Forest NEP simulation method and system based on three-branch EMA-CKAN and ecological constraint

Abstract

The invention relates to a forest NEP simulation method and system based on three-branch EMA-CKAN and ecological constraint, a three-branch EMA-CKAN network is constructed, the three-branch EMA-CKAN network comprises three parallel EMA-CKAN sub-networks, forest carbon flux is decomposed into three sub-problems of top canopy total primary productivity, bottom canopy total primary productivity and ecological respiration for modeling respectively, and the three sub-problems of top canopy total primary productivity, bottom canopy total primary productivity and ecological respiration are solved. Physical independence of each process is reserved, mutual restriction can be realized through sharing representation and a coupling mechanism, an EMA multi-scale attention mechanism is embedded behind the first two layers of three-dimensional CKANs of each subnet, then the two layers of three-dimensional CKANs are stacked, and deeper semantic extraction and fusion are performed on attention-enhanced features; nEP is synthesized in the network in a differentiable form based on an ecological constraint function, so that the model is constrained by physical consistency while pursuing statistical fitting, and training stability, noise immunity and physical interpretability are cooperatively improved.

Description

Technical Field

[0001] This invention relates to the field of forest carbon resource assessment technology, specifically to a forest NEP simulation method and system based on a three-branch EMA-CKAN and ecological constraints. Background Technology

[0002] As a core component of terrestrial ecosystems, forest ecosystems play an irreplaceable role in regulating the global carbon cycle, mitigating greenhouse gas concentrations, and mitigating global warming. Among these, the net energy exchange capacity (NEP) of forests directly characterizes their net carbon exchange capacity and is a key indicator for determining the carbon source / sink status of an ecosystem. However, because the carbon exchange process in forest ecosystems is complexly influenced by the coupling of multiple environmental factors such as light, temperature, water, and soil nutrients, current common light energy utilization rate models, while simplifying the calculation process to some extent, struggle to accurately represent the nonlinear relationships and heterogeneous response mechanisms of forest ecosystems to environmental factors. Consequently, the simulation accuracy and generalization ability in the complex and variable forest canopy are significantly limited. Summary of the Invention

[0003] The purpose of this invention is to provide a forest NEP simulation method and system based on three-branch EMA-CKAN and ecological constraints to solve the problems existing in the background art.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints, comprising: A three-branch EMA-CKAN network was constructed, comprising three parallel EMA-CKAN subnetworks. Forest carbon flux was decomposed into three subproblems: top canopy total primary productivity (GPP), bottom canopy GPP, and ecological respiration (Re). Each subproblem was modeled separately, and NEP was synthesized within the network in a differentiable form based on an ecological constraint function. The ecological constraint function is as follows: ; in, For NEP prediction bias, For quality constraints, For energy confinement, For temperature constraints, This represents the overall weight, with a value between 0 and 0.2. , , All are non-negative numbers, and + + =1; as well as An EMA multi-scale attention mechanism is embedded after the first two layers of 3D CKAN in each subnet, and two layers of 3D CKAN are stacked after the EMA multi-scale attention mechanism to perform deeper semantic extraction and fusion of attention-enhanced features.

[0005] Preferably, optimizing the construction of the three-branch EMA-CKAN network includes: Construct a dataset that includes in-situ measurement data of flux stations for the required forest types, remote sensing data of the area within 10km of the corresponding stations, meteorological data, and soil data; The dataset is divided into training dataset, test dataset, and validation dataset; Using remote sensing data, meteorological data, and soil data as inputs to a three-branch EMA-CKAN network, the network learns the correlation between total primary productivity (TPP) and ecological respiration (Re) of the top and bottom canopies and the data from remote sensing, meteorological, and soil data. The physical model parameter calibration process is transformed into an end-to-end neural network training task. Ecological constraint functions are calculated, and the network is trained repeatedly until the accuracy of the validation dataset of the three-branch EMA-CKAN network converges. The coefficient of determination and root mean square error index are used to examine the fitting performance of the three-branch EMA-CKAN network to the training samples, and the optimal three-branch EMA-CKAN network is obtained.

[0006] More preferably, the dataset is preprocessed before being partitioned, including: The effective photosynthetically active radiation intercepted by the top and bottom canopies of the forest at the calculation site; ; ; For photosynthetically effective radiation from the top canopy, This provides effective photosynthetic radiation for the bottom canopy. It is the surface albedo; This refers to the scattered photosynthetically effective radiation below the canopy; This refers to the radiation scattering term within the canopy; For the leaf angle; The solar zenith angle; and These are the diffuse and direct components of the incident solar radiation, respectively, estimated from the incident solar radiation and the clear sky index; The leaf area index of the top canopy. The leaf area index of the bottom canopy; It is a vegetation aggregation index, which depends on the forest type; The day-scale flux data published by the site were overlaid on the 8-day scale, while some abnormal data were deleted, including parameter observation gaps, data that did not conform to crop growth patterns, and data with abnormal observation values. The domain remote sensing data, meteorological data, and soil data were resampled to 500m using bilinear interpolation. The time resolution of remote sensing data, meteorological data, and soil data was altered using cubic spline interpolation to align them with the 8-day time of flux data.

