A regional crop yield spatial simulation method based on remote sensing water-carbon coupling process

Abstract

This invention provides a spatial simulation method for regional crop yield based on remote sensing water-carbon coupling processes. Driven by remotely sensed soil moisture data and multi-source meteorological data, this method constructs a coupled iterative framework of crop growth, remote sensing water-carbon coupling, and water balance modules. It achieves regional-scale spatial simulation of crop leaf area index, evapotranspiration, total primary productivity, and crop yield at a fixed time scale. The spatial distribution of crop yield is obtained by cumulatively calculating total primary productivity over the crop's growth period. This invention does not rely on field sample data, can reflect spatial differences in crop yield within a region, and is suitable for regional agricultural productivity assessment and agricultural drought impact analysis.

Description

Technical Field

[0001] This invention belongs to the technical field of agricultural remote sensing monitoring, crop growth simulation and farmland water and carbon cycle coupling analysis, and specifically relates to a regional crop yield spatial simulation method based on remote sensing water and carbon coupling process. Background Technology

[0002] Crop yield is influenced by a combination of factors, including meteorological conditions, soil moisture, and crop growth processes. Existing crop growth models are typically constructed based on crop physiological processes and have a certain simulation capability at the site or field scale. However, their operation often relies on detailed field observation data and soil parameters, making them difficult to apply directly at the regional scale.

[0003] At the regional scale, crop yields are usually obtained through statistical yearbooks. Such data can only reflect the average yield level at the city or county level, lacks spatial continuity, and cannot characterize the yield differences between different plots within the same administrative region. It is difficult to meet the spatial resolution requirements of regional agricultural monitoring and refined management.

[0004] With the development of remote sensing technology, some studies have attempted to use remote sensing data to estimate crop growth status or productivity. However, existing methods mostly focus on spatial inversion of single variables such as leaf area index or evapotranspiration, and have not yet formed a complete simulation framework that can describe the interaction between soil moisture, crop growth, and farmland evapotranspiration at the regional scale. At the same time, existing spatialized yield estimation methods generally suffer from coarse spatial resolution or rely on sample point extrapolation, making it difficult to obtain continuous and stable yield spatial distribution results.

[0005] Furthermore, the existing technology lacks a method that can dynamically update soil moisture at a fixed time scale and continuously feed it back into the crop growth and evapotranspiration simulation process, making it difficult to accurately express the spatial coupling relationship between crop growth process and regional water conditions.

[0006] Therefore, existing technologies cannot achieve continuous spatial simulation of soil moisture, crop growth, evapotranspiration and yield at the regional scale without relying on field sample data. In particular, it is difficult to obtain crop yield distribution results that can reflect the spatial differences in yield within the region. Summary of the Invention

[0007] To address the problem that existing technologies struggle to achieve continuous spatial simulation of crop yield at a regional scale, this invention aims to provide a spatial simulation method for regional crop yield based on remote sensing water-carbon coupling processes. This method uses remote sensing data and meteorological data as the main drivers, and achieves regional spatial simulation of crop growth status, evapotranspiration, and yield through a combination of crop growth simulation, water-carbon process simulation, and water balance calculation.

[0008] The objective of this invention is achieved through the following technical solution:

[0009] The first aspect of this invention provides a method for spatial simulation of regional crop yield based on remote sensing water-carbon coupling processes, comprising the following steps:

[0010] Step 1: Multi-source data processing

[0011] Based on the land use data of the study area, the spatial distribution of farmland planting areas and major crops was extracted, and soil moisture data, meteorological data and surface remote sensing parameters were obtained by remote sensing inversion.

[0012] The above data and parameters are processed using a spatial resampling method to form an input dataset with uniform spatial resolution.

[0013] Step 2: The plant growth module calculates the leaf area index (LAI).

[0014] From the input dataset formed in step 1, extract the initial soil moisture data and meteorological data, and input them into the crop growth module. Under the conditions of temperature stress and water stress, output the spatial distribution of crop leaf area index during this period.

[0015] The crop growth module, based on the crop growth process, divides the leaf area index into a leaf area index growth stage and a leaf area index decay stage for simulation.

