity, and a test salt marsh wetland at a third salinity, the first salinity being less than the second salinity, and the second salinity being less than the third salinity.

[0135] In some embodiments, the first salinity is 1 ppt, the second salinity is 10 ppt, and the third salinity is 30 ppt.

[0136] In some embodiments, for C3 vegetation or C4 vegetation, the electronic device can determine the first total primary productivity corresponding to the C3 vegetation and the second total primary productivity corresponding to the C4 vegetation respectively through formula (17); the electronic device uses the sum of the first total primary productivity and the second total primary productivity as the total primary productivity of the tested salt marsh wetland.

[0137] In some embodiments, the method for determining the second photosynthetic rate model includes: the electronic device determines the second photosynthetic rate model based on the following formula (13);

[0138] P I =E0×I Formula (13)

[0139] Among them, P I is the second photosynthetic rate model, and its unit is gC / (m 2 d), E0 is the canopy quantum yield of vegetation, its unit is (gC MJ -1 m -2 d -1 ), I is the incident shortwave radiation, its unit is (MJm -2 d -1 ), the canopy quantum yield can be determined by the following formula (14);

[0140]

[0141] Among them, q1 and q2 are the first canopy quantum yield coefficient (maximum canopy quantum yield) and the second canopy quantum yield coefficient (canopy quantum yield coefficient), which determine the shape of the curve corresponding to the E0-LAI function. This function is a monotonically increasing function of LAI. After LAI>5, the increasing trend of the function becomes slow, E0 is close to q1, and q1 is the largest at this time.

[0142] It should be understood that the canopy quantum yield of vegetation is used to describe the ability of the canopy to fix carbon using a unit amount of radiation, and the size of the canopy quantum yield is only related to the leaf area index of the vegetation. Since the vegetation in salt marsh wetlands is mostly herbaceous and gramineous, the vegetation distribution is mostly sparse and the canopy structure is vertical, and the relationship between the canopy quantum yield and the leaf area index is constructed as a monotonic positive correlation. It should also be understood that the above formula (14) for determining the canopy quantum yield is a rectangular hyperbolic monotonic function of LAI, which can simulate the growth characteristics of herbaceous vegetation and gramineous vegetation in salt marsh wetlands.

[0143] In some embodiments, q1 and q2 are 0.9 and 0.8, respectively.

[0144] In some embodiments, the electronic device couples the candidate evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model, including: the electronic device determines the candidate evaluation model based on the following formula (15);

[0145]

[0146] Among them, GPP is the candidate evaluation model corresponding to the daily scale, F hr It can be determined by the following formula (16):

[0147] F hr =s1×hr+s2 Formula (16)

[0148] Among them, F hr is the target sunshine duration, s1 and s2 are the first calibration coefficient and the second calibration coefficient affected by sunshine time, hr is the sunshine duration on a daily scale, and its unit is h.

[0149] In some embodiments, the first calibration coefficient and the second calibration coefficient are 0.3 and -1.9, respectively.

[0150] It should be understood that formula (15) is used to determine the total primary productivity of a salt marsh when the type of vegetation on the salt marsh includes only one. When the type of vegetation on the salt marsh includes multiple types, formula (15) can be corrected to the following formula (17);

[0151]

[0152] Among them, GPP i is the total primary productivity of vegetation i, in gC m -2 d -1 , P i,C The first photosynthetic rate model determined for vegetation i, P i,I The second photosynthetic rate model determined for vegetation i, F i,hr is the target sunshine duration corresponding to vegetation i.

[0153] In some embodiments, when the types of vegetation on the salt marsh wetland include multiple types, the electronic device couples the candidate evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model, including: the electronic device determines the candidate evaluation model based on the following formula (18);

[0154]

[0155] Among them, p i is the proportion of the distribution area of ​​vegetation i to the total area of ​​salt marsh wetlands, and n is the number of vegetation types in the salt marsh wetlands.

