CN110378521B - Construction and application of yield prediction model of winter wheat in northeast China of Henan province - Google Patents

Construction and application of yield prediction model of winter wheat in northeast China of Henan province Download PDF

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CN110378521B
CN110378521B CN201910575210.6A CN201910575210A CN110378521B CN 110378521 B CN110378521 B CN 110378521B CN 201910575210 A CN201910575210 A CN 201910575210A CN 110378521 B CN110378521 B CN 110378521B
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时雷
段其国
张娟娟
马新明
宋利红
秦雅倩
王健
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Abstract

The invention discloses construction and application of a winter wheat yield prediction model in northeast of Henan province, and aims to solve the technical problem that the yield of winter wheat cannot be accurately and precisely predicted by the conventional method. The invention provides a construction method of a winter wheat yield prediction model in a certain region and a winter wheat yield prediction method in the region. The method can accurately and precisely predict the yield of winter wheat in northeast of Henan province, provides a new idea for intelligent decision diagnosis of winter wheat production in Henan province, provides guidance and suggestion for agricultural production decision, can promote the efficiency of agricultural production, and avoids unnecessary loss.

Description

Construction and application of yield prediction model of winter wheat in northeast China of Henan province
Technical Field
The invention relates to the technical field of agricultural engineering, in particular to construction and application of a winter wheat yield prediction model in northeast Henan province.
Background
Winter wheat is a main food crop in China and is closely related to food safety in China. The Henan province is one of the core areas of grain production in China, the winter wheat planting area accounts for more than 70% of the cultivated land area, the winter wheat yield in the Henan province accounts for more than 25% of the total wheat yield in China, the northeast of the Henan province is also the main winter wheat planting area in the Henan province, and the stable yield and high yield of the winter wheat in the northeast planting area in the Henan province are of great importance to guarantee the grain safety of China.
The yield of winter wheat has a great impact on global economy. The traditional winter wheat yield estimation method mainly includes that regional investigation is carried out manually after winter wheat is harvested, and the yield of the winter wheat can be estimated relatively accurately under the condition of not using more agricultural information. Scholars at home and abroad begin to gradually estimate the yield of winter wheat by using a remote sensing technology. At present, the winter wheat is mainly estimated by a crop estimation model utilizing meteorological factors and a crop estimation model utilizing remote sensing, but the meteorological factors have strong regionality and the meteorological factors in different regions change greatly, so the crop estimation model utilizing the meteorological factors has certain limitation and cannot meet the estimation of the crop yield in a large range.
Therefore, in order to improve the prediction accuracy of the yield of the winter wheat, a method capable of timely, accurately and precisely predicting the yield of the winter wheat needs to be developed urgently, guidance and suggestions are provided for agricultural production decisions, so that the agricultural production efficiency can be promoted, and unnecessary loss is avoided.
Disclosure of Invention
The invention aims to solve the technical problem of providing construction and application of a winter wheat yield prediction model suitable for northeast of Henan province so as to solve the technical problem that the yield of winter wheat cannot be accurately and precisely predicted by the conventional method.
In order to solve the technical problems, the invention adopts the following technical scheme:
designing a construction method of a yield prediction model of winter wheat in northeast of Henan province, wherein the construction process comprises the temperature of each growth period of the winter wheat and a yield per unit prediction model of remote sensing elements; and acquiring historical data, solving the yield per unit model, and predicting the yield of the winter wheat according to the model. In the selection of the yield sensitive factors of the winter wheat, the air temperature in the growth process of the winter wheat is an extremely important factor, and the influence of the air temperature on the winter wheat is quantified by utilizing a Growth Degree Day (GDD) and an Extreme Degree Day (EDD), wherein the GDD represents the accumulated temperature in the growth process of the winter wheat, and the EDD represents the influence of the extreme air temperature on the growth of the winter wheat; in addition, the yield can be reflected by selecting the growth vigor of winter wheat, and then the plant growth vigor can be reflected by selecting the normalized vegetation index (NDVI).