[0016] Step 3: The remote sensing water-carbon coupling module calculates evapotranspiration (ET) and gross primary productivity (GPP).

[0017] From the input dataset formed in step 1, meteorological data and surface remote sensing parameters are extracted and combined with the leaf area index obtained in step 2, and then input into the remote sensing water-carbon coupling module.

[0018] By constructing the coupling relationship between photosynthetic and transpiration processes, the spatial distribution of crop evapotranspiration (ET) and total primary productivity (GPP) is calculated collaboratively.

[0019] Step 4: Update soil moisture in the water balance module.

[0020] Based on the crop evapotranspiration calculated in step 3, the remote sensing inversion precipitation extracted from the input dataset in step 1, and the calculated net irrigation water (Wang Yan et al., 2025), the data are input into the water balance module, and the soil moisture data is dynamically updated through the water balance relationship.

[0021] Step 5, iterate

[0022] By determining a fixed time scale, steps 2 to 4 are repeated to form a multi-cycle coupled iterative operation, and the spatial distribution sequence of leaf area index, evapotranspiration and total primary productivity during the crop growth period is obtained.

[0023] Step 6, Production Calculation

[0024] The total primary productivity obtained during the iterative process is accumulated and calculated to generate the regional spatial distribution results of crop yield; the results are compared and verified with statistical yield data, and finally a regional spatial distribution map of crop yield is output to realize the spatial simulation of crop yield in the study area.

[0025] Furthermore, in step 1, the meteorological data includes temperature, humidity, air pressure, shortwave radiation, downward longwave radiation, precipitation, and wind speed; the surface remote sensing parameters include albedo and emissivity.

[0026] Furthermore, in step 2, during the leaf area index growth phase, the leaf area index... Calculate using the following formula:

[0027]

[0028] In the formula: subscript , For day ordinal numbers, This represents the increase in leaf area index;

[0029] During the leaf area index decline phase, leaf area index Calculate using the following formula:

[0030]

[0031] In the formula: Indicates the first [number]th ... One time period; Indicates crops The parameter of decay rate; This represents the actual maximum leaf area index in the simulation. To achieve The thermal unit coefficient at that time;

[0032] Further considering the constraints of water and temperature stress on crop growth, crop growth is regulated through stress factors, specifically including:

[0033] From crop emergence to The previous stage before the descent, the first Crop growth stress factors include:

[0034] Water stress Considering the proportion of soil moisture content to field capacity, it is calculated using the following formula:

[0035]

[0036] In the formula: Indicates the first [number]th ... One time period; Is the crop to the first Soil moisture content over a given time period; It is the first Field water holding capacity over a given time period;

[0037] The temperature stress factor is calculated using the following formula:

[0038]

[0039] In the formula: Indicates the first [number]th ... One time period; The average daily temperature; The optimal temperature for crops; It is the base temperature for crops.

[0040] Furthermore, the increase in leaf area index Calculate using the following formula:

[0041]

[0042] In the formula: , Indicates the first [number]th ... The, the -1 time period; It is the crop's potentially largest leaf area index; It is a heat unit factor; It is the minimum crop stress factor.

[0043] Furthermore, in step 4, the remote sensing water-carbon coupling module combines the Penman-Monteith method with the stomatal conductance model. It first calculates the total assimilation rate of the leaf based on the photosynthesis model, and then obtains the total stomatal conductance and total primary productivity of the canopy through leaf area index aggregation.

[0044] Furthermore, the water supply and expenditure balance relationship is as follows:

[0045]

[0046] In the formula, Indicates the first [number]th ... One time period; For the first Soil moisture at different times For precipitation, This is the net irrigation amount. For actual evaporation, This refers to runoff.

[0047] Furthermore, in step 6, during the cumulative calculation of total primary productivity, carbon utilization efficiency φ is the ratio of net primary productivity (NPP) to total primary productivity (GPP), and the total dry matter of crops is obtained by converting the cumulative net primary productivity to the carbon content.

[0048] Furthermore, the crops mentioned include grain crops such as winter wheat and summer corn.

