A seagrass biomass prediction method based on the synergistic effect of multiple environmental factors

Abstract

The present invention discloses a method for predicting seagrass biomass based on the synergistic effect of multiple environmental factors, belonging to the field of seagrass biomass prediction. The method comprises the following steps: a. The leaf growth rate is affected by temperature, light, water nutrient salt concentration and plant density, the root growth rate is affected by temperature, soil nutrient salt concentration and plant density, and both the leaf respiration rate and the root respiration rate are affected by temperature; the seagrass biomass includes leaf biomass and root biomass, and considering the biomass transfer from roots to leaves, a seagrass biomass prediction model is constructed; b. Obtain the data of light, seawater temperature, water nutrient salt concentration, soil nutrient salt concentration and plant density at different time steps in the target area, and combine with the seagrass biomass prediction model constructed in step a to predict the seagrass biomass in the target area. The present invention establishes a seagrass biomass prediction model under the synergistic effect of multiple environmental factors, and realizes the accurate prediction of the change of the seagrass distribution area.

Description

Technical Field

[0001] The present invention relates to the field of seagrass biomass prediction, and more specifically, to a method for predicting seagrass biomass based on the synergistic effect of multiple environmental factors. Background Art

[0002] As an indispensable part of the marine ecosystem, seagrass beds play multiple key ecological functions such as maintaining biodiversity, purifying seawater quality, reducing wave energy, and resisting coastal erosion. However, under the dual pressures of global climate change and increasingly frequent human activities, seagrass beds are facing a severe degradation crisis. In this context, accurately predicting the distribution changes of seagrass beds is of great significance for formulating scientific and effective protection strategies and early warning of coastal disasters.

[0003] Currently, the research on seagrass beds mainly covers the following aspects: (1) evaluating the potential impact of seagrass restoration on the ecosystem based on the Ecopath with Ecosim model; (2) generating simulated satellite images based on the radiative transfer model to evaluate the visual detectability of seagrass beds under different seawater transparencies and seagrass coverages; (3) evaluating the environmental impact of mariculture on the seagrass bed ecosystem based on hydrodynamic-water quality models, light attenuation models, and seagrass bed dynamics models; (4) based on the self-organization theory model of the seagrass bed ecosystem, which mainly focuses on the formation mechanism of the spatial pattern of the seagrass ecosystem.

[0004] In summary, there is currently little research on seagrass growth models. Although some studies are involved, they are usually limited to the independent effects of single or a few environmental factors, and the non-linear response relationship of the multi-factor coupling mechanism on seagrass biomass has not been fully revealed. This limits the accuracy of predictions when facing complex and variable actual marine environments and makes it difficult to make reliable judgments on the growth status of seagrass. Summary of the Invention

[0005] In view of the above technical problems, the present invention proposes a method for predicting seagrass biomass based on the synergistic effect of multiple environmental factors.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A method for predicting seagrass biomass based on the synergistic effect of multiple environmental factors, comprising the following steps:

[0008] a. Construct a seagrass biomass prediction model;

[0009] Seagrass biomass includes leaf biomass and root biomass, and considering the biomass transfer from roots to leaves, a seagrass biomass prediction model is constructed as follows:

[0010] (1)

[0011] (2)

[0012] Among them, B l (t) and B l (t + 1) are the leaf biomass at time t and time t + 1 respectively, B r (t) and B r (t + 1) are the root biomass at time t and time t + 1 respectively, G l (t) and G r (t) are the leaf growth rate and root growth rate at time t respectively, R esl (t) and R esr (t) are the leaf respiration rate and root respiration rate at time t respectively, T rl (t) is the biomass transfer coefficient from roots to leaves at time t, and Δt is the time step;

[0013] The leaf growth rate is affected by temperature, light, water nutrient concentration, and plant density. The root growth rate is affected by temperature, soil nutrient concentration, and plant density. Both the leaf respiration rate and the root respiration rate are affected by temperature;

[0014] b. Predict the seagrass biomass in the target area;

[0015] Obtain the data of light, seawater temperature, water nutrient concentration, soil nutrient concentration, and plant density at different time steps in the target area, and combine with the seagrass biomass prediction model constructed in step a to predict the seagrass biomass in the target area.