The method specifically comprises the following steps:
(1) the following parameters or data of corresponding years are obtained aiming at a certain region in northeast China:
a. acquiring unit yield data of winter wheat corresponding to each local year;
b. calculating the apparent air temperature of the wheat in a certain hour in a certain day in the growth period of the corresponding year according to the historical measurement record or the following formula:
Figure 100002_DEST_PATH_IMAGE001
-formula (IV);
in the formula, TiIndicating the apparent temperature, T, within a certain hour (e.g., 0-1 hour, 1-2 hours, 2-3 hours, 3-4 hours, etc.) within a certain day of the wheat growth periodmaxIndicating the highest temperature, T, of the dayminDenotes the lowest air temperature of the day, T'minRepresents the lowest temperature on the next day, h0Represents the sunrise time, h 'of the local day'0Representing the sunset time of the current day;
c. the growth degree of the local winter wheat in the whole growth period of the corresponding year is calculated according to the following formula:
Figure 989529DEST_PATH_IMAGE002
-formula (II);
in the formula, GDD is the growth degree day of a certain day in the growth period of wheat; t isiIndicating the apparent temperature, T, within a certain hour of a certain day during the growth period of wheatoptRepresents the optimum growth temperature, T, of winter wheatbaseRepresenting the reference growth temperature of local winter wheat;
d. the extreme days of winter wheat during the full growth period of the corresponding year were calculated as follows:
Figure 100002_DEST_PATH_IMAGE003
-formula (III);
in the formula, EDD is an extreme degree day of a certain day in the growth period of wheat; t isiIndicating the apparent temperature, T, within a certain hour of a certain day during the growth period of wheatoptRepresents the optimal growth temperature, T, of local winter wheatcritThe minimum temperature limit value of the local winter wheat in each growth period is shown;
e. obtaining remote sensing data of local wheat during the growth period of 4 months, and calculating to obtain an NDVI peak value of the local wheat during the growth period of 4 months:
Figure 719719DEST_PATH_IMAGE004
-formula (VI);
in the formula, NDVI is a normalized vegetation index; xIRIs the reflection value of the near infrared band, XRIs the reflection value of the red light wave band;
NDVI is the best indicator of the vegetation growth state and the vegetation coverage, and for the main coverage of the land surface, cloud, water and snow have higher reflection action in a visible light wave band than a near infrared wave band, so the NDVI value is a negative value (< 0); the rock and the bare soil have similar reflection effects in two wave bands, and the NDVI value is close to 0; in the case of vegetation coverage, NDVI values are positive (> 0) and increase with increasing coverage of vegetation. Several typical types of ground cover are distinguished on large-scale NDVI images, and vegetation is effectively highlighted.
(2) Carrying out regression fitting on the obtained parameters or data corresponding to the years according to the following formula to obtain beta0、βG、βEAnd betaNAnd obtaining a corresponding winter wheat yield prediction model according to the corresponding value:
Figure 100002_DEST_PATH_IMAGE005
-formula (V);
wherein Y is the unit yield of winter wheat, and SGDD and SEDD are respectively the sum of length days and the sum of extreme days; NDVI is the peak value of the normalized vegetation index of winter wheat during 4 months of growth; beta is a0Is the intercept of the equation; beta is aG、βEThe weight coefficients of the single yield influence degree of the wheat on the yield of the winter wheat by SGDD and SEDD are respectively; beta is aNAnd the weight coefficient represents the influence degree of the normalized vegetation index on the yield of the winter wheat.
