A method, device, equipment, medium and product for evaluating the productivity of a saline cultivated land

Abstract

This application discloses a method, apparatus, equipment, medium, and product for assessing the productivity of saline-alkali arable land, relating to the field of agricultural resource management. The method includes acquiring multi-source remote sensing observation data of different crop growth stages in a target plot; jointly solving the multi-source remote sensing observation data to obtain soil salinity and vegetation index at different crop growth stages in the target plot; using an iterative ensemble smoothing method to assimilate and update the state variables and model parameters of the initial coupled model based on the soil salinity and vegetation index, obtaining an updated coupled model; and performing a time-step-based full-process mechanism simulation based on the updated coupled model to calculate the crop productivity of the target plot. This application solves the problem of traditional methods neglecting the influence of non-growing season salt accumulation on the productivity base at the sowing period, significantly improving the dynamics and accuracy of saline-alkali arable land productivity assessment at the plot scale.

Description

Technical Field

[0001] This application relates to the field of agricultural resource management, and in particular to a method, apparatus, equipment, medium, and product for assessing the productivity of saline-alkali arable land. Background Technology

[0002] Salinization is a key factor restricting arable land quality and food security. Due to climate change and unreasonable irrigation, the risk of secondary salinization in saline-alkali land is exacerbated. Accurately assessing the impact of salinization on arable land productivity is an important basis for formulating improvement measures, implementing the balance between arable land occupation and compensation, and grain subsidies.

[0003] Early assessments of saline-alkali land productivity primarily relied on field sampling at fixed monitoring stations and laboratory physicochemical analyses (such as using grid methods to measure soil electrical conductivity, alkalinity, and total salt content), combined with traditional statistical models to analyze the correlation between water-salt dynamics and yield. While this method can obtain precise parameters at the point scale, it suffers from inherent drawbacks such as long monitoring cycles, high labor costs, limited spatial coverage, and strong data lag, making it difficult to support the needs of large-scale, high-frequency dynamic assessments of saline-alkali land productivity. Remote sensing technology has improved monitoring efficiency, but it is mostly based on empirical or semi-empirical statistical models, which are insufficient in characterizing the cross-scale coupling mechanism of "freeze-thaw cycle - water-salt transport - crop response," and are easily affected by regional climate, soil texture, and crop type differences, limiting its spatiotemporal universality. In addition, crop growth models and data assimilation technologies face three core challenges: 1. Insufficient reflection of heterogeneity during the growth period; 2. Weak multi-process coupling mechanisms; 3. Low efficiency in parameter calibration and assimilation. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment, medium and product for assessing the productivity of saline-alkali arable land, which can solve the problem of the deviation of traditional methods that ignore the influence of non-growing season salt accumulation on the productivity base during the sowing period, and significantly improve the dynamism and accuracy of saline-alkali arable land productivity assessment at the plot scale.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a method for assessing the productivity of saline-alkali arable land, comprising: acquiring multi-source remote sensing observation data of different crop growth stages of a target plot; jointly solving the multi-source remote sensing observation data to obtain soil salinity and vegetation index of different crop growth stages of the target plot; assimilating and updating the state variables and model parameters of an initial coupled model using an iterative ensemble smoothing method based on the soil salinity and vegetation index to obtain an updated coupled model; the initial coupled model is a coupled model obtained by using a two-way coupling method based on the soil freeze-thaw process, water and salt transport process, and crop growth process; and performing a time-step-based full-process mechanism simulation based on the updated coupled model to calculate the crop productivity of the target plot.

[0007] Secondly, this application provides a device for assessing the productivity of saline-alkali arable land, comprising: a data acquisition module for acquiring multi-source remote sensing observation data of different crop growth stages of a target plot; a joint calculation module for performing joint calculation on the multi-source remote sensing observation data to obtain soil salinity and vegetation index of different crop growth stages of the target plot; a model update module for assimilating and updating the state variables and model parameters of an initial coupled model based on the soil salinity and the vegetation index using an iterative ensemble smoothing method to obtain an updated coupled model; the initial coupled model is a coupled model obtained by using a two-way coupling method based on the soil freeze-thaw process, water and salt transport process, and crop growth process; and a productivity assessment module for performing a time-step-based full-process mechanism simulation based on the updated coupled model to calculate the crop productivity of the target plot.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for assessing the productivity of saline-alkali arable land.

[0009] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for assessing the productivity of saline-alkali arable land.

[0010] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for assessing the productivity of saline-alkali arable land.

[0011] Based on the specific embodiments provided in this application, the following technical effects are disclosed.

[0012] This application provides a method, apparatus, equipment, medium, and product for assessing the productivity of saline-alkali arable land. By acquiring multi-source remote sensing observation data of different crop growth stages in a target plot, and jointly calculating the soil salinity and vegetation index at the corresponding crop growth stages, it obtains actual surface observation information that matches the crop growth sequence, avoiding the problem of insufficient representativeness of single-moment observation data and providing reliable measured constraints for subsequent data assimilation. Using an iterative ensemble smoothing method, the state variables and model parameters of the initial coupled model are assimilated and updated based on soil salinity and vegetation index. This simultaneously corrects the dynamically evolving state variables and key model parameters, reducing systematic errors caused by inherent model parameter settings and improving the model's ability to reproduce actual water and salinity conditions in the plot. The initial coupling model bidirectionally couples the soil freeze-thaw process, water and salt transport process, and crop growth process, which can fully restore the salt surface accumulation process driven by freeze-thaw in the non-growing season. It is no longer limited to the fragmented simulation of the crop growing season. On this basis, it performs time-step-based full-process mechanism simulation to calculate crop productivity, which can take into account the dynamic changes of soil water and salt and the stress response of crops at different growth stages, further ensuring that the assessment results of crop productivity in salinized plots are more in line with the actual situation. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is an application environment diagram of a method for assessing the productivity of saline-alkali arable land according to an embodiment of this application.

