Crop parameter updating and yield estimation method and device based on ensemble smooth multi-time data assimilation

Abstract

The application provides a crop parameter updating and yield estimation method and device based on ensemble smoothing multi-time data assimilation, and belongs to the cross field of agricultural remote sensing, crop model and data assimilation. The method comprises the following steps: obtaining basic information of a target plot or a target area; constructing a to-be-estimated vector containing variety parameters, soil moisture related parameters and an auxiliary initial state when the initial condition is uncertain, and forming a parameter prior sample set based on a prior distribution; running a whole-season crop model for each prior sample to obtain a whole-season state trajectory and a yield simulation value; combining leaf area index, soil moisture and available yield observation of the basic information into a joint observation vector, and extracting a joint prediction vector from the model output in the same order; performing multi-round ESMDA updating on the joint observation vector based on the joint prediction vector to obtain a parameter posterior sample set and a yield posterior sample set. The application improves the parameter updating stability, yield estimation consistency and result uncertainty expression capability.

Description

Technical Field

[0001] This application relates to the fields of agricultural remote sensing, crop modeling and data assimilation, and in particular to a method and apparatus for updating crop parameters and estimating yield by incorporating multiple smooth data assimilation. Background Technology

[0002] Among existing crop yield estimation methods, one type focuses on state correction, directly adjusting state variables such as leaf area index, soil moisture, or biomass upon observation; the other type focuses on posterior sampling, such as Markov Chain Monte Carlo (MCMC). In parameter update problems, the former type of method is prone to excessively large single corrections, posterior instability, and deviations from observations in subsequent periods; the latter type of method typically requires multiple reruns of the entire crop model, resulting in high computational costs and insufficient operational efficiency.

[0003] Crop yield estimation in agricultural production typically requires a full-season crop model to simultaneously explain both intra-season growth and final yield, and to provide the posterior parameters, yield distribution, and uncertainties within an acceptable computational timeframe. For this type of problem, where "parameters are primary and state is secondary," Ensemble Smoother with Multiple Data Assimilation (ESMDA) absorbs the same batch of observations in multiple rounds. This is generally more stable than a one-time Ensemble Kalman Filter (EnKF) update and is also more convenient for operational applications than long-term MCMC sampling.

[0004] However, existing schemes for using ESMDA for crop yield estimation generally still have the following shortcomings: First, the intra-season growth information and the end-season yield information have not been fully incorporated into the same parameter constraint framework; second, the mapping relationship between the parameter prior sample set, the vector to be estimated, the joint observation vector, and the joint prediction vector is not clear enough; third, soil moisture observation usually has a clear observation depth, while the model calculates soil moisture according to soil layers, and the correspondence between the observation depth and the model soil layer still needs to be further clarified; fourth, when the parameter posterior has obvious nonlinear characteristics, a large step update at once can easily lead to an excessive correction magnitude. Summary of the Invention

[0005] This application provides a method and apparatus for updating crop parameters and estimating yield through multiple rounds of smoothed data assimilation, primarily addressing the problem of the difficulty in stably constraining the same set of crop model parameters across multiple sources throughout the season. Specifically, this application clarifies the correspondence between parameter samples and joint observations and joint predictions by constructing a set of vectors to be estimated and prior parameter samples; it constrains the posterior parameters through leaf area index, soil moisture, and available yield constraint information; it achieves depth matching by using the overlap weight between soil moisture observation depth and the model soil layer; and it reduces the instability risk caused by a single large-step correction through multiple rounds of ESMDA small-step updates.

[0006] In a first aspect, this application provides a method for updating crop parameters and estimating yield using aggregated smoothed multiple data assimilation, including: Acquire basic information about the target plot or region, and perform data preprocessing on the basic information, including time alignment, missing data handling, and quality control. The basic information includes multi-temporal leaf area index observations, multi-temporal soil moisture observations, meteorological data, and agricultural management information during the crop growth period. If yield constraint information is obtained, the basic information also includes the yield constraint information, which includes measured crop yields at maturity or harvest time in historical years or for sample plots, or measured crop yields at maturity or harvest time when target season yield observations are obtained. The temporal sequence of the leaf area index observations may be the same as or different from the temporal sequence of the soil moisture observations. A vector to be estimated is constructed, and a first preset number of candidate sample groups are extracted according to the prior distribution of the vector to be estimated to form a parameter prior sample set; wherein, the vector to be estimated includes a variety parameter sub-vector and a soil moisture-related parameter sub-vector respectively composed of variety parameters and soil moisture-related parameter selected from the whole-season crop model, and when the initial conditions are uncertain, the vector to be estimated also includes an auxiliary initial state sub-vector; the variety parameters and soil moisture-related parameters are used to drive the operation of the whole-season crop model together with the meteorological driving and agricultural management information, and each candidate sample group is a set of specific values ​​of each sub-vector in the vector to be estimated; For each prior sample in the parameter prior sample set, the whole-season crop model is run based on the meteorological driving and agricultural management information to obtain the whole-season state trajectory from sowing to maturity and the simulated yield value at maturity or harvest corresponding to the prior sample. The preprocessed multi-temporal leaf area index observations, multi-temporal soil moisture observations, and available yield constraint information are arranged in a predetermined order to form a joint observation vector. From the whole-season state trajectory and the simulated yield values ​​at maturity or harvest time corresponding to each prior sample, simulated leaf area index values, simulated soil moisture values, and simulated yield values ​​at maturity or harvest time corresponding to the joint observation vector when the joint observation vector contains yield constraint information are extracted in the same order as the joint observation vector to form a joint prediction vector corresponding to the prior sample. The simulated soil moisture values ​​are composed of multi-temporal soil moisture extraction values ​​obtained by the soil moisture extraction function. When the soil moisture observation depth corresponds to multiple model soil layers, the soil moisture extraction value of each temporal phase is the weighted sum of the simulated soil moisture values ​​of each model soil layer corresponding to the observation depth. The layer weight is the proportion of the overlap thickness of the corresponding model soil layer with the observation depth interval to the total thickness of the observation depth interval, and the sum of the layer weights is 1. A second preset number of rounds of set smoothing and multiple data assimilation updates are performed on the same joint observation vector. The whole-season crop model is then rerun based on the updated estimate vector corresponding to each prior sample obtained in each round, to generate the joint prediction vector corresponding to the prior sample required for the next round of update or final output. For each prior sample, each round update includes: The perturbation observation vector of the prior sample in the current round is determined based on the random perturbation value of the prior sample in the current round and the joint observation vector; different random perturbation values ​​are used for different rounds and different prior samples, and the covariance of the random perturbation value is determined by the inflation factor of the current round and the joint observation error covariance matrix; The sample cross covariance and the sample covariance of the joint prediction vector for the current round are determined based on the estimated vector and the joint prediction vector corresponding to all prior samples in the current round. The estimated vector corresponding to the prior sample is corrected by using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample; wherein, the sum of the reciprocals of the inflation factors of all rounds is 1. After completing the second preset number of rounds of updates, the set of updated vectors to be estimated corresponding to all prior samples is used as the parameter posterior sample set of the target plot or the target area, and the set of simulated yield values ​​at maturity or harvest time obtained by rerunning the whole-season crop model after the last round of updates is used as the yield posterior sample set of the target plot or the target area.