The method for constructing the more accurate model for predicting the yield of winter wheat in northeast China of Henan province specifically comprises the following steps:
(1) the following parameters or data of corresponding years are obtained aiming at a certain region in northeast China:
a. acquiring unit yield data of winter wheat corresponding to each local year;
b. the apparent air temperature at a certain hour on a certain day during the whole growth period of the corresponding year is measured and recorded or calculated according to the following formula:
Figure 561773DEST_PATH_IMAGE001
-formula (IV);
in the formula, TiIndicating the apparent temperature, T, in a certain hour within a certain day of the wheat growth periodmaxIndicating the highest temperature, T, of the dayminDenotes the lowest air temperature of the day, T'minRepresents the lowest temperature on the next day, h0Represents the sunrise time, h 'of the local day'0Representing the sunset time of the current day;
c. the growth degree in the whole growth period of the winter wheat of the corresponding year is calculated according to the following formula:
Figure 956982DEST_PATH_IMAGE002
-formula (II);
in the formula, GDD is the growth degree day of a certain day in the growth period of wheat;Tiindicating the apparent temperature, T, within a certain hour of a certain day during the growth period of wheatoptRepresents the optimum growth temperature, T, of winter wheatbaseRepresenting the reference growth temperature of winter wheat;
d. the extreme degree days in the whole growth period of the winter wheat of the corresponding year are calculated according to the following formula:
Figure 581474DEST_PATH_IMAGE003
-formula (III);
in the formula, EDD is an extreme degree day of a certain day in the growth period of wheat; t isiIndicating the apparent temperature, T, within a certain hour of a certain day during the growth period of wheatoptRepresents the optimum growth temperature, T, of winter wheatcritRepresenting the lowest temperature threshold value of each growth period of the winter wheat;
e. obtaining remote sensing data of local wheat in a corresponding growth period, and respectively calculating to obtain normalized vegetation index average NDVI of the wheat in the periods of 14 th week, 17 th week and 19 th week of the natural year1、NDVI2、NDVI3；
(2) Carrying out regression fitting on the obtained corresponding parameters or data of the years according to the following formula to obtain beta0、βG、βE、βN1、βN2And betaN3And obtaining a corresponding winter wheat yield prediction model according to the corresponding value:
Y=β0+βGSGDD+βESEDD+βN1NDVI1+βN2NDVI2+βN3NDVI3-formula (VII);
wherein Y is the unit yield of winter wheat, and SGDD and SEDD are respectively the sum of length days and the sum of extreme days; NDVI1、NDVI2、NDVI3The normalized vegetation index average values of the wheat during the 14 th week, the 17 th week and the 19 th week of the natural year respectively; beta is a0Is the intercept of the equation; beta is aG、βEThe weight coefficients of the influence degrees of SGDD and SEDD on the yield of the winter wheat are respectively; beta is aN1、βN2And betaN3Are respectively provided withAnd the weight coefficient represents the influence degree of the normalized vegetation index of each growth period of the winter wheat on the yield of the winter wheat. Further: in the step b, the sunrise or sunset time of each place is calculated according to an astronomical calendar by combining longitude and latitude of the place and the date of the place.
Further: in the step c and/or d, the calculation is carried out in two stages by taking 4 months and 10 days as a boundary, the standard growth temperature of 4 months and 10 days and before is 2 ℃, and the optimal growth temperature is 16 ℃; after 4 months and 10 days, the standard growth temperature is 16 ℃, and the optimal growth temperature is 30 ℃.
Further: in the step e, the telemetry data is derived from MODIS satellite telemetry data of the website of the United states LAADS DAAC.
Further: in the step e, the NDVI value is analyzed based on MODIS data, then a distribution diagram of the winter wheat planting area of the area is obtained by combining the change characteristics that the NDVI value is increased and then decreased on a time sequence in the growth process of the winter wheat in the northeast of Henan province, and finally the NDVI value in the whole growth period of the winter wheat planting area of the area is obtained through one-step calculation by utilizing the distribution diagram of the winter wheat planting area.
Further, the identification method is based on the following winter wheat pixel identification method in the process of obtaining the winter wheat planting area distribution map:
NDVI8th week>0.2 and NDVI12th week>NDVI8th week and NDVI17th week >NDVI12th week and NDVI17th week>0.5 and NDVI19th week>0.5 and NDVI19th week>NDVI21th week and NDVI23th week<0.35; wherein NDVInth weekAnd (3) carrying out rule verification on pixels in different periods of the area where all Henan provinces are located for the NDVI value of the winter wheat in the nth week in the growth and green turning period of the winter wheat, and marking the pixels as winter wheat planting coverage pixels if the pixels meet the rule requirements.
The method for predicting the yield of the winter wheat comprises the following steps:
(1) calculating or counting SGDD and SEDD, NDVI or NDVI of the whole growth period of the wheat to be tested in a certain area1、NDVI2、NDVI3Parameter (d) ofOr data;
(2) and substituting each parameter or data into the obtained winter wheat yield prediction model to obtain the winter wheat yield prediction in the current year in the area.
Further: the GDD and the EDD are calculated in two stages by taking 4 months and 10 days as a boundary, the standard growth temperature of 4 months and 10 days and before is 2 ℃, and the optimal growth temperature is 16 ℃; after 4 months and 10 days, the standard growth temperature is 16 ℃, and the optimal growth temperature is 30 ℃.
