CN107609695B - Crop yield remote sensing estimation method based on adjustable vegetation index - Google Patents
Crop yield remote sensing estimation method based on adjustable vegetation index Download PDFInfo
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Abstract
A kind of crop yield remote sensing estimation method based on adjustable vegetation index.Multi-source multi-temporal remote sensing observation data of the crop in crop yield region to be evaluated within growth period are obtained, the adjustable vegetation index of the crop of multidate are determined based on the remote sensing observations data, and be finally inversed by the leaf area index LAI of the multidate based on remote sensing observations data；The growth model priori data of crop based on the region being obtained ahead of time sets the input parameter and primary condition of the crop growth model in the region, and crop growth model is run multiple times with a plurality of LAI curves for obtaining simulation and corresponding crop yield；The a plurality of LAI curves with the leaf area index LAI similarities of the multidate based on remote sensing observations data 75% or more and the LAI curves that are fitted are chosen, using the mean value of each crop yield of the similarity corresponding to 75% or more LAI curves as the corresponding crop yield of LAI curves being fitted；Using corresponding crop yield as area estimation yield.With the application of the invention, crop yield estimation precision can be promoted.
Description
Technical field
The present invention relates to the crop yield estimating techniques of agriculture field more particularly to a kind of works based on adjustable vegetation index
Produce amount remote sensing estimation method.
Background technology
Formulation and implementation, grain quality security evaluation, pest and disease damage and meteorology to field management strategy are estimated in crop yield
The loss appraisals such as disaster have important theoretical significance and application value realistic, are the research hotspots in precision agriculture field.
In precision agriculture field, the use of remote sensing technology is increasingly taken seriously.With time and space continuity remote sensing observations relative to consumption
When laborious ground surface sample for, can in time, it is accurate, obtain crop in large area and observe data, to estimate for crop yield
Calculation provides science data support.
Crop yield remote sensing estimation method has statistics class and mechanism class.Agronomy machine of the mechanism class method from process of crop growth
System triggering is input driving crop life with remote sensing observations data by choosing the mechanism class models such as rational crop growth model
Long model simulates process of crop growth, to quantitative estimation crop yield.Crop growth model is used as being capable of comprehensive and quantitative point
The tool of crop physiology process is analysed, is widely applied in crop yield estimation field, effectively provides the crop fields
Between the level of decision-making that manages.
Crop growth model is roughly divided into luminous energy driving model, carbon dioxide driving model and moisture driving model (such as
AquaCrop models).Input parameter needed for preceding two class model is numerous, needs to set tens to hundreds of primary condition and input
Parameter, and soil parameters, Cultivar parameter, genetic parameter etc. are not easy to obtain and be verified on regional scale, to reduce crop
Efficiency of the growth model in terms of area crops the yield by estimation, also limits the application of model.Third class model required input parameter compared with
It is few, and each parameter is easier to obtain and verify, in terms of agricultural output assessment more efficiently.Preceding two class models generally use leaf area refers to
Number affects the essence of the agricultural output assessment subsequently based on crop growth model as link parameter, the remote sensing inversion accuracy of leaf area index
Degree；Third class model generally use biomass affects subsequently as link parameter, the remote sensing inversion accuracy of biomass based on work
The agricultural output assessment precision of object growth model.However, being carried out using remote sensing observations data quantitative inverting leaf area index or biomass
There is the yield by estimation efficiency and the not high defect of precision in the mode of the yield by estimation.
Accordingly, it is considered to which timely, the high-precision application demand to crop yield remote sensing quantitative estimation, requires to carry in the prior art
For it is a kind of can in conjunction with each class model the advantages of, with more the yield by estimation efficiency and accuracy benefits crop yield remote sensing appraising side
Method.
Invention content
The embodiment of the present invention provides a kind of crop yield remote sensing estimation method based on adjustable vegetation index, with an improved
According to the technology of the Crop leaf area index in remotely-sensed data inverting region, in conjunction with crop leaf area index and third class is utilized
The advantages of model, carries out crop yield estimation, overcomes defect existing in the prior art.
