Construction method of summer corn layered upscaling model

Abstract

The invention discloses a construction method of a summer corn layered upscaling model, and relates to the technical field of agriculture. Comprising the steps of collecting key data of model construction; confirming a research growth period, screening typical days, and layering summer corn canopies; fitting a stomatal conductance model according to the collected observation results; evaluating the fitting stomatal conductance models of different layers, and screening the optimal stomatal conductance model of each layer; and carrying out upscaling integration on the optimal stomatal conductance model of each layer according to the layered LAI, and carrying out layer-by-layer accumulation to finally obtain a summer corn layered upscaling model. According to the method, the sample data is rich, the reliability of the estimation model is improved, the difference of the stomatal conductance between different canopy leaves is reflected, and the obtained conclusion has important significance on regional research.

Description

Technical Field

[0001] The invention relates to the technical field of agriculture, and in particular to a method for constructing a stratified upscaling model for summer corn. Background Art

[0002] Farmland evapotranspiration (ET) is a major component of surface water balance and energy balance, and it plays a vital role in the exchange of matter and energy between land and atmosphere. Changes in farmland evapotranspiration show significant spatiotemporal variability, which is affected by plant growth period and canopy height. Stomata are the main channels for plants to exchange water vapor with the outside world, and their opening and closing states directly affect the transmission process of soil moisture to the atmosphere, thereby affecting farmland evapotranspiration. Canopy conductance is an important indicator to measure the opening and closing of stomatal pores at the farmland canopy scale, which reflects the water vapor transmission capacity at the canopy level. Accurate estimation of canopy conductance is of great significance for predicting farmland evapotranspiration. It can help us better understand the water vapor exchange process between the canopy and the atmosphere, and how this process affects the water cycle of the entire farmland ecosystem.

[0003] Stomatal conductance models can be roughly divided into the following categories according to their theoretical basis: empirical models based on environmental factors, semi-empirical models regulated by photosynthesis or plant hormones, and leaf models based on optimal behavior theory. Each model has its own advantages and limitations, and the appropriate model should be selected according to the research objectives and conditions. Canopy-scale models can simulate the interaction between vegetation and the environment and are widely used in agriculture and forestry to study water, heat, carbon dioxide fluxes and photosynthesis. Its development and application require the integration of multidisciplinary knowledge, such as meteorology and biology, to improve the accuracy and applicability of the model and provide a comprehensive perspective for the study of the interaction between vegetation and the environment.

[0004] At present, there are few studies on conductance models that can reflect the stomatal differences between crop canopy leaves. In order to clarify the mechanism of the interaction between crop canopy and farmland environment, it is urgent to establish a layered canopy conductance upscaling estimation model. The key to upscaling lies in the upscaling of leaf area index. Canopy conductance is calculated by stomatal conductance and leaf area index or effective leaf area index, and then the crop evapotranspiration is estimated jointly. On this basis, many researchers are committed to exploring the upscaling of leaf stomatal conductance to canopy conductance using nonlinear models. There are actually differences in stomatal conductance between illuminated leaves and shaded leaves. Leaves at different positions in the vegetation canopy also have obvious differences in photosynthetic rate and transpiration. Therefore, the canopy is often divided into two categories in research: shade leaves and sun leaves; shade leaves refer to leaves that can only absorb scattered radiation, and sun leaves refer to leaves that can absorb direct and scattered radiation at the same time. This method can effectively reduce the errors in canopy photosynthesis and conductance simulation in large leaf models. However, these models need to use measured evapotranspiration data to infer canopy conductance for correction, and cannot reflect the differences in stomatal conductance between leaves in different canopies.