Compared with the prior art, the invention has the beneficial technical effects that:
the method can accurately and precisely predict the yield of winter wheat in the northeast of Henan province, can fully utilize the existing meteorological and remote sensing data, has strong operability, low prediction cost and strong timeliness, can further utilize big data to continuously improve the precision of a prediction model, provides a new idea for intelligent decision diagnosis of winter wheat production in the northeast of Henan province, provides guidance and suggestion for agricultural production decisions, can promote the efficiency of agricultural production, and avoids unnecessary loss.
Drawings
FIG. 1 is a graph showing the variation of winter wheat yield in Henan province over the years;
fig. 2 is a graph showing the variation trend of the growth length daily Sum (SGDD) and the extreme daily Sum (SEDD) of the southern Henan province over the years.
Detailed Description
The following examples are intended to illustrate the present invention in detail and should not be construed as limiting the scope of the present invention in any way. The parameters and terms referred to in the following examples are those conventional in the art, unless otherwise specified.
Example 1: model construction based on growth degree daily Sum (SGDD) and extreme daily Sum (SEDD)
The total sum of GDD and EDD of winter wheat from the sowing date to the harvest of winter wheat is SGDD and SEDD, respectively. The whole growth period of winter wheat from sowing to harvesting is 10 and 15 days in the first year to 6 and 1 days in the second year.
The sowing date of the whole Henan province is uniformly selected as 10 months and 15 days per year, and the harvesting date is determined as 6 months and 1 day per year. Establishing a regression model by taking the yield of the winter wheat as a dependent variable and taking SGDD and SEDD in the growth process of the winter wheat as independent variables, wherein the formula is as follows:
Figure DEST_PATH_IMAGE006
-formula (I);
in the formula, Y is the yield of winter wheat, and SGDD and SEDD respectively represent the sum of length days and the sum of extreme days; beta is a0Is the intercept of the equation; beta is aG、βEThe degrees of the influence of SGDD and SEDD on the yield of the winter wheat are respectively and correspondingly represent the change of the yield of the winter wheat when the SGDD and the SEDD change.
The calculation formula of the GDD and the EDD is as follows:
Figure 279302DEST_PATH_IMAGE002
-formula (II);
Figure 608653DEST_PATH_IMAGE003
-formula (III);
in the formula, TiIndicating the apparent temperature per hour, ToptRepresents the optimum growth temperature, T, of winter wheatbaseRepresents the reference growth temperature, T, of winter wheatcritRepresents the lowest air temperature limit of the winter wheat in each growth period.
Wherein the calculation formula of the air temperature per hour is as follows:
Figure 807553DEST_PATH_IMAGE001
-formula (IV);
in the formula, TiIndicating the apparent temperature per hour, TmaxIndicating the highest temperature, T, of the dayminDenotes the lowest air temperature of the day, T'minRepresents the lowest temperature on the next day, h0Representing longitude and latitude from various placesAnd sunrise time, h 'calculated by date for sunrise and sunset times of respective cities'0Representing the sunset time of each place;
through analyzing the jointing date of winter wheat in the past year, the fact that the jointing date of the winter wheat in 4 months and 10 days per year is approximately half a month of the jointing date of the winter wheat and is the last stage of late frost quantification of the winter wheat is found, therefore, the winter wheat is divided into two stages for calculation by taking the 10 days in 4 months per year as a boundary, the standard growth temperature selected in the first stage is 2 ℃, and the optimal growth temperature is 16 ℃; the reference growth temperature selected in the second stage is 16 ℃, and the optimal growth temperature is 30 ℃.
The change trends of the winter wheat yield, SGDD and SEDD of Henan province for continuous decades are shown in figures 1 and 2, and it can be seen from the figures that the winter wheat yield and SEDD of Henan province show a trend of rising year by year, the rising trend is obvious, and the SGDD also rises year by year, but the rising amplitude is not as large as the former two.
In the embodiment of the invention, a yield model of each region in Henan province is constructed by using yield data, SGDD data and SEDD data of 5 prefectures in northeast of Henan province from 2007 to 2016 for 10 years, and is specifically shown in Table 1:
table 1 winter wheat yield prediction model based on SGDD and SEDD
Figure DEST_PATH_IMAGE007
。
Example 2: multivariate based model construction
The yield in the growth process of the winter wheat is a result of comprehensive action of various factors, wherein a normalized vegetation index (NDVI) value can directly reflect photosynthesis and growth vigor of the winter wheat and is closely related to the yield of the winter wheat, the NDVI is used as an influence factor of a model, and the NDVI value adopted in the model construction is the peak value of the NDVI of the winter wheat in April.
