CN112749837A - Method for estimating grain crop yield based on post-flowering NDVI accumulation value - Google Patents

Method for estimating grain crop yield based on post-flowering NDVI accumulation value Download PDF

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CN112749837A
CN112749837A CN202011527929.1A CN202011527929A CN112749837A CN 112749837 A CN112749837 A CN 112749837A CN 202011527929 A CN202011527929 A CN 202011527929A CN 112749837 A CN112749837 A CN 112749837A
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王志敏
周晓楠
刘影
张英华
孙振才
李伟
张震
刘洋
胡乃月
王剑
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Abstract

The invention discloses a method for estimating grain crop yield based on a post-flowering NDVI accumulation value. The method comprises the following steps: the method comprises the steps of obtaining post-anthesis NDVI dynamics, smoothing data, obtaining post-anthesis delta NDVI dynamics, finding a point Q of a delta NDVI peak value corresponding to an NDVI curve, calculating an NDVI accumulation value of an NDVI curve section from anthesis to the point Q, removing the NDVI value in rainy days to obtain an Effective NDVI accumulation value Effective accumulation _ NDVI, and substituting the Effective NDVI accumulation value Effective accumulation _ NDVI into a model to predict yield. The method has better universality, can predict the yield of the crop groups with different years, different varieties and different management measures, and has better practicability. The method is used for breeding new crop varieties and remote sensing yield estimation, particularly, remote sensing information is obtained to predict the global grain crop yield by taking a satellite as a tool, so that the global grain crop yield information can be obtained more quickly, and important guarantee is made for grain safety and trade balance.

Description

Method for estimating grain crop yield based on post-flowering NDVI accumulation value
Technical Field
The invention relates to the field of crop remote sensing estimation, in particular to a method for estimating the yield of cereal crops by using a post-flowering remote sensing vegetation index NDVI.
Background
Yield prediction is an important component of the crop production field. With the continuous upgrading of global informatization and intellectualization, accurate acquisition of crop yield information will help to optimize resource allocation and trade balance in the agricultural field. Valuable conclusions or decisions must be based on reliable agricultural data, and remote sensing is the only efficient way to obtain reliable data. Scientists have now constructed a multi-type approach for yield estimation: for example, researchers initially found that yield could be estimated using spectral information acquired from crops at different growth stages (Wiegand et al, 1991); the yield can be effectively predicted by combining physiological and biochemical indexes to assist the spectral vegetation index (El-Hendawy et al, 2019; Kanning et al, 2018); the combination of vegetation index with crop growth models can improve the accuracy of yield prediction (Li et al, 2015); the combination of spectral information with mathematical models also allows to find the laws accurately as the interdisciplinary develops (Zeng et al, 2018; Petteri et al, 2019). Although the accuracy of the prediction result is improved by a plurality of methods, the complexity is increased continuously, which is difficult to avoid, and the popularization and application of the yield prediction model are necessarily limited. In the screening and use of the spectral information, people can easily find that the yield prediction is realized by most of the existing means based on the spectral information acquired by a certain growth time node or a plurality of time nodes and combining other methods, the crop growth is a continuous process and can be influenced by a plurality of external factors at any moment, and a large amount of important information related to the crop growth and development can be ignored or even lost only by using section data.
The remote sensing vegetation index NDVI was originally proposed by Rouse et al in 1973 to study vegetation growth dynamics. Through many years of research, the application of the remote sensing index NDVI is continuously widened. In large-scale airspace applications, in conjunction with crop phenological information, researchers can use the dynamic trends in the vegetation index over the life of the crop to distinguish between biological species (Lee et al, 2016; Gerstmann et al, 2016), such as weeds and crops (Lopez-Granados et al, 2016), by distinguishing similarities and differences in trends. Meanwhile, the scientific researchers can further extract the planting area of the crops (Pan et al, 2012) and draw a corresponding space distribution map (Gerstmann et al, 2018; Zhong et al, 2015). On a surface scale, NDVI has been widely used in global vegetation research, mostly for measuring coverage, biomass, physicochemical characteristics, health status, etc. of standard vegetation (wanli, 2013). Currently, the popularity of NDVI is high, and the NDVI is obtained in various ways and with low cost, and is increasingly paid more attention by researchers. The period from flowering to maturity of cereal crops is the determining period of seed yield, and the green area of the canopy after flowering, photosynthetic activity and duration thereof comprehensively determine the yield. The instantaneously measured canopy NDVI can characterize the canopy greenness and activity at that time, but because the canopy greenness and activity are dynamically changed and gradually decreased, it is difficult to establish a stable and highly correlated model with the final yield using the NDVI at a certain time point, and if the correlation between the canopy NDVI and the final yield is established, the duration of the canopy greenness and activity must be considered. However, to date, there has been no method of estimating crop yield using time series changes in post-anthesis NDVI.