[0005] Therefore, it is an urgent problem for those skilled in the art to propose a method for constructing a stratified upscaling model for summer corn to solve the difficulties existing in the prior art. Summary of the invention

[0006] In view of this, the present invention provides a method for constructing a stratified upscaling model for summer corn, which reflects the differences in stomatal conductance between leaves in different canopies, and the conclusions obtained are of great significance for regional research.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A method for constructing a summer corn stratification upscaling model comprises the following steps:

[0009] S1. Collect key data for model construction, including photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field;

[0010] S2. By confirming the growth period and selecting typical days, the summer corn canopy is divided into i layers and observed and recorded to obtain the observation results;

[0011] S3. Fitting the stomatal conductance model g based on the observation results s-i ;

[0012] S4. Stomatal conductance model g for different layers s-i Evaluate and select the optimal stomatal conductance model opt{g s-i};

[0013] S5. The optimal stomatal conductance model for layer i opt{g s-i}According to LAI i Perform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c .

[0014] Optionally, key data for model construction are collected in S1. The key data include photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field. The specific contents are:

[0015] The data measured by the photosynthetic instrument include stomatal conductance, transpiration rate, photosynthetic rate, and leaf temperature;

[0016] Weather station data include light and effective radiation, saturated water vapor pressure, relative humidity, and air temperature;

[0017] The crop physiological data measured in the field include plant height, leaf area, leaf area index, and extinction coefficient.

[0018] Optionally, in S2, by confirming the growth period and selecting typical days, the summer corn canopy is divided into i layers and observed and recorded, and the specific contents of the observation results are:

[0019] Combined with the actual sowing date of summer corn and the differences in the growth and development characteristics of summer corn, the growth period of summer corn stomatal conductance research is divided into jointing stage, tasseling stage, filling stage and maturity stage. Typical days need to be selected for different growth periods. The typical day is a clear, cloudless day with sufficient sunlight. According to the field measurement data, the summer corn canopy on a typical day is divided into i layers. The minimum value of i is 1, which means that the summer corn canopy is regarded as a uniform layer. The maximum value of i is the number of leaves on the summer corn plant on a typical day.

[0020] Optionally, based on field measurement data, the specific content of dividing the summer corn canopy into i layers on a typical day is:

[0021] During the crop growth period, n plants with uniform growth are selected at different growth stages for marking. The number of effective leaves, leaf length, leaf width and plant height are measured with a steel ruler within a set time, and the leaf area index is calculated.

[0022] Optional, stomatal conductance model g fitted in S3 s-i It is one or more of an empirical model and a semi-empirical model;

[0023] The empirical models include Jarvis two-factor model and Jarvis multi-factor model;

[0024] Semi-empirical models include BWB model, BBL model and USO unified stomatal model.

[0025] Optionally, the stomatal conductance model g for different layers in S4 s-i Evaluate and select the optimal stomatal conductance model opt{g s-i The specific content of} is:

[0026] All fitted stomatal conductance models of the i-th layer of the summer corn canopy were evaluated, and the stomatal conductance model with the best evaluation index of the i-th layer was selected by comprehensive comparison. The model evaluation indexes included the determination coefficient, root mean square error, Akaike information criterion and modified consistency coefficient. The calculation formula is as follows:

[0027]

[0028] Among them, R 2 is the determination coefficient, RMSE is the root mean square error, AIC is the Akaike information criterion, d is the modified consistency coefficient, E i is the measured value, O iis the simulated value, E is the average of the measured values, k is the number of parameters in the model, and n is the number of groups of measured values ​​and simulated values.

[0029] Optionally, the optimal stomatal conductance model for layer i in S5 opt{g s-i}According to LAI i Perform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c The specific content is:

[0030] gs-opt=opt{gs-i} (5)

[0031]

[0032] Where n is the number of layers into which the canopy is divided, LAI i-1 The leaf area index of the lower boundary of the division layer, LAI i is the leaf area index of the upper boundary of the division layer, ξ is the leaf area index from a certain height of the canopy to the top of the canopy, opt{g s-i} is the stomatal conductance model with the best simulation accuracy screened out in this canopy layer.