[0156] It should be understood that in a salt marsh wetland (salt marsh ecosystem) including multiple types of vegetation, the multiple types of vegetation are usually evenly distributed, and therefore the total primary productivity of the salt marsh wetland is the weighted average of the area proportions of the vegetation.

[0157] In one possible implementation, the electronic device constructs an evaluation index for evaluating the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity, including any one of the following: the electronic device constructs the model efficiency of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity; the electronic device constructs the root mean square error of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity; the electronic device constructs the determination coefficient of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity.

[0158] In the above technical solution, the model efficiency and / or the root mean square error and / or the determination coefficient are determined as the evaluation indicators for evaluating the candidate evaluation model, which can measure the prediction ability of the candidate evaluation model from a variety of different perspectives. In this way, it is helpful to obtain the GPP evaluation model with the best evaluation performance.

[0159] In some embodiments, the electronic device constructs the model efficiency of the candidate evaluation model based on the predicted total primary productivity and the corresponding tested total primary productivity, including: the electronic device determines the model efficiency of the candidate evaluation model based on the following formula (19);

[0160]

[0161] Among them, EF is the model efficiency of the candidate evaluation model, y j and f j are the tested total primary productivity and the corresponding predicted total primary productivity on day j, respectively. is the average of the total primary productivity of multiple tests corresponding to day J.

[0162] In some embodiments, the electronic device constructs a root mean square error of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity, including: the electronic device determines the root mean square error of the candidate evaluation model based on the following formula (20);

[0163]

[0164] Among them, RMSE is the root mean square error of the candidate evaluation model.

[0165] In some embodiments, the electronic device constructs a coefficient of determination of the candidate evaluation model based on the predicted total primary productivity and the corresponding tested total primary productivity, including: the electronic device determines the coefficient of determination of the candidate evaluation model based on the following formula (21);

[0166]

[0167] Among them, R 2 is the coefficient of determination of the candidate evaluation model, is the average of multiple predicted total primary productivity corresponding to day J.

[0168] In other embodiments, the electronic device may also construct a mean absolute error of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity.

[0169] In some embodiments, the electronic device constructs the mean absolute error of the candidate evaluation model based on the predicted total primary productivity and the corresponding tested total primary productivity, including: the electronic device determines the mean absolute error of the candidate evaluation model based on the following formula (22);

[0170]

[0171] Among them, MAE is the mean absolute error of the candidate evaluation model.

[0172] In one possible implementation, the electronic device determines the candidate evaluation model as the target evaluation model based on the evaluation index, including: when the model efficiency is greater than the preset model efficiency, the electronic device determines the candidate evaluation model as the target evaluation model; and / or, when the root mean square error is less than the preset root mean square error, the electronic device determines the candidate evaluation model as the target evaluation model; and / or, when the determination coefficient is greater than the preset determination coefficient, the electronic device determines the candidate evaluation model as the target evaluation model.

[0173] In the above technical solution, when the model efficiency is greater than the preset model efficiency, and / or when the root mean square error is less than the preset root mean square error, and / or when the determination coefficient is greater than the preset determination coefficient, the candidate evaluation model is determined as the target evaluation model. The above process evaluates the candidate evaluation model from a variety of different perspectives, and can obtain a GPP evaluation model with the best evaluation performance to accurately evaluate the total primary productivity of the target salt marsh wetland.

[0174] In some embodiments, after the electronic device determines the candidate evaluation model as the target evaluation model, the method 200 further includes: when the mean absolute error is less than a preset mean absolute error, the electronic device determines the candidate evaluation model as the target evaluation model.

[0175] In some embodiments, when the model efficiency is less than or equal to the preset model efficiency, the root mean square error is greater than or equal to the preset root mean square error, the determination coefficient is less than or equal to the preset determination coefficient, and the mean absolute error is greater than or equal to the preset mean absolute error, the electronic device continues to train the candidate evaluation model to adjust the weights in the candidate evaluation model to obtain the target evaluation model.