[0007] Preferably, the preprocessed 8-day scale remote sensing data, meteorological data, and soil data are combined with the 8-day scale throughput data to ensure time alignment; the data are divided into training set, validation set, and test set in a 6:2:2 ratio; and the data from each station appears in the training set, validation set, and test set for more than 2 months.

[0008] More preferably, the in-situ measurement data of the flux station includes net ecosystem productivity (NEP), ecological respiration (Re), and total primary productivity (GPP) data; Remote sensing data include leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), chlorophyll fluorescence index (SIF), and chlorophyll concentration (LCC). Meteorological data include: air temperature (Ta), saturated vapor pressure difference (VPD), and solar radiation (SW); Soil data include soil nitrogen content (Sn), soil pH, soil organic carbon concentration (SOC), and soil moisture (SM); The estimated data consist of air temperature Ta, enhanced vegetation index EVI, soil moisture SM, soil organic carbon concentration SOC, and soil pH Re. Effective photosynthetic radiation of the top canopy Composition of air temperature (Ta), saturated vapor pressure difference (VPD), saturated water vapor pressure (SW), soil moisture (SM), chlorophyll fluorescence (SIF), chlorophyll concentration (LCC), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil nitrogen content (Sn), and soil moisture (SM). Estimated data; Effective photosynthetic radiation of the bottom canopy Composition of air temperature (Ta), saturated vapor pressure difference (VPD), saturated water vapor pressure (SW), chlorophyll fluorescence (SIF), chlorophyll concentration (LCC), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil nitrogen content (Sn), and soil moisture (SM). Estimated data.

[0009] Preferably, in a subnet, based on the Re estimation data, the ecological respiration Re is calculated: ; In a subnet, based on Estimate data to calculate the total primary productivity of the top canopy. : ; In a subnet, based on Estimate data to calculate total primary productivity of the bottom canopy. : ; in, , The output results represent the EMA-CKAN subnetworks for the upper and lower canopies of the forest.

[0010] Preferably, based on ecological respiration (Re) and total primary productivity of the top canopy. and total primary productivity of the bottom canopy Calculate simulated values ​​of forest ecosystem NEP : ; ; in, , Simulated values ​​representing ecological respiration; Preferably, all input variables, including remote sensing, meteorological, and soil data, are normalized using min-max before being input into the deep learning model, linearly mapping the original values ​​to the range of 0-1, thereby improving the stability and convergence speed of model training; the Adam optimizer is selected as the network optimizer; after obtaining the loss, the weights in the entire network are modified, and the training is repeated multiple times until the accuracy of the validation dataset of the three-branch EMA-CKAN network converges, and the model parameters are saved.

[0011] More preferably, the coefficient of determination (R) 2 The root mean square error (RMSE) is used to test the linear correlation between predicted and observed values, and it is used to quantify the absolute deviation. The calculation is as follows: ; .

[0012] Preferably, each sub-network has the same overall structure. Their first layer is the input layer, which is adjusted to adapt the input data to the input network and connect with the subsequent network layers. Then, two three-dimensional CKAN layers are connected, with a convolution kernel size of 3*3*3 and filters of 16 and 32 respectively. The activation function is the leak ReLU function, and the α in the leak ReLU is selected in the range of 0.01-0.3. The EMA multi-scale attention mechanism divides the channels into four groups. For each group, parallel depthwise-separable convolutions with 1*3 kernels are used to calculate the multi-scale spatial response. The responses at each scale are concatenated and then subjected to 1*1 convolutions, followed by BatchNorm normalization to obtain the spatial attention map. The channel dimension is then restored by 1*1 convolution projection, and finally, the residual coefficients γ are added back to the original features. The EMA multi-scale attention mechanism enhances the mapping relationship between multi-scale spatial and multi-source ecological information, thereby improving the model's response capability to the nonlinear ecological relationships of multi-source ecological information. Preferably, the stacked two 3D CKAN convolutional layers have a kernel size of 3*3*3, with 64 and 128 filters respectively, and the Leak ReLU activation function is used; the following are added sequentially after the stacked two 3D CKAN layers: A reshap layer transforms three-dimensional features into two-dimensional features; A four-layer 2D CKAN network with a 3*3 kernel size, using the Leak ReLU activation function, where α is selected from 0.01 to 0.3, and the filters are 128, 128, 64, and 32 respectively. A 2x2 pooling layer; A deformable layer; The three fully connected layers have 32, 16, and 1 neural network layers, respectively.

