A construction method for a hierarchical upscaling model of summer maize

Abstract

The invention discloses a construction method of a summer maize hierarchical upscaling model, which relates to the technical field of agriculture. The method includes collecting key data for model construction; determining the growth period of research, screening typical days, and stratifying the summer maize canopy; fitting a stomatal conductance model according to the collected observation results; evaluating the fitted stomatal conductance models of different layers, and screening the optimal stomatal conductance model for each layer; performing upscaling integration on the optimal stomatal conductance models of each layer according to the stratified LAI, and accumulating layer by layer, and finally obtaining the summer maize hierarchical upscaling model. The invention has rich sample data, improves the reliability of the estimation model, reflects the differences in stomatal conductance among leaves of different canopies, and the obtained conclusions have important significance for regional research.

Description

Technical Field

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

[0002] Farmland evapotranspiration (ET) is a major component of the surface water balance and energy balance, and it plays a crucial role in the exchange of matter and energy between the land and the atmosphere. The variation of farmland evapotranspiration exhibits significant spatio-temporal variability, which is affected by the plant growth stage and canopy height. Stomata are the main channels for the exchange of water vapor between plants and the outside world, and their opening and closing states directly affect the process of soil water transfer to the atmosphere, thus affecting the amount of farmland evapotranspiration. Canopy conductance is an important indicator for measuring the opening and closing of stomata at the farmland canopy scale, and it reflects the water vapor transport capacity at the canopy level. Accurately estimating canopy conductance is of great significance for predicting farmland evapotranspiration, as 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 classified into the following categories according to their different theoretical bases: empirical models based on environmental factors, semi-empirical models based on photosynthesis or plant hormone regulation, and leaf models based on the theory of optimal behavior. Each model has its own advantages and limitations, and a suitable model needs to 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. Their development and application require the integration of multidisciplinary knowledge, such as meteorology, biology, etc., to improve the accuracy and applicability of the models, and to provide a comprehensive perspective for the study of the interaction between vegetation and the environment.

[0004] At present, there is little research on conductance models that can reflect the stomatal differences between the leaves of crop canopies. In order to clarify the mechanism between the crop canopy and the farmland environment, it is urgent to establish a hierarchical upscaling estimation model of canopy conductance. The key to scale-up lies in the upscaling of leaf area index. Canopy conductance is calculated from stomatal conductance and leaf area index or effective leaf area index, and then crop evapotranspiration is jointly estimated. On this basis, many researchers are committed to exploring the use of non-linear models to achieve the scale-up of the conversion from leaf stomatal conductance to canopy conductance. Due to the actual differences in stomatal conductance between sunlit leaves and shaded leaves, there are also obvious differences in photosynthetic rate and transpiration of leaves at different positions in the vegetation canopy. Therefore, in research, the canopy is often divided into two major categories: shaded leaves and sunlit leaves; among them, shaded leaves refer to the leaves that can only absorb scattered radiation, and sunlit leaves refer to the leaves that can absorb both direct and scattered radiation. Such a method can effectively reduce the errors in the simulation of canopy photosynthesis and conductance in the big leaf model. However, these models need to be calibrated by using measured evapotranspiration data to infer canopy conductance, and cannot reflect the stomatal conductance differences between different canopy leaves.

[0005] Therefore, it is an urgent problem for those skilled in the art to propose a method for constructing a hierarchical upscaling model of summer maize 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 hierarchical upscaling model of summer maize, which reflects the differences in stomatal conductance between leaves of different canopy layers, 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 solutions:

[0008] A method for constructing a hierarchical upscaling model of summer maize, comprising the following steps:

[0009] S1. Collect key data for model construction, where the key data includes photosynthesis meter measurement data, meteorological station data, and crop physiological data measured in the field;

[0010] S2. By confirming the growth period and screening typical days, divide the summer maize canopy into i layers, observe and record, and obtain the observation results;

[0011] S3. Fit the stomatal conductance model g s-i ;

[0012] S4. Evaluate the stomatal conductance models g s-i for different layers, and screen the optimal stomatal conductance model opt{g s-i} for the i-th layer;

[0013] S5. Perform upscaling integration on the optimal stomatal conductance model opt{g s-i} for the i-th layer according to LAI i , and accumulate layer by layer, and finally obtain the hierarchical upscaling model g c of summer maize.