[0176] It should be understood that the "adjustment of weights in the candidate evaluation model" in the above scheme is to adjust the metabolic efficiency coefficient a1, temperature influence coefficient c1, first canopy quantum yield coefficient q1, second canopy quantum yield coefficient q2, first calibration coefficient s1, second calibration coefficient s2 and salinity threshold m in the candidate evaluation model.

[0177] Figure 4 It is a schematic diagram of a candidate evaluation model based on determination coefficient provided in an embodiment of the present application.

[0178] For example, Figure 4 As shown, taking multiple test salt marsh wetlands including a test salt marsh wetland under a first salinity (low salinity), a test salt marsh wetland under a second salinity (medium salinity) and a test salt marsh wetland under a third salinity (high salinity), the main vegetation parameters of the multiple test salt marsh wetlands are as shown in Table 1 below, and the multi-site parameter calibration method is used to describe the process of evaluating candidate evaluation models based on the determination coefficient. Among them, the first salinity is 1ppt, the second salinity is 10ppt, and the third salinity is 30ppt.

[0179] It should be understood that after evaluating the candidate evaluation model based on the determination coefficient, when the determination coefficient does not meet the preset conditions (for example, is much smaller than the preset determination coefficient), the metabolic efficiency coefficient a1, temperature influence coefficient c1, first canopy quantum yield coefficient q1, second canopy quantum yield coefficient q2, first calibration coefficient s1, second calibration coefficient s2 and salinity threshold m in the candidate evaluation model can be adjusted until the determination coefficient corresponding to the adjusted evaluation model meets the preset conditions.

[0180] Table 1 Main vegetation parameters of several tested salt marsh wetlands

[0181]

[0182] Where a is the third salinity, -4.08 a It means that the physiological characteristic parameter of the vegetation of Spartina alterniflora in the test salt marsh wetland under the third salinity is -4.08. b is the second salinity, -3 b It means that the physiological characteristic parameter of the vegetation of Spartina alterniflora in the test salt marsh wetland under the second salinity is -3. c is the first salinity, -2.0417 c It means that the vegetation physiological characteristic parameter of Spartina alterniflora in the test salt marsh wetland under the first salinity is -2.0417. min It is a vegetation parameter closely related to the soil environment. Different soil salinity will result in different PWP. min The soil salinity gradients of the distribution of Spartina alterniflora and Spartina vulgaris vary greatly, and there are different measured values ​​corresponding to high salinity (30ppt), medium salinity (10ppt), and low salinity (1ppt). It should be understood that the above Table 1 only describes the main vegetation parameters of multiple test salt marsh wetlands by way of example, and the changes in the main vegetation parameters of the multiple test salt marsh wetlands do not affect the conception of the present application.

[0183] Specifically, the parameters shown in Table 2 are calibrated by measuring the measured GPP of the salt marsh wetland for at least one year (i.e., the test GPP of the test salt marsh wetland) and the corresponding predicted GPP, and the simulation effects of different evaluation models obtained under different parameters are evaluated to obtain the optimal target model, i.e., the target evaluation model. The optimal target model should be applicable to any year and be able to simulate the GPP of the salt marsh wetland in any year. Among them, the test GPP of the test salt marsh wetland for at least one year can be determined by multiple parameters, and the multiple parameters include two types of parameters. The first type is parameters obtained by field measurement or theoretical calculation: vegetation physiological characteristic parameters of vegetation on the test salt marsh wetland, seawater density, soil salinity, salinity-chlorinity conversion coefficient, molar mass of chloride ions, Boltzmann constant, average temperature of the latitude zone where the test salt marsh wetland is located, daily average temperature, temperature difference between day and night, highest temperature on a certain day, lowest temperature on a certain day, concentration of carbon dioxide in the atmosphere, concentration of carbon dioxide in cells of vegetation, carbon dioxide compensation point, leaf nitrogen content of vegetation, leaf area index of vegetation, degree of influence of atmospheric temperature on vegetation, canopy quantum yield of vegetation, incident shortwave radiation, and sunshine duration on a certain day scale; the second type is parameters obtained by calibration: salinity threshold, temperature influence coefficient, metabolic efficiency coefficient, first canopy quantum yield coefficient, second canopy quantum yield coefficient, first calibration coefficient and second calibration coefficient affected by sunshine time.