[0013] Preferably, The mass constraint is calculated as follows: ; The quality constraint is used to constrain the joint simulation accuracy of the three components of carbon flux by the three-branch EMA-CKAN network. It requires that the predicted NEP, GPP, and respiration (Re) satisfy material budget consistency at both the sample and aggregate levels. The constraint is defined as follows: ; Where n is the number of samples used for calculation; , , These represent net ecosystem productivity, gross primary productivity, and ecological respiration, as predicted by the three-branch EMA-CKAN model. , , These are the corresponding station observations; The energy-constrained calculation is used to determine whether the canopy's efficiency in utilizing light energy conforms to known physical mechanisms; its loss is defined as: ; Where n is the number of samples used for calculation; This represents the net ecosystem production predicted by the three-branch EMA-CKAN, while To correspond to the relationship between light energy absorbed by the forest canopy and its conversion into fixed biomass energy, the total primary productivity of the entire canopy is calculated using the following method: ; In the formula, , and are correction factors for maximum VPD and minimum photosynthetic temperature, respectively, representing the effects of saturated vapor-water pressure difference and minimum air temperature on vegetation photosynthesis; and The data was obtained through bilinear regression of flux data. Minimum temperature, The soil moisture content correction factor is calculated as follows: ; ; In the formula, , , , These are parameters related to forest type, obtained through time-series variation analysis of station flux data. Temperature constraints are used to characterize the sensitivity of ecological respiration to temperature and to constrain the prediction of Re. These constraints introduce a physical reference model for the temperature response. The parameters of this model can be initialized station-by-site or by ecological class using field data from flux observation stations, and can be fine-tuned during training within physically acceptable ranges. The constraint expression is as follows: ; in This is a predicted value for ecological respiration. The physical reference values ​​for ecological respiration, taking temperature into account, are calculated as follows: ; in Select 10°C or 15°C to represent the reference temperature. As the baseline breathing intensity, The eco-respiration intensity coefficient is measured by selecting flux observation stations. S is obtained through regression estimation, as shown in the following formula: ; ; ; In the formula, T represents the flux station measurement data.

[0014] This invention also discloses a forest NEP simulation system based on a three-branch EMA-CKAN and ecological constraints, comprising: Acquisition of sample data unit: Acquire the in-situ measurement data of the required forest type flux stations, the area remote sensing data within 10km of the corresponding stations, meteorological data and soil data, and match them with the flux observation scale; Data preprocessing unit: Calculates photosynthetically active radiation of the top canopy. and photosynthetically effective radiation of the bottom canopy The system unifies data from different sources and with different time resolutions to the 8-day time scale required by the model, and performs outlier cleaning and imputation; it overlays or averages the daily throughput data to the 8-day scale according to rules, while removing abnormal training data; it outputs aligned multi-source image patch data, performs min-max normalization on it, and divides it into training set, validation set and test set in a 6:2:2 ratio. Model training unit: Construct and train a three-branch EMA-CKAN model, combine ecological constraint functions to estimate forest NEP in real end-to-end, use Adam optimizer to update weights during training, save model weights and training history after training, and finally use the coefficient of determination and root mean square error index on an independent test set to check the fitting performance of the three-branch EMA-CKAN network to the training samples, and save the optimal three-branch EMA-CKAN network. NEP simulation unit: Configured with an optimal three-branch EMA-CKAN network for batch inference and spatialized output of the target region.

[0015] Preferably, remote sensing, meteorological, and soil data of the target area are acquired and resampled using the same preprocessing procedure as during training to obtain an 8-day scale prediction dataset, which is then input into the model pixel-by-pixel or in blocks for calculation. , The NEP spatiotemporal sequence is synthesized with Re and finally generated to produce NEP data for the target forest region.

[0016] Beneficial Effects: This invention employs a three-branch structure to decompose forest carbon flux into three sub-problems: top canopy GPP, bottom canopy GPP, and ecological respiration (Re). These sub-problems are modeled separately, preserving the physical independence of each process while enabling mutual constraints through shared representations and coupling mechanisms. This structurally reduces feature confounding between processes and improves the estimation accuracy of sub-processes and the overall NEP. Secondly, the EMA-CKAN sub-network embeds an EMA multi-scale attention mechanism after the first two layers of 3D CKAN to capture features that have not been over-downsampled, enhancing the discriminability of local photosynthetic and respiration-related features. Subsequently, two more layers of 3D CKAN are stacked to perform deeper semantic extraction and fusion of the attention-enhanced features. Furthermore, this invention constructs an ecological constraint system comprised of the synergistic effects of mass, energy, and temperature constraints, and integrates it as a differentiable loss function into the training process of the three-branch EMA-CKAN network. This allows the model to pursue statistical fitting while being constrained by physical consistency, synergistically improving training stability, noise resistance, and physical interpretability. Attached Figure Description

[0017] Figure 1 This is a flowchart of the forest NEP simulation method based on the three-branch EMA-CKAN and ecological constraints of the present invention; Figure 2 This is a diagram of the three-branch EMA-CKAN network of the present invention; Figure 3 This is a diagram verifying the simulation accuracy of the three-branch EMA-CKAN model in this invention; Detailed Implementation

[0018] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe a forest NEP simulation method and system based on a three-branch EMA-CKAN and ecological constraints, or several specific implementations of this invention, and does not strictly limit the scope of protection specifically claimed by this invention.