[0015] Figure 2 This is a flowchart illustrating a method for assessing the productivity of saline-alkali arable land, provided as an embodiment of this application.

[0016] Figure 3 This is a schematic diagram of the overall framework of a method for assessing the productivity of saline-alkali arable land, provided as another embodiment of this application.

[0017] Figure 4 This is a schematic diagram of the functional modules of a saline-alkali arable land productivity assessment device provided in an embodiment of this application.

[0018] Figure 5 A schematic diagram of the functional modules of a saline-alkali arable land productivity assessment device provided in another embodiment of this application.

[0019] Figure 6This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Remote sensing technology, with its advantages of macroscopic coverage, rapid response, and non-destructive detection, provides a new approach for regional-scale salinization monitoring: by using multi-source satellite imagery from Landsat, Sentinel series satellites, and the SDGSAT-1TIS thermal infrared imager, combined with techniques such as thermal infrared salinity index inversion and deep learning information extraction, dynamic monitoring of soil salinization levels, spatial distribution of water and salt, and net primary productivity (NPP) can be achieved. Although such methods significantly improve monitoring efficiency, they are mostly based on empirical or semi-empirical statistical models, which are easily affected by regional climate, soil texture, and crop type differences, thus limiting their spatiotemporal universality.

[0022] In recent years, the combination of crop growth models and data assimilation technology has provided a new approach to overcoming the aforementioned bottlenecks. Crop growth models (such as Decision Support Systems for Agrotechnology Transfer (DSSAT) and Agricultural Production Systems Simulator (APSIM)) possess strong mechanistic and process simulation capabilities, enabling dynamic simulation at the plot scale of soil water and salt migration, crop nitrogen uptake and distribution, growth stages, and final productivity formation under freeze-thaw cycles. Meanwhile, multi-source remote sensing data (optical, radar, and thermal infrared) can provide high spatiotemporal resolution of key parameters such as canopy structure, biomass, and soil moisture. By integrating the advantages of both through data assimilation technology, the initial conditions, key parameters (such as salt stress coefficient and root water uptake threshold), and state variables of the model can be effectively optimized, significantly improving the accuracy and reliability of regional-scale simulation of productivity in saline-alkali farmland.

[0023] However, applying assimilation methods to assess the productivity of saline-alkali farmland still faces three major challenges: First, the heterogeneity of growth stages is not fully reflected. Existing models often ignore the differences in sensitivity to salt stress at different growth stages of crops (such as the jointing and grain-filling stages of winter wheat). This difference is often simplified to a static stress coefficient in existing models, leading to simulation bias. Second, the multi-process coupling mechanism is weak. The cross-scale interaction of "freeze-thaw-water-salt-crop" is complex: freeze-thaw cycles change soil porosity and salt accumulation patterns, and water-salt dynamics reduce productivity through both direct and indirect pathways. Existing models struggle to accurately quantify the interaction effects of these nonlinear processes. Third, parameter calibration and assimilation efficiency are low. Currently used manual calibration or single Markov chain Monte Carlo (MCMC) methods are computationally extremely time-consuming and prone to getting trapped in local optima. Furthermore, existing assimilation methods often employ standard ensemble Kalman filters (EnKF), which are prone to filter divergence when dealing with the strong nonlinear relationship between salt content and yield.

[0024] Therefore, there is an urgent need to construct a freeze-thaw-water-salt-crop coupling model that considers differences in growth stages. This model should combine multi-source remote sensing assimilation technology with multi-dimensional observation data such as hyperspectral (nitrogen monitoring), radar (soil moisture), and thermal infrared (salt inversion) to accurately simulate the physiological and ecological response processes of crops throughout their entire growth period under salinization. Furthermore, advanced assimilation algorithms should be used to dynamically optimize model parameters, ultimately achieving high-precision and timely assessment of the productivity of salinized arable land at the regional scale, thus providing scientific support for the improvement of saline-alkali land and the enhancement of its productivity.

[0025] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] The method for assessing the productivity of saline-alkali arable land provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send multi-source remote sensing observation data of different crop growth stages of the target plot to server 104. After receiving the multi-source remote sensing observation data of different crop growth stages of the target plot, server 104 jointly calculates the soil salinity and vegetation index, and updates the initial coupled model parameters based on the soil salinity and vegetation index to obtain an updated coupled model. Finally, based on the updated coupled model, it performs a full-process mechanism simulation to calculate the crop productivity of the target plot. Server 104 can feed back the obtained crop productivity of the target plot to terminal 102. In addition, in some embodiments, the method for assessing the productivity of saline-alkali arable land can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly use multi-source remote sensing observation data of different crop growth stages of the target plot to jointly calculate the soil salinity and vegetation index, and update the initial coupling model parameters based on the soil salinity and vegetation index to obtain an updated coupling model. Finally, the whole-process mechanism simulation is performed based on the updated coupling model to calculate the crop productivity of the target plot. Alternatively, the server 104 can obtain multi-source remote sensing observation data of different crop growth stages of the target plot from the data storage system, jointly calculate the soil salinity and vegetation index, and update the initial coupling model parameters based on the soil salinity and vegetation index to obtain an updated coupling model. Finally, the whole-process mechanism simulation is performed based on the updated coupling model to calculate the crop productivity of the target plot.

[0027] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0028] In one exemplary embodiment, such as Figure 2 As shown, a method for assessing the productivity of saline-alkali arable land is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204.