[0007] As an example, the estimated vector corresponding to the prior sample is corrected using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample, including: Determine the residual between the perturbation observation vector and the joint prediction vector; Determine the product between the expansion factor and the joint observation error covariance matrix, and the sum of the sample covariance and the product, and determine the inverse matrix of the sum, the Moore-Penrose generalized inverse matrix, the Tikhonov regularized inverse matrix, or the generalized inverse matrix after singular value truncation; The product of the sample cross covariance, the inverse matrix, the Moore-Penrose generalized inverse matrix, the Tikhonov regularized inverse matrix or the generalized inverse matrix after singular value truncation, and the residual is used as the correction vector. The sum of the vector to be estimated and the corrected vector is used as the updated vector to be estimated.

[0008] As an example, the sample cross covariance of each round is the product of the first deviation matrix of the un-updated vector corresponding to all prior samples of the round and the transpose of the second deviation matrix of the joint prediction vector corresponding to all prior samples of the round, divided by the first preset number minus 1. The sample covariance is the product of the second deviation matrix and the transpose of the second deviation matrix, divided by the first preset quantity minus 1.

[0009] As an example, the soil moisture-related parameters include at least one or more of the following: soil water holding capacity, infiltration, evaporation, root water uptake, or water stress-related parameters.

[0010] As one embodiment, the joint observation vector comprises elements arranged in the following order: Leaf area index observations were performed according to multiple time phases arranged in a phase-wise manner; Soil moisture observations were conducted in multiple phases, arranged according to time phase and observation depth. Given the yield constraint information, the measured crop yields at maturity or harvest time are arranged by maturity or harvest time.

[0011] As an example, the simulated leaf area index value is composed of multi-temporal leaf area index extracted values ​​obtained by the leaf area index extraction function; When the whole-season crop model directly outputs the leaf area index state, the extracted leaf area index value for each time phase is the leaf area index state of the time phase output by the whole-season crop model.

[0012] As an example, when the set smooths multiple data assimilation updates using uniform updates, the inflation factor for all rounds is equal to the second preset number; When the set smooths multiple data assimilation updates adopt a multi-round update strategy with increasing information share, the information share of the early rounds is less than the information share of the later rounds, and the sum of the information shares of all rounds is 1, wherein the expansion factor of each round is the reciprocal of the information share of that round.

[0013] As an example, the joint observation error covariance matrix is ​​a diagonal matrix constructed according to the observation errors corresponding to each element in the joint observation vector, or it is a block diagonal matrix composed of leaf area index observation error sub-matrices, soil moisture observation error sub-matrices, and yield observation error sub-matrices.

[0014] As an example, the variety parameters and the soil moisture-related parameters are selected from the whole-season crop model based on sensitivity, identifiability, and prior uncertainty.

[0015] As one embodiment, after obtaining the posterior sample set of yields for the target plot or the target area, the method further includes: Based on the production posterior sample set, determine one or more of the following: production mean, quantile interval, confidence interval, production reduction probability, or risk level of the target plot or the target area; wherein, the production reduction probability is the proportion of samples in the production posterior sample set that are lower than the preset benchmark production, historical average production, or management target production, and the risk level is determined based on the production reduction probability or the quantile interval.

[0016] Secondly, this application also provides a crop parameter update and yield estimation device that incorporates multiple smoothed data assimilation, comprising: The data access and preprocessing module is used to acquire basic information of the target plot or target area and perform data preprocessing on the basic information. The data preprocessing includes time alignment, missing data processing, and quality control. The basic information includes multi-temporal leaf area index observations, multi-temporal soil moisture observations, meteorological driving data, and agricultural management information during the crop growth period. If yield constraint information is acquired, the basic information also includes the yield constraint information, which includes the measured crop yield at maturity or harvest time in historical years or for the sample plot, or the measured crop yield at maturity or harvest time if target season yield observations are acquired. The temporal sequence of the leaf area index observations may be the same as or different from the temporal sequence of the soil moisture observations. The sample generation module is used to construct the vector to be estimated and extract a first preset number of candidate sample groups according to the prior distribution of the vector to be estimated to form a parameter prior sample set. The vector to be estimated includes a variety parameter sub-vector and a soil moisture-related parameter sub-vector, respectively composed of variety parameters and soil moisture-related parameter parameters selected from the whole-season crop model. When the initial conditions are uncertain, the vector to be estimated also includes an auxiliary initial state sub-vector. The variety parameters and soil moisture-related parameters are used to drive the whole-season crop model together with the meteorological and agricultural management information. Each candidate sample group is a set of specific values ​​for each sub-vector in the vector to be estimated. The whole-season crop model running module is used to run the whole-season crop model based on the meteorological driving and agricultural management information for each prior sample in the parameter prior sample set, and to obtain the whole-season state trajectory from sowing to maturity and the simulated yield value at maturity or harvest corresponding to the prior sample. A joint observation and joint prediction module is used to construct a joint observation vector by combining preprocessed multi-temporal leaf area index observations, multi-temporal soil moisture observations, and available yield constraint information in a predetermined order; from the whole-season state trajectory and mature or harvested yield simulation values ​​corresponding to each prior sample, leaf area index simulation values, soil moisture simulation values, and mature or harvested yield simulation values ​​corresponding to the yield constraint information in the joint observation vector are extracted in the same order as the joint observation vector to form the joint prediction vector corresponding to the prior sample; wherein, the soil moisture simulation values ​​are composed of multi-temporal soil moisture extraction values ​​obtained by the soil moisture extraction function; when the soil moisture observation depth corresponds to multiple model soil layers, the soil moisture extraction value of each temporal phase is the weighted sum of the soil moisture simulation values ​​of each model soil layer corresponding to the observation depth, and the layer weight is the proportion of the overlap thickness of the corresponding model soil layer with the observation depth interval to the total thickness of the observation depth interval, and the sum of the layer weights is 1; The ensemble smoothing multiple data assimilation update module is used to perform a second preset number of ensemble smoothing multiple data assimilation updates on the same joint observation vector, and to rerun the whole-season crop model based on the updated estimate vector corresponding to each prior sample obtained in each round, so as to generate the joint prediction vector corresponding to the prior samples required for the next round of update or final output; wherein, each round of update includes: The perturbation observation vector of the prior sample in the current round is determined based on the random perturbation value of the prior sample in the current round and the joint observation vector; different random perturbation values ​​are used for different rounds and different prior samples, and the covariance of the random perturbation value is determined by the inflation factor of the current round and the joint observation error covariance matrix; The sample cross covariance and the sample covariance of the joint prediction vector for the current round are determined based on the estimated vector and the joint prediction vector corresponding to all prior samples in the current round. The estimated vector corresponding to the prior sample is corrected by using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample; wherein, the sum of the reciprocals of the inflation factors of all rounds is 1. The result output module is used to, after completing the second preset number of rounds of updates, use the set of updated vectors to be estimated corresponding to all prior samples as the parameter posterior sample set of the target plot or the target area, and use the set of simulated yield values ​​at maturity or harvest time obtained by rerunning the whole-season crop model after the last round of updates as the yield posterior sample set of the target plot or the target area.