[0033] It can be seen from the above technical solution that, compared with the prior art, the present invention provides a method for constructing a stratified upscaling model for summer corn, which has the following beneficial effects: the present invention is based on measured data, takes into account the stratified difference characteristics, improves the reliability of the estimation model, and the constructed stratified upscaling model can reflect the differences in stomatal conductance between leaves in different canopies, and the conclusions obtained are of great significance to regional research. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1 A flow chart of a method for constructing a stratified upscaling model for summer corn provided by the present invention;

[0036] Figure 2 The hierarchical upscaling model provided by the present invention shows a good fitting effect in simulating canopy conductance. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Reference Figure 1 As shown, the present invention discloses a method for constructing a summer corn stratified scaling model, comprising the following steps:

[0039] S1. Collect key data for model construction, including photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field;

[0040] S2. By confirming the growth period and selecting typical days, the summer corn canopy is divided into i layers and observed and recorded to obtain the observation results;

[0041] S3. Fitting the stomatal conductance model g based on the observation results s-i ;

[0042] S4. Stomatal conductance model g for different layers s-i Evaluate and select the optimal stomatal conductance model opt{g s-i};

[0043] S5. The optimal stomatal conductance model for layer i opt{g s-i}According to LAI i Perform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c .

[0044] Furthermore, S1 collects key data for model construction, including photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field. The specific contents are as follows:

[0045] The data measured by the photosynthetic instrument include stomatal conductance, transpiration rate, photosynthetic rate, and leaf temperature;

[0046] Weather station data include light and effective radiation, saturated water vapor pressure, relative humidity, and air temperature;

[0047] The crop physiological data measured in the field include plant height, leaf area, leaf area index (LAI), and extinction coefficient.

[0048] Furthermore, in S2, by confirming the growth period and selecting typical days, the summer corn canopy is divided into i layers and observed and recorded. The specific contents of the observation results are as follows:

[0049] Combined with the actual sowing date of summer corn and the differences in the growth and development characteristics of summer corn, the growth period of summer corn stomatal conductance research is divided into jointing stage, tasseling stage, filling stage and maturity stage. Typical days need to be selected for different growth periods. The typical day is a clear, cloudless day with sufficient sunlight. According to the field measurement data, the summer corn canopy on a typical day is divided into i layers. The minimum value of i is 1, which means that the summer corn canopy is regarded as a uniform layer. The maximum value of i is the number of leaves on the summer corn plant on a typical day.

[0050] Specifically, experimental observations were conducted on the stomatal conductance, net photosynthetic rate and related environmental factors of summer corn leaves. In each experimental observation, a typical corn plants with uniform distribution were randomly selected as representative plants, and b functional leaves with uniform size and consistent light receiving direction were selected for each plant. The measurement position was the middle of the leaf, and the target leaf was kept perpendicular to the sunlight during the measurement. Recording began after the data stabilized, and the average values ​​were taken as the measurement results of the stomatal conductance, net photosynthetic rate and related environmental factors of summer corn leaves.

[0051] The Li-6400 photosynthesis measurement system (Li-COR, USA) was used to measure the stomatal conductance (g / cm2) of a group of leaves every 10 days. s ), net photosynthetic rate (A n ), photosynthetically active radiation (PAR), leaf surface CO2 concentration (C s ), ambient CO2 concentration (C a ), and use a temperature gun to record the corresponding ambient temperature (T a ) and record the corresponding hygrometer humidity (h s ) data. The time range of each measurement is from 8:00 to 17:00, and the measurement is made every 1 hour. The observation time is fine-tuned according to the specific weather conditions.

[0052] Furthermore, based on the field measurement data, the specific content of dividing the summer corn canopy into i layers on a typical day is as follows:

[0053] During the crop growth period, n plants with uniform growth are selected at different growth stages for marking. The number of effective leaves, leaf length, leaf width and plant height are measured with a steel ruler within a set time, and the leaf area index is calculated.

[0054] Specifically, the test time is from June to October. The geographical coordinates of the test base are 39°37.28′N, 116°25.57′E. The test area is located in the temperate semi-arid continental monsoon climate zone. The summer is characterized by high temperature and heavy rain, while the winter presents cold and dry climate conditions. The average annual temperature is 12.2℃, the average annual relative humidity is about 56.5%, the average annual precipitation is about 478mm, the average annual water surface evaporation is more than 1800mm, the average annual sunshine hours are about 2502h, and the frost-free period is about 185d. The soil type is mainly sandy loam.