[0184] The process of evaluating the candidate evaluation model is as follows: 1) based on the parameter range, initial value and iteration step, the parameters to be calibrated are divided to obtain n groups of parameters; 2) based on the first group of parameters in the n groups of parameters and the candidate evaluation model, the predicted GPP of each test salt marsh wetland is determined, and based on formula (21), the determination coefficient between the predicted GPP of each test salt marsh wetland and the corresponding test GPP is determined. When the determination coefficient does not meet the preset conditions, the weight in the candidate evaluation model is adjusted to obtain the adjusted candidate evaluation model. According to the above method, based on the i-th group of parameters (i=2, 3, ..., n) in the n groups of parameters and the adjusted candidate evaluation model, the adjusted candidate evaluation model is further evaluated until the determination coefficient meets the preset conditions, and the final candidate evaluation model is determined as the target evaluation model. The target evaluation model can be applied to salt marsh wetlands with different salinity gradients (soil salinity) in any year, that is, the GPP of salt marsh wetlands with different soil salinities in any year is evaluated.

[0185] Furthermore, you can Figure 4The three figures in the graph (the curve between the test GPP and date of the test salt marsh corresponding to low salinity, the curve between the test GPP and date of the test salt marsh corresponding to medium salinity, and the curve between the test GPP and date of the test salt marsh corresponding to high salinity) show that the coefficient of determination between the predicted total primary productivity (Modeled) predicted by the candidate evaluation model and the corresponding test total primary productivity (Observed) is relatively large, specifically 0.9, 0.96, and 0.79. Therefore, the final target evaluation model has a good effect in evaluating GPP in salt marsh wetlands with high, medium, and low soil salinity levels. At the same time, the environmental driving data of the target evaluation model is very simple, only some basic meteorological data and basic parameters such as leaf nitrogen concentration and vegetation water potential, so it is conducive to promotion. Among them, the horizontal axis of each of the three figures is time (in days) and the vertical axis of each figure is GPP (in gC m -2 d -1 ).

[0186] In addition, the weights in the obtained target evaluation model can be seen in Table 2 below.

[0187] Table 2 Parameter values ​​in the target evaluation model

[0188]

[0189] exist Figure 4 In the illustrated embodiment, the total primary productivity of the test salt marsh wetland can be obtained based on the FLUXNET data set and the public data in the literature corresponding to the salt marsh wetland. The FLUXNET data set and the public data in the literature corresponding to the salt marsh wetland can also obtain the latitude of the salt marsh wetland, soil salinity, the minimum temperature, maximum temperature and optimum temperature for vegetation photosynthesis, leaf area index, etc. The annual average concentration of carbon dioxide in the atmosphere comes from the "Greenhouse Gas Bulletin" document published by the World Meteorological Organization. According to the vegetation type in the test salt marsh wetland, the vegetation water potential and leaf nitrogen content of the vegetation are selected in the parameter database, and the proportion of the dominant vegetation species in the test salt marsh wetland is obtained through literature research. In addition, the method 200 in the present application establishes a parameter database of the main vegetation in salt marsh wetlands based on observations (Table 1) based on the vegetation physiological characteristics of special vegetation in salt marsh wetlands; it also introduces a characteristic salinity stress impact section based on the impact of soil salinity on the GPP of salt marsh wetlands; it also uses a multi-site parameter calibration method to construct a parameterized scheme for GPP assessment of salt marsh wetlands with different salinity gradients, and has good verification effects in salt marsh wetlands with high, medium and low salinity gradients.

[0190] Figure 5 It is a structural schematic diagram of a device for determining total primary productivity provided in an embodiment of the present application.