[0014] Optionally, the specific content of the key data for model construction collected in S1, where the key data includes photosynthesis meter measurement data, meteorological station data, and crop physiological data measured in the field is as follows:

[0015] The photosynthesis meter measurement data includes stomatal conductance, transpiration rate, photosynthetic rate, and leaf temperature;

[0016] The meteorological station data includes photosynthetically active radiation, saturation vapor pressure, relative humidity, and air temperature;

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

[0018] Optionally, in S2, by confirming the growth period and screening typical days, the summer maize canopy is divided into i layers for observation and recording. The specific content of the observation results is as follows:

[0019] Combined with the actual sowing date of summer maize and the differences in the growth and development characteristics of summer maize, the growth periods for the study of the stomatal conductance of summer maize are divided into the jointing stage, tasseling stage, filling stage, and maturity stage. For different growth periods, typical days need to be screened. The typical days are weather conditions with clear skies and sufficient sunlight. According to the field measurement data, the summer maize canopy within the typical days is divided into i layers. The minimum value of i is 1, which means the summer maize canopy is regarded as a uniform layer, and the maximum value of i is the number of leaves on the summer maize plants on the typical day.

[0020] Optionally, the specific content of dividing the summer maize canopy into i layers within the typical days according to the field measurement data is as follows:

[0021] During the crop growth period, n plants with uniform growth are selected and marked during different growth periods. The effective leaf number, leaf length, leaf width, and plant height are measured using a steel ruler within a set time, and the leaf area index is calculated.

[0022] Optionally, the stomatal conductance model g fitted in S3 s-i is one or more of the empirical model and the semi-empirical model;

[0023] The empirical model includes the Jarvis two-factor model and the Jarvis multi-factor model;

[0024] The semi-empirical model includes the BWB model, the BBL model, and the USO unified stomatal model.

[0025] Optionally, in S4, the stomatal conductance models g for different layers s-i are evaluated to screen the optimal stomatal conductance model opt{g s-i} for the i layer. The specific content is as follows:

[0026] Evaluate all the fitted stomatal conductance models for the i-th layer of the summer maize canopy, and comprehensively compare and select the stomatal conductance model with the best index evaluation for the i-th layer. The model evaluation indexes include the coefficient of determination, root mean square error, Akaike information criterion, and modified consistency coefficient. The calculation formulas are as follows:

[0027]

[0028] 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 iwhere $\hat{y}$ is the simulated value, $\bar{y}$ is the average value of the measured values, $k$ is the number of parameters in the model, and $n$ is the number of groups of measured and simulated values.

[0029] Optionally, in S5, the optimal stomatal conductance model opt{gs-i} for layer i s-i is upscaled and integrated according to LAI i and accumulated layer by layer. Finally, the specific content of the upscaled model g c for summer maize by layer is as follows:

[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 is the leaf area index at the lower boundary of the divided layer, LAI i is the leaf area index at the upper boundary of the divided layer, $\xi$ is the leaf area index from a certain height in the canopy to the top of the canopy, and opt{gs} s-i is the stomatal conductance model with the best simulation accuracy selected in this layer of the canopy.

[0033] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a method for constructing an upscaled model for summer maize by layer, which has the following beneficial effects: Based on measured data, the present invention takes into account the characteristics of layer differences, improves the reliability of the estimation model, and the constructed upscaled model by layer can reflect the differences in stomatal conductance between leaves in different canopies. The conclusions obtained are of great significance for regional research. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0035] Figure 1 is a flowchart of a method for constructing an upscaled model for summer maize by layer provided by the present invention;

[0036] Figure 2 is the fitting effect presented by the upscaled model by layer provided by the present invention in simulating canopy conductance. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Referring to Figure 1 as shown, the present invention discloses a method for constructing a hierarchical upscaling model of summer maize, including the following steps:

[0039] S1. Collect key data for model construction, where the key data includes data measured by a photosynthesis meter, meteorological station data, and crop physiological data measured in the field;

[0040] S2. By confirming the growth stage and screening typical days, divide the summer maize canopy into i layers, observe and record, and obtain the observation results;

[0041] S3. Fit the stomatal conductance model g s-i ;

[0042] S4. Evaluate the stomatal conductance models g s-i for different layers, and screen the optimal stomatal conductance model opt{g s-i} for the i-th layer;

[0043] S5. Integrate the optimal stomatal conductance model opt{g s-i} for the i-th layer according to LAI i and accumulate layer by layer, and finally obtain the hierarchical upscaling model g c of summer maize.