[0017] Thirdly, this application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned methods for updating crop parameters and estimating yield through multiple sets of smooth data assimilation.

[0018] Fourthly, this application also provides a non-transitory computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for updating crop parameters and estimating yield through multiple sets of smooth data assimilation.

[0019] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements any of the above-mentioned methods for updating crop parameters and estimating yield through multiple sets of smooth data assimilation.

[0020] This application first includes a small number of sensitive parameters in the vector to be estimated, and then samples them from their prior distribution to form a parameter prior sample set. A full-season crop model is then run based on this parameter prior sample set. Predictions corresponding to leaf area index, soil moisture, and yield are extracted from the full-season state trajectory and simulated yield values ​​output by the full-season crop model. Finally, ESMDA is used to stably absorb the same batch of observations in multiple rounds, directly obtaining the parameter posterior and yield posterior, thereby improving the stability of parameter updates, the consistency of yield estimation results, and the ability to express uncertainty. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the crop parameter update and yield estimation method based on multiple data assimilation with set smoothing provided in this application. Figure 2 This is a structural diagram of the crop parameter update and yield estimation device with multiple data assimilation and set smoothing provided in this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0023] The present application will now be described in conjunction with the accompanying drawings and embodiments. It should be understood that the following embodiments are used to explain the technical solutions of the present application and should not be construed as limiting the scope of protection.

[0024] (I) Overall Approach and Applicable Scenarios This application applies to agricultural yield estimation and parameter updating scenarios under multi-source observation constraints throughout the entire season. This scenario typically includes multi-temporal leaf area index, soil moisture, meteorological drivers, and agricultural management information during the growing season. Historical year, sample plot, or post-harvest yield observations can be incorporated as yield constraint information based on data conditions. When the target season yield is unknown, it is not a necessary observation input; instead, the updated parameter set and the posterior yield distribution output by the whole-season crop model are used. Compared to frequent adjustments to the instantaneous state, this application focuses on using multiple rounds of small-step updates via ESMDA to stably incorporate multi-source information throughout the season into the parameter posterior. This method mainly includes six interconnected steps: modeling a small number of sensitive parameters, prior sample sampling, running the whole-season crop model, constructing joint observations and joint predictions, multi-round small-step updates via ESMDA, and outputting the parameter posterior and yield posterior.

[0025] For ease of explanation, the following notation will be used consistently throughout the text: This is the number of the prior sample. The total number of samples in the prior sample set of parameters (i.e., the first preset number, please see the following explanation). For ESMDA round number, This is the total number of rounds in ESMDA (i.e., the second preset number, see the explanation below). The time for leaf area index observation, For soil moisture observation time, In order to be with the first The observation depth corresponding to each soil moisture observation. This refers to the ripening or harvesting period.

[0026] like Figure 1 As shown, the crop parameter update and yield estimation method for ensemble smoothing multiple data assimilation provided in this application includes: S110, Data Preparation: Acquire basic information of the target plot or target area, and perform data preprocessing on the basic information; the data preprocessing includes time alignment, missing data handling, and quality control; wherein, the basic information includes multi-temporal leaf area index observations, multi-temporal soil moisture observations, meteorological driving and agricultural management information during the crop growth period; if yield constraint information is obtained, the basic information also includes the yield constraint information, which includes the measured crop yield at maturity or harvest time in historical years or sample plots; or the yield constraint information includes the measured crop yield at maturity or harvest time if the target season yield observation is obtained; when the target season yield observation is not available, the target season yield is not a necessary input; the temporal sequence of the leaf area index observation may be the same as or different from the temporal sequence of the soil moisture observation. S120, Parameter Selection and Prior Set: Construct the vector to be estimated, and extract a first preset number of candidate sample groups according to the prior distribution of the vector to be estimated to form a parameter prior sample set; the first preset number can be set according to the dimension of the parameter to be estimated and the observation dimension, preferably 50 to 200. The vector to be estimated includes a variety parameter sub-vector and a soil moisture-related parameter sub-vector, respectively composed of variety parameters and soil moisture-related parameters selected from the whole-season crop model. When the initial conditions are uncertain, the vector to be estimated also includes an auxiliary initial state sub-vector; the variety parameters and soil moisture-related parameters are used to drive the whole-season crop model together with meteorological and agricultural management information, and each candidate sample group is a specific set of values ​​for each sub-vector in the vector to be estimated. S130, Forward running of the whole season model: For each prior sample in the parameter prior sample set, the whole season crop model is run based on the meteorological driving and agricultural management information to obtain the whole season state trajectory from sowing to maturity and the simulated yield value at maturity or harvest corresponding to the prior sample. S140, Construction of LAI / SM / Yield Joint Observation: Preprocessed multi-temporal leaf area index (LAI) observations, multi-temporal soil moisture (SM) observations, and available yield constraint information are combined into a joint observation vector in a predetermined order. From the full-season state trajectory and mature or harvested yield simulation values ​​corresponding to each prior sample, simulated leaf area index values, simulated soil moisture values, and mature or harvested yield simulation values ​​corresponding to the yield constraint information in the joint observation vector are extracted in the same order as the joint observation vector, forming a joint prediction vector corresponding to the prior sample. The simulated soil moisture values ​​are composed of multi-temporal soil moisture extraction values ​​obtained from the soil moisture extraction function. When the soil moisture observation depth corresponds to multiple model soil layers, the soil moisture extraction value for each temporal phase is the weighted sum of the simulated soil moisture values ​​of each model soil layer corresponding to the observation depth. The layer weight is the proportion of the overlap thickness between the corresponding model soil layer and the observation depth interval to the total thickness of the observation depth interval, and the sum of the layer weights is 1. S150, ensemble smoothing multiple data assimilation multiple rounds of small-step update: A second preset number of rounds of ensemble smoothing multiple data assimilation update are performed on the same joint observation vector, and the whole-season crop model is rerun based on the updated estimate vector corresponding to each prior sample obtained in each round, to generate the joint prediction vector corresponding to the prior sample required for the next round of update or final output; wherein, for each prior sample, each round of update includes: S1510: Determine the perturbation observation vector of the prior sample in the current round based on the random perturbation value of the prior sample in the current round and the joint observation vector; different random perturbation values ​​are used for different rounds and different prior samples, and the covariance of the random perturbation value is determined by the inflation factor of the current round and the joint observation error covariance matrix; S1520: Determine the sample cross covariance and the sample covariance of the joint prediction vector for the current round based on the estimated vector and joint prediction vector corresponding to all prior samples in the current round. S1530: The estimated vector corresponding to the prior sample (i.e., the estimated vector before update) is corrected using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample; wherein, the sum of the reciprocals of the inflation factors of all rounds is 1; S160, Obtain the posterior parameters and yield distribution: After completing the update of the second preset number of rounds, the set of updated vectors to be estimated corresponding to all prior samples is taken as the posterior parameter sample set of the target plot or the target area, and the set of simulated yield values ​​at maturity or harvest time obtained by rerunning the whole-season crop model after the last round of update is taken as the posterior yield sample set of the target plot or the target area.