[0049] The advantages of this invention compared to the prior art are as follows:

[0050] (1) The regional crop yield spatial simulation method based on remote sensing water-carbon coupling process described in this invention can realize continuous synchronous spatial simulation of crop growth, evapotranspiration, soil moisture and yield at the regional scale;

[0051] (2) The method described in this invention establishes a dynamic feedback relationship between soil moisture and crop growth process through multi-module coupling and iterative operation;

[0052] (3) Compared with the annual scale yield data of the Rural Statistical Yearbook, this invention, by coupling remote sensing data with higher temporal resolution, continuously simulates the crop growth period at a fixed time scale, which can characterize the dynamic changes of the crop growth process and provide more refined time-series information support for the analysis of agricultural production processes.

[0053] (4) The method of the present invention does not require field sample data as input during operation and is suitable for spatial simulation of crop yield at the regional scale;

[0054] (5) The method of the present invention adopts a raster modeling method. Its spatial resolution is mainly affected by the resolution of the input remote sensing data and the setting of the computing unit. In the embodiment, it is described in terms of the regional scale. When the remote sensing data with higher spatial resolution is obtained and the computing unit is adjusted accordingly, the method of the present invention can be extended to a more refined scale, including the spatial simulation of crop yield at the plot scale.

[0055] (6) It can obtain the results of crop yield distribution with spatial continuity, reflecting the yield differences within the region. Attached Figure Description

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0057] Figure 1This is a flowchart of the overall process for spatial simulation of regional crop yield based on remote sensing water-carbon coupling process as described in this invention.

[0058] Figure 2 This is a comparison chart of simulated production data and statistical data from this invention;

[0059] Figure 3 This is a schematic diagram of the spatial distribution of regional crop yields obtained by the method of the present invention. Detailed Implementation

[0060] The embodiments described are provided to better illustrate the present invention, but are not intended to limit the scope of the invention to the embodiments described. Therefore, non-essential improvements and adjustments made to the embodiments by those skilled in the art based on the above description are still within the scope of protection of the present invention.

[0061] The endpoints and any values ​​of the ranges disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed herein.

[0062] The present invention will be described in detail below through embodiments. It should be understood that the following embodiments are only used to exemplify and further explain and illustrate the content of the present invention, and are not intended to limit the present invention.

[0063] Example 1

[0064] This embodiment provides a spatial simulation method for regional crop yield based on remote sensing water-carbon coupling processes. Taking winter wheat in southern Hebei Province as the research object, the simulation was conducted in 2014. The spatial resolution was uniformly set to 5 km, and the time range was from the end of the winter wheat overwintering period to the end of the growing season. The model was run on a fixed time scale of 8 days. Figure 1 As shown, the specific steps include:

[0065] Step 1: Multi-source data processing

[0066] In this embodiment, farmland planting areas are first extracted based on land use data of the study area, and the spatial distribution of the main winter wheat crop is also extracted. Then, soil moisture data retrieved via remote sensing is used as the initial input condition for the crop growth module. Simultaneously, meteorological data such as temperature, humidity, air pressure, shortwave radiation, downward longwave radiation, precipitation, and wind speed, as well as remote sensing parameters such as surface albedo and emissivity, are acquired. The above data are processed using a spatial bilinear interpolation method and unified to a spatial resolution of 5 km. See Table 1 for details of the data sources.

[0067] Table 1. Data Sources and Specific Information

[0068]

[0069] Step 2: The plant growth module calculates the leaf area index (LAI).

[0070] From the input dataset formed in step 1, the initial soil moisture data and meteorological data are extracted and input into the crop growth module. Under the conditions of temperature stress and water stress, the spatial distribution of crop leaf area index during this period is output.

[0071] The crop growth module, based on the crop growth process, divides the leaf area index into a leaf area index growth stage and a leaf area index decay stage for simulation.

[0072] S21, Leaf Area Index Growth Stage:

[0073] The stage from crop emergence to the point before LAI declines, this stage is the first The formula for calculating LAI on each day is as follows: [Formula for calculating LAI on each day of this stage] The sky Calculate according to formula (1):

[0074]

[0075] In the formula: subscript , It is the ordinal number of the day.