Disclosure of Invention
The invention aims to provide a method for estimating yield by dynamically extracting effective NDVI values and calculating accumulation values through acquiring the NDVI of the postanthesis canopy of a cereal crop group. The method of the invention is obviously different from other analysis methods, has better universality and can monitor the yield of cereal crop groups under different years, different cultivars and different management measures.
The method for estimating the yield of the cereal crops comprises the following steps:
1) arranging crop cultivation tests of different years, different cultivars and different management measures;
2) recording the specific flowering date of the crop group by field investigation to be accurate to a certain natural day;
3) obtaining the post-canopy flower NDVI dynamics of crops, namely the NDVI dynamics from blooming to plant withering, and drawing a post-canopy NDVI value curve;
4) carrying out data smoothing treatment on the acquired post-anthesis NDVI value curve;
5) calculating the daily decline of the NDVI value after the blossom, namely the delta NDVI dynamic;
6) synchronizing the peak occurrence time of the delta NDVI dynamic curve to a post-anthesis NDVI value curve, determining a corresponding NDVI value, and marking the value as a Q point;
7) calculating the cumulative value of NDVI (relationship of NDVI) of the interval from flowering to the point Q;
8) removing the NDVI value of the rainy day in the cumulant _ NDVI, and taking the residual part as an Effective NDVI accumulation value Effective accumulation _ NDVI (Effective accumulation of NDVI);
9) after crops are mature, obtaining crop grains in the same area of NDVI and converting the crop grains into yield with the water content of 13%;
10) constructing a model relationship between Effective evaluation _ NDVI and yield of each crop population by using unary linear regression,
(1) establishing an Effective cumulant _ NDVI-based yield prediction model and other parameters;
(2) validating the model using the independent test data;
11) and (3) obtaining an Effective NDVI accumulation value Effective accumulation _ NDVI of the crop group to be detected by referring to the operations of the steps 2) -8), inputting a model which is already built for the corresponding crop, and predicting the yield.
In the step 2) of the method, the specific flowering date is the date of entering GS65 by referring to the Zalooks standard crop group, namely the date of entering the flowering period by 50% of ears of the field crop or the loose powder;
in the step 3), a spectral device is used for testing the NDVI of the canopy of the crop, and the NDVI value which cannot be tested due to weather reasons is supplemented by a mathematical interpolation Hermite (Hermite) interpolation method;
the NDVI is obtained by calculating the reflectivity of the red light wave band and the reflectivity of the near infrared light wave band, and the calculation formula is as follows: NDVI (NIR-Red)/(NIR + Red), NIR being the reflectivity of a near infrared light band, and Red being the reflectivity of a Red light band;
in the step 4), a Savitzky-Golay filtering method is adopted to perform data smoothing on the patterned NDVI value curve, and specifically, the Savitzky-Golay filtering method in Origin software is used to perform data smoothing on the curve;
in step 5), the Δ NDVI is obtained by subtracting the NDVI value of the next day from the NDVI value of the current day, and the calculation formula is as follows: delta NDVIi＝NDVIi-NDVIi+1，i≥0，NDVIiRepresents the NDVI value at day i post-anthesis;
in the step 8), the percentage of the area of the cloud in the sky occupying the sky is used as a basis for judging the weather: the cloud cover is clear when the cloud cover is 0-10%; 10-30% is cloudiness; 30-70% is cloudy; more than 70% is cloudy.