[0044] Furthermore, the specific content of the key data for model construction collected in S1, where the key data includes data measured by a photosynthesis meter, meteorological station data, and crop physiological data measured in the field is as follows:

[0045] The data measured by the photosynthesis meter includes stomatal conductance, transpiration rate, photosynthetic rate, and leaf temperature;

[0046] The meteorological station data includes photosynthetically active radiation, saturation vapor pressure, relative humidity, and air temperature;

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

[0048] Furthermore, the specific content of obtaining the observation results by confirming the growth stage, screening typical days, dividing the summer maize canopy into i layers, observing and recording in S2 is as follows:

[0049] Combined with the actual sowing dates of summer maize and the differences in the growth and development characteristics of summer maize, the growth stages for the study of the stomatal conductance of summer maize are divided into the jointing stage, tasseling stage, filling stage, and maturity stage. For different growth stages, typical days need to be selected. The typical days are those with clear and cloudless skies and sufficient sunlight. According to the field measurement data, the canopy of summer maize within a typical day is divided into i layers. The minimum value of i is 1, which means the canopy of summer maize is regarded as a uniform layer, and the maximum value of i is the number of leaves on the summer maize plants on a typical day.

[0050] Specifically, experimental observations are carried out on the stomatal conductance, net photosynthetic rate, and related environmental factors of summer maize leaves; during each experimental observation, a randomly selected a typical maize plants evenly distributed are used as representative plants, and b functional leaves of uniform size and consistent light-receiving direction are selected for each plant. The measurement position is in the middle of the leaf, and during the measurement, the target leaf is always kept perpendicular to the sun's rays; after the data is stable, recording starts, and the mean values are taken respectively as the measurement results of the stomatal conductance, net photosynthetic rate, and related environmental factors of summer maize leaves.

[0051] The Li-6400 photosynthesis measurement system is used to conduct experimental observations on summer maize leaves. The Li-6400 photosynthesis measurement system (Li-COR, USA) is used to measure a set of stomatal conductance (g s ), net photosynthetic rate (A n ), photosynthetically active radiation (PAR), leaf surface CO2 concentration (C s ), ambient CO2 concentration (C a ) of leaves every 10 days, and at the same time, a thermometer gun is used to record the corresponding ambient temperature (T a ), and the humidity meter humidity (h s ) data is recorded. The time range for each measurement is from 8:00 to 17:00, and measurements are taken every 1 hour, and the observation time is adjusted accordingly according to the specific weather conditions.

[0052] Furthermore, the specific content of dividing the canopy of summer maize within a typical day into i layers according to the field measurement data is as follows:

[0053] During the crop growth period, n plants with uniform growth are marked during different growth stages. Within the set time, a steel ruler is used to measure the number of effective leaves, leaf length, leaf width, and plant height, and the leaf area index is calculated.

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

[0055] The test base covers an area of 200 m × 200 m. The test crop of summer maize is Xuenuo No. 2, which is planted in the middle and late June every year and harvested in early October. The growth period is usually from late June to early October. The growth period division of summer maize during the research period is shown in Table 1. There is sufficient precipitation during the growth period of summer maize, and irrigation is basically not required.

[0056] Table 1 Growth period division of summer maize

[0057] Year Jointing stage Tasseling stage Filling stage Maturity stage 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 is one or more of the empirical model and the semi-empirical model;

[0059] The empirical model includes the Jarvis two-factor model and the Jarvis multi-factor model;

[0060] The semi-empirical model includes the BWB model, the BBL model, and the USO unified stomatal model.