[0076] in, Increment Calculate according to formula (2):

[0077]

[0078] In the formula: , Indicates the first [number]th ... The, the -1 time period; It is the crop's potentially largest leaf area index; It is a heat unit factor; is the minimum crop stress factor, and is the minimum value of water and temperature stress factors.

[0079] Heat unit function Calculate according to formula (3):

[0080]

[0081] In the formula: Indicates the first [number]th ... One time period; It is a heat unit factor; For the first The thermal unit coefficient of a day; and The parameters used to control the changes in the leaf area curve.

[0082] S22, LAI begins to decline until the end of the reproductive period.

[0083] In this stage, the first LAI per day is calculated using formula (4):

[0084]

[0085] In the formula: Indicates the first [number]th ... One time period; Indicates crops The parameter of decay rate; This represents the actual maximum leaf area index in the simulation. To achieve The thermal unit coefficient at that time.

[0086] S23, Considering the effects of moisture and temperature stress on LAI

[0087] Crops are subjected to environmental stresses such as water, salinity, temperature, and nutrients during their actual growth process, which affect their growth, development, and final yield. This invention mainly considers the constraints of water stress and temperature stress on crop growth.

[0088] From crop emergence to The previous stage before the descent, the first Crop growth stress factors include:

[0089] Water stress ( Based on the proportion of soil moisture content to field capacity, the calculation is performed using formula (5):

[0090]

[0091] In the formula: Indicates the first [number]th ... A time period; where, Is the crop to the first Soil moisture content over a given time period; It is the first Field water holding capacity over a given time period.

[0092] The temperature stress factor is calculated using formula (6):

[0093]

[0094] In the formula: Indicates the first [number]th ... One time period; The average daily temperature; The optimal temperature for crops; These are the basic temperatures for crops, all measured in °C.

[0095] Through the above stress factors The growth process was adjusted to obtain the 8-day time period. Spatial distribution.

[0096] Step 3: The remote sensing water-carbon coupling module calculates evapotranspiration (ET) and gross primary productivity (GPP).

[0097] From the input dataset formed in step 1, meteorological data and surface remote sensing parameters are extracted and combined with the leaf area index obtained in step 2, and then input into the remote sensing water-carbon coupling module.

[0098] By constructing the coupling relationship between photosynthetic and transpiration processes, the spatial distribution of crop evapotranspiration (ET) and total primary productivity (GPP) is calculated collaboratively.

[0099] In this embodiment, the leaf area index output by the crop growth module, along with meteorological data and surface remote sensing parameters, is input into the remote sensing water and carbon coupling module. By constructing the coupling relationship between the photosynthetic process and the transpiration process, the crop ET and GPP are calculated.

[0100] The specific process is as follows: By combining the PM method with porosity conductivity... By combining these models, a unified theoretical system can be constructed that can simultaneously estimate the composition of vegetation evapotranspiration and total primary productivity. The stomatal conductance model can dynamically simulate... Physiological responses to variables such as photosynthetically active radiation, ambient temperature, and total leaf assimilation rate. Taking into account the total leaf assimilation rate... Sensitivity to environmental factors can be initially assessed by calculating the assimilation rate based on a photosynthesis model. Subsequently, the stomatal conductance at the leaf scale was calculated using the leaf area index (LAI). With assimilation rate Extending this to the canopy scale, we can obtain the total stomatal conductance of the canopy. and canopy total primary productivity (GPP).

[0101] Pore ​​conductance Calculate according to formula (7):

[0102]

[0103] In the formula, It is the porosity conductivity. It is the total assimilation of the leaves; It is in the air concentration; It reflects the pressure deficit of the stomata in atmospheric water vapor ( ) Parameters of the response.

[0104] Total assimilation rate of blades Calculate according to formula (8):

[0105]

[0106] In the formula, It is the initial slope of the light response curve. ; yes Initial slope of the response curve I is the photon density of PAR ( ); yes and The maximum photosynthetic rate when all components are saturated.