According to the present invention, a kind of crop yield remote sensing estimation method based on adjustable vegetation index, this method packet are provided
It includes：
For needing the region of estimated crops, the crop in the region is obtained in early growth period, growth medium and life
Multi-source multi-temporal remote sensing in the long maturity period observes data, and the work of multidate is determined based on multi-source multi-temporal remote sensing observation data
The adjustable vegetation index of object, and referred to according to the leaf area of multidate of the adjustable vegetation index inverting acquisition based on remote sensing observations data
Number LAI；
The growth model priori data of crop based on the region being obtained ahead of time sets the plant growth mould in the region
The input parameter and primary condition of type；
Crop growth model is run multiple times to obtain a plurality of LAI of simulation in input parameter based on setting and primary condition
Curve and the corresponding crop yield of every LAI curve；LAI curve tables are shown as the LAI of object and the adjustable vegetation index of different phases
Correspondence；
According to multidate LAI data, the blade face with the multidate based on remote sensing observations data is chosen from a plurality of LAI curves
The a plurality of LAI curves of selection are fitted and are fitted by a plurality of LAI curve of the product index LAI similarities 75% or more
LAI curves, and the average value of each crop yield using similarity corresponding to 75% or more a plurality of LAI curves is as fitting
The corresponding crop yield of LAI curves；
Using the corresponding crop yield of LAI curves of fitting as the estimation yield in the region.
Preferably, determining that the adjustable vegetation index of crop includes based on multi-source multi-temporal remote sensing observation data：
The near-infrared for obtaining crop of the crop in the region in early growth period, growth medium and growth and maturity phase is anti-
Penetrate rate and reflection to red light rate；
Calculate separately crop the crop of early growth period, growth medium and growth and maturity phase reflection to red light rate and near-infrared
Reflectivity ratio；
By crop the crop of early growth period, growth medium and growth and maturity phase reflection to red light rate and near infrared reflectivity
Ratio is summed and is averaged, and the average value of acquirement is multiplied by predetermined weighting coefficient δ as Dynamic gene α；And it seeks
Average value ρ of the crop in the reflection to red light rate of the crop of early growth period, growth medium and growth and maturity phasered, crop is sought in life
Long initial stage, growth medium and growth and maturity phase crop near infrared reflectivity average value ρNIR；
The adjustable vegetation index VDVI of crop is determined according to formula 1：
Wherein, weighting coefficient δ is determined previously according to empirical value, and value range is 1.02 to 1.08.
Preferably, and according to the leaf area index of multidate of the adjustable vegetation index inverting acquisition based on remote sensing observations data
LAI includes：
If α is less than or equal to 0.1, the leaf area index LAI of phase is determined according to formula 2,
Y=0.9336x+0.7442 formula 2
If α is more than 0.1, the leaf area index LAI of phase is determined according to formula 3,
Y=0.902x+0.4576 formula 3
In formula, y is leaf area index, and x is the corresponding adjustable vegetation index value of different time in crop growth period.
Wherein, under the conditions of same phase, if the adjustable vegetation of the corresponding LAI values of LAI curves and the crop determined
The ratio of the leaf area index LAI for the multidate that the corresponding inverting of index obtains is not less than 0.75, then using the LAI curves as phase
LAI curves like degree 75% or more.
Preferably, a plurality of LAI curves of selection be fitted the LAI curves being fitted including：
For each phase of crop growth period, it is assumed that a plurality of LAI curves of selection are N items, and N is the integer more than 2,
If LAI1, LAI2, L, LAINThe corresponding LAI values of respectively N LAI curve, by LAI1, LAI2, L, LAINAverage value conduct
The LAI values of the fitting of corresponding phase；
The fitting LAI values of each phase are depicted as to the LAI curves of fitting.
Wherein, the crop growth model is that moisture drives AquaCrop.
Wherein, multi-source multi-temporal remote sensing observation data are that multi-source multi-temporal remote sensing observes image data.