[0029] Step 201: Acquire multi-source remote sensing observation data of different crop growth stages in the target plot. Specifically, the multi-source remote sensing observation data includes Synthetic Aperture Radar (SAR) data and optical remote sensing data. Crop growth stages include seedling stage, jointing stage, and grain-filling stage.

[0030] Step 202: Perform joint calculation on the multi-source remote sensing observation data to obtain the soil salinity and vegetation index of different crop growth stages in the target plot.

[0031] In one example, the soil salinity is obtained by fusing the backscattering coefficient and coherence characteristics of SAR data with the red-edge reflectance of optical remote sensing data using a random forest regression algorithm.

[0032] Step 203: Based on the soil salinity and the vegetation index, an iterative ensemble smoothing method is used to assimilate and update the state variables and model parameters of the initial coupled model to obtain an updated coupled model. The initial coupled model is a coupled model obtained using a two-way coupling method based on the soil freeze-thaw process, water and salt transport process, and crop growth process.

[0033] Step 204: Based on the updated coupling model, perform a time-step-based full-process mechanism simulation to calculate the crop productivity of the target plot.

[0034] In another example, such as Figure 3 As shown, the method for assessing the productivity of saline-alkali arable land includes the following steps S1 to S6.

[0035] Step S1: Acquire temporal synthetic aperture radar data and temporal optical remote sensing data of the target plot. Preprocess and feature fusion are performed on the multi-source remote sensing data to retrieve the topsoil salinity and vegetation index at key growth stages of the crop, including seedling, jointing, and grain-filling stages.

[0036] In this example, Sentinel-1 Synthetic Aperture Radar (Sentinel-1 SAR) data (Vertical-Vertical Polarization (VV) / Vertical-Horizontal Polarization (VH)) and Sentinel-2 optical satellite optical data were acquired during the current growing season. Field sampling was conducted at the seedling, jointing, and grain-filling stages to determine soil salinity and alkalinity in the 0-20 cm soil layer, and leaf area index was measured. Using a random forest regression algorithm, the backscattering coefficient of SAR (reflecting surface roughness and dielectric constant) and the optical red-edge band (reflecting vegetation status) were fused to establish an inversion model and output a plot-scale surface salinity distribution map.

[0037] Step S2: Based on the Soil-Water-Atmosphere-Plant (SWAP) model (used to realize water and salt transport processes), the Simultaneous Heat and Water (SHAW) model (used to realize water and salt transport processes and soil freeze-thaw processes), and the Environmental Policy Integrated Climate (EPIC) model, a two-way coupling is performed to obtain the freeze-thaw-water-salt-crop coupled model (i.e., the SWAP-SHAW-EPIC coupled model). That is, the freeze-thaw model (i.e., the SHAW model) is embedded in the water-salt-crop model (i.e., the SWAP-EPIC model) to simulate the water and salt migration during the soil freezing-thawing process in the non-growing season and the salt tolerance response mechanism of crops throughout their entire growth period.

[0038] Step S3: Sensitivity analysis of the input parameters of the freeze-thaw-water-salt-crop coupling model is performed based on the Sobol global variance decomposition method to identify the set of sensitive parameters. A surrogate model (Emulator) based on a Gaussian process (GP) is constructed to approximate the input-output mapping relationship of the freeze-thaw-water-salt-crop coupling model. The Calibrate-Emulate-Sample (CES) Bayesian inference framework is used to perform posterior sampling and uncertainty quantification of the sensitive parameter set to obtain the posterior distribution of the sensitive parameters.

[0039] Step S4: Using the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) method, the surface soil salinity and vegetation indices obtained from the inversion in Step S1 at different crop growth stages are constructed into a multi-source observation vector. The sensitive parameter set is expanded into a state vector and jointly updated with the water and salt state variables of the SWAP-SHAW-EPIC coupled model. During the assimilation iteration, the surrogate model trained in Step S3 is used for computation to accelerate convergence.

[0040] Step S5: Based on the daily root zone salt stress factors and crop salt tolerance response curves obtained from the SWAP-SHAW-EPIC coupled model simulation, calculate the marginal contribution weight of salt stress to final yield at each key growth stage, and obtain the comprehensive salinization capacity impact coefficient (or capacity loss rate) of the field by weighting according to the growth stage sensitivity weight.

[0041] Step S6: Input the optimal parameter combination corrected in step S4 into the SWAP-SHAW-EPIC coupled model to obtain the updated coupled model, run the simulation to update the coupled model, and output the crop productivity calculation results of the target plot considering the freeze-thaw salt return base and the cumulative effect of multi-growth period stress.

[0042] In one example, the soil freeze-thaw process is realized using the freeze-thaw modified hydraulic conductivity formula and the Clapeyron thermodynamic relationship. The water-salt transport process is realized using the soil water-salt coupled transport equation set. The crop growth process is realized using the biomass growth differential equation.

[0043] The method for building the initial coupling model includes the following steps 301 to 304.

[0044] Step 301: Based on the freeze-thaw corrected hydraulic conductivity formula, the Clapeyron thermodynamic relation, the soil water-salt coupled transport equation set, and the biomass growth differential equation, a two-way coupling method is used to obtain the freeze-thaw-water-salt-crop coupled model, and a surrogate model of the freeze-thaw-water-salt-crop coupled model is constructed.

[0045] In one example, a Gaussian process-based surrogate model is constructed to approximate the input-output mapping relationship of the freeze-thaw-water-salt-crop coupling model. 500 sets of sensitive parameter combinations are generated using Latin hypercube sampling, and the SWAP-SHAW-EPIC coupling model is run to obtain the corresponding crop yield and leaf area index (LAI) outputs, which are then used to train the GP surrogate model.

[0046] In one example, the freeze-thaw corrected hydraulic conductivity formula is as follows, used to simulate the process of salt migrating upwards with water (salinization) during the winter freezing period.