[0019] Traditional forest NEP estimation models have shortcomings in their mechanism representation: existing methods mostly rely on data-driven regression, treating NEP as a single objective, while ignoring... , The physiological processes associated with respiration (Re) make it difficult to guarantee a reasonable decomposition and cross-regional generalization of carbon budget processes. Furthermore, traditional methods typically rely on single loss functions such as RMSE to optimize parameters, failing to explicitly incorporate key ecological mechanisms such as mass conservation, energy balance, and temperature dependence into the training process, thus exhibiting deficiencies in physical consistency and physiological rationality. To overcome these problems, this invention proposes an ecologically constrained three-branch EMA-CKAN network. This method decomposes forest carbon flux into three branches: top canopy GPP, bottom canopy GPP, and ecological respiration. A multi-scale attention module (EMA) is introduced during convolutional feature extraction to fully preserve the spatial information of multi-source ecological variables that have not yet been weakened by pooling, enhancing the model's responsiveness to key environmental features. Simultaneously, an ecological constraint loss function is introduced into the optimization objective, comprehensively considering mass conservation, energy balance, and temperature effects. This allows the model to maintain high-precision fitting while adhering to the fundamental physical laws of ecosystem processes, achieving a more accurate and generalized simulation of forest NEP. Example 1: As Figure 1 As shown, a forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints includes the following steps: S1. Obtain in-situ measurement data of flux stations for the required forest types, and obtain area remote sensing, meteorological and soil data within 10km of the corresponding stations; The in-situ measurement data from flux stations are publicly available data on net ecosystem productivity (NEP), ecological respiration (Re), and gross primary productivity (GPP) measured at flux stations. The required remote sensing data include: leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), chlorophyll fluorescence (SIF), and chlorophyll concentration (LCC). The required remote sensing data include: air temperature (Ta), vapor pressure difference (VPD), and solar radiation (SW). The required soil data include: soil nitrogen content (Sn), soil pH, soil organic carbon concentration (SOC), and soil moisture (SM). The required remote sensing, meteorological, and soil data for the site locations need to be extracted from spatial data within a 10km radius. In a specific case, flux data came from 54 flux sites in the China Flux Consortium and the FLUX2015 dataset. Specifically, LAI data used MODIS MOD15A2H; NDVI and EVI data used MODIS MCD12C1; SIF data used GOSIF; LCC data used the 2002-2019 global 8-day 500-meter leaf chlorophyll content product dataset; SIF data used the GOSIF dataset; soil moisture (SM), soil organic carbon (SOC), soil pH, and soil total nitrogen (Sn) data used the SoilGrids dataset; SM data was downloaded from the GLEAM dataset; shortwave incident radiation (SR), air temperature (Ta), and dew point temperature (Td) came from the ERA5 dataset; VPD was obtained from atmospheric temperature and dew point temperature. S2. Calculate the photosynthetically active radiation intercepted by the top and bottom canopies of the forest at the site. , Furthermore, the remote sensing, meteorological, and soil data around the station obtained in step S1 are preprocessed to generate a specified k*k image patch with a spatial resolution of 500m and a temporal resolution of 8-day, and the time is aligned with the flux data; thus, the preprocessed flux, remote sensing, meteorological, and soil datasets are obtained. Regarding the setting of k, in this embodiment k=7, in other embodiments k can be 9 or 11; Preprocessing includes: Data within a 10km range is resampled to 500m using bilinear interpolation. If the spatial resolution of the dataset used is exactly 500m, resampling is not necessary. The day-scale flux data published by the site were overlaid on the 8-day scale, while some abnormal data were deleted, including parameter observation gaps, data that did not conform to crop growth patterns, and data with abnormal observation values. Remote sensing data, meteorological data, and soil moisture are adjusted using cubic spline interpolation to change the temporal resolution, aligning the 8-day data of their flux. , The APAR absorbed by the top and bottom canopies of a forest canopy is calculated as follows: ; ; In the above formula, It is the surface albedo; This refers to the scattered photosynthetically effective radiation below the canopy; This refers to the radiation scattering term within the canopy; The leaf tilt angle is set to 60°. The solar zenith angle; and These are the diffuse and direct components of the incident PAR, estimated from the incident PAR and the clear sky index. For the top canopy LAI, For the basal canopy LAI; It is a vegetation aggregation index, which depends on the forest type; S3. Combine the flux, remote sensing, meteorological, and soil datasets obtained in step S2, and divide them into training, testing, and validation datasets; specifically: The preprocessed 8-day scale meteorological, remote sensing, and soil data obtained in step S2 are combined with the 8-day scale flux data to achieve time alignment; Ta, EVI, SM, SOC, and pH constitute the Re estimation data, with a data structure of k*k*5; APARsu, Ta, VPD, SW, SIF, LCC, NDVI, EVI, Sn, and SM constitute The estimated data has a k*k*10 structure; where APARsh, Ta, VPD, SW, SIF, LCC, NDVI, EVI, Sn, and SM constitute the data. The estimated data has a k*k*10 structure and is divided into training, validation, and test sets in a 6:2:2 ratio to obtain the training data for the three-branch EMA-CKAN model. Simultaneously, it should be ensured that data from each site appears in the training, validation, and test sets for at least two months. S4. Construct a three-branch EMA-CKAN network, refer to... Figure 2 As shown, the three-branch EMA-CKAN network comprises three branch subnets: Branch (input is k×k×10) Branch (input is k×k×10) and Re branch (input is k×k×5); and The calculation method is as follows: ; ; in, , The output results of the EMA-CKAN subnetworks representing the upper and lower canopies of the forest are provided by... , , , , , , , and The simulation yielded the following results; and The actual forest top and bottom canopy GPP.