The use of post-anthesis NDVI accumulation values in the estimation of the yield of a cereal crop population is also within the scope of the present invention.
The application of the method for estimating the grain crop yield based on the post-flowering NDVI accumulation value in grain crop variety screening and precise cultivation management also belongs to the protection scope of the invention.
The invention provides a method for estimating the yield of cereal crops based on post-flowering NDVI accumulation values, which has the following obvious advantages compared with the existing determination method:
1) the method has the advantages of simple operation, rapidness, high efficiency, low cost, simple and convenient data processing and the like;
2) the method has higher universality, and can realize unified yield prediction under different year types, different varieties and different cultivation measures;
3) the method does not use toxic chemical reagents in the operation process, has no pollution to the environment, does not need destructive sampling, and can realize no damage to the crop canopy.
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FIG. 1 is a diagram illustrating the method for obtaining the Effective cumulative _ NDVI of the canopy layer according to the present invention.
FIG. 2 is a wheat model constructed by the method of the present invention and an independent experimental verification.
Detailed Description
The present invention will be described below with reference to specific examples, but the present invention is not limited thereto.
The experimental methods used in the following examples are all conventional methods unless otherwise specified; reagents, materials and the like used in the following examples are commercially available unless otherwise specified.
Example 1 estimation of winter wheat yield based on post-anthesis NDVI accumulation values
1. Test set-up
The winter wheat test is carried out at the Wuqiao experimental station of the Chinese agriculture university in 2016 + 2020, different population canopies are created by setting different years, different cultivation varieties and different management measure tests, and the specific information is as follows:
EXP.1 Water Nitrogen test 2016-2017 (field 1) the test physicochemical properties are shown in Table 1. The test variety is Jimai 22, and three nitrogen fertilizer gradients are set, namely 0 and 180kgN/hm respectively2，300kgN/hm2. Under each nitrogen application condition, three water treatments are respectively set, namely W1 (water saving by only irrigation and drainage in spring), W2 (water saving by irrigation and flowering in spring), W3 (water of body irrigation and booting and water of grouting in spring) and 75mm of irrigation each time. 50% of nitrogenous fertilizer and phosphatic fertilizer (P)2O5112.5kg/hm2) And a potassium fertilizer (K)2O 112.5kg/hm2) Applied before sowing, in additionThe outer 50% of nitrogen fertilizer is broadcast in the form of urea before the first irrigation in spring. The test is designed according to random block, the area of the cell is 3.5m multiplied by 10.0m, the line spacing is 15cm, and the test is repeated for three times. The test is carried out in 2016, 10 months and 15 days, the seeding rate is 450 strains/m2。
EXP.2 moisture test 2018 and 2019 (field 2) the physicochemical properties of the test are shown in Table 1. The tested varieties are Jimai 22 and Nongda 399, each variety is provided with 5 irrigation treatments, namely W0 (no irrigation in spring), W1U (irrigation water only in spring), W1J (irrigation water only in spring), W2UA (irrigation water only in spring and flower water), W2JA (irrigation water saving in spring and flower water), and the irrigation quantity of each time is 75 mm. All fertilizers are applied once before sowing, wherein the dosage of the nitrogen fertilizer is N240 kg/hm2The dosage of the phosphate fertilizer is P2O5 112.5kg/hm2K is used as potash fertilizer2O112.5kg/hm2. The test is designed according to random block, the area of the cell is 3.5m multiplied by 8.0m, the line spacing is 15cm, and the test is repeated for three times. The test is carried out in 2018, 10 months and 13 days, and the seeding rate is 600 plants/m2。
EXP.3 variety + Water Nitrogen test 2017-2018 (field 3) the field physicochemical properties are shown in Table 1. The number of the tested varieties is 5 (Jimai 22, Gao you 2018, Shimai 22, Zhongmai 1062 and nong Da 399 respectively), and each variety is provided with two water treatments (spring 0 water and spring water saving) and four nitrogen fertilizer gradients (0, 120 kgN/hm)2，195kgN/hm2，270kgN/hm2). All fertilizers are applied once before sowing, wherein the dosage of the phosphate fertilizer is P2O5 112.5kg/hm2K is used as potash fertilizer2O 112.5kg/hm2. Each treatment was 10 m long by 2 m wide, repeated three times, with a row spacing of 18 cm. Sowing in 2017, 10 months and 22 days, and controlling the sowing amount to 700 plants/m2。