[0191] For example, Figure 5 As shown, the device 500 includes:

[0192] Building block 501: for:

[0193] Based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the conversion coefficient of salinity-chlorinity and the physiological characteristic parameters of vegetation, a water potential difference model corresponding to the sample salt marsh wetland was constructed. The water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential.

[0194] Based on the soil salinity, a salinity stress model corresponding to the sample salt marsh wetland is constructed, and the salinity stress model is used to describe the impact of soil salinity on vegetation;

[0195] Coupling module 502: used to couple a target evaluation model based on the water potential difference model and the salinity stress model, wherein the target evaluation model is used to evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data and the physiological data of the vegetation in the area where the target salt marsh wetland is located.

[0196] Optionally, the construction module 501 is specifically used to: determine the water potential difference model based on the following formula;

[0197] F WPD =PWP min -SWP;

[0198] Among them, F WPD is the water potential difference model, PWP min is the physiological characteristic parameter of the vegetation, specifically the plant water potential of the vegetation on the sample salt marsh wetland, SWP is the soil water potential of the sample salt marsh wetland, and the soil water potential can be determined by the following formula;

[0199]

[0200] Among them, ρ is the density of seawater, s is the salinity of the soil, k Cl is the conversion coefficient of salinity-chlorinity, M Cl is the molar mass of chloride ions, R is the Boltzmann constant, and T is the average temperature of the latitude zone where the sample salt marsh is located.

[0201] Optionally, the construction module 501 is also specifically used to construct a canopy stomatal conductance model of the vegetation on the sample salt marsh wetland based on the water potential difference model and the salinity stress model; the device 500 also includes: a determination module, used to determine a first photosynthetic rate model for being limited by carbon dioxide based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland and the concentration of carbon dioxide in the cells of the vegetation; the coupling module 502 is specifically used to couple the target evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light.

[0202] Optionally, the determination module is specifically used to determine the first photosynthetic rate model based on the following formula;

[0203] P C =g c ×(C a -C i );

[0204] Among them, P C is the first photosynthetic rate model, g c is the canopy stomatal conductance model, C a is the concentration of carbon dioxide in the atmosphere, C i is the concentration of carbon dioxide in the cell, and the concentration of carbon dioxide in the cell can be determined by the following formula;

[0205]

[0206] A=(C a +qp) 2 -4×(C a ×qp×C comp );

[0207] q=C half -C comp ;

[0208] p=P N ×F T / g c ;

[0209] Among them, C half is the carbon dioxide concentration in the vegetation canopy when photosynthesis limited by carbon dioxide reaches half of its maximum photosynthetic rate, C comp The carbon dioxide compensation point is used to describe the concentration of carbon dioxide in the cells of vegetation when photosynthesis and respiration are balanced. N is the third photosynthetic rate model limited by leaf nitrogen content and leaf area index of vegetation, g c is the canopy stomatal conductance model, F T Used to describe the impact of atmospheric temperature on vegetation.T It can be determined by the following formula;

[0210]

[0211] in, is the daily average temperature, T min , T max , T opt They are respectively used to describe the minimum temperature, maximum temperature and optimum temperature for vegetation to carry out photosynthesis.

[0212] Optionally, the coupling module 502 is further specifically used to: couple a candidate evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model; the determination module is further specifically used to: input the test environment driving data and the test physiological data of the vegetation in the area where each test salt marsh wetland is located in a plurality of test salt marsh wetlands into the candidate evaluation model respectively to obtain the predicted total primary productivity of each test salt marsh wetland; based on the predicted total primary productivity and the corresponding test total primary productivity, construct an evaluation index for evaluating the candidate evaluation model, and based on the evaluation index, determine the candidate evaluation model as the target evaluation model.

[0213] Optionally, the construction module 501 is also specifically used for any of the following: constructing the model efficiency of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity; constructing the root mean square error of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity; constructing the determination coefficient of the candidate evaluation model based on the predicted total primary productivity and the corresponding test total primary productivity.