Wherein, the growth model priori data includes：Profile data, crop historical data and the crop life in the region
Performance data of science, the growth model priori data of the crop based on the region being obtained ahead of time, sets the region
The input parameter and primary condition of crop growth model include：
Priori is built according to the profile data in the region, crop historical data and crop physiology performance data
Library；The priori library is used for as the input parameter of the crop growth model and the basis of design of primary condition；According to
The priori library sets the input parameter and primary condition of crop growth model.
As seen from the above technical solution, the crop yield remote sensing provided in an embodiment of the present invention based on adjustable vegetation index is estimated
Calculation method.According to the present invention, determine that the leaf area of the multidate based on remote sensing observations data refers to using adjustable vegetation index inverting
Number LAI, and compared with the multidate LAI curves obtained based on operation crop growth model, similarity is chosen 75% or more
LAI curves are fitted, and carry out crop yield estimation using the LAI curves that fitting obtains.The crop yield estimation of the present invention
Plant page index and the model usually using crop biomass can be utilized in method so that crop yield is estimated due to taking into account
Many factors and it is more accurate.In addition, in the determination of adjusting plant index, due to being utilized in crop growth period not
Reflection to red light rate and near infrared reflectivity ratio with the stage determine adjustable vegetation index, avoid traditional normalization vegetation
The problem of index is saturated, so that more accurate come the page index LAI values of inverting based on adjustable vegetation index.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described.It should be evident that the attached drawing in being described below is only this
Some embodiments of invention illustrated embodiment can also obtain according to these attached drawings for those of ordinary skills
Other embodiments and its attached drawing.
Fig. 1 is the crop yield remote sensing estimation method flow diagram based on adjustable vegetation index of the embodiment of the present invention.
Specific implementation mode
Clear, complete description is carried out to the technical solution of various embodiments of the present invention below with reference to attached drawing, it is clear that retouched
The embodiment stated is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention,
Those of ordinary skill in the art's obtained all other embodiment without making creative work belongs to this
The protected range of invention.
Fig. 1 shows the crop yield remote sensing estimation method flow signal based on adjustable vegetation index of the embodiment of the present invention
Figure.As shown in Figure 1, in step 10, for needing the region of estimated crops, obtain the crop in the region early growth period,
Multi-source multi-temporal remote sensing in growth medium and growth and maturity phase observes data, and data are observed based on the multi-source multi-temporal remote sensing
It determines the adjustable vegetation index of the crop of multidate, and is obtained based on the more of remote sensing observations data according to adjustable vegetation index inverting
The leaf area index LAI of phase.
Preferably, the adjustable vegetation index for determining crop based on multi-source multi-temporal remote sensing observation data is implemented as follows.It is first
First, the near infrared reflectivity of crop of the crop in the region in early growth period, growth medium and growth and maturity phase is obtained
With reflection to red light rate.Then, calculate separately crop the crop of early growth period, growth medium and growth and maturity phase reflection to red light
Rate and near infrared reflectivity ratio.Later, crop is anti-in the feux rouges of the crop of early growth period, growth medium and growth and maturity phase
It penetrates rate and near infrared reflectivity ratio is summed and is averaged, the average value of acquirement, which is multiplied by predetermined weighting coefficient δ, to be made
For Dynamic gene α, and seeks crop and be averaged in the reflection to red light rate of the crop of early growth period, growth medium and growth and maturity phase
Value ρred, seek crop the near infrared reflectivity of the crop of early growth period, growth medium and growth and maturity phase average value ρNIR。
Finally, the adjustable vegetation index VDVI of crop is determined according to formula 1：
Wherein, weighting coefficient δ is determined previously according to empirical value, and value range is 1.02 to 1.08.