[0027] The specific explanation is as follows: (ii) The vector to be estimated and the prior sample set of parameters The vector to be estimated is represented as follows: (1); In the formula, For the first The prior sample at the th ... The estimated vector to be updated in each round; For the first The prior sample at the th ... The updated subvector of variety parameters; For the first The prior sample at the th ... The subvector of soil moisture-related parameters updated in each cycle; For the first The prior sample at the th ... The auxiliary initial state subvector updated in each round; ; .

[0028] In one possible implementation, the variety parameters and the soil moisture-related parameters are selected from the whole-season crop model based on sensitivity, identifiability, and prior uncertainty.

[0029] In one possible implementation, the soil moisture-related parameters include at least one or more of soil water holding capacity, infiltration, evaporation, root water uptake, or water stress-related parameters.

[0030] Preferably, the variety parameter sub-vector This may include one or more of the following: accumulated temperature from emergence to tasseling, accumulated temperature from tasseling to maturity, specific leaf area index, and biomass allocation coefficient; subvectors of soil moisture-related parameters. It may include one or more of the following: field capacity correction coefficient, wilting point correction coefficient, root water uptake stress coefficient, soil evaporation coefficient, and infiltration correction coefficient; auxiliary initial state subvector. The initial root zone soil moisture is preferred, but the initial leaf area index or other small initial conditions may also be included if necessary.

[0031] Preferably, the number of principal parameters in the vector to be estimated is indivual.

[0032] Equation (1) defines which parameters need to be estimated. In this embodiment, a small set of stable model parameters is estimated, not all model parameters. Only a small number of auxiliary initial states are added when the initial conditions are truly uncertain. The reason for this approach is that if a large number of instantaneous states are included in the estimation, the observation residuals are often absorbed by short-term states, and the truly stable parameters that need to be identified become obscured. Therefore, by clearly limiting the estimation objects to a small number of sensitive model parameters and adding a small number of auxiliary initial states only when necessary, the problem of poor interpretability of the estimation results caused by estimating all parameters is avoided from the source.

[0033] The prior sample set of parameters is expressed as follows: (2); In the formula, For the parameter prior sample set; For the first A prior sample (i.e., a candidate sample group).

[0034] Equation (2) does not give the observation set, but rather the vector to be estimated. Candidate values ​​for each group. Each prior sample They all have the same structure as the vector to be estimated in equation (1), only their specific values ​​differ. In other words, The parameter "prior sample set" represents the subset of vectors that make up a single prior sample (i.e., a single candidate sample group). This indicates how many prior samples are grouped together.

[0035] (III) Operation of the whole-season crop model The whole-season crop model is used to predict the whole-season state trajectory and simulated yield at maturity or harvest by using meteorological drivers, agricultural management information and vectors to be estimated during the crop growth period.

[0036] The results of the full-season crop model are as follows: (3); In the formula, For whole-season crop models; Driven by weather conditions; For agricultural management information; For the first The prior sample at the th ... The overall seasonal trajectory corresponding to the cycle; For the first The prior sample at the th ... Simulated yield values ​​for the corresponding maturity or harvest period of each round.

[0037] Equation (3) shows that each prior sample uses the whole-season crop model to simulate the entire growing season from sowing to maturity, rather than just performing local state repairs at the observation time. Since the estimation object of this application is the whole-season crop model parameter, parameter changes will simultaneously affect the subsequent growth trajectory and the final yield. Therefore, after each update of the vector to be estimated, the whole-season crop model needs to be rerun to obtain the simulation results of intra-season growth and end-season yield corresponding to the updated parameters.

[0038] (iv) Joint observation and joint forecasting In one possible implementation, the joint observation vector comprises elements arranged in the following order: Leaf area index observations were performed according to multiple time phases arranged in a phase-wise manner; Soil moisture observations were conducted in multiple phases, arranged according to time phase and observation depth. Given the yield constraint information, the measured crop yields at maturity or harvest time are arranged by maturity or harvest time.

[0039] Specifically, the joint observation vector is represented as follows: (4); in, For joint observation vectors; For the first Leaf area index observation at time; For the first Time and corresponding observation depth Soil moisture observation; The measured crop yield at maturity or harvest.

[0040] In the formula, the joint observation vector may include the first... Leaf area index observation at time 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... Soil moisture observations at specific times and corresponding depths, and yield observations at maturity or harvest when yield constraint information is available; when target season yield observations are unavailable, the yield observation item is not included in the target season joint observation vector.

[0041] Equation (4) incorporates leaf area index, soil moisture, and available yield constraint information during the growth period into the same observation framework, enabling multi-source observations to jointly constrain the same set of parameters to be estimated. It should be noted that the observation time series of leaf area index and soil moisture do not need to be the same; they can be the same or different. The original observation times of leaf area index and soil moisture can be retained, as long as they maintain a consistent arrangement order in the joint observation vector and joint prediction vector.

[0042] The joint prediction vector is expressed as follows: (5); in: For the first The prior sample at the th ... The joint prediction vector corresponding to the round; For the first The prior sample at the th ... The leaf area index prediction component corresponding to the wheel (i.e., the simulated value of leaf area index); For the first The prior sample at the th ... The corresponding predicted soil moisture component (i.e., simulated soil moisture value) for each cycle. For the first The prior sample at the th ... The corresponding yield forecast component (i.e., the simulated yield value at maturity or harvest).

[0043] In one possible implementation, the simulated leaf area index is composed of multi-temporal leaf area index extracted values ​​obtained by the leaf area index extraction function; the simulated soil moisture is composed of multi-temporal soil moisture extracted values ​​obtained by the soil moisture extraction function; and the simulated yield is obtained by the yield prediction function.