[0020] Another independent EMA-CKAN subnetwork estimates Re by combining Ta, EVI, SM, SOC, and pH parameters; among these variables, Ta represents the influence of climatic factors, SM, SOC, and pH are used to characterize the influence of soil factors, and EVI is used to characterize the influence of remote sensing factors; the Re expression is shown below: ; After obtaining GPPsu, GPPsh, and Re through the three EMA-CKAN subnetworks mentioned above, the estimated value of forest ecosystem NEP was calculated. : ; ; in, This is the simulated value for NEP; , The sum of the GPP of the top and bottom canopies of the forest is: ; Simulated values ​​representing ecological respiration; Each sub-network has the same overall structure. Their first layer is the input layer, which adjusts the input data to fit the network and connects with subsequent network layers. Two 3D CKAN layers are then connected, with 3x3x3 kernels and 16 and 32 filters respectively. The Leak ReLU activation function is used, with α ranging from 0.01 to 0.3.

[0021] Subsequently, the EMA multi-scale attention mechanism is embedded, dividing the channels into 4 groups. For each group, parallel depthwise-separable convolutions with 1*3 kernels are used to calculate the multi-scale spatial response. The responses at each scale are concatenated and then subjected to 1*1 convolutions, followed by BatchNorm normalization to obtain the spatial attention map. The channel dimension is then restored by 1*1 convolution projection, and finally the residual coefficients γ are added back to the original features. EMA enhances the mapping relationship between multi-scale spatial and multi-source ecological information, thereby improving the model's response capability to the nonlinear ecological relationships of multi-source ecological information. Subsequently, two 3D CKAN layers are connected, with convolutional kernel size of 3*3*3 and filters of 64 and 128 respectively. The activation function used is the Leak ReLU function, with α ranging from 0.01 to 0.3. After this, a reshaping layer is connected to convert the 3D features into 2D features. Then, four 2D CKAN networks are added, with convolutional kernel size of 3*3 and the activation function used is the Leak ReLU function, with α ranging from 0.01 to 0.3. In this embodiment, α is 0.1, and filters are 128, 128, 64, and 32 respectively. After this, a 2*2 pooling layer is added. Then, a reshaping layer is added, followed by three fully connected layers with 32, 16, and 1 neural network in each layer. The two-dimensional and three-dimensional CKAN network layers mentioned above are both constructed using KAN (Kolmogorov-Arnold Network). Compared with conventional CNNs, they can achieve essentially the same accuracy with fewer parameters.

[0022] Optimization of the three-branch EMA-CKAN network includes: The training and validation datasets obtained in steps S3 and S4 are imported into the three-branch EMA-CKAN network. The model takes remote sensing, meteorological, and soil data as input and then uses the three-branch EMA-CKAN network to learn the total primary productivity of the top and bottom canopies. Using Re data, remote sensing data, meteorological data, and soil data, the physical model parameter calibration process is transformed into an end-to-end neural network training task, combined with ecological constraint functions: ; The training process is repeated multiple times until the accuracy of the validation dataset for the three-branch EMA-CKAN network converges. The coefficient of determination (R²) is then used. 2 The root mean square error (RMSE) metric is used to check the fitting performance of the three-branch EMA-CKAN network to the training samples, and the trained three-branch EMA-CKAN network is saved. In this embodiment, λ is 0.1. A higher overall weight will increase the physical generalization of the network, but will make the network difficult to converge. m It is 0.6, w e The value is 0.3, w t It is 0.1.

[0023] In the ecological constraint function, the mass constraint forces consistency of material budget when predicting each carbon flux component, preventing the model from reducing the overall error by adjusting a single component; the energy constraint drives the network's GPP prediction to be statistically consistent with this physical reference, incorporating the light energy-GPP ecological process into the training process to improve the model's generalization ability under different light conditions and canopy structures; the temperature constraint embeds the sensitivity of ecological respiration to temperature as a physical prior into the network, providing the physical reference of Re through a differentiable temperature response submodule.