[0016] The beneficial technical effects of the present invention are as follows:

[0017] By constructing a seagrass biomass prediction model with the synergistic effect of multiple environmental factors, the present invention optimizes the limitation of traditional models that only focus on single or a few environmental factors. By considering the influence process of temperature, light, water nutrient concentration, soil nutrient concentration, and plant density factors on seagrass biomass, a seagrass biomass prediction model with the synergistic effect of multiple environmental factors is established, realizing the accurate prediction of the change of seagrass distribution area. In the process of optimizing model parameters, key parameters with greater influence on model results are found through sensitivity analysis, and then these key parameters are optimized specifically. This modeling method of comprehensive analysis of multiple factors and parameter optimization effectively compensates for the prediction deviation problem caused by one-sided data in traditional models, greatly improves the accuracy of prediction results, and provides more reliable support for seagrass ecological research and protection work.

[0018] The accurate prediction results provided by the present invention offer a solid scientific basis for the protection and restoration of seagrass beds. With the prediction data of this model, relevant management departments can deeply understand the development trends of seagrass beds under different environmental conditions, and then formulate more targeted and effective management measures. For example, based on the prediction results, reasonably plan marine protected areas, accurately allocate restoration resources, avoid waste of resources caused by blind decision-making, and provide strong guarantee for the sustainable development of the seagrass bed ecosystem. Description of the Drawings

[0019] Figure 1 It is a schematic diagram of the framework of the seagrass biomass change model;

[0020] Figure 2 It is the sensitivity of the main parameters of the model;

[0021] Figure 3 It shows the decline of the cost function during the assimilation process;

[0022] Figure 4 It is a comparison chart of the predicted leaf biomass of the model and the measured results;

[0023] Figure 5 It is a comparison chart of the predicted root biomass of the model and the measured results;

[0024] Figure 6 It is the seagrass bed coverage observed by shipborne radar in a certain area in a certain month;

[0025] Figure 7 It is the average sea surface temperature, light intensity in the study area and the simulated average leaf biomass situation. Among them, (a) shows the sea surface temperature situation, (b) shows the light intensity situation, and (c) shows the leaf biomass situation;

[0026] Figure 8 It shows the distribution of leaf biomass in the study area in four seasons. Among them, (a) shows the leaf biomass in spring, (b) shows the leaf biomass in summer, (c) shows the leaf biomass in autumn, and (d) shows the leaf biomass in winter. Detailed Embodiments

[0027] Traditional modeling methods are usually limited to the independent effects of single or a few environmental factors, and fail to fully reveal the non-linear response relationship of the multi-factor coupling mechanism to seagrass biomass. This makes the accuracy of the model limited when facing the complex and changeable actual marine environment, and it is difficult to make a reliable prediction of the growth status of seagrass. In view of this situation, the present invention proposes a method and application for predicting seagrass biomass based on the synergistic effect of multiple environmental factors. By considering the effects of multiple key environmental variables such as temperature, light, water nutrient concentration, soil nutrient concentration, and plant density on seagrass biomass, a prediction model of seagrass biomass under the synergistic influence of multiple environmental factors is established, and the parameter uncertainty is further evaluated and optimized, thus filling the deficiencies of existing models in multi-factor comprehensive analysis and accurate prediction, and providing an innovative and effective technical means for seagrass ecosystem research and protection.

[0028] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0029] A method for predicting seagrass biomass based on the synergistic effect of multiple environmental factors includes the following steps:

[0030] a. Construct a prediction model of seagrass biomass;

[0031] Considering the environmental influencing factors such as temperature, light, water nutrient concentration, soil nutrient concentration, and plant density on seagrass leaf biomass and root biomass, a seagrass biomass model is constructed, and the specific model framework of this model is as Figure 1 shown.

[0032] The leaf growth rate is affected by light, temperature, plant density, and water nutrient concentration, while the root growth rate is not affected by light and water nutrient concentration, but will be additionally affected by soil nutrient concentration. For the respiration rate, it is mainly affected by temperature. The growth rates and respiration rates of leaves and roots are respectively expressed as:

[0033] (1)

[0034] (2)

[0035] (3)

[0036] (4)

[0037] Among them, G l (t) and G r (t) are the leaf and root growth rates at time t respectively, R esl (t) and R esr (t) are the leaf and root respiration rates at time t respectively, f I (t), fTl (t), f Tr (t), f S (t), f Nw (t) and f Ns (t) and f are the light influence coefficient, leaf temperature influence coefficient, root temperature influence coefficient, plant density influence coefficient, water body nutrient salt concentration influence coefficient, and soil nutrient salt concentration influence coefficient at time t, respectively, and are expressed as:

[0038] (5)

[0039] (6)

[0040] (7)

[0041] (8)

[0042] (9)

[0043] (10)

[0044] Among them, I(t) is the light intensity at time t, which is obtained by measuring with a field illuminometer; k el is the leaf light semi-saturation constant; T(t) is the sea temperature at time t, which is calculated by an ocean numerical model; T optl and T optr are the optimal growth temperatures of the leaf and root, respectively; s tl and s tr are the temperature dependence coefficient of leaf growth and root growth, respectively; n w (t) and n s (t) are the nutrient salt concentrations of the water body and soil at time t, respectively. During model calculation, the nitrate concentration value is mainly used, which is obtained by measuring and analyzing seawater and soil samples collected on-site; n lmin and n lcrit are the minimum internal nitrogen content of the leaf and the critical internal nitrogen content of the leaf, respectively; n rmin and n rcrit are the minimum internal nitrogen content of the root and the critical internal nitrogen content of the root, respectively; B l (t) is the leaf biomass at time t. First, the leaf biomass B l0 at the initial time, i.e., t = 0, is obtained by on-site collection, and then subsequent calculations are carried out through the iterative calculation model constructed by formula (1) to obtain the leaf biomass B l (t) at different times; B lmax is the maximum leaf biomass; k lis the dependence degree coefficient of leaf growth on space.

[0045] The light intensity I(t,z) at depth z at time t can be calculated from the sea surface light intensity I surface (t) as follows:

[0046] (11)

[0047] where k z is the light attenuation coefficient.

[0048] The biomass transfer from roots to leaves at time t is represented by the biomass transfer coefficient T rl , expressed as:

[0049] (12)

[0050] T rl (t) is the biomass transfer coefficient from roots to leaves at time t, and T rlmean is the average biomass transfer coefficient from roots to leaves.

[0051] The model represents biomass in terms of dry weight per square meter (g DW m - ²). The biomass of seagrass consists of leaf biomass and root biomass, and the change in biomass is related to growth, respiration, and biomass transport. For a given time step Δt, the biomass at time t+1 can be calculated from the biomass at time t as follows:

[0052] (13)

[0053] (14)

[0054] where B l (t) and B l (t+1) are the leaf biomass at time t and t+1 respectively, B r (t) and B r (t+1) are the root biomass at time t and t+1 respectively, G l (t) and G r (t) are the growth rates of leaves and roots at time t respectively, R esl (t) and R esr (t) are the respiration rates of leaves and roots at time t respectively, and T rl (t) is the biomass transfer coefficient from roots to leaves at time t.

[0055] In summary, by considering the effects of temperature, light, water nutrient concentration, soil nutrient concentration and plant density on leaf biomass and root biomass, a method for calculating seagrass biomass was given, thus constructing a complete seagrass biomass change model framework.

[0056] b. Optimizing the parameters of the seagrass biomass prediction model constructed in step a;

[0057] Optimization of model parameters is a key step in improving the accuracy and reliability of the seagrass biomass change model. The present invention uses sensitivity analysis and measured data fitting to collaboratively optimize model parameters.

[0058] b1. Screen key parameters based on sensitivity analysis;

[0059] In order to study the impact of model parameters on the results, it is necessary to conduct a sensitivity analysis on the main parameters of the model, and explore the extent of its impact on leaf biomass by increasing and decreasing the model parameters by 20%.

[0060] The present invention sets up two sets of sensitivity experiments (SE-1 and SE-2). In these two sets of experiments, relative random errors of +20% and -20% are applied to the main parameters of the model respectively to simulate the errors that may occur in the actual observation process. Each set of experiments is repeated 100 times to ensure the stability and representativeness of the experimental results through multiple repetitions. At the same time, the uncertainty (RU) of the sensitivity experiment results is calculated, and the calculation formula is:

[0061] (15)

[0062] Among them, CE is the result of the control experiment, SE is the result of the sensitive experiment, N is the number of simulation days, that is, 365 days, and the calculation of the result uncertainty (RU) can reveal the fluctuation range of the model prediction results under different scenarios, and thus reflect the uncertainty level of the model.