[0107] The Penman-Monteith method (PM equation) is used to estimate soil evaporation. Canopy transpiration Calculate using formulas (9) and (10):

[0108]

[0109]

[0110] In the formula, Is S and The ratio; It is the humidity constant; It is the slope of the saturated vapor pressure versus temperature curve; net radiation is determined by the extinction coefficient of available energy ( ) is divided into canopy ( ) and soil ( Effectiveness; It is air density; It is the specific heat of air at constant pressure; Air vapor pressure deficit is defined as the difference (kPa) between the saturated vapor pressure and the actual vapor pressure under the given temperature conditions. It is aerodynamic conductivity; It is the stomatal conductance of the canopy; It is a dimensionless variable, which determines Availability when considering precipitation and equilibrium evaporation rates.

[0111] Then, the stomatal conductance at the leaf scale was analyzed using the leaf area index (LAI). ) and total assimilation rate ( By aggregating to the canopy scale, the total stomatal conductance of the canopy can be calculated. The formulas for canopy total primary productivity (GPP) are as follows:

[0112] Canopy stomatal conductance GPP is calculated using formulas (11) and (12):

[0113]

[0114]

[0115] In the formula, It is the porosity conductivity. It is the extinction coefficient of photosynthetically active radiation (PAR); , , and It is a simplified form of the composite parameter, derived from the maximum photosynthetic rate ( ), initial slope of the light response curve ( ), Initial slope of response curve Physiological parameters and PAR flux density at the top of the canopy ( ) and air concentration( This model is composed of environmental variables such as [list of variables]. Furthermore, it introduces a parameter [reference needed]. and The constraint function is used to characterize the saturated water vapor pressure difference ( ) on total primary productivity ( The effect of water vapor pressure deficit in the above equations. This indicates that stomatal aperture closes as D increases. In this model's water-carbon coupling mechanism, the atmospheric vapor pressure deficit (VPD) effect, characterizing water limitation, is further introduced. Soil moisture affects canopy conductance (…). The limitations can be reflected in light interception capabilities or changes in LAI, as well as in atmospheric VPD.

[0116] The spatial distribution of crop evapotranspiration and total primary productivity at the current time is obtained through the above calculations.

[0117] Step 4: Update soil moisture in the water balance module.

[0118] Based on the crop evapotranspiration calculated in step 3, the remote sensing inversion precipitation extracted from the input dataset in step 1, and the calculated net irrigation water volume (Wang Yan, Wang Xingwang, Qu Yanping, et al. Estimation of farmland irrigation water volume and its contribution to crop water consumption and yield analysis - a case study of southern Hebei [J]. Journal of Irrigation and Drainage, 2025, 44(11):1-9.DOI:10.13522 / j.cnki.ggps.2025137.), input the data into the water balance module to dynamically update the soil moisture data through the water balance relationship.

[0119] The water supply and expenditure balance relationship is expressed by the following formula:

[0120]

[0121] In the formula, Indicates the first [number]th ... One time period; For the first Soil moisture at different times For precipitation, Net irrigation water volume For actual evaporation, For runoff, the central and southern Hebei region belongs to the North China Plain, and the runoff is negligible.

[0122] Step 5, iterate

[0123] By repeating steps 2 to 4 on a fixed timescale of 8 days, a multi-cycle coupled iterative operation is formed to obtain the spatial distribution sequence of leaf area index, evapotranspiration and total primary productivity during the crop growth period.

[0124] Step 6, Production Calculation

[0125] The total primary productivity obtained during the iterative process is accumulated and calculated to generate the regional spatial distribution results of crop yield; the results are compared and verified with statistical yield data, and finally a regional spatial distribution map of crop yield is output to realize the spatial simulation of crop yield in the study area.

[0126] Crop growth, development, and yield are products of carbon assimilation through photosynthesis. Therefore, a quantitative relationship can be established between GPP and crop growth and yield accumulation processes. In the carbon cycle of farmland ecosystems, net primary productivity (NPP) is obtained by subtracting the carbon loss from vegetation respiration from GPP.

[0127] This embodiment uses 8-day GPP to calculate cumulative net primary productivity (NPP):

[0128]

[0129] In the formula, Indicates the first [number]th ... One time period; yes The total number of sequences; It is carbon utilization efficiency, which is the ratio of NPP to GPP.