[0055] The experimental base covers an area of ​​200m×200m. The summer corn tested is Xuenuo No. 2, which is planted in mid-to-late June and harvested in early October. The growing period is usually from late June to early October. The growth period of summer corn during the study is shown in Table 1. There is sufficient precipitation during the growth period of summer corn, and basically no irrigation is required.

[0056] Table 1 Division of summer corn growth period

[0057] years Jointing stage Male development period Grouting period Maturity 2016 7.25—8.12 8.13—8.28 8.29—9.13 9.14—9.27 2017 7.15—8.6 8.7—8.22 8.23—9.6 9.7—9.23 2021 7.19—8.4 8.5—8.20 8.21—9.8 9.9—9.23

[0058] Furthermore, the stomatal conductance model g fitted in S3 s-i It is one or more of an empirical model and a semi-empirical model;

[0059] The empirical models include Jarvis two-factor model and Jarvis multi-factor model;

[0060] Semi-empirical models include BWB model, BBL model and USO unified stomatal model.

[0061] Furthermore, the stomatal conductance model g for different layers in S4 s-i Evaluate and select the optimal stomatal conductance model opt{g s-i The specific content of} is:

[0062] All fitted stomatal conductance models of the i-th layer of the summer corn canopy were evaluated, and the stomatal conductance model with the best evaluation index of the i-th layer was selected by comprehensive comparison. The model evaluation indexes included the determination coefficient, root mean square error, Akaike information criterion and modified consistency coefficient. The calculation formula is as follows:

[0063]

[0064] Among them, R 2 is the determination coefficient, RMSE is the root mean square error, AIC is the Akaike information criterion, d is the modified consistency coefficient, E i is the measured value, O i is the analog value, is the average of the measured values, k is the number of parameters in the model, and n is the number of groups of measured values ​​and simulated values.

[0065] Furthermore, in S5, the optimal stomatal conductance model for layer i, opt{g s-i}According to LAI i Perform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c The specific content is:

[0066] gs-opt=opt{gs-i} (5)

[0067]

[0068] Where n is the number of layers into which the canopy is divided, LAI i-1 The leaf area index of the lower boundary of the division layer, LAI i is the leaf area index of the upper boundary of the division layer, ξ is the leaf area index from a certain height of the canopy to the top of the canopy, opt{g s-i} is the stomatal conductance model with the best simulation accuracy screened out in this canopy layer.

[0069] Embodiment 1:

[0070] Based on the observation data of summer corn jointing period in 2016 at Beijing Daxing Irrigation Experiment Station, Jarvis models with different forms or combinations of environmental factors were screened, and combined with different types of stomatal conductance models. The model simulation accuracy was analyzed using the observation data in 2017. On this basis, the stomatal conductance model of summer corn in different growth periods was evaluated and a hierarchical upscaling model was constructed, which was verified using data from 2021.

[0071] (I) Collect key data for model construction, including photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field, specifically:

[0072] 1.1) The stomatal conductance (g) of a group of leaves was measured every 10 days using the Li-6400 photosynthesis measurement system (Li-COR, USA). s ), net photosynthetic rate (A n ), photosynthetically active radiation (PAR), leaf surface CO2 concentration (C s ), ambient CO2 concentration (C a ), and use a temperature gun to record the corresponding ambient temperature (T a ) and record the corresponding hygrometer humidity (h s ) data. The time range of each measurement is from 8:00 to 17:00, and the measurement is made every 1 hour. The observation time is fine-tuned according to the specific weather conditions.

[0073] 1.2) The latent heat flux was measured using an eddy covariance system, which was installed in the central and southern part of the study area and consisted of a three-dimensional ultrasonic anemometer (CAST3, Campbell Scientific, USA), an open-circuit CO2 / H2O gas analyzer (LI-7500, Li-Cor-Inc., USA), and a data logger (CR5000, Campbell Scientific, USA).

[0074] 1.3) During the crop growth period, select 3 representative plants with uniform growth in each plot and mark them. Use a steel ruler to measure the leaf length, leaf width and plant height of all effective leaves every 4-7 days, and calculate the leaf area index.