It will be appreciated by those skilled in the art that usually using normalized site attenuation in the prior art.However, returning in determination
When one change difference vegetation index, if the near infrared reflectivity of crop is much larger than reflection to red light rate, normalizes difference vegetation and refer to
Number shows " saturation " phenomenon, causes identified normalized site attenuation that cannot really reflect vegetation state, thus adopts
The problem of estimated value inaccuracy is also resulted in when estimated crops with normalized differential vegetation index.In the embodiment of the present invention
In, using crop the crop of early growth period, growth medium and growth and maturity phase reflection to red light rate and near infrared reflectivity ratio
Value simultaneously determines vegetation index in conjunction with weighting coefficient determining based on experience value so that no matter crop is in early growth period, growth
How is difference between the reflection to red light rate and near infrared reflectivity of the crop of phase and growth and maturity phase, can vegetation be referred to
Number delicately changes with the variation of reflection to red light rate and near infrared reflectivity value, it is entirely avoided due to the near-infrared of crop
" saturation " phenomenon that normalized site attenuation caused by difference of the reflectivity much larger than reflection to red light rate shows.
According to practical experience, the value of weighting coefficient δ should not be too big, and optimum value is between 1.02 to 1.04.
According to an embodiment of the invention, the multidate based on remote sensing observations data is obtained according to adjustable vegetation index inverting
Leaf area index LAI includes：
If α is less than or equal to 0.1, the leaf area index LAI of phase is determined according to formula 2,
Y=0.9336x+0.7442 formula 2
If α is more than 0.1, the leaf area index LAI of phase is determined according to formula 3,
Y=0.902x+0.4576 formula 3
In formula, y is leaf area index, and x is the corresponding adjustable vegetation index value of different time in crop growth period.
Then, in step 20, the growth model priori data of the crop based on the region being obtained ahead of time sets the area
The input parameter and primary condition of the crop growth model in domain.
In the embodiment of the present invention, crop growth model is moisture driving model AquaCrop.The region that step 20 obtains
The growth model priori data of crop includes the data numbers such as regional general situation, crop historical data and the crop physiology characteristic collected
According to.
Regional general situation data can include but is not limited to research area's longitude and latitude, soil types, soil moisture, soil nutrient,
The information such as crop planting model, crop water and fertilizer management mode, meteorological data, weather conditions；Crop historical data may include but
It is long to be not limited to crop sowing time, seeding method, application rate, crop irrigation time, irrigation method, irrigation volume, Crop growing stage
The information such as gesture situation, crop yield.Crop physiology performance data includes the breeding time of crop, root depth, plant type, plant height, leaf color
Deng.
After the datas such as collecting zone overview, crop historical data and crop physiology characteristic, it can utilize and receive
The various data of collection build priori library, and constructed priori library is used for as biological growth model AquaCrop's
The basis of design of input parameter and primary condition.Later, in the input parameter for setting AquaCrop models based on priori library
When with primary condition, the information of setting includes at least：
(1) 8 input file：Weather file, the file that evapotranspires, precipitation file, carbon dioxide file, is irrigated at temperature file
File, management file and soil profile file；
(2) 51 fixed value input parameters, including soil and Root Parameters, temperature and date parameter, yield/canopy/receipts
Obtain figure parameters etc.；
The numberical range of (3) 3 adjustable input parameters：Maximum effectively root is deep, blade face growth factor and maximum blade face are planted
By index.
In step 30, crop growth model is run multiple times to be simulated in the input parameter based on setting and primary condition
A plurality of LAI curves and the corresponding crop yield of every LAI curve；LAI curve tables are shown as the adjustable of the LAI of object and different phases
The correspondence of vegetation index.
On the basis of setting the input parameter and primary condition of AquaCrop models, by the way that 3 adjustable inputs are arranged
The different numerical value of parameter run an AquaCrop model based on setting each time, and a plurality of LAI for obtaining corresponding crop is bent
Line and the corresponding crop yield of every LAI curve.It will be appreciated by those skilled in the art that look-up table may be used to replace above-mentioned song
Line scheme.
Next, in step 40, according to multidate LAI data, is chosen from a plurality of LAI curves and be based on remote sensing observations number
According to multidate a plurality of LAI curve of the leaf area index LAI similarities 75% or more, by a plurality of LAI curves of selection into
The LAI curves that row is fitted, and each crop yield by similarity corresponding to 75% or more a plurality of LAI curves
LAI curve corresponding crop yield of the average value as fitting.