[0130] The NPP accumulation was converted to the total dry matter of crops (DM). ).

[0131]

[0132] In the formula, Indicates the first [number]th ... One time period; yes The total number of sequences; NPP is net primary productivity; Carbon content, of which, winter wheat The value is 0.43.

[0133] To quantify crop development rate and dry matter distribution, define For each With the general ( The ratio of )

[0134]

[0135] In the formula, Indicates the first [number]th ... One time period; For the total , For the current stage .

[0136] Then, using The software determined the dry biomass allocation coefficients for maize and wheat during the growing season, using the following formulas:

[0137]

[0138]

[0139] In the formula, , Represents stem (s), leaf (l), grain (g), and underground ( (Location). And coefficient , and It is defined as the ratio of each component to aboveground biomass (AB). It is defined as the ratio of underground biomass (BB, i.e., roots) to DM.

[0140] According to relevant materials, the specific values ​​of parameters a, b, c, and d in each allocation coefficient function are shown in the table below.

[0141]

[0142] Changes in total dry matter during the crop's growth period were obtained. After considering the distribution coefficients for each part, the dry matter mass of the underground part ( ) and aboveground dry matter ( Including stems, leaves and fruit. It can be calculated using the following formula:

[0143] Based on the allocation coefficients, BB and AB (allocated as l, s, and g components) can be obtained:

[0144]

[0145]

[0146] The final formula for calculating crop yield is as follows:

[0147]

[0148] In the formula, The dry biomass of cereals at the end of the season; To determine the moisture content of grains, consult relevant materials, specifically for winter wheat. It was set to 0.11.

[0149] The final calculation results are compared and verified with statistical yield data to generate the regional spatial distribution results of crop yield for the target year.

[0150] The above embodiments are illustrated using winter wheat as an example. The method of the present invention is also applicable to other major grain crops such as summer corn, and its implementation process is the same as that of the above embodiments.

[0151] like Figure 2 As shown, the winter wheat yield simulated by the method of this invention is compared with the winter wheat yield of the corresponding region in the statistical yearbook. A scatter plot is drawn and linear regression analysis is performed. The results show that there is a good linear relationship between the simulated yield and the statistical yield, and the regression equation is y=1.0542x, with a coefficient of determination of [missing information]. =0.9986, indicating that the method of the present invention can better reflect the overall level and spatial distribution characteristics of winter wheat yield at the regional scale.

[0152] like Figure 3As shown, the simulated winter wheat yield obtained by the method of this invention exhibits significant spatial distribution differences within the study area. Overall, the simulated yield levels in major winter wheat planting areas such as Shijiazhuang and Handan are relatively high, consistent with the actual production situation in the corresponding regions, indicating that the method of this invention can reasonably reflect the spatial distribution characteristics of winter wheat yield within the study area. Furthermore, since the southern Hebei region administratively only includes a portion of Langfang City (excluding the three northern counties), while the statistical yearbook data for Langfang City covers the entire city, this embodiment does not compare the simulated yield of Langfang City with the statistical yearbook data. These factors do not affect the spatial simulation results of yield in other regions or the overall applicability of the method of this invention.