[0044] Specifically: in, This is the leaf area index extraction function. For the first The prior sample at the th ... Observation time of wheel and leaf area index The corresponding leaf area index extracted value, This is a soil moisture extraction function. For the first The prior sample at the th ... Rotation, soil moisture observation time Observation depth The corresponding soil moisture extraction value, This is the production forecast function. For the first The prior sample at the th ... Yield forecasts for the maturity or harvest period of the crop.

[0045] When the whole-season crop model directly outputs the leaf area index (LAI) state, the LAI extracted value for each time phase is the LAI state of that time phase output by the whole-season crop model: When soil moisture observation depth When dealing with multiple soil layers in a model, a weighted average method is used to obtain the soil moisture extraction values: In the formula, For the first The prior sample at the th ... Rotation, soil moisture observation time , No. Simulated soil moisture values ​​for each soil layer; To be related to the observation depth The corresponding layer weights.

[0046] Equation (5) essentially maps the model output to the observation space. The joint prediction vector can include three levels: extracting the leaf area index (LAI) according to the leaf area index observation time, extracting soil moisture (SM) according to the soil moisture observation time and observation depth, and extracting the corresponding simulated yield value according to the maturity or harvest period when the joint observation vector contains yield constraint information.

[0047] (v) Set smoothing, multiple data assimilation, and multiple rounds of small-step updates The disturbance observation vector is expressed as follows: (12); In the formula, For the first The prior sample at the th ... The disturbance observation vector corresponding to the wheel; For the first The prior sample at the th ... The random disturbance value corresponding to the wheel; The joint observation error covariance matrix; For the first The expansion factor of the wheel.

[0048] Equation (12) shows that the physical content of the same batch of observations remains unchanged (i.e., the joint observation vector remains unchanged). What changes is the degree of amplification of the observation error in each round and the resulting perturbation observation vector. The larger the inflation factor, the larger the corresponding perturbation observation error covariance, and the weaker the intensity of the absorption of observation information in a single round; the smaller the inflation factor, the smaller the corresponding perturbation observation error covariance, and the stronger the intensity of the absorption of observation information in a single round. In this way, the observation absorption process, which was originally completed in one go, is divided into several controllable small steps.

[0049] In one possible implementation, in S1530, the expansion factor of the current round is utilized. Joint observation error covariance matrix The disturbance observation vector The sample cross covariance The sample covariance and the joint prediction vector corresponding to the prior samples For the vector to be estimated corresponding to the prior sample The correction is performed to obtain the updated vector to be estimated corresponding to the prior sample. ,include: Determine the disturbance observation vector With the joint prediction vector residuals between ; Determine the product between the expansion factor and the joint observation error covariance matrix, and the sum of the sample covariance and the product, and determine the inverse matrix of the sum, the Moore-Penrose generalized inverse matrix, the Tikhonov regularized inverse matrix, or the generalized inverse matrix after singular value truncation; The product of the sample cross covariance, the inverse matrix, the Moore-Penrose generalized inverse matrix, the Tikhonov regularized inverse matrix or the generalized inverse matrix after singular value truncation, and the residual is used as the correction vector. The sum of the vector to be estimated and the corrected vector is used as the updated vector to be estimated.

[0050] Specifically, the core update formula for set smoothing multiple data assimilation is as follows: (13); In the formula, For the first The prior sample at the th ... The updated vector to be estimated obtained in the round. For the first The sample cross covariance between the un-estimated vector and the joint prediction vector corresponding to all prior samples; For the first The sample covariance of the joint prediction vector of the wheels.

[0051] In one possible implementation, the sample cross covariance for each round is the product of the first deviation matrix of the un-updated vector corresponding to all prior samples of the round and the transpose of the second deviation matrix of the joint prediction vector corresponding to all prior samples of the round, divided by the first preset number minus 1; the sample covariance is the product of the second deviation matrix and the transpose of the second deviation matrix, divided by the first preset number minus 1.

[0052] Specifically, the sample cross covariance and sample covariance are calculated using the following formulas: (14); in, For the first The first deviation matrix of the vector to be estimated before the update corresponding to all prior samples of the round. For the first The second deviation matrix of the joint prediction vector corresponding to all prior samples of the round. It is the transpose of the second deviation matrix.

[0053] Equation (13) shows that parameters in the vector to be estimated that are more strongly correlated with the leaf area index, soil moisture and yield residual will receive a greater correction; parameters with weaker correlation will receive a smaller correction.

[0054] After each round of updates, the updated vector to be estimated is substituted back into equation (3), the whole-season crop model is run (i.e., return to S130), and the joint prediction vector is reconstructed according to equation (5) (S140) before entering the next round of updates. If this rerun step is omitted, the new parameter samples still correspond to the old intra-season trajectory and the old yield prediction, and the forward model will lose its self-consistency after multiple data assimilation updates of the ensemble smoothing.

[0055] Therefore, by using multiple rounds of small updates, the risk of making a single large correction is reduced, making the update process more robust.

[0056] Preferably, after obtaining the posterior sample set of yields for the target plot or the target area (S160), the method further includes: Based on the production posterior sample set, determine one or more of the following: production mean, quantile interval, confidence interval, production reduction probability, or risk level of the target plot or the target area; wherein, the production reduction probability is the proportion of samples in the production posterior sample set that are lower than the preset benchmark production, historical average production, or management target production, and the risk level is determined based on the production reduction probability or the quantile interval.

[0057] (vi) Determination of the number of rounds and expansion factor Inflation factor The constraints are as follows: (16); In the formula, Preferred selection .

[0058] When the model has relatively weak nonlinearity, it is preferable to choose... or When the nonlinearity is moderate, it is preferable to choose... or When the posterior distribution of the parameters exhibits significant nonlinear characteristics or the sample dispersion decreases too rapidly after the first trial calculation, the preferred value is... or .

[0059] In one possible implementation, when the set is smoothed through multiple data assimilation updates using uniform updates, the inflation factor for all rounds is equal to the second preset number.

[0060] Specifically, when using uniform updates, it can be made .

[0061] In one possible implementation, when the set smooths multiple data assimilation updates using a multi-round update strategy with increasing information share, the information share of the earlier rounds is less than the information share of the later rounds, and the sum of the information shares of all rounds is 1, wherein the inflation factor of each round is the reciprocal of the information share of that round.

[0062] Specifically, when you want to reduce the instability risk caused by large-scale corrections in the early stages, you can first set an increasing information share: (18); Again (19).

[0063] For example, when At that time, it can be taken (20); correspond (twenty one).

[0064] when At that time, it can be taken (twenty two); correspond (twenty three).

[0065] The incremental information share update strategy here is specifically manifested as follows: a larger inflation factor is used in the early rounds, allowing the observed information to enter the parameter set with smaller step sizes; the inflation factor is gradually reduced in later rounds, causing the sample set to converge towards the posterior high-probability region. This arrangement is particularly suitable for situations where the model response is highly nonlinear or the initial sample dispersion decreases too rapidly.