[0075] 1.4) To determine the extinction coefficient of the canopy, the SunScan canopy analysis system (Dynamax, Inc., USA) was used. In order to obtain accurate extinction coefficient values, a certain measurement frequency and time period were used. During the measurement process, measurements were taken every 15 days or so, and continuous measurements were selected between 10:00 am and 12:00 pm.

[0076] (II) Confirm that the growth period of the study is the jointing stage, the typical screening day is August 4, and the summer corn canopy is divided into three layers: upper, middle and lower. The specific methods are:

[0077] Based on the data collected in the first step, the stomatal conductance of leaves on a typical day (g s ), net photosynthetic rate (A n ), photosynthetically active radiation (PAR), leaf surface CO2 concentration (C s ), ambient CO2 concentration (C a ), ambient temperature (T a ), hygrometer humidity (h s ), latent heat flux, upper LAI, middle LAI, lower LAI, extinction coefficient, etc.

[0078] (III) Fitting the stomatal conductance model based on the collected observation results, the specific methods are:

[0079] Based on the data collected in the first step, the Jarvis two-factor model, Jarvis multi-factor model, BWB model, BBL model and USO unified stomatal model were used for the upper, middle and lower layers of the summer corn canopy. The fitting parameters of each model are shown in Table 2.

[0080] Table 2 Stomatal conductance model fitting parameters (α=0.01)

[0081]

[0082] 3.1) The Jarvis model is the product of the combined effects of several environmental factors. It is a multiplication model based on the assumption that the effects of environmental factors on stomatal conductance are independent of each other. The specific expression of the model is as follows:

[0083]

[0084] Among them, g smax is the maximum leaf stomatal conductance, in mol·m -2 s -1 , f1~f5 are empirical functions used to consider T a (℃), PAR (μmol m -2 s -1 )、VPD(kPa)、C a (μmol·mol), leaf water potential φ (kPa) on stomatal opening and closing, in the present invention, f4 (C a )=1, take f5(θ)=1.

[0085] There are different expressions for the single factor response model of stomatal conductance. In the present invention, a linear or nonlinear response function is adopted for each environmental factor. The response function of stomatal conductance to temperature is calculated using formulas (8) and (9):

[0086] f 1-1 (T a )=a1·T a +a2 (8)

[0087]

[0088] The response function of stomatal conductance to photosynthetically active radiation was calculated using equations (10) and (11):

[0089] f 2-1 (PAR) = b1 · PAR + b2 (10)

[0090]

[0091] The response function of stomatal conductance to saturated vapor pressure difference can be calculated using formulas (12) and (13):

[0092] f 3-1 (VPD) = c1·VPD + c2 (12)

[0093]

[0094] 3.2) The BWB model formula is as follows:

[0095]

[0096] Among them, P n is the net photosynthetic rate, expressed in μmol m 2 s -1 ,h s is the relative humidity of the air on the leaf surface, unit is %, C s is the CO2 concentration on the leaf surface, in μmol·mol -1 , m and g0 are unknown parameters.

[0097] The BBL model formula is as follows:

[0098]

[0099] Where Г is the CO2 compensation point, in μmol·mol -1 , VPD is the leaf surface water vapor pressure difference, the unit is kPa; m, VPD0 and g0 are unknown parameters.

[0100] 3.2) Unified pore optimization model

[0101] The formula of the unified stomatal optimization model (USO) is as follows:

[0102]

[0103] Among them, C a is the ambient CO2 concentration, in μmol·CO2·mol -1 , g0 and g1 are fitting parameters. It is worth mentioning that g1 of the unified stomatal model has biological significance, which can characterize the water use decision of plants.

[0104] (IV) Fitting stomatal conductance model g for different layers s-i Evaluate and select the optimal stomatal conductance model opt{g s-i}, as follows:

[0105] The present invention divides the corn canopy into three layers: upper, middle and lower, and compares the simulation accuracy of different stomatal conductance models in each layer. The accuracy evaluation is shown in Table 3. Therefore, it can be judged that the optimal stomatal conductance models for the upper, middle and lower layers are BBL model, USO model, g sd-2 Model.