[0153] Finally, it should be noted that the above is only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention (such as the application of various formulas, the order of steps, etc.) without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for spatial simulation of regional crop yield based on remote sensing water-carbon coupling processes, characterized in that, The method includes the following steps: Step 1: Multi-source data processing Based on the land use data of the study area, the spatial distribution of farmland planting areas and major crops was extracted, and soil moisture data, meteorological data and surface remote sensing parameters were obtained by remote sensing inversion. The above data and parameters are processed using a spatial resampling method to form an input dataset with uniform spatial resolution; Step 2: Plant growth module calculates leaf area index From the input dataset formed in step 1, extract the initial soil moisture data and meteorological data, and input them into the crop growth module. Under the conditions of temperature stress and water stress, output the spatial distribution of crop leaf area index during this period. The crop growth module, based on the crop growth process, divides the leaf area index into a leaf area index growth stage and a leaf area index decay stage for simulation. Step 3: The remote sensing water-carbon coupling module calculates evapotranspiration and total primary productivity. From the input dataset formed in step 1, meteorological data and surface remote sensing parameters are extracted and combined with the leaf area index obtained in step 2, and then input into the remote sensing water-carbon coupling module. By constructing the coupling relationship between photosynthetic and transpiration processes, the spatial distribution of crop evapotranspiration (ET) and total primary productivity (GPP) is calculated collaboratively. Step 4: Update soil moisture in the water balance module. Based on the crop evapotranspiration calculated in step 3, the remote sensing inversion precipitation extracted from the input dataset in step 1, and the calculated net irrigation water, input them into the water balance module to dynamically update the soil moisture data through the water balance relationship. Step 5, iterate By determining a fixed time scale, steps 2 to 4 are repeated to form a multi-cycle coupled iterative operation, and the spatial distribution sequence of leaf area index, evapotranspiration and total primary productivity during the crop growth period is obtained. Step 6, Production Calculation The total primary productivity obtained during the iterative process is accumulated and calculated to generate the regional spatial distribution results of crop yield; the results are compared and verified with statistical yield data, and finally a regional spatial distribution map of crop yield is output to realize the spatial simulation of crop yield in the study area.

2. The method for spatial simulation of regional crop yield according to claim 1, characterized in that, In step 1, the meteorological data includes temperature, humidity, air pressure, shortwave radiation, downward longwave radiation, precipitation, and wind speed; the surface remote sensing parameters include albedo and emissivity.

3. The method for spatial simulation of regional crop yield according to claim 1, characterized in that, In step 2, during the leaf area index growth phase, the leaf area index... Calculate using the following formula: In the formula: subscript , For day ordinal numbers, This represents the increase in leaf area index; During the leaf area index decline phase, leaf area index Calculate using the following formula: In the formula: Indicates the first [number]th ... One time period; Indicates crops The parameter of decay rate; This represents the actual maximum leaf area index in the simulation. To achieve The thermal unit coefficient at that time; Further considering the constraints of water and temperature stress on crop growth, crop growth is regulated through stress factors, specifically including: From crop emergence to The previous stage before the descent, the first Crop growth stress factors include: Water stress Considering the proportion of soil moisture content to field capacity, it is calculated using the following formula: In the formula: Indicates the first [number]th ... One time period; Is the crop to the first Soil moisture content over a given time period; It is the first Field water holding capacity over a given time period; The temperature stress factor is calculated using the following formula: In the formula: Indicates the first [number]th ... One time period; The average daily temperature; The optimal temperature for crops; It is the base temperature for crops.

4. The method for spatial simulation of regional crop yield according to claim 3, characterized in that, Leaf area index increase Calculate using the following formula: In the formula: , Indicates the first [number]th ... The, the -1 time period; It is the crop's potentially largest leaf area index; It is a heat unit factor; It is the minimum crop stress factor.

5. The method for spatial simulation of regional crop yield according to claim 1, characterized in that, In step 3, the remote sensing water-carbon coupling module combines the Penman-Monteith method with the stomatal conductance model. First, it calculates the total assimilation rate of the leaves based on the photosynthesis model, and then obtains the total stomatal conductance and total primary productivity of the canopy by aggregating the leaf area index.

6. The method for spatial simulation of regional crop yield according to claim 1, characterized in that, In step 4, the water balance relationship is as follows: In the formula, Indicates the first [number]th ... One time period; For the first Soil moisture at different times For precipitation, This is the net irrigation amount. For actual evaporation, This refers to runoff.

7. The method for spatial simulation of regional crop yield according to claim 1, characterized in that, In step 6, during the cumulative calculation of total primary productivity, carbon utilization efficiency φ is the ratio of net primary productivity (NPP) to total primary productivity (GPP), and the total dry matter of crops is calculated by converting the cumulative net primary productivity to the carbon content.

8. The method for spatial simulation of regional crop yield according to any one of claims 1 to 7, characterized in that, The crops mentioned are grain crops, including winter wheat and summer corn.

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