CN109508693A - Unmanned aerial vehicle remote sensing rice yield estimation method based on imaging EO-1 hyperion vegetation index and breeding time length information - Google Patents
Unmanned aerial vehicle remote sensing rice yield estimation method based on imaging EO-1 hyperion vegetation index and breeding time length information Download PDFInfo
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Abstract
A kind of unmanned aerial vehicle remote sensing rice yield estimation method based on imaging EO-1 hyperion vegetation index and breeding time length information, the described method comprises the following steps: 1) the imaging EO-1 hyperion vegetation index for rice the yield by estimation determines；2) growth period duration of rice length information extracts；3) breeding time length information normalizes；4) the rice Yield Estimation Model building based on imaging EO-1 hyperion vegetation index and breeding time length information；5) the yield by estimation precision test.This method has comprehensively considered remote sensing information and breeding time information, considers change of production brought by the difference of breeding time length on the basis of traditional remote sensing variable, provides a kind of new idea and method for the accurate Crop Estimation that carries out.
Description
Technical field
The crops reflectivity information that the present invention is obtained using remote sensing technology extracts the breeding time length of crop, and combines
Remote sensing Variational Design has gone out a kind of Crop Estimation Method based on remotely-sensed data, breeding time length, realizes the field of crops
Accurate the yield by estimation.
Background technique
The stabilization of food supply and the development of social economy are closely related, and grain-production safety is always also that every country is high
Degree payes attention to obtaining problem.Reduction, water resource pollution and the shortage of water resources of the size of population, cultivated area that sharply increase, environment
Deteriorate, global climate warms, seriously affect agricultural production and jeopardize grain security.Grain security has become many countries at present
With the maximum challenge faced in area.Under the conditions of current complicated and changeable, accurately grain yield acquisition of information is for country
Grain security, the formulation of agricultural policy, the regulation of national food price, water resource rational allocation there is important guidance to anticipate
Justice.
Currently, the most commonly used is be based on remote sensing light when carrying out region production estimation force evaluating using satellite remote sensing date
The Crop Estimation statistical model of spectrum information, it is intended to obtain the reflected radiation value of crops using remote sensing technology or reflectivity is established
Different times vegetation index and Relationship with Yield, to obtain the yield by estimation result in target time.But this yield estimation method does not consider
To difference of the crops due to breeding time length of different cultivars, the dry matter that warm light utilization efficiency has differences, therefore accumulated
Amount and yield are also not quite similar.If to production estimation power predicting that result often generates according to the conventional method
Biggish error.
Summary of the invention
Accuracy in order to overcome the shortcomings of existing rice yield estimation by remote sensing method is poor, the invention proposes one kind based at
The unmanned aerial vehicle remote sensing rice yield estimation method of image height spectral vegetation indexes and breeding time length information, effectively eliminate different cultivars it
Between as breeding time length the different differences caused by yield.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of unmanned aerial vehicle remote sensing rice yield estimation method based on imaging EO-1 hyperion vegetation index and breeding time length information, packet
Include following steps:
1) the imaging EO-1 hyperion vegetation index for rice the yield by estimation determines
For the Imaging Hyperspectral Data that Rise's boot period, heading stage, pustulation period obtain, using the method for exhaustion it will be seen that light model
All wave bands enclosed and all wave bands of near infrared range carry out combination of two and calculate separately DVI, RVI, NDVI and EVI2 vegetation
Then the calculated vegetation index of all combinations and rice yield data are carried out correlation analysis, most by related coefficient by index
For high band combination as the best vegetation index for rice the yield by estimation, the calculation formula of each vegetation index is as follows:
DVIi,j=NIRi-REDj (2)
EVI2I, j=2.5 (NIRi-REDj)/(NIRi+2.4REDj+1) (4)
In formula, NIRiFor near infrared band range a certain wave band i, REDjFor a certain wave band j of red spectral band range；
2) growth period duration of rice length information extracts
Vegetation index EVI2 is reduced to given threshold to determine growth period duration of rice length, i.e., is set when EVI2 reduction reaches
Determine to think that the accumulation of dry matter has been completed when threshold value, rice entire breeding time also terminates to determine breeding time length；
3) breeding time length information normalizes
In formula, T is the growth period duration of rice length parameter after normalization, LExtract breeding time lengthFor the fertility extracted by vegetation index
Phase length, LNormal reproduction phase lengthFor the kind normal reproduction phase length, f () indicates to be used for modeling functions；
4) the rice Yield Estimation Model building based on imaging EO-1 hyperion vegetation index and breeding time length information
Combine imaging of more breeding times EO-1 hyperion vegetation index as independent variable using the breeding time length information after normalization,
It is as follows to establish Yield Estimation Model as dependent variable for rice yield:
Y=f (VIBoot stage, VIHeading stage, VIPustulation period, T) and (6)
In formula, Y is rice yield, VBoot stage, VHeading stage, VPustulation period, it is Rise's boot period, the imaging bloom at heading stage, pustulation period
Vegetation index is composed, T is normalization breeding time length；
5) the yield by estimation precision test
By the V of target fieldBoot stage, VHeading stage, VPustulation period, T, which is brought into model (6) and obtains, estimates rice yield YEstimation.Utilize formula
(7) the yield by estimation precision test is carried out,
In formula, RE is the yield by estimation relative error, YEstimationFor the rice yield that target field is estimated by model, YActual measurementFor target field
The rice yield of block actual measurement.