Predicting the yield of the winter wheat based on multivariate, and establishing a regression model of the yield of the winter wheat and SGDD, SEDD and NDVI, wherein the formula is as follows:
Figure 289481DEST_PATH_IMAGE005
-formula (V);
wherein Y is winter wheat yield per unit, beta0Is the intercept of the equation; beta is aG、βEThe degree of influence of SGDD and SEDD on the yield of winter wheat, betaNRepresenting the degree of influence of NDVI on winter wheat yield.
And calculating the NDVI value by using the MODIS remote sensing image. The value of NDVI can be obtained by calculating the red wave band and the near infrared wave band of the MODIS image, and the specific calculation formula of the NDVI is as follows:
Figure 282845DEST_PATH_IMAGE004
-formula (VI);
in the formula, XIRIs the reflection value of the near infrared band, XRIs the reflection value of the red light wave band.
Calculating the vegetation indexes of all green plants on the ground according to the formula (VI), and then combining the change characteristics that the NDVI value is increased firstly and then reduced on a time sequence in the growing process of winter wheat in the northeast of Henan province to obtain a planting area distribution diagram of the winter wheat in the Henan province; and finally, further calculating to obtain the full-growth-period NDVI value of the winter wheat planting area in the northeast region of Henan province by using the planting area distribution map, and solving the NDVI peak value of the winter wheat for later use in a period of 4 months.
Combining the characteristic of an NDVI curve of a typical ground feature in Henan province, and gradually increasing the NDVI value of the planting area of the winter wheat from 2 months to 5 months and reducing the NDVI value from 5 months to 6 months according to the change rule of the NDVI in the growth process of the winter wheat. The NDVI values of the green turning period, the elongation period, the booting period, the heading period, the flowering period and the filling period in the growth process of the winter wheat are different, based on the low threshold value comparison of the green turning period and the filling period in the period with higher NDVI, the identification rule of the winter wheat pixel is established, the areas of the winter wheat, the evergreen forest and the grassland are extracted, and therefore the planting information of the winter wheat in Henan province is extracted, and the specific identification method is as follows:
NDVI8th week>0.2 and NDVI12th week>NDVI8th week and NDVI17th week >NDVI12th week and NDVI17th week>0.5 and NDVI19th week>0.5 and NDVI19th week>NDVI21th week and NDVI23th week<0.35。
wherein NDVInth weekAnd carrying out rule verification on pixels in different periods of the area where all Henan provinces are located for NDVI values of the winter wheat in the nth week in the growth and green turning period of the winter wheat, marking the pixels as winter wheat planting coverage pixels if the pixels meet the rule requirements, and finally counting the areas of the pixels in all the winter wheat planting areas, wherein the result shows that the ratio of the winter wheat planting area counted by the pixels to the winter wheat planting area counted by the Henan province officially counted by the Henan province is 95-105%.
The statistical yield data, the SGDD data, the SEDD data and the NDVI data of 5 prefectures in northeast of the south of the river from 2007 to 2016 for 10 years are used to construct yield models of various regions in the south of the river, as shown in table 2.
TABLE 2 multivariate-based winter wheat yield prediction model
Figure 99491DEST_PATH_IMAGE008
。
Example 3: improvements to multivariate models
The normalized vegetation index (NDVI) value in the growth process of the winter wheat can directly reflect photosynthesis and vigor of the winter wheat, the winter wheat grows more prosperous from 4 months to 5 months, and the correlation between the vigor of the winter wheat and the yield of the winter wheat is stronger, so that the NDVI values at 14 weeks, 17 weeks and 19 weeks are selected as an influence factor of the model.