[0047] .

[0048] .

[0049] in, For soil temperature, For soil temperature Freeze-thaw corrected hydraulic conductivity For saturated hydraulic conductivity, For soil temperature The unfrozen water content below For ice suction, The function of unfrozen water content is an empirical power function obtained from measured data. This refers to the residual volumetric water content of the soil. This refers to the saturated volumetric water content of the soil. Empirical parameters for describing pore tortuosity and connectivity are used To achieve the correction of saturated hydraulic conductivity by unfrozen water.

[0050] The expressions for the soil water-salt coupled transport equations are as follows. The first equation is the one-dimensional Richards equation, used to control soil water movement, and the second equation is the convection-dispersion equation, used to describe soil solute transport.

[0051] .

[0052] .

[0053] in, For soil matrix potential, This refers to the soil volumetric water content. Input the saturated hydraulic conductivity into the model. For soil depth, For time, For depth z ,time t The rate at which crop roots absorb water from a unit volume of soil. This refers to the salt concentration in the soil solution. The hydrodynamic dispersion coefficient, For Darcy flux, for The corresponding source and sink terms. In this example, the van Genuchten-Mualem formula is used for calculation. - - The relationship between them.

[0054] The expression for the differential equation of biomass growth is as follows.

[0055] .

[0056] in, For biomass, To maximize radiation utilization, In order to intercept photosynthetically active radiation, Temperature stress factor It is a water stress factor. Salt stress factor, This refers to the soil electrical conductivity.

[0057] Salt stress factors It is defined by the Maas-Hoffman model as shown in the following equation.

[0058] .

[0059] in, This represents the salt tolerance threshold for crops. This represents the yield decline slope. In this example, different crop growth stages are used with differentiated methods. and The values ​​are used to reflect the differences in salt tolerance response curves throughout the entire growth period.

[0060] In one example, the SHAW model is introduced into the SWAP model to solve the soil energy balance equation, obtain the soil temperature profile and the location of the freezing front, and calculate the correction of soil matrix potential and saturated hydraulic conductivity by the unfrozen water content. The surface accumulation (salt return) effect of liquid water redistribution during the freeze-thaw process is simulated, and the initial daily root zone salinity is fed back to the starting conditions of the growing season.

[0061] Specifically, using the day as the time step, the SHAW model outputs daily soil temperature profiles and unfrozen water content. The freeze-thaw corrected hydraulic conductivity and freeze-thaw corrected soil matrix potential were obtained. The corrected hydraulic parameters (unfrozen water content, freeze-thaw corrected soil matrix potential, and freeze-thaw corrected hydraulic conductivity) and the initial root zone salinity obtained from the non-growing season simulation were then substituted back into the SWAP model to update the initial field and salinity boundary conditions of the soil water-salt coupled transport equations. The SWAP model then solved the Richards equation and the convection-diffusion equation based on the corrected parameters to obtain the daily root zone soil volumetric water content. and soil electrical conductivity and will Weighted average and soil volumetric water content The data is fed into the EPIC model to calculate the daily salt stress factor and water stress factor, and the minimum value of the two is used as the growth regulator to correct the leaf area index, transpiration and biomass increment. The LAI and transpiration obtained from the updated EPIC model are fed back into the SWAP model as the crop resistance term for potential evapotranspiration, thus realizing the two-way coupling of water-heat-salt-crop.

[0062] Step 302: Perform parameter sensitivity analysis on the freeze-thaw-water-salt-crop coupling model to screen and obtain the sensitive parameters of the freeze-thaw-water-salt-crop coupling model.

[0063] In this example, the Sobol global variance decomposition method is used to calculate the first-order effect index and the total-order effect index of each input parameter of the freeze-thaw-water-salt-crop coupling model on crop productivity output. Parameters whose contribution to the simulation results is greater than a preset threshold are selected as sensitive parameters. In one example, sensitive parameters include the soil thermal conductivity in the SHAW module, the van Genuchten soil hydraulic parameter in the SWAP module, and the salt response curve threshold parameter in the EPIC module.

[0064] Step 303: Determine the posterior distribution of the sensitive parameters based on the surrogate model.

[0065] In one example, the CES Bayesian inference framework is used to perform posterior sampling and uncertainty quantification on the sensitive parameter set. Under the Bayesian framework, the posterior distribution of the sensitive parameters is obtained through MCMC sampling. The execution process of the CES Bayesian inference framework includes the following steps S31 to S33.

[0066] S31: Calibrate phase, based on prior knowledge or the results of running a small number of mechanistic models, initialize the prior distribution of sensitive parameters.

[0067] S32: Emulate stage, train the Gaussian process surrogate model of the freeze-thaw-water-salt-crop coupling model based on the initial sampling points, so that it can quickly predict the model output under any combination of parameters.

[0068] S33: In the sampling stage, based on the likelihood function predicted by the Gaussian surrogate model, the MCMC algorithm is used to sample the posterior distribution of the sensitive parameters to obtain a set of samples representing the uncertainty of the parameters.

[0069] Step 304: Determine the value of the sensitive parameter based on the posterior distribution, and assign parameter values ​​to the freeze-thaw-water-salt-crop coupling model based on the value of the sensitive parameter to obtain the initial coupling model.

[0070] In one example, the state variables of the initial coupled model include soil volumetric water content and soil electrical conductivity. The model parameters of the initial coupled model are sensitive parameters. These sensitive parameters are model parameters obtained by performing parameter sensitivity analysis on the initial coupled model and then filtering them. The iterative ensemble smoothing method is an ensemble smoother multiple data assimilation method.

[0071] The above step 203 can be replaced by the following steps 2031 and 2032.

[0072] Step 2031: Construct an observation vector based on the soil salinity and the vegetation index, and construct a state vector based on the soil volumetric water content, the soil electrical conductivity, and the sensitive parameters.