EXP.4/EXP.5 variety + moisture test (field 4)2017 and 2019, the geographical characterization of the test is shown in Table 1. 2017-2018 wheat season test (EXP.4) has 60 varieties (lines), and each variety is provided with two water treatments (water conservation in spring 0 and spring water). 2018-2019 wheat season test (EXP.5) variety (line) 0 water treatment is 25, and the jointing water treatment is 30. All fertilizers are applied once before sowing, wherein the dosage of the nitrogen fertilizer is N210 kg/hm2The dosage of the phosphate fertilizer is P2O5 112.5kg/hm2K is used as potash fertilizer2O 112.5kg/hm2. Each treatment was 10 m long by 2 m wide, repeated three times, with a row spacing of 18 cm. Sowing in 2017, 10 months and 22 days, and controlling the sowing amount to 700 plants/m2. Sowing in 2018, 10 months and 15 days, wherein the sowing quantity is controlled to be 600 plants/m2。
EXP.6 seed test 2017 and 2018 (field 5) the physicochemical properties of the tests are shown in Table 1. The variety to be tested is Jimai 22, 5 sowing periods are respectively set as 10-month 6 days, 10-month 16 days, 10-month 23 days, 10-month 30 days and 11-month 6 days in 2017, and the sowing amount of the sowing periods one by one is respectively controlled at 400 strains/m2500 strains/m2750 strains/m2750 strains/m2750 strains/m2The seeding row spacing is 15 cm.
EXP.7 validation tests, which were performed in 2019-2020, included various water nitrogen species and density treatments.
All other management measures of the test treatment are the same as those of the general high-yield field.
TABLE 1 nutrient status of 0-20cm soil layer of test field
Figure BDA0002851373730000051
2. Starting from wheat heading, experimentally recording flowering dates of different wheat populations at 9 am every day, wherein the specific method refers to a Zadaks method, and the date of the population entering GS 65;
3. dynamic acquisition of NDVI value after wheat population florescence
(1) Determining NDVI values for the canopy population using a two-band spectrometer greenseker (Trimble, USA);
(2) and (3) testing conditions are as follows: clear without clouds or with few clouds, no wind or differentiation; the tester makes dark clothes and caps; 11 am to 1 pm; the distance between the sensor probe and the upper part of the canopy is 70-90cm and the sensor probe is kept horizontal; the tester avoids the body shadow from shielding the top of the tested canopy region; continuously testing the length of more than 3 meters, reciprocating for 1 time respectively, and calculating an average value to be used as a record value of each test;
(3) canopy NDVI data not tested but allowed by rainy weather or environmental conditions were filled using Hermite (Hermite) interpolation;
4. carrying out data smoothing treatment on the acquired post-anthesis NDVI value curve
Carrying out data smoothing treatment on the NDVI time sequence curve by using a Savitzky-Golay filtering method in Origin software;
5. calculating the delta NDVI value of each day after the flower blossom, wherein the delta NDVI is obtained by subtracting the NDVI value of the next day from the NDVI value of the current day, and the calculation formula is as follows: delta NDVIi＝NDVIi-NDVIi+1，i≥0，NDVIiRepresents the NDVI value at day i post-anthesis;
6) synchronizing the peak occurrence time of the delta NDVI dynamic curve to the NDVI curve, determining the corresponding NDVI value, and marking the value as a Q point;
7) calculating the cumulative value of NDVI (relationship of NDVI) of the interval from flowering to the point Q;
8) removing the NDVI value of the rainy day in the cumulant _ NDVI, and taking the residual part as an Effective NDVI accumulation value Effective accumulation _ NDVI (Effective accumulation of NDVI);
9) after crops are mature, obtaining crop grains in the same area of NDVI and converting the crop grains into yield with the water content of 12.5%;
10) construction of a model relationship between Effect summary _ NDVI and yield for each crop population Using unitary Linear regression
(1) Establishing an Effective cumulant _ NDVI-based yield prediction model and other parameters;
(2) validating the model using the independent test data;
fig. 2 shows the constructed wheat model and independent experimental verification. In model, linearly fitted R2Is 0.9412. Determining coefficient R of linear regression of predicted value and true value in verification model20.9499 and a root mean square error RMSE of 261.8, better accuracy was obtained.