[0214] Optionally, the determination module is further specifically used to: when the model efficiency is greater than the preset model efficiency, determine the candidate evaluation model as the target evaluation model; and / or, when the root mean square error is less than the preset root mean square error, determine the candidate evaluation model as the target evaluation model; and / or, when the determination coefficient is greater than the preset determination coefficient, determine the candidate evaluation model as the target evaluation model.

[0215] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0216] For example, Figure 6 As shown, the electronic device 600 includes: a memory 601 and a processor 602, wherein the memory 601 stores an executable program code 603, and the processor 602 is used to call and execute the executable program code 603 to perform a method for determining total primary productivity.

[0217] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute a method for determining total primary productivity provided in an embodiment of the present application.

[0218] In this embodiment, the functional modules of the device can be divided according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0219] In the case of dividing each functional module according to each function, the device may also include a construction module, a coupling module, a determination module, etc. It should be noted that all relevant contents involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0220] It should be understood that the device provided in this embodiment is used to execute the above-mentioned method for determining the total primary productivity, and thus can achieve the same effect as the above-mentioned implementation method.

[0221] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to an electronic device, the processing module may be used to control and manage the actions of the electronic device. The storage module may be used to support the electronic device in executing related executable program codes, etc.

[0222] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits shown in conjunction with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0223] In addition, the device provided in the embodiments of the present application may specifically be a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a method for determining the total primary productivity provided in the above embodiment.

[0224] This embodiment also provides a computer-readable storage medium, in which executable program code is stored. When the executable program code is run on a computer, the computer executes the above-mentioned related method steps to implement a method for determining total primary productivity provided in the above embodiment.

[0225] This embodiment also provides a computer program product. When the computer program product runs on a computer, it enables the computer to execute the above-mentioned related steps to implement a method for determining total primary productivity provided in the above embodiment.

[0226] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0227] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0228] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0229] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining gross primary productivity, characterized in that The method comprises: Based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the conversion coefficient of salinity-chlorinity and the physiological characteristic parameters of vegetation, a water potential difference model corresponding to the sample salt marsh wetland is constructed, and the water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential; Based on the soil salinity, a salinity stress model corresponding to the sample salt marsh wetland is constructed, wherein the salinity stress model is used to describe the degree of influence of soil salinity on vegetation; Based on the water potential difference model and the salinity stress model, a target evaluation model is coupled, and the target evaluation model is used to evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data and the physiological data of the vegetation in the area where the target salt marsh wetland is located.

2. The method according to claim 1, characterized in that The method constructs a water potential difference model corresponding to the sample salt marsh wetland based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the salinity-chlorinity conversion coefficient and the physiological characteristic parameters of vegetation, including: Based on the following formula, the water potential difference model is determined; F WPD =PWP min -SWP; Among them, F WPD is the water potential difference model, PWP min is the physiological characteristic parameter of the vegetation, specifically the plant water potential of the vegetation on the sample salt marsh wetland, SWP is the soil water potential of the sample salt marsh wetland, and the soil water potential can be determined by the following formula; Where ρ is the density of seawater, s is the soil salinity, k Cl is the conversion coefficient of salinity-chlorinity, M Cl is the molar mass of chloride ions, R is the Boltzmann constant, and T is the average temperature of the latitude zone where the sample salt marsh wetland is located.

3. The method according to claim 1 or 2, characterized in that: The coupling target evaluation model based on the water potential difference model and the salinity stress model includes: Based on the water potential difference model and the salinity stress model, constructing a canopy stomatal conductance model of vegetation on the sample salt marsh wetland; Determining a first photosynthetic rate model for being limited by carbon dioxide based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of intracellular carbon dioxide in vegetation; The target evaluation model is coupled based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light.