[0073] Step 2032: Based on the observation vector and the state vector, the soil volumetric water content, soil electrical conductivity and the sensitive parameters are updated by using the ensemble smoother multiple data assimilation method to obtain the update coupling model.

[0074] In one example, soil salinity obtained from crop seedling, jointing, and grain-filling stages is used as salinity observation value, and vegetation index obtained from the same stage is used as growth observation value, together forming a multidimensional observation vector; multiple assimilation times are set. N In the k During the second assimilation, the observation error covariance matrix is ​​expanded using the formula... To be updated, among which, For the first k The dilated observation error covariance matrix obtained during the next assimilation iteration This represents the original observation error covariance matrix. By repeatedly assimilating the same set of observation vectors, the observation error covariance matrix is ​​gradually shrunk and iteratively updated, thereby achieving the joint assimilation and update of the water and salt state variables (including soil volumetric water content and soil electrical conductivity) and sensitive parameters of the SWAP-SHAW-EPIC coupled model.

[0075] Specifically, the ensemble size was set to 100, and the number of Multiple Data Assimilation (MDA) iterations was 5. In each iteration, the GP surrogate model was used to quickly calculate the simulated observations, and the extended state vector (containing soil volumetric water content, soil electrical conductivity, and sensitive parameters) was updated using Kalman gain. Due to the use of the GP surrogate model, the calculation of a single assimilation, which originally took several hours, was reduced to a few seconds.

[0076] Step 204 above can be replaced by steps 2041 and 2048.

[0077] Step 2041: Obtain the initial soil electrical conductivity of the target plot, and set the initial leaf area index and initial transpiration rate.

[0078] Step 2042: For the i-th simulation time step, obtain the soil temperature of the target plot.

[0079] Step 2043: Calculate the unfrozen water content of the target plot based on the soil temperature.

[0080] Step 2044: Based on the unfrozen water content, the saturated hydraulic conductivity and soil matrix potential of the target plot are corrected using the freeze-thaw corrected hydraulic conductivity formula and the Clapeyron thermodynamic relationship, respectively, to obtain the freeze-thaw corrected hydraulic conductivity and the freeze-thaw corrected soil matrix potential.

[0081] Step 2045: Based on the soil electrical conductivity, unfrozen water content, freeze-thaw corrected hydraulic conductivity, and freeze-thaw corrected soil matrix potential corresponding to the i-th simulation time step, and using the leaf area index and transpiration rate corresponding to the i-th simulation time step as boundary constraints, solve the soil water-salt coupled transport equations to calculate the soil volumetric water content and soil electrical conductivity. i>0. The soil electrical conductivity corresponding to the first simulation time step is the initial soil electrical conductivity. The leaf area index and transpiration rate corresponding to the first simulation time step are the initialized leaf area index and the initialized transpiration rate, respectively.

[0082] Step 2046: Based on the soil conductivity, obtain the soil conductivity corresponding to the (i+1)th simulation time step.

[0083] Step 2047: Based on the soil volumetric water content and the soil electrical conductivity, solve the differential equation for biomass growth to calculate the biomass increment corresponding to the i-th simulation time step. Then, based on the soil volumetric water content, the soil electrical conductivity, the leaf area index corresponding to the i-th simulation time step, and the meteorological temperatures obtained from the previous i simulation time steps, update the leaf area index and transpiration corresponding to the (i+1)-th simulation time step using a preset update rule.

[0084] Step 2048: If i is greater than the preset time step threshold, then the crop productivity of the target plot is obtained by time integration based on the biomass increments corresponding to all i simulation time steps. Otherwise, proceed to the (i+1)th simulation time step.

[0085] In one example, the biomass growth differential equation includes salt stress factors and water stress factors.

[0086] Step 2047 above can be replaced by steps 0471 to 0475.

[0087] Step 0471: Based on the soil electrical conductivity, the salt stress factor corresponding to the i-th simulation time step is calculated using the Maas-Hoffman model.

[0088] Step 0472: Based on the soil volumetric water content, the root water absorption model is used to calculate the water stress factor corresponding to the i-th simulation time step.

[0089] Step 0473: Based on the salt stress factor and water stress factor corresponding to the i-th simulation time step, solve the biomass growth differential equation to calculate the biomass increment corresponding to the i-th simulation time step.

[0090] Step 0474: Calculate the comprehensive stress factor corresponding to the i-th simulation time step based on the salt stress factor and water stress factor corresponding to the i-th simulation time step.

[0091] Step 0475: Based on the comprehensive stress factor, the leaf area index corresponding to the i-th simulation time step, and the meteorological temperature obtained from the previous i simulation time steps, the leaf area index corresponding to the (i+1)-th simulation time step is updated using a preset leaf area index update rule. Based on the leaf area index corresponding to the (i+1)-th simulation time step, the transpiration rate corresponding to the (i+1)-th simulation time step is updated using a preset transpiration rate update rule.

[0092] Specifically, the accumulated temperature for the i-th simulation time step is calculated by first obtaining the meteorological temperatures from the first i simulation time steps, and then the accumulated temperature index is determined based on the ratio of accumulated temperature to total potential accumulated temperature. Then, the leaf area index for the (i+1)-th simulation time step is updated using the following preset leaf area index update formula.

[0093] .

[0094] .

[0095] in, Let be the leaf area index corresponding to the i-th simulation time step. The leaf area index corresponds to the (i+1)th simulation time step. For the increase in leaf area index, The accumulated temperature index, The potential leaf area growth function, To preset the maximum leaf area index, The comprehensive stress factor has a value range of 0-1 and is determined by the minimum value of the salt stress factor and the water stress factor corresponding to the i-th simulation time step.