[0106] Table 3 Accuracy evaluation of various stomatal conductance models at different canopy layers during the jointing period

[0107]

[0108] (V) For the optimal stomatal conductance model opt{g s-i}According to LAI iPerform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c , as follows:

[0109] According to the fourth step, the summer corn canopy is divided into three countable layers: upper, middle and lower, as shown in Table 4. Each layer uses the optimal stomatal conductance model (g s-opt ), and then use PAR as the scale conversion factor in each layer, assuming that the underlying surface is uniformly distributed and ignoring the influence of soil evaporation and the change of VPD in the canopy. s-opt Integrate, and then add up the integrals of the three layers to form a hierarchical upscaling model. Under this premise, the following formula is obtained:

[0110]

[0111] Where n is the number of layers into which the canopy is divided, and 3 is taken in the calculation. i-1 , LAI i are the leaf area index of the lower boundary and the leaf area index of the upper boundary of the layer respectively, and ξ is the leaf area index from a certain height of the canopy to the top of the canopy.

[0112] The radiation attenuation model was fitted using the measured data of the jointing stage in 2016, and the extinction coefficient of corn at the jointing stage and the correction coefficient of the light response correction model were obtained. Referring to the light response correction model, the relevant expressions are as follows

[0113]

[0114] In the formula, PAR a is the photosynthetically active radiation intercepted by the leaf surface, in μmol·m- 2 ·s -1 , R d is the dark respiration rate, in μmol·m -2 ·s -1 , a, b and c are correction coefficients;

[0115] In formula (18), the photosynthetically active radiation intercepted by the leaves, PARa, is calculated by the following formula:

[0116]

[0117] Table 4 Radiation attenuation model parameters

[0118]

[0119] Among them, the extinction coefficient K is 0.926, the correction coefficient a is 0.864, b is 0.421, and c is 0.248; the dark respiration rate R dThe values ​​of in different layers are different. Expanding equation (17) and substituting it into the specific model, we can get the summer corn stratification upscaling conductance g c The estimation model is as follows:

[0120]

[0121] Embodiment 2:

[0122] In another specific embodiment, in order to more intuitively reflect the accuracy of the constructed summer corn stratified upscaling model, the specific contents are as follows:

[0123] (I) Actual canopy conductance estimated by the Penman-Monteith model

[0124] The canopy-scale latent heat flux measured by the eddy covariance system is used to infer the actual canopy conductance g based on the Penman-Monteith formula. c

[0125]

[0126] d=0.63h c (twenty three)

[0127] z0=0.13h c (twenty four)

[0128] Among them, g a is the aerodynamic conductivity, in m / s; VPD is the saturated vapor pressure difference, in kPa; γ is the hygrometer constant, in kPa / ℃; Δ is the slope of the saturated vapor pressure-temperature curve, in kPa / ℃; Rn is the net radiation, in W / m 2 , G is the soil heat flux, unit is W / m 2 , Cp is the constant pressure specific heat of air, unit is J·kg -1 K -1 , k is the Karman constant, the value is 0.41, z is the reference height, the value is 2m, u z is the wind speed at the reference height, in m / s, h c is the canopy height in m, the zero plane displacement d and the roughness length z0 vary with the crop height h c changes with the changes.

[0129] 2. Evaluation of summer corn canopy conductance model

[0130] The measured value g obtained in the previous step c Compared with the model value calculated in Example 1, the results are as follows Figure 2 In the statistical analysis of simulated and measured values, R 2The value is 0.593. Although this value is not very close to 1, it does show that the model captures the variation of the measured values ​​to some extent. The RMSE is 1.13 mm·s -1 , which is a measure of the difference between the model prediction and the measured value. -1 The RMSE value indicates that the prediction error of the model is small. The modified d is 0.827, which indicates that there is good consistency between the model prediction and the measured values. According to the given statistical indicators, the performance of the model for canopy conductance at the jointing stage is acceptable. It captures the trend of the measured values ​​and maintains consistency with the measured values ​​to a certain extent.