[0024] All input variables of the network, including remote sensing, meteorological and soil data, are normalized using min-max before being input into the deep learning model, which linearly maps the original values ​​to the range of 0-1, improving the stability and convergence speed of model training; the Adam optimizer is selected as the network optimizer. refer to Figure 3 As shown, the coefficient of determination (R²) 2 The root mean square error (RMSE) is used to test the linear correlation between predicted and observed values, and it is used to quantify the absolute deviation. The calculation is as follows: ; ; like Figure 3 As shown, the NEP R2 of the four forest types (mixed forest in the upper left, deciduous broad-leaved forest in the upper right, evergreen coniferous forest in the lower left, and evergreen broad-leaved forest in the lower right) are all above 0.7. The scattered points are concentrated and closely distributed on both sides of the reference line, which shows that the model has good consistency and accuracy throughout the entire NEP value range, and does not show any obvious systematic overestimation or underestimation.

[0025] For the ecological constraint function: ; in, For NEP prediction bias, For quality constraints, For energy confinement, For temperature constraints, The overall weight λ ranges from 0 to 0.2. In this embodiment, the overall weight λ is 0.1. A higher overall weight increases the network's physical generalization ability but makes the network more difficult to converge. , , All are non-negative numbers, and + + =1; The NEP prediction bias is represented by the RMSE loss function. This loss function penalizes larger errors more severely to avoid extreme errors, and is calculated as follows: ; Loss mass As a quality constraint, a variation of RMSE loss, it is used to constrain the joint simulation accuracy of the three-branch EMA-CKAN network for the three components of carbon flux. It requires that the predicted NEP, GPP, and respiration (Re) satisfy material budget consistency at both the sample and aggregate levels. The constraint is defined as follows: ; Where n is the number of samples used for calculation; , , These represent net ecosystem productivity, gross primary productivity, and ecological respiration, as predicted by the three-branch EMA-CKAN model. , , These are the corresponding station observations; The energy-constrained calculations are used to determine whether the canopy's efficiency in utilizing light energy conforms to known physical mechanisms; the loss is defined as: ; Where n is the number of samples used for calculation; This represents the net ecosystem production predicted by the three-branch EMA-CKAN, while To correspond to the relationship between light energy absorbed by the forest canopy and its conversion into fixed biomass energy, the total primary productivity of the entire canopy is calculated using the following method: ; In the formula, , and are correction factors for maximum VPD and minimum photosynthetic temperature, respectively, representing the effects of saturated vapor-water pressure difference and minimum air temperature on vegetation photosynthesis; and The data was obtained through bilinear regression of flux data. Minimum temperature, The soil moisture content correction factor is calculated as follows: ; ; In the formula, , , , These are parameters related to forest type, obtained through time-series variation analysis of station flux data. Temperature constraints are used to characterize the sensitivity of ecological respiration to temperature and to constrain the prediction of Re. These constraints introduce a physical reference model for the temperature response. The parameters of this model can be initialized station-by-site or by ecological class using field data from flux observation stations, and can be fine-tuned during training within physically acceptable ranges. The constraint expression is as follows: ; in This is a predicted value for ecological respiration. The physical reference values ​​for ecological respiration, taking temperature into account, are calculated as follows: ; in Select 10°C or 15°C to represent the reference temperature. As the baseline breathing intensity, The eco-respiration intensity coefficient is measured by selecting flux observation stations. S is obtained through regression estimation, as shown in the following formula: ; ; ; In the formula, T represents the flux station measurement data; S5. Acquire remote sensing, meteorological and soil data of the target forest area, preprocess all data of the target area using the method in step S2, and obtain a multi-source prediction dataset of the target area with the same format as the training dataset. Use the trained three-branch EMA-CKAN network saved in step S4 to simulate the NEP of the target forest area and obtain the forest NEP results. In this embodiment, the selected study area is a global mixed forest, deciduous broad-leaved forest, evergreen coniferous forest and evergreen broad-leaved forest region, and the selected time period is the whole year of 2015.

[0026] Example 2: This invention also discloses a forest NEP simulation system based on a three-branch EMA-CKAN and ecological constraints, characterized in that it includes: Sample Data Acquisition Unit: This unit is responsible for acquiring station flux data for training, as well as collecting remote sensing, soil, and meteorological data within a 10 km radius of the station to match the flux observation scale. The collected data includes LAI, EVI, NDVI, SIF, and LCC remote sensing data; Ta, VPD, and PAR meteorological variables; and Sn, pH, SOC, and SM soil variables.

[0027] Data Preprocessing Unit: This unit calculates APARsu and APARsh, unifies data from different sources and with different time resolutions to the 8-day time scale required by the model, and performs outlier cleaning and imputation. Daily throughput data is superimposed or averaged to the 8-day scale according to rules, while outlier training data is removed. Finally, this unit outputs aligned multi-source image patch data, performs min-max normalization on it to improve training stability and convergence speed, and divides it into training, validation, and test sets in a 6:2:2 ratio.