[0066] In one possible implementation, the joint observation error covariance matrix is ​​a diagonal matrix constructed according to the observation errors corresponding to each element in the joint observation vector, or it is a block diagonal matrix composed of leaf area index observation error sub-matrices, soil moisture observation error sub-matrices, and yield observation error sub-matrices.

[0067] The joint observation error covariance matrix is ​​expressed as: (twenty four); in, , and These correspond to the leaf area index observation error submatrix, soil moisture observation error submatrix, and yield observation error submatrix, respectively. Construct a diagonal matrix. The leaf area index, soil moisture, and yield observation errors should be entered into the joint observation error covariance matrix according to the order of elements in the joint observation vector. When the same type of observation includes multiple time phases or multiple observation depths, the corresponding errors can be written into the same diagonal matrix, or a block-based diagonal matrix can be constructed. For parameters to be estimated or auxiliary initial states with upper and lower bounds (e.g., soil moisture-related parameters, initial root zone soil moisture), it is preferable to update them in the normalized space and then inversely transform them back to physical quantities, applying upper and lower bound constraints or non-negativity constraints.

[0068] Taking spring maize yield estimation as an example, multi-temporal leaf area index, multi-temporal soil moisture, meteorological drivers, sowing date, and irrigation records can be obtained. If historical year, sample plot, or post-harvest yield observations exist, they are used as yield constraint information in parameter updates. It is preferable to select approximately 6–10 parameters as the main estimation targets. Variety parameters may include accumulated temperature from emergence to tasseling, accumulated temperature from tasseling to maturity, specific leaf area index, and grain allocation coefficient. Soil moisture-related parameters may include field capacity correction coefficient, wilting point correction coefficient, root water uptake stress coefficient, and soil evaporation coefficient. If soil moisture before sowing is uncertain, an additional initial root zone soil moisture is added as an auxiliary initial state. After sampling 50–200 candidate sample groups according to the prior range, a full-season spring maize model is run on each prior sample, and updates are performed using 4 or 6 rounds of ESMDA. If the sample variance decreases too rapidly after the first update, an expansion factor setting with increasing information share is used. After the iteration is completed, the posterior distribution of parameters, the posterior distribution of output, the mean output, the quantile interval, and the probability of reduced output can be obtained.

[0069] This application addresses the agricultural assimilation problem with parameters as the primary factor and states as secondary factors. It acquires multi-temporal leaf area index (LAI) observations, multi-temporal soil moisture observations, meteorological data, and agricultural management information throughout the crop's growth period, incorporating these into a joint constraint when yield constraints are available. Variety parameters and soil moisture-related parameters are selected from the whole-season crop model as the primary estimation objects, with a small amount of auxiliary initial states added when initial conditions are uncertain, forming a vector to be estimated. A prior sample set is formed by sampling the prior distribution of the vector to be estimated. For each sample, the whole-season crop model from sowing to maturity is run, extracting the simulated leaf area index, simulated soil moisture, and optional simulated yield values ​​corresponding to the observations, forming a joint prediction vector. ESMDA is used to perform multiple rounds of small-step updates on the same batch of joint observation vectors, and the whole-season crop model is rerun after each update to gradually obtain the posterior sample sets of parameters and yield, providing operational results such as interval estimation and yield reduction risk. This method can simultaneously utilize intra-season growth information and available end-season results information, enabling the whole-season crop model to not only fit intra-season leaf area index and soil moisture changes, but also output the final yield posterior distribution. Without relying on long-term MCMC sampling, it improves parameter update stability, yield estimation consistency, and the ability to express result uncertainty.

[0070] Based on the above, this application also provides a crop parameter update and yield estimation device that incorporates multiple data assimilation with set smoothing. This crop parameter update and yield estimation device with set smoothing and multiple data assimilation can be used in conjunction with the aforementioned crop yield estimation method.

[0071] As an example, such as Figure 2 As shown, the crop parameter update and yield estimation device that integrates multiple data assimilation and smoothing includes: The data access and preprocessing module 210 is used to acquire basic information of the target plot or target area and perform data preprocessing on the basic information. The data preprocessing includes time alignment, missing data processing, and quality control. The basic information includes multi-temporal leaf area index observations, multi-temporal soil moisture observations, meteorological data, and agricultural management information during the crop growth period. If yield constraint information is available, the basic information also includes the yield constraint information, which includes the measured crop yield at maturity or harvest time in historical years or for the sample plot; or the yield constraint information includes the measured crop yield at maturity or harvest time if target season yield observations are available. When target season yield observations are unavailable, the target season yield is not considered a necessary input. The temporal sequence of the leaf area index observations may be the same as or different from the temporal sequence of the soil moisture observations. The sample generation module 220 is used to construct the vector to be estimated and extract a first preset number of candidate sample groups according to the prior distribution of the vector to be estimated to form a parameter prior sample set; wherein, the vector to be estimated includes a variety parameter sub-vector and a soil moisture related parameter sub-vector respectively composed of variety parameters and soil moisture related parameters selected from the whole-season crop model; when the initial conditions are uncertain, the vector to be estimated also includes an auxiliary initial state sub-vector; the variety parameters and soil moisture related parameters are used to drive the operation of the whole-season crop model together with the meteorological driving and agricultural management information, and each candidate sample group is a set of specific values ​​of each sub-vector in the vector to be estimated; The whole-season crop model running module 230 is used to run the whole-season crop model based on the meteorological driving and agricultural management information for each prior sample in the parameter prior sample set, and to obtain the whole-season state trajectory from sowing to maturity and the simulated yield value at maturity or harvest corresponding to the prior sample. The joint observation and joint prediction construction module 240 is used to form a joint observation vector by combining the preprocessed multi-temporal leaf area index observations, multi-temporal soil moisture observations, and the available yield constraint information in a predetermined order; from the whole-season state trajectory and the simulated yield values ​​at maturity or harvest time corresponding to each prior sample, the simulated leaf area index value, the simulated soil moisture value, and the simulated yield values ​​at maturity or harvest time corresponding to the joint observation vector when the joint observation vector contains yield constraint information are extracted in the same order as the joint observation vector, forming the joint prediction vector corresponding to the prior sample; wherein, the simulated soil moisture value is composed of multi-temporal soil moisture extraction values ​​obtained by the soil moisture extraction function; when the soil moisture observation depth corresponds to multiple model soil layers, the soil moisture extraction value of each temporal phase is the weighted sum of the simulated soil moisture values ​​of each model soil layer corresponding to the observation depth, the layer weight is the proportion of the overlap thickness of the corresponding model soil layer with the observation depth interval to the total thickness of the observation depth interval, and the sum of the weights of each layer is 1; The ensemble smoothing multiple data assimilation update module 250 is used to perform a second preset number of ensemble smoothing multiple data assimilation updates on the same joint observation vector, and to rerun the whole-season crop model based on the updated estimate vector corresponding to each prior sample obtained in each round, so as to generate the joint prediction vector corresponding to the prior samples required for the next round of update or final output; wherein, each round of update includes: The perturbation observation vector of the prior sample in the current round is determined based on the random perturbation value of the prior sample in the current round and the joint observation vector; different random perturbation values ​​are used for different rounds and different prior samples, and the covariance of the random perturbation value is determined by the inflation factor of the current round and the joint observation error covariance matrix; The sample cross covariance and the sample covariance of the joint prediction vector for the current round are determined based on the estimated vector and the joint prediction vector corresponding to all prior samples in the current round. The estimated vector corresponding to the prior sample is corrected by using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample; wherein, the sum of the reciprocals of the inflation factors of all rounds is 1. The result output module 260 is used to, after completing the second preset number of rounds of updates, use the set of updated vectors to be estimated corresponding to all prior samples as the parameter posterior sample set of the target plot or the target area, and use the set of simulated yield values ​​at maturity or harvest time obtained by rerunning the whole-season crop model after the last round of updates as the yield posterior sample set of the target plot or the target area.