[0063] Figure 2 is the sensitivity of the main parameters of the model determined by the above method in the present invention. Figure 2 It can be seen that the model results are more sensitive to parameters related to nutrients, and the sensitivity to root-related parameters is higher than that to leaf-related parameters. This is because the root system plays an important role in the absorption and storage of seaweed nutrients. The root system absorbs nutrients from the pore water of the sediment, and its absorption rate is regulated by relevant parameters. When these parameters change, the nutritional status of the seaweed will change, which will in turn affect the overall growth and biomass of the seaweed. The leaves have a relatively single way of obtaining nutrients, so they are less sensitive to parameter changes. Therefore, the parameter G that has a greater impact on the model results is selected. lmax , G rmax , nlmin , n lcrit , R eslmax , R esrmax , s tl , s tr As key parameters, and further optimize and correct the key parameters.

[0064] b2. Optimization of key parameters;

[0065] The adjoint assimilation model is an efficient parameter optimization method. By combining forward simulation and adjoint simulation, it can quickly find the parameter values that make the simulation results closest to the observed data. The main steps of the adjoint assimilation model include: forward simulation, adjoint simulation, and parameter optimization.

[0066] (1) Forward simulation:

[0067] Forward simulation refers to running the seagrass biomass prediction model according to the current parameter values to generate simulation results. The steps of forward simulation are as follows:

[0068] According to the key parameters (G lmax , G rmax , n lmin , n lcrit , R eslmax , R esrmax , s tl , s tr ) screened in step b1 for initialization. Run the seagrass biomass prediction model according to formulas (1)-(12) to generate the time series of leaf biomass B l (t) and root biomass B r (t).

[0069] Establish the difference function between simulation and observation, denoted as the cost function MSE, expressed as:

[0070] (16)

[0071] where C (t) = [B l (t), B r (t)] is the model state variable at time t, i.e., leaf biomass and root biomass, and C * (t) = [B l * (t), B r * (t)] is the observed data corresponding to the model state variable at time t, i.e., the observed data of leaf biomass and root biomass, and N is the number of simulation time steps.

[0072] (2) Adjoint simulation:

[0073] Adjoint simulation refers to calculating the gradient of the cost function with respect to the model parameters through the adjoint equation, thereby determining the direction of parameter optimization. First, according to the kinetic equation of the seagrass biomass prediction model, the adjoint equation is constructed. The adjoint equation describes the sensitivity of the cost function to the model state variables (i.e., leaf biomass B l (t) and root biomass B r (t)), and then, through the chain rule, the sensitivity of the cost function to the model parameters is derived. The specific form of the adjoint equation is as follows:

[0074] (17)

[0075] (18)

[0076] where λ l (t) and λ r (t) are the adjoint variables of leaf and root biomass at time t, respectively.

[0077] Subsequently, the adjoint equation is solved. The solution of the adjoint equation starts from the final time point t = N, denoted as t N , and is integrated backward to the initial time point t = 0, denoted as t 0 . First, the initial values of the adjoint variables at the final time point t N need to be determined:

[0078] (19)

[0079] (20)

[0080] The time interval [t 0 , t N is discretized into N time points with a time step of Δt. Any intermediate time step t = i is denoted as t i . Starting from t N , the values of the adjoint variables at each time point are calculated step by step backward. For any time point t i , using the Euler method to solve the adjoint equation, the update formula for the adjoint variables is:

[0081] (21)

[0082] (22)

[0083] During the backward integration process, the values of the adjoint variables λ l (t) and λ r (t) at each time point are stored.

[0084] (3) Parameter optimization

[0085] After completing the reverse solution of the adjoint equation, the gradient of the cost function with respect to the model parameters is calculated using the adjoint variables. For each parameter θ (i.e., G lmax G rmax n lmin n lcrit R eslmax R esrmax s tl s tr ), the calculation formula for the gradient is:

[0086] (23)

[0087] According to the calculated parameter gradients, the gradient descent method is used to update the model parameters to minimize the cost function MSE. The formula for parameter optimization is:

[0088] (24)

[0089] Where θ(i + 1) and θ(i) are the values of the parameter θ at the (i + 1)-th and i-th iteration steps respectively, η represents the learning rate used to determine the size of the gradient descent step, and the size of the learning rate η will be adjusted in specific numerical simulations. The present invention uses the adjoint assimilation method to optimize each parameter in turn. The error between the observed data and the simulation results drives the continuous operation of the model, continuously optimizing the parameters in the adjoint assimilation model, and obtaining the optimal learning rate η for each parameter while optimizing the parameters.