Although the invention has been described in detail hereinabove by way of general description, specific embodiments and experiments, it will be apparent to those skilled in the art that many modifications and improvements can be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (8)

1. A method of estimating grain crop yield, comprising the steps of:
1) arranging crop cultivation tests of different years, different cultivars and different management measures;
2) recording the specific flowering date of the crop group by field investigation to be accurate to a certain natural day;
3) obtaining the post-canopy flower NDVI dynamics of crops, namely the NDVI dynamics from blooming to plant withering, and drawing a post-canopy NDVI value curve;
4) carrying out data smoothing treatment on the acquired post-anthesis NDVI value curve;
5) calculating the daily decline of the NDVI value after the blossom, namely the delta NDVI dynamic;
6) synchronizing the peak occurrence time of the delta NDVI dynamic curve to a post-anthesis NDVI value curve, determining a corresponding NDVI value, and marking the value as a Q point;
7) calculating the cumulative value of the NDVI (cumulative _ NDVI) of the interval from flowering to the point Q;
8) removing the NDVI value of the precipitation _ NDVI in the precipitation _ NDVI, and taking the residual part as an Effective NDVI accumulation value Effective precipitation _ NDVI;
9) after crops are mature, obtaining crop grains in the same area of NDVI and converting the crop grains into yield with the water content of 13%;
10) constructing a model relationship between Effective evaluation _ NDVI and yield of each crop population by using unary linear regression,
(1) establishing an Effective cumulant _ NDVI-based yield prediction model and other parameters;
(2) validating the model using the independent test data;
11) and (3) obtaining an Effective NDVI accumulation value Effective accumulation _ NDVI of the crop group to be detected by referring to the operations of the steps 2) -8), inputting a model which is already built for the corresponding crop, and predicting the yield.
2. The method of claim 1, wherein: in the step 2), the specific flowering date is the date when the standard Zalooks crop population enters GS65, namely the date when 50% of ears of the field crop blossom or loose powder enters the flowering period.
3. The method according to claim 1 or 2, characterized in that: in step 3), testing the NDVI of the crop canopy by using a spectral device,
the NDVI is obtained by calculating the reflectivity of the red light wave band and the reflectivity of the near infrared light wave band, and the calculation formula is as follows: NDVI ═ NIR-Red)/(NIR + Red), NIR for near infrared band reflectance and Red for Red band reflectance.
4. The method according to any one of claims 1-3, wherein: and 4) performing data smoothing treatment on the patterned NDVI value curve by adopting a Savitzky-Golay filtering method.
5. The method according to any one of claims 1-4, wherein: in step 5), the delta NDVI is obtained by subtracting the NDVI value of the next day from the NDVI value of the current day, and the calculation formula is as follows: delta NDVIi＝NDVIi-NDVIi+1，i≥0，NDVIiRepresents the NDVI value at day i after anthesis.
6. The method according to any one of claims 1-5, wherein: in step 8), the percentage of the area of the cloud in the sky occupying the sky is taken as the basis for judging the weather: the cloud cover is clear when the cloud cover is 0-10%; 10-30% is cloudiness; 30-70% is cloudy; more than 70% is cloudy.
7. Use of post-anthesis NDVI accumulation values in the estimation of grain crop population yield.
8. Use of the method for estimating grain crop yield based on post-anthesis NDVI accumulation values according to any one of claims 1-6 for grain crop variety screening and precision cultivation management.
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