[0072] Figure 3 This is a schematic diagram of the structure of the electronic device provided in this application, such as... Figure 3 As shown, the electronic device may include a memory 320, a processor 310, and a computer program stored in the memory 320 and executable on the processor 310. When the processor executes the computer program, it implements any of the above-mentioned methods for updating crop parameters and estimating yield through multiple sets of smooth data assimilation.

[0073] This application also provides a non-transitory computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for updating crop parameters and estimating yield through multiple sets of smooth data assimilation.

[0074] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements any of the above-mentioned methods for updating crop parameters and estimating yield through multiple sets of smooth data assimilation.

[0075] The above embodiments are used to illustrate the technical solutions of this application, and are not intended to limit them. Those skilled in the art can combine, substitute, or make equivalent modifications to the technical features in the embodiments without departing from the technical concept of this application; such combinations, substitutions, or equivalent modifications should all fall within the protection scope of this application.

Claims

1. A crop parameter updating and yield estimation method of ensemble smoothing multi-time data assimilation, characterized in that, include: Acquire basic information about the target plot or region, and perform data preprocessing on the basic information, including time alignment, missing data handling, and quality control. The basic information includes multi-temporal leaf area index observations, multi-temporal soil moisture observations, meteorological data, and agricultural management information during the crop growth period. If yield constraint information is obtained, the basic information also includes the yield constraint information, which includes measured crop yields at maturity or harvest time in historical years or for sample plots, or measured crop yields at maturity or harvest time when target season yield observations are obtained. The temporal sequence of the leaf area index observations may be the same as or different from the temporal sequence of the soil moisture observations. A vector to be estimated is constructed, and a first preset number of candidate sample groups are extracted according to the prior distribution of the vector to be estimated to form a parameter prior sample set; wherein, the vector to be estimated includes a variety parameter sub-vector and a soil moisture-related parameter sub-vector respectively composed of variety parameters and soil moisture-related parameter selected from the whole-season crop model, and when the initial conditions are uncertain, the vector to be estimated also includes an auxiliary initial state sub-vector; the variety parameters and soil moisture-related parameters are used to drive the operation of the whole-season crop model together with the meteorological driving and agricultural management information, and each candidate sample group is a set of specific values ​​of each sub-vector in the vector to be estimated; For each prior sample in the parameter prior sample set, the whole-season crop model is run based on the meteorological driving and agricultural management information to obtain the whole-season state trajectory from sowing to maturity and the simulated yield value at maturity or harvest corresponding to the prior sample. The preprocessed multi-temporal leaf area index observations, multi-temporal soil moisture observations, and the obtained yield constraint information are arranged in a predetermined order to form a joint observation vector. From the whole-season state trajectory and the simulated yield values ​​at maturity or harvest time corresponding to each prior sample, the simulated leaf area index value, simulated soil moisture value, and the simulated yield value at maturity or harvest time corresponding to the joint observation vector when the joint observation vector contains yield constraint information are extracted in the same order as the joint observation vector to form the joint prediction vector corresponding to the prior sample. The simulated soil moisture value is composed of the multi-temporal soil moisture extraction value obtained by the soil moisture extraction function. When the soil moisture observation depth corresponds to multiple model soil layers, the soil moisture extraction value of each temporal phase is the weighted sum of the simulated soil moisture values ​​of each model soil layer corresponding to the observation depth. The layer weight is the proportion of the overlap thickness of the corresponding model soil layer with the observation depth interval to the total thickness of the observation depth interval, and the sum of the layer weights is 1. A second preset number of rounds of set smoothing and multiple data assimilation updates are performed on the same joint observation vector. The whole-season crop model is then rerun based on the updated estimate vector corresponding to each prior sample obtained in each round, to generate the joint prediction vector corresponding to the prior sample required for the next round of update or final output. For each prior sample, each round update includes: The perturbation observation vector of the prior sample in the current round is determined based on the random perturbation value of the prior sample in the current round and the joint observation vector; different random perturbation values ​​are used for different rounds and different prior samples, and the covariance of the random perturbation value is determined by the inflation factor of the current round and the joint observation error covariance matrix; The sample cross covariance and the sample covariance of the joint prediction vector for the current round are determined based on the estimated vector and the joint prediction vector corresponding to all prior samples in the current round. The estimated vector corresponding to the prior sample is corrected by using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample; wherein, the sum of the reciprocals of the inflation factors of all rounds is 1. After completing the second preset number of rounds of updates, the set of updated vectors to be estimated corresponding to all prior samples is used as the parameter posterior sample set of the target plot or the target area, and the set of simulated yield values ​​at maturity or harvest time obtained by rerunning the whole-season crop model after the last round of updates is used as the yield posterior sample set of the target plot or the target area.

2. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The estimated vector corresponding to the prior samples is corrected using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior samples, to obtain the updated estimated vector corresponding to the prior samples, including: Determine the residual between the perturbation observation vector and the joint prediction vector; Determine the product between the expansion factor and the joint observation error covariance matrix, and the sum of the sample covariance and the product, and determine the inverse matrix of the sum, the Moore-Penrose generalized inverse matrix, the Tikhonov regularized inverse matrix, or the generalized inverse matrix after singular value truncation; The product of the sample cross covariance, the inverse matrix, the Moore-Penrose generalized inverse matrix, the Tikhonov regularized inverse matrix or the generalized inverse matrix after singular value truncation, and the residual is used as the correction vector. The sum of the vector to be estimated and the corrected vector is used as the updated vector to be estimated.

3. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The sample cross covariance of each round is the product of the first deviation matrix of the un-updated vector corresponding to all prior samples of the round and the transpose of the second deviation matrix of the joint prediction vector corresponding to all prior samples of the round, divided by the first preset number minus 1. The sample covariance is the product of the second deviation matrix and the transpose of the second deviation matrix, divided by the first preset quantity minus 1.

4. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The soil moisture-related parameters include at least one or more of the following: soil water holding capacity, infiltration, evaporation, root water uptake, or water stress-related parameters.

5. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The joint observation vector comprises the following elements arranged in the following order: Leaf area index observations were performed according to multiple time phases arranged in a phase-wise manner; Soil moisture observations were conducted in multiple phases, arranged according to time phase and observation depth. Given the yield constraint information, the measured crop yields at maturity or harvest time are arranged by maturity or harvest time.

6. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The simulated leaf area index value is composed of multi-temporal leaf area index extracted values ​​obtained by the leaf area index extraction function; When the whole-season crop model directly outputs the leaf area index state, the extracted leaf area index value for each time phase is the leaf area index state of the time phase output by the whole-season crop model.

7. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, When the set is smoothed through multiple data assimilation updates using uniform updates, the inflation factor for all rounds is equal to the second preset number; When the set smooths multiple data assimilation updates adopt a multi-round update strategy with increasing information share, the information share of the early rounds is less than the information share of the later rounds, and the sum of the information shares of all rounds is 1, wherein the expansion factor of each round is the reciprocal of the information share of that round.

8. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The joint observation error covariance matrix is ​​a diagonal matrix constructed according to the observation errors corresponding to each element in the joint observation vector, or it is a block diagonal matrix composed of leaf area index observation error sub-matrices, soil moisture observation error sub-matrices, and yield observation error sub-matrices.

9. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, The variety parameters and soil moisture-related parameters were selected from the whole-season crop model based on sensitivity, identifiability, and prior uncertainty.

10. The ensemble-smoothed multi-time data assimilated crop parameter update and yield estimation method of claim 1, wherein, After obtaining the posterior sample set of yields for the target plot or the target area, the method further includes: Based on the production posterior sample set, determine one or more of the following: production mean, quantile interval, confidence interval, production reduction probability, or risk level of the target plot or the target area; wherein, the production reduction probability is the proportion of samples in the production posterior sample set that are lower than the preset benchmark production, historical average production, or management target production, and the risk level is determined based on the production reduction probability or the quantile interval.

11. A crop parameter update and yield estimation device that incorporates smoothed multiple data assimilation, characterized in that, include: The data access and preprocessing module is used to acquire basic information of the target plot or target area and perform data preprocessing on the basic information. The data preprocessing includes time alignment, missing data processing, and quality control. The basic information includes multi-temporal leaf area index observations, multi-temporal soil moisture observations, meteorological driving data, and agricultural management information during the crop growth period. If yield constraint information is acquired, the basic information also includes the yield constraint information, which includes the measured crop yield at maturity or harvest time in historical years or for the sample plot, or the measured crop yield at maturity or harvest time if target season yield observations are acquired. The temporal sequence of the leaf area index observations may be the same as or different from the temporal sequence of the soil moisture observations. The sample generation module is used to construct the vector to be estimated and extract a first preset number of candidate sample groups according to the prior distribution of the vector to be estimated to form a parameter prior sample set. The vector to be estimated includes a variety parameter sub-vector and a soil moisture-related parameter sub-vector, respectively composed of variety parameters and soil moisture-related parameter parameters selected from the whole-season crop model. When the initial conditions are uncertain, the vector to be estimated also includes an auxiliary initial state sub-vector. The variety parameters and soil moisture-related parameters are used to drive the whole-season crop model together with the meteorological and agricultural management information. Each candidate sample group is a set of specific values ​​for each sub-vector in the vector to be estimated. The whole-season crop model running module is used to run the whole-season crop model based on the meteorological driving and agricultural management information for each prior sample in the parameter prior sample set, and to obtain the whole-season state trajectory from sowing to maturity and the simulated yield value at maturity or harvest corresponding to the prior sample. A joint observation and joint prediction module is used to construct a joint observation vector by combining the preprocessed multi-temporal leaf area index observations, multi-temporal soil moisture observations, and the obtained yield constraint information in a predetermined order; from the whole-season state trajectory and the simulated yield values ​​at maturity or harvest time corresponding to each prior sample, the simulated leaf area index value, simulated soil moisture value, and the simulated yield value at maturity or harvest time corresponding to the joint observation vector when the joint observation vector contains yield constraint information are extracted in the same order as the joint observation vector, forming a joint prediction vector corresponding to the prior sample; wherein, the simulated soil moisture value is composed of multi-temporal soil moisture extraction values ​​obtained by the soil moisture extraction function; when the soil moisture observation depth corresponds to multiple model soil layers, the soil moisture extraction value of each temporal phase is the weighted sum of the simulated soil moisture values ​​of each model soil layer corresponding to the observation depth, and the layer weight is the proportion of the overlap thickness of the corresponding model soil layer with the observation depth interval to the total thickness of the observation depth interval, and the sum of the weights of each layer is 1; The ensemble smoothing multiple data assimilation update module is used to perform a second preset number of ensemble smoothing multiple data assimilation updates on the same joint observation vector, and to rerun the whole-season crop model based on the updated estimate vector corresponding to each prior sample obtained in each round, so as to generate the joint prediction vector corresponding to the prior samples required for the next round of update or final output; wherein, each round of update includes: The perturbation observation vector of the prior sample in the current round is determined based on the random perturbation value of the prior sample in the current round and the joint observation vector; different random perturbation values ​​are used for different rounds and different prior samples, and the covariance of the random perturbation value is determined by the inflation factor of the current round and the joint observation error covariance matrix; The sample cross covariance and the sample covariance of the joint prediction vector for the current round are determined based on the estimated vector and the joint prediction vector corresponding to all prior samples in the current round. The estimated vector corresponding to the prior sample is corrected by using the inflation factor of the current round, the joint observation error covariance matrix, the perturbation observation vector, the sample cross covariance, the sample covariance, and the joint prediction vector corresponding to the prior sample, to obtain the updated estimated vector corresponding to the prior sample; wherein, the sum of the reciprocals of the inflation factors of all rounds is 1. The result output module is used to, after completing the second preset number of rounds of updates, use the set of updated vectors to be estimated corresponding to all prior samples as the parameter posterior sample set of the target plot or the target area, and use the set of simulated yield values ​​at maturity or harvest time obtained by rerunning the whole-season crop model after the last round of updates as the yield posterior sample set of the target plot or the target area.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the crop parameter update and yield estimation method for set smoothing multiple data assimilation as described in any one of claims 1 to 10.

13. A non-transitory computer-readable storage medium, wherein a computer program is stored on the non-transitory computer-readable storage medium, characterized in that, When the computer program is executed by the processor, it implements the crop parameter update and yield estimation method for set smoothing multiple data assimilation as described in any one of claims 1 to 10.

14. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the crop parameter update and yield estimation method for set smoothing multiple data assimilation as described in any one of claims 1 to 10.

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