[0061] Furthermore, in S4, the stomatal conductance models g for different layers s-i are evaluated, and the specific content of screening the optimal stomatal conductance model opt{g s-i} for the i-th layer is as follows:

[0062] Evaluate all the fitted stomatal conductance models for the i-th layer of the summer maize canopy, and comprehensively select the stomatal conductance model with the best index evaluation for the i-th layer. The model evaluation indexes include the coefficient of determination, the root mean square error, the Akaike information criterion, and the modified consistency coefficient. The calculation formulas are as follows:

[0063]

[0064] 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 simulated value, is the average of measured values, k is the number of parameters in the model, and n is the number of groups of measured and simulated values.

[0065] Further, in S5, the optimal stomatal conductance model opt{g s-i} for layer i is integrated for upscaling according to LAI i and accumulated layer by layer. Finally, the specific content of the upscaling model g c for summer maize by layer is as follows:

[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 is the leaf area index at the lower boundary of the divided layer, LAI i is the leaf area index at the upper boundary of the divided layer, ξ is the leaf area index from a certain height in the canopy to the top of the canopy, and opt{g s-i} is the stomatal conductance model with the best simulation accuracy selected in this layer of the canopy.

[0069] Example 1:

[0070] Based on the observed data of summer maize at the jointing stage in 2016 at the Beijing Daxing Irrigation Experiment Station, Jarvis models with different forms or combinations of environmental factors were screened, combined with different types of stomatal conductance models. The simulation accuracy of the models was analyzed using the observed data in 2017. On this basis, the stomatal conductance models for different growth stages of summer maize were evaluated and the upscaling model by layer was constructed, and verified using the data in 2021.

[0071] (1) Collect key data for model construction, including photosynthesis meter measurement data, meteorological station data, and crop physiological data measured in the field, specifically:

[0072] 1.1) Use a Li-6400 photosynthesis measurement system (Li-COR, USA) to measure a set of leaf stomatal conductance (g s ), net photosynthetic rate (A n ), photosynthetically active radiation (PAR), leaf surface CO2 concentration (C s ), ambient CO2 concentration (C a ) every 10 days, and at the same time record the corresponding ambient temperature (T a ) with a temperature gun and the humidity (h s ) data of the hygrometer. The time range for each measurement is from 8:00 to 17:00, and measurements are taken every 1 hour, with corresponding fine-tuning of the observation time 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 south-central part of the study area and consisted of a three-dimensional ultrasonic anemometer (CAST3, Campbell Scientific, USA), an open-path CO2 / H2O gas analyzer (LI-7500, Li-Cor-Inc., USA), and a data logger (CR5000, Campbell Scientific, USA), etc.

[0074] 1.3) During the crop growth period, three representative plants with uniform growth were selected and marked in each plot. The leaf length, leaf width, and plant height of all effective leaves were measured with a steel ruler every 4 - 7 days, and the leaf area index was calculated.

[0075] 1.4) To measure the extinction coefficient of the canopy, a SunScan canopy analysis system (Dynamax, Inc., USA) was used. To obtain accurate extinction coefficient values, a certain measurement frequency and time period were adopted. During the measurement process, measurements were taken about every 15 days, and continuous measurements were carried out between 10:00 and 12:00 in the morning.

[0076] (2) It was confirmed that the growth period was the jointing stage, and the typical day was August 4th. The summer maize canopy was divided into three layers: upper, middle, and lower. The specific method was as follows:

[0077] Based on the data collected in the first step, the leaf stomatal conductance (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 ), humidity (h s ), latent heat flux, upper layer LAI, middle layer LAI, lower layer LAI, extinction coefficient, etc. of the typical day were screened and sorted out.

[0078] (3) According to the collected observation results, the stomatal conductance model was fitted. The specific method was as follows:

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

[0080] Table 2 Fitting parameters of the stomatal conductance model (α = 0.01)

[0081]

[0082] 3.1) Among them, the Jarvis model is the product of the combined action of several environmental factors. It is a multiplicative model established 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] where g smax is the maximum leaf stomatal conductance, with the unit of mol·m -2 s -1 , and f1 to f5 are empirical functions used to consider the effects of T a (°C), PAR (μmol m -2 s -1 ), VPD (kPa), C a (μmol·mol), and leaf water potential φ (kPa) on stomatal opening and closing. In the present invention, f4(C a ) = 1 and f5(θ) = 1 are taken.