[0096] The preset transpiration update rule is based on the Penman-Monteith (PM) model. It calculates the potential transpiration based on meteorological conditions, and then corrects the potential transpiration based on the leaf area index corresponding to the (i+1)th simulation time step. Finally, it further constrains and reduces the corrected transpiration by combining root water uptake and salt stress factors to obtain the actual transpiration, which is used as the transpiration corresponding to the (i+1)th simulation time step.

[0097] In one example, during the full-process mechanism simulation, the updated coupling model, based on the biomass growth differential equation at each simulation time step, obtains a salt stress factor and a non-stress biomass increment. The biomass growth differential equation is used to realize the crop growth process.

[0098] Following step 204 above, the method for assessing the productivity of saline-alkali arable land further includes steps 205 to 207.

[0099] Step 205: Based on the simulation results of the whole process mechanism of the updated coupling model, determine all salt stress factors corresponding to each crop growth stage of the target plot and the non-stress biomass increment corresponding to each salt stress factor.

[0100] Step 206: For any crop growth stage, based on all salt stress factors corresponding to the crop growth stage and the non-stress biomass increment corresponding to each salt stress factor, the yield loss corresponding to the crop growth stage is obtained by using the time integral conversion method.

[0101] Step 207: Perform a weighted calculation on the yield loss corresponding to all crop growth stages to obtain the salinization capacity impact index of the target plot, and determine the degree of salinization impact on the target plot based on the salinization capacity impact index.

[0102] In one example, the daily root zone salt stress factor was extracted from the simulation of the SWAP-SHAW-EPIC coupled model. The salt stress factor was determined by combining the Maas-Hoffman type full-growth period salt tolerance response curve with the root zone soil electrical conductivity.

[0103] Based on the physiological sensitivity of different crop growth stages to salt, salt stress sensitivity factors were set for the seedling stage, jointing stage, and grain-filling stage.

[0104] Using the marginal contribution analysis method, the salt stress impact of each crop growth stage is quantified into a salinization capacity impact coefficient using the following formula.

[0105] .

[0106] in, The coefficient representing the impact of salinization on production capacity. This represents the yield loss corresponding to the seedling stage. This represents the yield loss corresponding to the jointing stage. This represents the production loss during the grouting period. , and They are respectively , and The corresponding marginal contribution weighting coefficient of salt stress factor to final crop yield, in this example, , and The values ​​are 0.5, 0.3 and 0.2 respectively (the sum of the weights is 1, and the weight of seedling is the largest). It is a multivariate mapping function.

[0107] In another example, the specific formula for calculating the impact coefficient of salinization capacity is as follows.

[0108] .

[0109] in, For the first c Average salt stress factor at each crop growth stage For the first c The marginal contribution weighting coefficients of salt stress factors at each crop growth stage to final crop productivity. c =1, 2, 3 correspond to the seedling stage, jointing stage, and grain-filling stage, respectively.

[0110] Final output capacity loss rate = , This represents the actual crop yield under salt stress conditions. This represents the potential crop productivity under salt-free stress.

[0111] In one example, the final assessment results of the impact of salinized farmland on productivity include: crop productivity prediction results under salinization stress, non-salinized potential productivity, salinization productivity loss rate or salinization productivity impact coefficient, and the marginal contribution weight of salt stress to final productivity at different crop growth stages.

[0112] Based on the same inventive concept, this application also provides a saline-alkali land productivity assessment device for implementing the above-mentioned method for assessing the productivity of saline-alkali land. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the saline-alkali land productivity assessment device provided below can be found in the limitations of the saline-alkali land productivity assessment method described above, and will not be repeated here.

[0113] In one exemplary embodiment, such as Figure 4 As shown, a device for assessing the productivity of saline-alkali arable land is provided, including a data acquisition module 001, a joint calculation module 002, a model update module 003, and a productivity assessment module 004.

[0114] The data acquisition module 001 is used to acquire multi-source remote sensing observation data of different crop growth stages in the target plot.

[0115] The joint calculation module 002 is used to perform joint calculation on the multi-source remote sensing observation data to obtain the soil salinity and vegetation index of different crop growth stages of the target plot.

[0116] The model update module 003 is used to assimilate and update the state variables and model parameters of the initial coupled model based on the soil salinity and the vegetation index using an iterative ensemble smoothing method, thereby obtaining an updated coupled model. The initial coupled model is a coupled model obtained using a two-way coupling method based on the soil freeze-thaw process, water and salt transport process, and crop growth process.

[0117] The production capacity assessment module 004 is used to perform a time-step-based full-process mechanism simulation based on the updated coupling model to calculate the crop production capacity of the target plot.

[0118] In another example, such as Figure 5 As shown, Figure 3 The salinized arable land productivity assessment device corresponding to the salinized arable land productivity assessment method includes a raw data acquisition module 1, a model construction module 2, a parameter calibration module 3, an assimilation and update module 4, an assessment calculation module 5, and an output module 6.

[0119] The raw data acquisition module 1 is used to acquire time-series SAR data and time-series optical remote sensing data of the target plot, and to invert the surface soil salinity and vegetation index at each key growth stage.

[0120] Model building module 2 is used to build a SWAP-SHAW-EPIC coupled model that embeds a SHAW model.

[0121] The parameter calibration module 3 is used to perform global sensitivity analysis of Sobol and to quantify the uncertainty of sensitive parameters using the CES Bayesian inference framework combined with the Gaussian process surrogate model.

[0122] The assimilation update module 4 is used to perform ES-MDA multiple data assimilation, accelerates iteration using a proxy model, and jointly updates the model state and sensitive parameters.

[0123] The evaluation calculation module 5 is used to run the corrected model and calculate the crop productivity and salinization productivity impact coefficient of the target plot.