The model in the example 2 is improved, the yield of the winter wheat is predicted, and a regression model is established for the yield of the winter wheat, SGDD, SEDD and NDVI of each period, wherein the formula is as follows:
Y=β0+βGSGDD+βESEDD+βN1NDVI1+βN2NDVI2+βN3NDVI3-formula (VII);
in the formula, Y is the unit yield of winter wheat, and SGDD and SEDD are respectively the sum of length days and the sum of extreme days; NDVI1Is the average of the 14 week NDVI calculated using formula (VI) in example 2; NDVI2Is the average of the week 17 NDVI calculated using formula (VI) in example 2; NDVI3Is the average of the week 19 NDVI calculated using formula (VI) in example 2; beta is a0Is the intercept of the equation; beta is aG、βEThe degree of influence of SGDD and SEDD on the yield of winter wheat, betaN1、βN2And betaN3Representing the influence degree of the normalized vegetation index of each growth period of the winter wheat on the yield of the winter wheat.
The statistical yield data, SGDD data, SEDD data and NDVI data of each period from 2007 to 2016 10 years of 5 prefectures in northeast of the south of the river are used to construct a yield model of each region in the south of the river, as shown in table 3.
TABLE 3 improvement of multivariate model
Figure DEST_PATH_IMAGE009
。
Compared with the model, long-term experimental research shows that the precision of the model is obviously improved after multivariate improvement, the NDVI value is considered separately, the relation between the NDVI value and the yield of winter wheat can be embodied, errors generated by single variables can be weakened, and compared with the table 2 and the table 3, the precision of the model in the market is improved to a certain extent, and the precision of the model is more than 0.6, so that the model has more reference significance compared with the model in the embodiment 2.
While the present invention has been described in detail with reference to the drawings and the embodiments, those skilled in the art will understand that various specific parameters in the above embodiments can be changed without departing from the spirit of the present invention, and a plurality of specific embodiments are formed, which are common variation ranges of the present invention, and will not be described in detail herein.

Claims (9)

1. A method for constructing a model for predicting yield of winter wheat in North Henan comprises the following steps:
(1) the following parameters or data of corresponding years are obtained aiming at a certain region in northeast China:
a. acquiring winter wheat yield per unit corresponding to each year in the region;
b. calculating the apparent air temperature T in a certain hour of a certain day in the winter wheat growth period of the corresponding year in the region according to the historical measurement record or the following formulai：
Figure DEST_PATH_IMAGE001
-formula (IV);
in the formula, TmaxIndicating the highest temperature, T, of the dayminDenotes the lowest air temperature of the day, T'minRepresents the lowest temperature on the next day, h0Represents the sunrise time, h 'of the current day in the area'0Representing the sunset time of the area on the day;
c. calculating the GDD of the winter wheat in the corresponding year in the region in the growing period of a certain day according to the following formula:
Figure 471639DEST_PATH_IMAGE002
-formula (II);
in the formula, TiIndicating the apparent temperature, T, of the winter wheat within a certain hour of a certain day during the growth periodoptRepresents the optimal growth temperature, T, of local winter wheatbaseRepresenting the reference growth temperature of local winter wheat;
d. calculating the EDD of the winter wheat in the corresponding year in the area on an extreme day in the whole growth period according to the following formula:
Figure DEST_PATH_IMAGE003
-formula (III);
in the formula, TiIndicating the apparent temperature, T, of the winter wheat within a certain hour of a certain day during the growth periodoptRepresents the optimal growth temperature, T, of local winter wheatcritThe minimum temperature limit value of the local winter wheat in each growth period is shown;
e. remote sensing data of the local winter wheat during the growth period of 4 months are obtained, and the NDVI peak value of the local winter wheat during the growth period of 4 months is calculated:
(2) carrying out regression fitting on the obtained parameters or data corresponding to the years according to the following formula to obtain beta0、βG、βEAnd betaNAnd obtaining a corresponding winter wheat yield prediction model according to the corresponding value:
Figure 975170DEST_PATH_IMAGE004
-formula (V);
wherein Y is the unit yield of winter wheat, and SGDD and SEDD are respectively the sum of length days and the sum of extreme days; NDVI is the peak value of the normalized vegetation index of winter wheat during 4 months of growth; beta is a0Is the intercept of the equation; beta is aG、βEThe weight coefficients of the single yield influence degree of the wheat on the yield of the winter wheat by SGDD and SEDD are respectively; beta is aNAnd the weight coefficient represents the influence degree of the normalized vegetation index on the yield of the winter wheat.