[0085] The single-factor response models of stomatal conductance have different expressions. In the present invention, linear or non-linear response functions are 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 is calculated using formulas (10) and (11):

[0089] f 2-1 (PAR) = b1·PAR + b2 (10)

[0090]

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

[0092] f 3-1 (VPD) = c1·VPD + c2 (12)

[0093]

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

[0095]

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

[0097] The formula of the BBL model is as follows:

[0098]

[0099] Among them, Г is the CO2 compensation point, with the unit of μmol·mol -1 , and VPD is the vapor pressure difference on the leaf surface, with the unit of kPa; m, VPD0 and g0 are undetermined parameters.

[0100] 3.2) Unified stomatal optimization model

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

[0102]

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

[0104] (IV) Evaluate the fitting stomatal conductance models g s-i for different layers and screen the optimal stomatal conductance model opt{g s-i} for the i-th layer, as follows:

[0105] In the present invention, the maize canopy is divided into upper, middle and lower layers, and the simulation accuracies of different stomatal conductance models in each layer are compared respectively. 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 the BBL model, the USO model, and the g sd-2 model, respectively.

[0106] Table 3 Accuracy evaluation of each stomatal conductance model in different canopies at the jointing stage

[0107]

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

[0109] According to the fourth step, the summer maize canopy is divided into three countable upper, middle, and lower layers, as shown in Table 4. The optimal stomatal conductance model (g s-opt ) in each layer is selected. Then, PAR is used as the scale conversion factor in each layer, and assuming a uniform distribution of the underlying surface and ignoring the influence of soil evaporation and the change of VPD within the canopy, the g s-opt in each layer is integrated. After that, the integrals of the three layers are accumulated to obtain the hierarchical upscaling model. Under this prerequisite, the following formula is obtained:

[0110]

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

[0112] Use the measured data at the jointing stage in 2016 to fit the radiation attenuation model, and the extinction coefficient and the correction coefficient of the light response correction model for maize at the jointing stage can be 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, with the unit of μmol·m- 2 ·s -1 , R d is the dark respiration rate, with the unit of μmol·m -2 ·s -1 , and a, b, and c are correction coefficients;

[0115] The photosynthetically active radiation PARa intercepted by the leaf in formula (18) 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, c is 0.248; the dark respiration rate R at the jointing stage dThere are differences in the values at different layers. Expand Equation (17) and substitute it into the specific model to obtain the canopy conductance g for the hierarchical upscaling of summer maize c The estimation model is as follows:

[0120]

[0121] Example 2:

[0122] In another specific embodiment, to more intuitively reflect the accuracy of the constructed hierarchical upscaling model for summer maize, the specific content is as follows:

[0123] (1) The actual canopy conductance calculated by the Penman-Monteith model

[0124] Use the latent heat flux at the canopy scale measured by the eddy covariance system and inversely calculate the actual canopy conductance g based on the Penman-Monteith formula c

[0125]

[0126] d = 0.63h c (23)

[0127] z0 = 0.13h c (24)

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

[0129] (2) Evaluation of the summer maize canopy conductance model

[0130] Compare the measured value g c obtained in the previous step with the model value calculated in Example 1, and the result is as Figure 2 shown. In the statistical analysis of the simulated value and the measured value, R 2The value is 0.593. Although this value is not very close to 1, it does show that the model has captured the changes in the measured values to a certain extent. The RMSE is 1.13 mm·s -1 , which is a measure of the difference between the model predictions and the measured values. Based on the given range of measured and simulated values (approximately between 8 and 17 mm·s -1 ), the RMSE value indicates that the prediction error of the model is small. The corrected d is 0.827, which indicates good agreement between the model predictions and the measured values. According to the given statistical metrics, the model performs reasonably well in terms of canopy conductance at the jointing stage. It captures the trend of the measured values and shows a certain degree of consistency with the measured values.