[0124] Output module 6 is used to output the farmland productivity assessment results calculated by assessment calculation module 5.

[0125] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores multi-source remote sensing observation data of different crop growth stages in the target plot. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for assessing the productivity of saline-alkali farmland.

[0126] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment to which the present application is applied. Specific computer equipment may include, for example, [the following is a list of possible additional structures]. Figure 6 The diagram shows more or fewer components, or combinations of certain components, or different component arrangements.

[0127] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0128] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0129] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0130] This application provides a multi-process coupled model of freeze-thaw, water-salt, and crop considering differences in growth stages, and uses multi-source remote sensing assimilation to collaboratively update the coupled model parameters for calculating the productivity and impact assessment of saline-alkali farmland. Based on the backscattering coefficient (reflecting surface roughness and dielectric constant) and coherence characteristics of SAR data, combined with the red-edge band (reflecting vegetation status) and vegetation indices of optical remote sensing data, this application uses a random forest regression algorithm to invert surface soil salinity at multiple growth stages, including seedling, jointing, and grain-filling stages. A SWAP-SHAW-EPIC coupled model is constructed, embedding a freeze-thaw (SHAW) module into the water-salt-crop (SWAP-EPIC) model to simulate the salt accumulation process caused by freeze-thaw cycles during the non-growing season and the salt tolerance response of crops throughout their entire growth period. The Sobol global variance decomposition method is used to identify sensitive parameters of the model, and a surrogate model (Em) based on a Gaussian process is constructed. This paper proposes a method to approximate the input-output mapping relationship of the updated coupled model using a Bayesian calibrator-emulate-sample (CES) inference framework. This framework performs posterior sampling and uncertainty quantification of sensitive parameters, replacing the time-consuming iteration of traditional mechanistic models. A multi-data assimilation method using an ensemble smoother is employed to construct multi-source observation vectors from salinity and vegetation indices retrieved from multiple growth stages, and to expand sensitive parameters into state vectors, assimilating and updating the root zone water and salt state variables of the coupled model. The updated coupled model, after assimilation and correction, is run to output predicted crop yield and salinization loss rate / impact index of salinized farmland, considering the base effect of freeze-thaw salinization and the cumulative effect of multi-growth stage stress. By introducing the SHAW freeze-thaw mechanism, this application effectively solves the problem of traditional models neglecting the influence of non-growing season salinity accumulation on the yield base at the sowing stage. Through multi-source data assimilation and consideration of differences across multiple growth stages, the dynamics and accuracy of salinized farmland yield and impact assessment at the plot scale are significantly improved. This application can be primarily applied to the assessment of plot-level productivity loss for major crops such as corn and rice in seasonally frozen and thawed saline areas (such as soda saline-alkali land and inland irrigation areas).

[0131] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0132] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0133] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0134] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for assessing the productivity of saline-alkali arable land, characterized in that, The method includes: Acquire multi-source remote sensing data of different crop growth stages in the target plot; By jointly solving the multi-source remote sensing observation data, the soil salinity and vegetation index of the target plot at different crop growth stages are obtained; Based on the soil salinity and the vegetation index, an iterative ensemble smoothing method is used to assimilate and update the state variables and model parameters of the initial coupled model to obtain an updated coupled model; the initial coupled model is a coupled model obtained by a two-way coupling method based on the soil freeze-thaw process, water and salt transport process and crop growth process. Based on the aforementioned update coupling model, a time-step-based full-process mechanism simulation is performed to calculate the crop productivity of the target plot.

2. The method for assessing the productivity of saline-alkali arable land according to claim 1, characterized in that, The soil freeze-thaw process is realized through the freeze-thaw modified hydraulic conductivity formula and the Clapeyron thermodynamic relationship; the water and salt transport process is realized through the soil water and salt coupled transport equation set; the crop growth process is realized through the biomass growth differential equation. The method for constructing the initial coupling model includes: Based on the freeze-thaw corrected hydraulic conductivity formula, the Clapeyron thermodynamic relation, the soil water-salt coupled transport equation set, and the biomass growth differential equation, a freeze-thaw-water-salt-crop coupled model is obtained using a two-way coupling method, and a surrogate model of the freeze-thaw-water-salt-crop coupled model is constructed. Sensitivity analysis of the freeze-thaw-water-salt-crop coupling model was performed to screen out the sensitive parameters of the freeze-thaw-water-salt-crop coupling model; Based on the surrogate model, determine the posterior distribution of the sensitive parameters; The values ​​of the sensitive parameters are determined based on the posterior distribution, and the parameters of the freeze-thaw-water-salt-crop coupling model are assigned according to the values ​​of the sensitive parameters to obtain the initial coupling model.

3. The method for assessing the productivity of saline-alkali arable land according to claim 1, characterized in that, The state variables of the initial coupling model include soil volumetric water content and soil electrical conductivity; the model parameters of the initial coupling model are sensitive parameters; the sensitive parameters are model parameters obtained by performing parameter sensitivity analysis on the initial coupling model and screening them; the iterative ensemble smoothing method is an ensemble smoother multiple data assimilation method. Based on the soil salinity and vegetation index, an iterative ensemble smoothing method is used to assimilate and update the state variables and model parameters of the initial coupled model to obtain an updated coupled model, specifically including: An observation vector is constructed based on the soil salinity and the vegetation index, and a state vector is constructed based on the soil volumetric water content, the soil electrical conductivity, and the sensitive parameters. Based on the observation vector and the state vector, the soil volumetric water content, soil electrical conductivity and the sensitive parameters are updated by using the ensemble smoother multiple data assimilation method to obtain the update coupling model.