Further, in the step 4), the statistical model for being fitted modeling is multivariate linear model.
Beneficial effects of the present invention are mainly manifested in: remote sensing information and breeding time length information have been comprehensively considered, in tradition
The variation of yield brought by the difference of breeding time length is considered on the basis of remote sensing variable, accuracy is higher.
Detailed description of the invention
Fig. 1 is breeding time length information extraction process.
Fig. 2 is actual measurement yield and forecast production comparison diagram.
Fig. 3 is the unmanned aerial vehicle remote sensing rice yield estimation method based on imaging EO-1 hyperion vegetation index and breeding time length information
Flow chart.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig.1~Fig. 3, a kind of unmanned aerial vehicle remote sensing water based on imaging EO-1 hyperion vegetation index and breeding time length information
Rice yield estimation method, comprising the following steps:
1) the imaging EO-1 hyperion vegetation index for rice the yield by estimation determines
It, will using the method for exhaustion for the Imaging Hyperspectral Data that Rise's boot period, heading stage, pustulation period, dough stage obtain
All wave bands of visible-range and all wave bands of near infrared range carry out combination of two calculate separately DVI, RVI, NDVI and
Then the calculated vegetation index of all combinations and rice yield data are carried out correlation analysis, by phase by EVI2 vegetation index
For the highest band combination of relationship number as the best vegetation index for rice the yield by estimation, the calculation formula of each vegetation index is as follows:
DVIi,j=NIRi-REDj (2)
EVI2I, j=2.5 (NIRi-REDj)/(NIRi+2.4REDj+1) (4)
In formula, NIRiFor near infrared band range a certain wave band i, REDjFor a certain wave band j of red spectral band range；
With best vegetation index of the calculated each breeding time of Deqing rice Growing state survey test data in 2017 and its most
Good band combination is the NDVI in boot stage[840,728], heading stage NDVI[840,728]With the RVI of pustulation period[840,728]。
2) growth period duration of rice length information extracts
In view of the rice growing way of different cultivars even Different Fertilization situation is also not quite similar, effective breeding time length is also deposited
In certain difference, therefore breeding time length information is added to the robustness that can be further improved model in model and accurate
Degree.
Since the vegetation index variation characteristic of different growing is more obvious, growth phase normally behaves as vegetation index and passs
The process of increasing, dry matter initially forms yield after biomass reaches maximum, and the information of vegetation blade starts to weaken at this time, performance
The trend then to taper off on vegetation index.We mainly consider the period that vegetation index successively decreases, when dry matter is transformed into one
We are defined as the end of entire breeding time after determining degree, and measurement index then can obviously be reduced by vegetation index
It is determined to a certain threshold value.Since the information that EVI2 index can make up blue light missing can accurately reflect that vegetation is entire
Upgrowth situation, therefore choose sensitive vegetation index of the EVI2 as breeding time length.Through comprehensively considering, it is believed that when EVI2 reaches threshold
The accumulation of dry matter has been basically completed when value 0.15, and rice entire breeding time also terminates substantially.A most apparent appearance table
Show, is that rear blade of the rice in late growth stage i.e. milking maturity full ripe stage starts product that is a large amount of withered and yellow, therefore no longer carrying out dry matter
Tired, yield has kept relative stability at this time.And the EVI2 vegetation index in decrement states can then pass through certain modeling
Method interpolation show that it is equal to corresponding transplanting number of days when 0.15.
By taking No. 2 fields in Deqing experiment in 2017 as an example, the specific calculation of breeding time length such as 1 institute of attached drawing
Show, EVI2 time series vegetation index is modeled first, the study find that the EVI2 after rice reaches growth animated period
Exist between index and breeding time length (transplanting number of days) than stronger linear relationship, R2Reachable 0.88, therefore this project
Time series EVI2 index is modeled according to linear relationship, recycling EVI2=0.15 is that threshold value extracts No. 2 Tian Shengyu
Phase length is 121 days.
3) breeding time length information normalizes
In formula, T is the growth period duration of rice length parameter after normalization, LExtract breeding time lengthFor the fertility extracted by vegetation index
Phase length, LNormal reproduction phase lengthFor the kind normal reproduction phase length.By taking No. 2 fields as an example, calculated breeding time length is 121 days,
And the normal reproduction phase length of the kind is 156 days, therefore the normalization breeding time length variable T in No. 2 fields is 121/156=
0.7756。
4) the rice Yield Estimation Model building based on imaging EO-1 hyperion vegetation index and breeding time length information
Combine imaging of more breeding times EO-1 hyperion vegetation index as independent variable using the breeding time length information after normalization,
It is as follows to establish Yield Estimation Model as dependent variable for rice yield:
Y=f (VIBoot stage, VIHeading stage, VIPustulation period, T) and (6)
In formula, Y is rice yield, VBoot stage, VHeading stage, VPustulation period, it is Rise's boot period, the imaging bloom at heading stage, pustulation period
Vegetation index is composed, T is normalization breeding time length.