[0131] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can 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 hierarchical upscaling model of summer maize, characterized in that, It includes the following steps: S1. Collect the key data for model construction, where the key data includes the data measured by a photosynthesis meter, the data from a weather station, and the crop physiological data measured in the field; S2. By confirming the growth stage, screening typical days, dividing the summer maize canopy into i layers, observing and recording, and obtaining the observation results; S3. Fit the stomatal conductance model g according to the observation results s-i ; S4. Evaluate the stomatal conductance models \(g\) for different layers s-i and screen the optimal stomatal conductance model \(\text{opt}\{g\) for layer \(i\) s-i \}; S5. Upscale and integrate the optimal stomatal conductance model opt{g s-i} of layer i according to LAI i and accumulate layer by layer. Finally, the upscaled model g c of summer maize by layer is obtained; Evaluate the stomatal conductance model g for different layers in S4 s-i and screen the optimal stomatal conductance model opt{g s-i} for layer i. The specific content is as follows: Evaluate all the fitted stomatal conductance models for the i-th layer of the summer maize canopy, and comprehensively select the stomatal conductance model with the best index evaluation for the i-th layer. The model evaluation indexes include the coefficient of determination, root mean square error, Akaike information criterion, and modified agreement coefficient, and the calculation formulas are 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 simulated value, is the average value of the measured values, k is the number of parameters in the model, and n is the number of groups of measured and simulated values; In S5, the optimal stomatal conductance model opt{g s-i} for layer i is upscaled and integrated according to LAI i , and accumulated layer by layer. Finally, the specific content of the upscaled model g c for summer maize by layer is as follows: gs -opt = opt{gs -i} (5) where n is the number of layers into which the canopy is divided, LAI i-1 is the leaf area index at the lower boundary of the divided layer, LAI i is the leaf area index at the upper boundary of the divided layer, ξ is the leaf area index from a certain height in the canopy to the canopy top, opt{g s-i} is the stomatal conductance model with the best simulation accuracy selected in this layer of the canopy.

2. According to the method for constructing a hierarchical upscaling model of summer maize described in claim 1, wherein The specific content of the key data for model construction collected in S1, where the key data includes the data measured by a photosynthesis meter, the data from a weather station, and the crop physiological data measured in the field is: The data measured by a photosynthesis meter includes stomatal conductance, transpiration rate, photosynthetic rate, and leaf temperature; The data from a weather station includes photosynthetically active radiation, saturation vapor pressure, relative humidity, and air temperature; The crop physiological data measured in the field includes plant height, leaf area, leaf area index, and extinction coefficient.

3. According to the method for constructing a hierarchical upscaling model of summer maize described in claim 1, wherein The specific content of S2, where by confirming the growth stage, screening typical days, dividing the summer maize canopy into i layers, observing and recording, and obtaining the observation results is: Combining the actual sowing date of summer maize and the differences in the growth and development characteristics of summer maize, the growth stages for studying the stomatal conductance of summer maize are divided into the jointing stage, tasseling stage, filling stage, and maturity stage. Different growth stages require screening typical days, and the typical days are sunny and cloudless days with sufficient sunlight. According to the field measurement data, the summer maize canopy within the typical day is divided into i layers. The minimum value of i is 1, which means regarding the summer maize canopy as a uniform layer, and the maximum value of i is the number of leaves on the summer maize plant on the typical day.

4. According to the method for constructing a hierarchical upscaling model of summer maize described in claim 3, wherein The specific content of dividing the summer maize canopy into i layers within the typical day according to the field measurement data is: During the crop growth stage, select n plants with uniform growth vigor for marking during different growth stages. Measure the number of effective leaves, leaf length, leaf width, and plant height using a steel ruler within a set time, and calculate the leaf area index.

5. According to the method for constructing a hierarchical upscaling model of summer maize described in claim 1, wherein The stomatal conductance model g fitted in S3 s-i is one or more of empirical models and semi-empirical models; The empirical models include the Jarvis two-factor model and the Jarvis multi-factor model; The semi-empirical models include the BWB model, the BBL model, and the USO unified stomatal model.

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