2. A method for constructing a model for predicting yield of winter wheat in North Henan comprises the following steps:
(1) the following parameters or data of corresponding years are obtained aiming at a certain region in northeast China:
a. acquiring unit yield data of winter wheat corresponding to each local year;
b. measuring and recording or calculating the apparent temperature T in a certain hour of a certain day in the winter wheat growth period of the corresponding year in the region according to the following formulai：
Figure DEST_PATH_IMAGE005
-formula (IV);
in the formula, TmaxIndicating the highest temperature, T, of the dayminDenotes the lowest air temperature of the day, T'minIndicates the lowest dayAir temperature, h0Represents the sunrise time, h 'of the current day in the area'0Representing the sunset time of the area on the day;
c. calculating the GDD of the winter wheat in the corresponding year in the region in the growing period of a certain day according to the following formula:
Figure 507652DEST_PATH_IMAGE002
-formula (II);
in the formula, TiIndicating the apparent temperature, T, of the winter wheat within a certain hour of a certain day during the growth periodoptRepresents the optimal growth temperature, T, of local winter wheatbaseRepresenting the reference growth temperature of local winter wheat;
d. calculating the EDD of the extreme degree day of a certain day in the whole growth period of the winter wheat of the corresponding year according to the following formula:
Figure 454748DEST_PATH_IMAGE003
-formula (III);
in the formula, TiIndicating the apparent temperature, T, of the winter wheat within a certain hour of a certain day during the growth periodoptRepresents the optimal growth temperature, T, of local winter wheatcritThe minimum temperature limit value of the local winter wheat in each growth period is shown;
e. obtaining remote sensing data of the winter wheat in the corresponding growth period of the area, and respectively calculating to obtain normalized vegetation index average values NDVI of the winter wheat in the 14 th week, the 17 th week and the 19 th week of the 2 nd natural year after sowing1、NDVI2、NDVI3；
(2) Carrying out regression fitting on the obtained corresponding parameters or data of the years according to the following formula to obtain beta0、βG、βE、βN1、βN2And betaN3And obtaining a corresponding winter wheat yield prediction model according to the corresponding value:
Y=β0+βGSGDD+βESEDD+βN1NDVI1+βN2NDVI2+βN3NDVI3-formula (VII);
wherein Y is the unit yield of the winter wheat, SGDD is the sum of growing days in the growing period of the winter wheat, and SEDD is the sum of extreme days in the growing period of the winter wheat; NDVI1、NDVI2、NDVI3The normalized vegetation index average values of winter wheat in the 14 th week, 17 th week and 19 th week of the 2 nd natural year after sowing are respectively obtained; beta is a0Is the intercept of the equation; beta is aG、βEThe weight coefficients of the influence degrees of SGDD and SEDD on the yield of the winter wheat are respectively; beta is aN1、βN2And betaN3And the weight coefficients respectively represent the influence degree of the normalized vegetation indexes of the winter wheat in each growth period on the yield of the winter wheat.
3. The method for constructing a model for predicting yield of winter wheat in northeast of Henan province according to claim 1 or 2, wherein: in the step b, the sunrise or sunset time of each place is calculated according to an astronomical calendar by combining longitude and latitude of the place and the date of the place.
4. The method for constructing a model for predicting yield of winter wheat in northeast of Henan province according to claim 1 or 2, wherein: in the step c and/or d, the calculation is carried out in two stages by taking 4 months and 10 days as a boundary, the standard growth temperature of 4 months and 10 days and before is 2 ℃, and the optimal growth temperature is 16 ℃; after 4 months and 10 days, the standard growth temperature is 16 ℃, and the optimal growth temperature is 30 ℃.
5. The method for constructing a model for predicting yield of winter wheat in northeast of Henan province according to claim 1 or 2, wherein: in the step e, the telemetry data is derived from MODIS satellite telemetry data of the website of the United states LAADS DAAC.
6. The method for constructing the model for predicting yield of winter wheat in northeast China Yu according to claim 5, wherein: in the step e, the NDVI value is analyzed based on MODIS data, then a distribution diagram of the winter wheat planting area of the area is obtained by combining the change characteristics that the NDVI value is increased and then decreased on a time sequence in the growth process of the winter wheat in the northeast of Henan province, and finally the NDVI value in the whole growth period of the winter wheat planting area of the area is obtained through one-step calculation by utilizing the distribution diagram of the winter wheat planting area.