By taking Deqing experimental data in 2017 as an example, the test block totally 22 pieces of experimental fields are loaded using unmanned plane as needed
Hyperspectral imager obtain different times remotely-sensed data and carry out the rice reflectivity that different growing is calculated.We
The reflectivity data in boot stage, three periods of heading stage and pustulation period is selected.Then using calculating needed for each experimental field
Vegetation index, and the breeding time length for extracting each field simultaneously was returned divided by the kind normal reproduction phase length 156 days again
One changes breeding time length, and specific data are as shown in table 1.
Table 1
It is as follows with this calculated Production Forecast Models:
Y=10620.741+15299.2 × NDVI[840,728](Booting)+9644.025× NDVI[840,728](Heading)-
10169.009×RVI[840,728](Filling)+10613.654×T
In formula, Y is rice prediction per unit area yield, the modeling R of the model2Reach 0.83.
5) the yield by estimation precision test
By the V of target fieldBoot stage, VHeading stage, VPustulation period, T, which is brought into model (6) and obtains, estimates rice yield YEstimation.Utilize formula
(7) the yield by estimation precision test is carried out,
In formula, RE is the yield by estimation relative error, YEstimationFor the rice yield that target field is estimated by model, YActual measurementFor target field
The rice yield of block actual measurement.
By taking Deqing experimental data in 2017 as an example, the yield by estimation result and survey production result average relative error are 2.52%, are put down
Equal absolute error is 11.24Kg/ mus, and RMSE is 12.55Kg/ mus.Concrete outcome is as shown in table 2 and figure 2.
Table 2.
Claims (2)
1. a kind of unmanned aerial vehicle remote sensing rice yield estimation method based on imaging EO-1 hyperion vegetation index and breeding time length information, special
Sign is, the described method comprises the following steps:
1) the imaging EO-1 hyperion vegetation index for rice the yield by estimation determines
For the Imaging Hyperspectral Data that Rise's boot period, heading stage, pustulation period obtain, using the method for exhaustion it will be seen that optical range
All wave bands and all wave bands of near infrared range carry out combination of two and calculate separately DVI, RVI, NDVI and EVI2 vegetation index,
Then the calculated vegetation index of all combinations and rice yield data are subjected to correlation analysis, by the highest wave of related coefficient
For Duan Zuhe as the best vegetation index for rice the yield by estimation, the calculation formula of each vegetation index is as follows:
DVIi,j=NIRi-REDj (2)
EVI2i,j=2.5 (NIRi-REDj)/(NIRi+2.4REDj+1)(4)
In formula, NIRiFor near infrared band range a certain wave band i, REDjFor a certain wave band j of red spectral band range；
2) growth period duration of rice length information extracts
Vegetation index EVI2 is reduced to given threshold to determine growth period duration of rice length, i.e., when EVI2 reduction reaches given threshold
When think that the accumulation of dry matter has been completed, rice entire breeding time also terminates to determine breeding time length；
3) breeding time length information normalizes
In formula, T is the growth period duration of rice length parameter after normalization, LExtract breeding time lengthIt is long for breeding time for being extracted by vegetation index
Degree, LNormal reproduction phase lengthFor the kind normal reproduction phase length；
4) the rice Yield Estimation Model building based on imaging EO-1 hyperion vegetation index and breeding time length information
Combine imaging of more breeding times EO-1 hyperion vegetation index as independent variable, rice using the breeding time length information after normalization
It is as follows to establish Yield Estimation Model as dependent variable for yield:
Y=f (VIBoot stage, VIHeading stage, VIPustulation period, T) and (6)
In formula, Y is rice yield, VBoot stage, VHeading stage, VPustulation period, it is the imaging EO-1 hyperion plant of Rise's boot period, heading stage, pustulation period
By index, T is normalization breeding time length, and f () indicates to be used for modeling functions；
5) the yield by estimation precision test
By the V of target fieldBoot stage, VHeading stage, VPustulation period, T, which is brought into model (6) and obtains, estimates rice yield YEstimation, utilize formula (7)
The yield by estimation precision test is carried out,
In formula, RE is the yield by estimation relative error, YEstimationFor the rice yield that target field is estimated by model, YActual measurementIt is real for target field
The rice yield of border measurement.
2. the unmanned aerial vehicle remote sensing rice as described in claim 1 based on imaging EO-1 hyperion vegetation index and breeding time length information
Yield estimation method, which is characterized in that the statistical model in the step 4) for being fitted modeling is multivariate linear model.
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