[0028] Model Training Unit: This unit is responsible for building and training the three-branch EMA-CKAN model, combining ecological constraints with real-end-to-end forest NEP estimation, using the Adam optimizer for weight updates during training, saving model weights and training history after training, and finally providing quantitative indicators such as the coefficient of determination (R²) and RMSE on the independent test set as the basis for performance evaluation. The model parameters are saved after training.

[0029] NEP Simulation Unit: This unit primarily uses a pre-trained three-branch EMA-CKAN for batch inference and spatialization output of the target area. It acquires and resamples remote sensing, meteorological, and soil data of the target area using the same preprocessing procedure as during training, obtaining an 8-day scale prediction dataset. This dataset is then input into the model pixel-by-pixel or in blocks for computation. , The NEP spatiotemporal sequence is synthesized with Re and finally generated to produce NEP data for the target forest region.

[0030] The embodiments of the present invention have been described in detail above with reference to the examples. However, the present invention is not limited to the above embodiments. For those skilled in the art, after learning the contents described in the present invention, several equivalent changes and substitutions can be made without departing from the principle of the present invention. These equivalent changes and substitutions should also be considered to fall within the protection scope of the present invention.

Claims

1. A forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints, characterized in that, include: A three-branch EMA-CKAN network was constructed, comprising three parallel EMA-CKAN subnetworks. Forest carbon flux was decomposed into three subproblems: top canopy total primary productivity (GPP), bottom canopy GPP, and ecological respiration (Re). Each subproblem was modeled separately, and NEP was synthesized within the network in a differentiable form based on an ecological constraint function. The ecological constraint function is as follows: ； in, For NEP prediction bias, For quality constraints, For energy confinement, For temperature constraints, This represents the overall weight, with a value between 0 and 0.

2. , , All are non-negative numbers, and + + =1; as well as An EMA multi-scale attention mechanism is embedded after the first two layers of 3D CKAN in each subnet, and two layers of 3D CKAN are stacked after the EMA multi-scale attention mechanism to perform deeper semantic extraction and fusion of attention-enhanced features.

2. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 1, characterized in that: Optimizing the construction of the three-branch EMA-CKAN network includes: Construct a dataset that includes in-situ measurement data of flux stations for the required forest types, remote sensing data of the area within 10km of the corresponding stations, meteorological data, and soil data; The dataset is divided into training dataset, test dataset, and validation dataset; Using remote sensing data, meteorological data, and soil data as inputs to a three-branch EMA-CKAN network, the network learns the correlation between total primary productivity (TPP) and ecological respiration (Re) of the top and bottom canopies and the data from remote sensing, meteorological, and soil data. The physical model parameter calibration process is transformed into an end-to-end neural network training task. Ecological constraint functions are calculated, and the network is trained repeatedly until the accuracy of the validation dataset of the three-branch EMA-CKAN network converges. The coefficient of determination and root mean square error index are used to examine the fitting performance of the three-branch EMA-CKAN network to the training samples, and the optimal three-branch EMA-CKAN network is obtained.

3. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 2, characterized in that: Before splitting the dataset, the dataset is preprocessed, including: The effective photosynthetically active radiation intercepted by the top and bottom canopies of the forest at the calculation site; ； ； For photosynthetically effective radiation from the top canopy, This provides effective photosynthetic radiation for the bottom canopy. It is the surface albedo; This refers to the scattered photosynthetically effective radiation below the canopy; This refers to the radiation scattering term within the canopy; For the leaf angle; The solar zenith angle; and These are the diffuse and direct components of the incident solar radiation, respectively, estimated from the incident solar radiation and the clear sky index; The leaf area index of the top canopy. The leaf area index of the bottom canopy; It is a vegetation aggregation index, which depends on the forest type; The day-scale flux data published by the site were overlaid on the 8-day scale, while some abnormal data were deleted, including parameter observation gaps, data that did not conform to crop growth patterns, and data with abnormal observation values. The domain remote sensing data, meteorological data, and soil data were resampled to 500m using bilinear interpolation. The temporal resolution of remote sensing data, meteorological data, and soil data was altered using cubic spline interpolation to align them with the 8-day flux data. The processed 8-day scale remote sensing data, meteorological data, and soil data were combined with the 8-day scale flux data to ensure time alignment; they were divided into training, validation, and test sets in a 6:2:2 ratio; and the data from each station appeared in the training, validation, and test sets for more than two months.

4. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 3, characterized in that: The in-situ measurement data from the flux stations include net ecosystem productivity (NEP), ecological respiration (Re), and total primary productivity (GPP) data. Remote sensing data include leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), chlorophyll fluorescence index (SIF), and chlorophyll concentration (LCC). Meteorological data include: air temperature (Ta), saturated vapor pressure difference (VPD), and solar radiation (SW); Soil data include soil nitrogen content (Sn), soil pH, soil organic carbon concentration (SOC), and soil moisture (SM); The estimated data consist of air temperature Ta, enhanced vegetation index EVI, soil moisture SM, soil organic carbon concentration SOC, and soil pH Re. Effective photosynthetic radiation of the top canopy Composition of air temperature (Ta), saturated vapor pressure difference (VPD), solar radiation (SW), soil moisture (SM), chlorophyll fluorescence (SIF), chlorophyll concentration (LCC), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil nitrogen content (Sn), and soil moisture (SM) Estimated data; Effective photosynthetic radiation of the bottom canopy Composition of air temperature (Ta), saturated vapor pressure difference (VPD), solar radiation (SW), chlorophyll fluorescence (SIF), chlorophyll concentration (LCC), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil nitrogen content (Sn), and soil moisture (SM). Estimated data.

5. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 4, characterized in that: In a subnet, calculate the ecorespiration Re based on Re estimation data: ； In a subnet, based on Estimate data to calculate the total primary productivity of the top canopy. : ； In a subnet, based on Estimate data to calculate total primary productivity of the bottom canopy. : ； in, , The output results represent the EMA-CKAN subnetworks for the upper and lower canopies of the forest.

6. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 5, characterized in that: Based on ecological respiration (Re) and total primary productivity of the top canopy and total primary productivity of the bottom canopy Calculate simulated values ​​of forest ecosystem NEP : ； ； in, , The simulated value represents ecological respiration.

7. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 1, characterized in that: The EMA multi-scale attention mechanism divides the channels into 4 groups. For each group, parallel depthwise-separable convolutions with 1*3 kernels are used to calculate the multi-scale spatial response. The responses of each scale are concatenated and then subjected to 1*1 convolutions, followed by BatchNorm normalization to obtain the spatial attention map. The channel dimension is restored by 1*1 convolution projection, and finally the residual coefficients γ are added back to the original features.

8. The forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints according to claim 7, characterized in that: The stacked two 3D CKAN convolutional layers have kernel sizes of 3*3*3, with 64 and 128 filters respectively, and use the leaky ReLU activation function. After the stacked two 3D CKAN layers, the following are added sequentially: A deformation layer converts three-dimensional features into two-dimensional features; A four-layer 2D CKAN network with a 3*3 kernel size, using the Leak ReLU activation function, and filters of 128, 128, 64 and 32 respectively; A 2x2 pooling layer; A deformable layer; The three fully connected layers have 32, 16, and 1 neural network layers, respectively.

9. A forest NEP simulation method based on three-branch EMA-CKAN and ecological constraints as described in claim 8, characterized in that: In the ecological constraint function: Energy constraints Specifically: ； Where n is the number of samples used for calculation; This represents the net ecosystem productivity predicted by the three-branch EMA-CKAN network, while To correspond to the relationship between light energy absorbed by the forest canopy and its conversion into fixed biomass energy, the total primary productivity of the entire canopy was calculated using the following method: ； in, , and are correction factors for maximum VPD and minimum photosynthetic temperature, respectively, representing the effects of saturated vapor-water pressure difference and minimum air temperature on vegetation photosynthesis; and Obtained by bilinear regression of flux data; minimum temperature, The soil moisture content correction factor is calculated as follows: ； ； in, , , , These are parameters related to forest type, obtained through time-series variation analysis of station flux data; Temperature constraint Specifically: ； in, This is a predicted value for ecological respiration. The physical reference value for ecological respiration, taking temperature into account, is calculated as follows: ； in, As the reference temperature, As the baseline breathing intensity, The eco-respiration intensity coefficient is measured by selecting flux observation stations. S is obtained through regression estimation, as shown in the following formula: ； ； ； Where T represents flux station measurement data.

10. A forest NEP simulation system based on a three-branch EMA-CKAN and ecological constraints, characterized in that: include: Acquisition of sample data unit: Acquire the in-situ measurement data of the required forest type flux stations, the area remote sensing data within 10km of the corresponding stations, meteorological data and soil data, and match them with the flux observation scale; Data preprocessing unit: Calculates photosynthetically active radiation of the top canopy. and photosynthetically effective radiation of the bottom canopy The data from different sources and with different time resolutions were unified to the 8-day time scale required by the model, and outlier cleaning and imputation were completed. The daily throughput data is superimposed or averaged to an 8-day scale according to rules, while abnormal training data is removed. The output is aligned multi-source image patch data, min-max normalized, and divided into training, validation and test sets in a 6:2:2 ratio; Model training unit: Construct and train a three-branch EMA-CKAN model, combine ecological constraint functions to estimate forest NEP in real end-to-end, use Adam optimizer to update weights during training, save model weights and training history after training, and finally use the coefficient of determination and root mean square error index on an independent test set to check the fitting performance of the three-branch EMA-CKAN network to the training samples, and save the optimal three-branch EMA-CKAN network. NEP simulation unit: Configured with an optimal three-branch EMA-CKAN network for batch inference and spatialized output of the target region.

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