4. The method for assessing the productivity of saline-alkali arable land according to claim 1, characterized in that, Based on the aforementioned update coupling model, a time-step-based full-process mechanism simulation is performed to calculate the crop productivity of the target plot, specifically including: Obtain the initial soil electrical conductivity of the target plot, and set the initial leaf area index and initial transpiration rate; For the i-th simulation time step, obtain the meteorological temperature and the soil temperature of the target plot; Based on the soil temperature, the unfrozen water content of the target plot is calculated; Based on the unfrozen water content, the saturated hydraulic conductivity and soil matrix potential of the target plot are corrected by using the freeze-thaw corrected hydraulic conductivity formula and the Clapeyron thermodynamic relationship, respectively, to obtain the freeze-thaw corrected hydraulic conductivity and the freeze-thaw corrected soil matrix potential. Based on the soil electrical conductivity, unfrozen water content, freeze-thaw corrected hydraulic conductivity, and freeze-thaw corrected soil matrix potential corresponding to the i-th simulation time step, and using the leaf area index and transpiration rate corresponding to the i-th simulation time step as boundary constraints, the soil water-salt coupled transport equations are solved to calculate the soil volumetric water content and soil electrical conductivity; i>0; the soil electrical conductivity corresponding to the 1st simulation time step is the initial soil electrical conductivity; the leaf area index and transpiration rate corresponding to the 1st simulation time step are the initialized leaf area index and the initialized transpiration rate, respectively. Based on the soil electrical conductivity, the soil electrical conductivity corresponding to the (i+1)th simulation time step is obtained; Based on the soil volumetric water content and the soil electrical conductivity, the differential equation for biomass growth is solved to calculate the biomass increment corresponding to the i-th simulation time step. Based on the soil volumetric water content, the soil electrical conductivity, the leaf area index corresponding to the i-th simulation time step, and the meteorological temperatures obtained in the previous i simulation time steps, the leaf area index and transpiration corresponding to the (i+1)-th simulation time step are updated using a preset update rule. If i is greater than the preset time step threshold, the crop productivity of the target plot is obtained by time integration based on the biomass increment corresponding to all i simulation time steps; otherwise, proceed to the (i+1)th simulation time step.

5. The method for assessing the productivity of saline-alkali arable land according to claim 4, characterized in that, The biomass growth differential equation includes salt stress factors and water stress factors. Based on the soil volumetric water content and soil electrical conductivity, the differential equation for biomass growth is solved to calculate the biomass increment corresponding to the i-th simulation time step. Then, based on the soil volumetric water content, soil electrical conductivity, leaf area index corresponding to the i-th simulation time step, and the meteorological temperatures obtained from the previous i simulation time steps, a preset update rule is used to update the leaf area index and transpiration rate corresponding to the (i+1)-th simulation time step, specifically including: Based on the soil electrical conductivity, the salt stress factor corresponding to the i-th simulation time step is calculated using the Mas-Hoffmann model. Based on the soil volumetric water content, the water stress factor corresponding to the i-th simulation time step is calculated using a root water absorption model. Based on the salt stress factor and water stress factor corresponding to the i-th simulation time step, solve the biomass growth differential equation and calculate the biomass increment corresponding to the i-th simulation time step. Based on the salt stress factor and water stress factor corresponding to the i-th simulation time step, the comprehensive stress factor corresponding to the i-th simulation time step is calculated. Based on the comprehensive stress factor, the leaf area index corresponding to the i-th simulation time step, and the meteorological temperature obtained from the previous i simulation time steps, the leaf area index corresponding to the (i+1)-th simulation time step is updated using a preset leaf area index update rule. Based on the leaf area index corresponding to the (i+1)-th simulation time step, the transpiration rate corresponding to the (i+1)-th simulation time step is updated using a preset transpiration rate update rule.

6. The method for assessing the productivity of saline-alkali arable land according to claim 1, characterized in that, During the full-process mechanism simulation, the updated coupling model, based on the biomass growth differential equation at each simulation time step, obtains a salt stress factor and a non-stress biomass increment; the biomass growth differential equation is used to realize the crop growth process. After calculating the crop productivity of the target plot by performing a time-step-based full-process mechanism simulation based on the updated coupling model, the method for assessing the productivity of saline-alkali arable land further includes: Based on the simulation results of the whole process mechanism of the updated coupling model, all salt stress factors corresponding to each crop growth stage of the target plot and the non-stress biomass increment corresponding to each salt stress factor are determined. For any crop growth stage, the yield loss corresponding to the crop growth stage is obtained by using the time integral conversion method based on all salt stress factors corresponding to the crop growth stage and the non-stress biomass increment corresponding to each salt stress factor. The yield loss corresponding to all crop growth stages is weighted and calculated to obtain the salinization capacity impact index of the target plot, and the degree of salinization impact on the target plot is determined based on the salinization capacity impact index.

7. A device for assessing the productivity of saline-alkali arable land, characterized in that, The device includes: The data acquisition module is used to acquire multi-source remote sensing observation data of different crop growth stages in the target plot; The joint solution module is used to perform joint solution on the multi-source remote sensing observation data to obtain the soil salinity and vegetation index of the target plot at different crop growth stages; The model update module is used to assimilate and update the state variables and model parameters of the initial coupled model based on the soil salinity and the vegetation index using an iterative set smoothing method to obtain an updated coupled model; the initial coupled model is a coupled model obtained by using a two-way coupling method based on the soil freeze-thaw process, water and salt transport process and crop growth process. The production capacity assessment module is used to perform a time-step-based full-process mechanism simulation based on the updated coupling model to calculate the crop production capacity of the target plot.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for assessing the productivity of saline-alkali arable land according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for assessing the productivity of saline-alkali arable land as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for assessing the productivity of saline-alkali arable land as described in any one of claims 1-6.

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