[0090] Figure 3 Shows the distribution of the inverted parameters and the decrease in the cost function. The decrease in the cost function confirms the feasibility and effectiveness of the model in simulating the parameters. The cost functions corresponding to the 8 parameters selected through sensitivity analysis have all decreased by about 50%, indicating that effective parameter inversion has been achieved. The specific optimized parameter values are shown in Table 1.

[0091] Table 1 Optimized values of model parameters

[0092]

[0093] c. Predict the seagrass biomass in the target area;

[0094] Obtain the data of light, seawater temperature, water body nutrient concentration, soil nutrient concentration and plant density at different time steps in the target area, and combine with the seagrass biomass prediction model constructed in step a and optimized in parameters in step b to predict the seagrass biomass in the target area.

[0095] To determine whether the model with optimized parameters can truly reflect the growth law of seagrass, the present invention further verified in terms of simulating leaf and root biomass. In the present invention, the initial leaf biomass was set according to the approximate conditions in the Bay of Calvi in 1993 (data from Gobert et al., 1995). The temperature and light data from Gobert et al. (1995) were used as inputs. The measured results of leaf biomass are as Figure 4 shown by the dots. In summer, in the areas with depths of 10 m, 20 m, and 30 m respectively, there were significant differences in the maximum accumulation of leaf biomass, which were 700, 550, and 200 g·DW·m -2 respectively. Under the conditions of 10 m, 20 m, and 30 m, the growth of leaves usually starts in April, reaches the peak in June, and starts to decline from early July.

[0096] By comparing the biomass data at different depths, it can be found that the biomass at 20 m and 30 m is less than that at 10 m. Among them, the time when the peak of leaf biomass appears at 30 m depth is about 1 month and 1.5 months later than that at 10 m depth respectively. This phenomenon fully shows that the relative importance of biomass not only changes over time but also varies with the depth of the community. Further analysis shows that the phenomenon of biomass decreasing with increasing depth is closely related to the low light compensation point. In the deep water environment, the ratio of leaf biomass to root biomass may change, and leaf biomass may be more or more dominant, which also affects the distribution of biomass at different depths to a certain extent.

[0097] The model prediction results are as Figure 4 shown by the solid line. The comparison results between the model prediction results and the on-site measurement data show that the size and appearance time of the peak of leaf biomass in the model are highly close to the on-site measurement results. The dynamic change curves obtained by simulating the leaf and epiphyte biomass match well with the actual measurement data at the three depths of 10 m, 20 m, and 30 m, fully verifying that the model has good accuracy and reliability in simulating the change of seagrass biomass.

[0098] In the research process of seagrass biomass, compared with the measurement of leaf biomass, the measurement of root biomass is relatively less. Limited by the lack of observational data, the present invention limited the simulation depth of root biomass to 10 m. Through the simulation of root biomass at 10 m depth ( Figure 5 ), it was found that compared with leaf biomass, the seasonal change range of root biomass is extremely small, and its annual average biomass is about 3000 g·DW·m -2This result indicates that in seagrass ecosystems, root biomass exhibits relative stability over time series, in sharp contrast to the obvious seasonal fluctuations of leaf biomass. This stability is of great significance for deeply understanding the structure and function of seagrass ecosystems and evaluating the changing trends of seagrass beds under different environmental conditions.

[0099] The present invention will be further described below in conjunction with specific application examples.

[0100] 1. Determine the study sea area;

[0101] To verify the practicality of the method of the present invention in complex real-world scenarios, the present invention is based on the survey data of a certain month by the North Sea Forecasting and Disaster Reduction Center of the Ministry of Natural Resources in a certain area ( Figure 6 ), and discusses the performance of the present invention in actual situations.

[0102] 2. Model input;

[0103] According to the seagrass bed numerical simulation area, environmental information of the study area is further collected. The light, temperature, and water body nutrient data for model input are from the data provided by the Copernicus Marine Environment Monitoring Service (CMEMS). By calling the data interface of CMEMS, the sea surface light intensity at different times, seawater temperature, and water body nutrient data information at different positions and depths can be obtained. The soil nutrient concentration and plant density information for model input are obtained through multiple on-site survey data.