7. The method for constructing the model for predicting yield of winter wheat in northeast China Yu according to claim 6, wherein: the identification method is based on the following winter wheat pixel identification method in the process of obtaining the winter wheat planting area distribution map:
NDVI8th week>0.2 and NDVI12th week>NDVI8th week and NDVI17th week >NDVI12th week and NDVI17th week>0.5 and NDVI19th week>0.5 and NDVI19th week>NDVI21th week and NDVI23th week<0.35; wherein NDVInth weekAnd (3) carrying out rule verification on pixels in different periods of the area where all Henan provinces are located for the NDVI value of the winter wheat in the nth week in the growth and green turning period of the winter wheat, and marking the pixels as winter wheat planting coverage pixels if the pixels meet the rule requirements.
8. A method for predicting yield of winter wheat comprises the following steps:
(1) calculating or counting SGDD, SEDD, NDVI or NDVI of the whole growth period of the wheat to be tested in a certain area1、NDVI2、NDVI3The parameters or data of (a);
(2) and (3) substituting each parameter or data into the corresponding winter wheat yield prediction model obtained in the claim 1 or 2 to obtain the predicted yield of the winter wheat in the current year in the area.
9. The winter wheat yield prediction method of claim 8, wherein: calculating the SGDD and the SEDD in two stages by taking 4 months and 10 days as a limit, wherein the standard growth temperature of 4 months and 10 days and before is 2 ℃, and the optimal growth temperature is 16 ℃; after 4 months and 10 days, the standard growth temperature is 16 ℃, and the optimal growth temperature is 30 ℃.
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CN111062526B (en) * 2019-12-09 2022-03-08 北京师范大学 Winter wheat yield per unit prediction method and system
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CN112579981B (en) * 2020-12-24 2022-05-31 青岛大学 Remote sensing simulation method for yield of winter wheat in coupling irrigation area scale

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2004119487A (en) * 2004-06-25 2006-01-10 ФГОУ ДПОС "Белгородский институт переподготовки и повышени квалификации кадров агробизнеса" (БИППККА) (RU) METHOD FOR FORECASTING YIELD OF WINTER WHEAT
CN104134095A (en) * 2014-04-17 2014-11-05 中国农业大学 Crop yield estimation method based on scale transformation and data assimilation
CN108764255A (en) * 2018-05-21 2018-11-06 二十世纪空间技术应用股份有限公司 A kind of extracting method of winter wheat planting information
CN109615149A (en) * 2018-12-29 2019-04-12 航天信息股份有限公司 A kind of method and system of determining beet Meteorological Output
CN109815959A (en) * 2018-12-27 2019-05-28 中国农业科学院农业环境与可持续发展研究所 A kind of Yield Forecast of Winter Wheat method and device
CN109919395A (en) * 2019-04-01 2019-06-21 安徽大学 A kind of winter wheat yield monitoring method based on short cycle remote sensing area data

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2004119487A (en) * 2004-06-25 2006-01-10 ФГОУ ДПОС "Белгородский институт переподготовки и повышени квалификации кадров агробизнеса" (БИППККА) (RU) METHOD FOR FORECASTING YIELD OF WINTER WHEAT
CN104134095A (en) * 2014-04-17 2014-11-05 中国农业大学 Crop yield estimation method based on scale transformation and data assimilation
CN108764255A (en) * 2018-05-21 2018-11-06 二十世纪空间技术应用股份有限公司 A kind of extracting method of winter wheat planting information
CN109815959A (en) * 2018-12-27 2019-05-28 中国农业科学院农业环境与可持续发展研究所 A kind of Yield Forecast of Winter Wheat method and device
CN109615149A (en) * 2018-12-29 2019-04-12 航天信息股份有限公司 A kind of method and system of determining beet Meteorological Output
CN109919395A (en) * 2019-04-01 2019-06-21 安徽大学 A kind of winter wheat yield monitoring method based on short cycle remote sensing area data

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
基于GIS的县域小麦产量潜力评价系统设计与实现;H.Zhang 等;《2010年世界自动化大会》;20101210;全文 *
基于MODIS数据的河南省冬小麦产量遥感估算模型;李军玲等;《生态环境学报》;20121018(第10期);第15-19页 *
基于物联网和遥感的河南省冬小麦晚霜冻害监测及产量预测;宋利红;《中国优秀博硕士学位论文全文数据库(硕士)》;20200415(第04期);第A009-122页 *

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