4. The method according to claim 3, characterized in that The method of determining a first photosynthetic rate model limited by carbon dioxide based on the canopy stomatal conductance model, the concentration of carbon dioxide in the atmosphere of the sample salt marsh wetland, and the concentration of carbon dioxide in cells of vegetation comprises: Based on the following formula, the first photosynthetic rate model is determined; P c =g c ×(C a -C i ); Among them, P C is the first photosynthetic rate model, g c is the canopy stomatal conductance model, C a is the concentration of carbon dioxide in the atmosphere, C i is the concentration of carbon dioxide in the cell, and the concentration of carbon dioxide in the cell can be determined by the following formula; A=(C a +q-p) 2 -4×(C a ×q-p×C comp ); q=C half -C comp ; p=P N ×F T / g c ; Among them, C half is the carbon dioxide concentration in the vegetation canopy when photosynthesis limited by carbon dioxide reaches half of its maximum photosynthetic rate, C comp The carbon dioxide compensation point is used to describe the concentration of carbon dioxide in the cells of vegetation when photosynthesis and respiration are balanced. N is the third photosynthetic rate model limited by leaf nitrogen content and leaf area index of vegetation, g c is the canopy stomatal conductance model, F T Used to describe the impact of atmospheric temperature on vegetation. T It can be determined by the following formula; in, is the daily average temperature, T min , T max , T opt They are respectively used to describe the minimum temperature, maximum temperature and optimum temperature for vegetation to carry out photosynthesis.

5. The method according to claim 3, characterized in that: The method of coupling the target evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model for being limited by light comprises: coupling a candidate evaluation model based on the first photosynthetic rate model and the second photosynthetic rate model; Inputting the test environmental driving data and the test physiological data of the vegetation in the area where each of the multiple test salt marsh wetlands is located into the candidate evaluation model respectively to obtain the predicted total primary productivity of each test salt marsh wetland; Based on the predicted total primary productivity and the corresponding tested total primary productivity, an evaluation index for evaluating the candidate evaluation model is constructed, and based on the evaluation index, the candidate evaluation model is determined as the target evaluation model.

6. The method according to claim 5, characterized in that The evaluation index for evaluating the candidate evaluation model based on the predicted total primary productivity and the corresponding tested total primary productivity is constructed, including any one of the following: constructing a model efficiency of the candidate evaluation model based on the predicted gross primary productivity and the corresponding tested gross primary productivity; constructing a root mean square error of the candidate assessment model based on the predicted gross primary productivity and the corresponding tested gross primary productivity; Based on the predicted gross primary productivity and the corresponding tested gross primary productivity, a coefficient of determination of the candidate assessment model is constructed.

7. The method according to claim 6, characterized in that The step of determining the candidate evaluation model as the target evaluation model based on the evaluation index includes: In a case where the model efficiency is greater than a preset model efficiency, determining the candidate evaluation model as the target evaluation model; and / or, When the root mean square error is less than a preset root mean square error, determining the candidate evaluation model as the target evaluation model; and / or, When the determination coefficient is greater than a preset determination coefficient, the candidate evaluation model is determined as the target evaluation model.

8. A device for determining gross primary productivity, characterized in that The device comprises: Building blocks for: Based on the soil salinity of the sample salt marsh wetland, the average temperature of the latitude zone, the conversion coefficient of salinity-chlorinity and the physiological characteristic parameters of vegetation, a water potential difference model corresponding to the sample salt marsh wetland is constructed, and the water potential difference model is used to describe the deviation between the vegetation water potential and the soil water potential; Based on the soil salinity, a salinity stress model corresponding to the sample salt marsh wetland is constructed, wherein the salinity stress model is used to describe the degree of influence of soil salinity on vegetation; A determination module is used to couple a target evaluation model based on the water potential difference model and the salinity stress model, wherein the target evaluation model is used to evaluate the total primary productivity of the target salt marsh wetland based on the environmental driving data and the physiological data of the vegetation in the area where the target salt marsh wetland is located.

9. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an executable program code, and when the executable program code is executed, the method according to any one of claims 1 to 7 is implemented.

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