[0104] 3. Changes in seagrass biomass;

[0105] The data obtained above are input into the seagrass biomass prediction model, and the seagrass bed biomass of this month is used as the reference data to conduct a simulation study on the seasonal changes of the seagrass bed biomass in this area. The simulation results are as Figure 7 shown.

[0106] As Figure 7 can be seen, the model has good predictive ability for the seasonal changes of leaf biomass. Specifically, the biomass reaches a peak from July to early August, then gradually decreases, and reaches a trough from the end of January to early February. Figure 8 It generally presents the spatial distribution status of seagrass, growth state, and the influence of environmental factors on it. During the growth process of seagrass, hydrodynamic force plays a direct regulatory role. In the mudflat area near the main tidal channel, due to the influence of shear stress, the probability of seagrass leaves being torn and scoured increases significantly, resulting in a slowdown in the growth trend of seagrass in this area.

[0107] In summary, one of the key technologies of the present invention is that when constructing the seagrass biomass change model, various environmental factors such as temperature, light, water nutrient concentration, soil nutrient concentration, and plant density are comprehensively considered. By deeply analyzing the interaction between each factor, the growth status of seagrass under different environmental combinations is accurately simulated. In the model framework, the leaf growth rate is jointly affected by light, temperature, plant density, and water nutrient concentration, and the root growth rate is also related to multiple factors. This multi-factor coupling modeling method breaks through the limitation of traditional models that only consider single or a few factors, and comprehensively reflects the complexity and dynamic change characteristics of the seagrass growth environment.

[0108] Furthermore, the model parameters are optimized by combining sensitivity analysis with fitting of measured data. First, through sensitivity analysis, the uncertainty of the calculation results is calculated, and the key parameters that have a greater impact on the model results are screened out. Then, using the gradient descent algorithm and the adjoint assimilation method, a cost function is constructed based on the difference between simulation and observation, and the screened parameters are optimized, significantly improving the accuracy and reliability of the model.

[0109] The present invention protects the seagrass distribution area change prediction method based on the construction of the seagrass biomass change model, including the construction idea of the model framework, the comprehensive consideration method of multiple environmental factors, the specific method of parameter optimization, and the operation process of the entire prediction method, preventing others from using, copying, or improving this complete technical solution without authorization.

[0110] In addition, the present invention also protects the application rights of this method in the marine ecological fields such as seagrass bed ecosystem scientific research, monitoring and protection, and sustainable development. It is ensured that in these fields, without authorization, others are not allowed to use this technology for related activities such as seagrass distribution area change prediction and seagrass bed protection strategy formulation, providing forward-looking and practical key technical support for the marine ecological fields and ensuring the exclusive application value of the technology of the present invention in related fields.

Claims

1. A method for predicting seagrass biomass based on the synergistic effect of multiple environmental factors, characterized in that The following steps are involved: a. Construct a seagrass biomass prediction model; Seagrass biomass includes leaf biomass and root biomass, and considering the biomass transfer from roots to leaves, a seagrass biomass prediction model is constructed as follows: （1） （2） Among them, B l (t) and B l (t+1) are the leaf biomass at time t and time t+1, B r (t) and B r (t+1) are the root biomass at time t and time t+1, G l (t) and G r (t) are the leaf growth rate and root growth rate at time t, R esl (t) and R esr (t) are the leaf respiration rate and root respiration rate at time t, T rl (t) is the biomass transfer coefficient from roots to leaves at time t, and Δt is the time step; The leaf growth rate is affected by temperature, light, water nutrient concentration and plant density, the root growth rate is affected by temperature, soil nutrient concentration and plant density, and both the leaf respiration rate and root respiration rate are affected by temperature. b. To predict seagrass biomass in the target area; Obtain the light, seawater temperature, water nutrient concentration, soil nutrient concentration and plant density data at different time steps in the target area, and use the seagrass biomass prediction model constructed in step a to predict the seagrass biomass in the target area.

2. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 1, characterized in that: The leaf growth rate, root growth rate, leaf respiration rate and root respiration rate were calculated using the following formulas: （3） （4） （5） （6） Among them, f I (t), f Tl (t), f Tr (t), f S (t), f Nw (t) and f Ns (t) are the light influence coefficient, leaf temperature influence coefficient, root temperature influence coefficient, plant density influence coefficient, water nutrient concentration influence coefficient and soil nutrient concentration influence coefficient at time t; G lmax is the maximum growth rate of leaves; G rmax is the maximum root growth rate; R eslmax is the maximum respiration rate coefficient of the leaf; R esrmax is the maximum root respiration rate coefficient.

3. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 2, characterized in that: Light influence coefficient, leaf temperature influence coefficient, root temperature influence coefficient, plant density influence coefficient, water nutrient concentration influence coefficient and soil nutrient concentration influence coefficient: （7） （8） （9） （10） （11） （12） Where I(t) is the light intensity at time t; k el is the leaf illumination half-saturation constant; T(t) is the sea temperature at time t; T optl and T optr are the optimum growth temperatures for leaves and roots, respectively; tl and tr are the coefficients of dependence of leaf growth and root growth on temperature; n w (t) and n s (t) are the nutrient concentrations of water and soil at time t; n lmin and n lcrit are the minimum internal nitrogen content of leaves and the critical internal nitrogen content of leaves, respectively; n rmin and n rcrit are the minimum internal nitrogen content of the root system and the critical internal nitrogen content of the root system, respectively; B l (t) is the leaf biomass at time t; B lmax is the maximum leaf biomass; k l is the coefficient of leaf growth dependence on space.

4. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 1, characterized in that: The biomass transfer from roots to leaves at time t is represented by the biomass transfer coefficient, which is calculated as follows: （13） Among them, T rl (t) is the biomass transfer coefficient from roots to leaves at time t, T rlmean is the average biomass transfer coefficient from roots to leaves.

5. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 1, characterized in that: It also includes a model parameter optimization step, using sensitivity analysis and measured data fitting to synergistically optimize seagrass biomass prediction model parameters.

6. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 5, characterized in that: The model parameter optimization specifically includes the following steps: a11. Screen key parameters based on sensitivity analysis; A sensitivity experiment was set up to impose relative random errors on the parameters of the seagrass biomass prediction model to simulate the errors that may occur in the actual observation process and calculate the uncertainty of the experimental results. The calculation formula is as follows: （14） Among them, CE is the result of the control experiment, SE is the result of the sensitive experiment, N is the number of simulation days, and RU is the result uncertainty; the key parameters that affect the seagrass biomass prediction model are screened out through the RU value; a12. Use measured data fitting method to optimize key parameters; First, the difference function between simulation and observation is established, denoted as the cost function MSE, which is expressed as: （15） Where C (t) = [B l (t), B r (t)] is the model state variable at time t, C * (t) = [B l * (t), B r * (t)] is the observed data corresponding to the model state variables at time t, and N is the number of simulation time steps; Secondly, the adjoint assimilation method is used to carry out forward simulation and adjoint simulation. The gradient of the cost function with respect to the model parameters is obtained by solving the adjoint equation, thereby determining the direction of parameter updating, and the gradient descent method is used to iteratively adjust the parameters to minimize the cost function.

7. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 6, characterized in that: To achieve gradient descent, you need to calculate the gradient of the cost function of the model with respect to the parameter θ, so as to obtain the gradient vector corresponding to the parameter , and subtract the parameter θ from To achieve parameter optimization, it is expressed as: （16） Among them, θ(i+1) and θ(i) are the values ​​of the parameter θ for the i+1th iteration and the ith iteration respectively. η represents the learning rate, which is used to determine the size of the gradient descent step. The size of the learning rate η will be adjusted in the specific numerical simulation.

8. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 7, characterized in that: The adjoint assimilation method is used to optimize each parameter in turn. The error between the observed data and the simulation results drives the continuous operation of the model. The parameters are continuously optimized in the adjoint assimilation model. While optimizing the parameters, the optimal learning rate η of each parameter is also obtained.

9. The method for predicting seaweed biomass based on the synergistic effect of multiple environmental factors according to claim 1, characterized in that: Collect environmental information of the target area, use the data provided by the Copernicus Marine Environment Monitoring Service, and obtain the sea surface light intensity at different times, sea water temperature and water nutrient data information at different locations and depths by calling the data interface of the Copernicus Marine Environment Monitoring Service. Obtain soil nutrient and plant density information through multiple field survey data.

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