[0131] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a summer corn stratification upscaling model, characterized in that: The following steps are involved: S1. Collect key data for model construction, including photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field; S2. By confirming the growth period and selecting typical days, the summer corn canopy is divided into i layers and observed and recorded to obtain the observation results; S3. Fitting the stomatal conductance model g based on the observation results s-i ; S4. Stomatal conductance model g for different layers s-i Evaluate and select the optimal stomatal conductance model opt{g s-i }; S5. The optimal stomatal conductance model for layer i opt{g s-i }According to LAI i Perform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c .

2. The method for constructing a summer corn stratification upscaling model according to claim 1, characterized in that: S1 collects key data for model construction, including photosynthetic instrument measurement data, weather station data, and crop physiological data measured in the field. The specific contents are as follows: The data measured by the photosynthetic instrument include stomatal conductance, transpiration rate, photosynthetic rate, and leaf temperature; Weather station data include light and effective radiation, saturated water vapor pressure, relative humidity, and air temperature; The crop physiological data measured in the field include plant height, leaf area, leaf area index, and extinction coefficient.

3. The method for constructing a summer corn stratification upscaling model according to claim 1, characterized in that: In S2, by confirming the growth period and selecting typical days, the summer corn canopy is divided into i layers and observed and recorded. The specific contents of the observation results are as follows: Combined with the actual sowing date of summer corn and the differences in the growth and development characteristics of summer corn, the growth period of summer corn stomatal conductance research is divided into jointing stage, tasseling stage, filling stage and maturity stage. Typical days need to be selected for different growth periods. The typical day is a clear, cloudless day with sufficient sunlight. According to the field measurement data, the summer corn canopy on a typical day is divided into i layers. The minimum value of i is 1, which means that the summer corn canopy is regarded as a uniform layer. The maximum value of i is the number of leaves on the summer corn plant on a typical day.

4. The method for constructing a summer corn stratification upscaling model according to claim 3, characterized in that: According to the field measurement data, the specific content of dividing the summer corn canopy into i layers in a typical day is: During the crop growth period, n plants with uniform growth are selected at different growth stages for marking. The number of effective leaves, leaf length, leaf width and plant height are measured with a steel ruler within a set time, and the leaf area index is calculated.

5. The method for constructing a summer corn stratification upscaling model according to claim 1, characterized in that: Stomatal conductance model g fitted in S3 s-i It is one or more of an empirical model and a semi-empirical model; The empirical models include the Jarvis two-factor model and the Jarvis multi-factor model; Semi-empirical models include BWB model, BBL model and USO unified stomatal model.

6. The method for constructing a summer corn stratification upscaling model according to claim 1, characterized in that: Stomatal conductance model g for different layers in S4 s-i Evaluate and select the optimal stomatal conductance model opt{g s-i The specific content of} is: All fitted stomatal conductance models of the i-th layer of the summer corn canopy were evaluated, and the stomatal conductance model with the best evaluation index of the i-th layer was selected by comprehensive comparison. The model evaluation indexes included the determination coefficient, root mean square error, Akaike information criterion and modified consistency coefficient. The calculation formula is as follows: Among them, R 2 is the coefficient of determination, RMSE is the root mean square error, AIC is the Akaike information criterion, d is the modified consistency coefficient, E i is the measured value, O i is the analog value, is the average of the measured values, k is the number of parameters in the model, and n is the number of groups of measured values ​​and simulated values.

7. The method for constructing a summer corn stratification upscaling model according to claim 1, characterized in that: The optimal stomatal conductance model for layer i in S5 is opt{g s-i }According to LAI i Perform upscaling integration and accumulate layer by layer to finally obtain the summer corn stratified upscaling model g c The specific content is: Where n is the number of layers into which the canopy is divided, LAI i-1 The leaf area index of the lower boundary of the division layer, LAI i is the leaf area index of the upper boundary of the division layer, ξ is the leaf area index from a certain height of the canopy to the top of the canopy, opt{g s-i } is the stomatal conductance model with the best simulation accuracy screened out in this canopy layer.

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