Wherein, a plurality of LAI curves of selection are fitted the LAI curves being fitted includes：
For each phase of crop growth period, it is assumed that a plurality of LAI curves of selection are N items, and N is the integer more than 2,
If LAI1, LAI2, L, LAINThe corresponding LAI values of respectively N LAI curve, by LAI1, LAI2, L, LAINAverage value conduct
The LAI values of the fitting of corresponding phase；
The fitting LAI values of each phase are depicted as to the LAI curves of fitting.
Wherein, under the conditions of same phase, if the adjustable vegetation of the corresponding LAI values of LAI curves and the crop determined
The ratio of the leaf area index LAI for the multidate that the corresponding inverting of index obtains is not less than 0.75, then using the LAI curves as phase
LAI curves like degree 75% or more.
In the present invention, by choosing through running in a plurality of LAI curves that AquaCrop models obtain and being based on adjustable vegetation
Curve of the similarity for the LAI curves that index VDVI invertings obtain 75% or more, and the LAI curves of selection are fitted
To the LAI curves of fitting, so that the LAI curves of fitting more precisely react actual crop yield, it is more enough so that making
The estimation of produce amount is more accurate.
Finally, using the corresponding crop yield of LAI curves of fitting as the estimation yield in the region, step 50.
According to the method for the present invention, in the determination of adjustable vegetation index, due to considering crop in early growth period, growth
The reflection to red light rate and near infrared reflectivity ratio of the crop of phase and growth and maturity phase and and weighting determining based on experience value
Coefficient, so that adjustable vegetation index showed when not being much larger than reflection to red light rate by the near infrared reflectivity due to vegetation
The influence of " saturation " phenomenon, so that more accurate according to the LAI curves of adjustable vegetation index inverting vegetation.Further, by
In a plurality of LAI curves of selection obtained through operation AquaCrop models obtained with based on adjustable vegetation index VDVI invertings
LAI curves curve of the similarity 75% or more so that being fitted the LAI curves of selection to be fitted
LAI curves more precisely react actual crop yield, are conducive to the estimation precision for improving crop yield.
Obviously, various changes and modifications can be made to the invention without departing from spirit of the invention by those skilled in the art
And range.If in this way, these modification and variation of the present invention are belonged to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention also includes these modifications and variations.
Claims (6)
1. a kind of crop yield remote sensing estimation method based on adjustable vegetation index, this method include：
For needing the region of estimated crops, obtaining the crop in the region in early growth period, growth medium and growing into
Multi-source multi-temporal remote sensing in the ripe phase observes data, and the crop of multidate is determined based on multi-source multi-temporal remote sensing observation data
Adjustable vegetation index, and according to the leaf area index of multidate of the adjustable vegetation index inverting acquisition based on remote sensing observations data
LAI；
The growth model priori data of crop based on the region being obtained ahead of time, sets the crop growth model in the region
Input parameter and primary condition；
Crop growth model is run multiple times to obtain a plurality of LAI curves of simulation in input parameter based on setting and primary condition
Crop yield corresponding with every LAI curve；LAI curve tables are shown as the correspondence of the LAI and different phases of object；
According to multidate LAI data, chooses from a plurality of LAI curves and refer to the leaf area of the multidate based on remote sensing observations data
The a plurality of LAI curves of selection, it is bent to be fitted the LAI being fitted by a plurality of LAI curve of the number LAI similarities 75% or more
Line, and the average value of each crop yield using similarity corresponding to 75% or more a plurality of LAI curves is bent as the LAI of fitting
The corresponding crop yield of line；
Using the corresponding crop yield of LAI curves of fitting as the estimation yield in the region；
Wherein, determine that the adjustable vegetation index of crop includes based on multi-source multi-temporal remote sensing observation data：
Obtain the near infrared reflectivity of crop of the crop in the region in early growth period, growth medium and growth and maturity phase
With reflection to red light rate；
Calculate separately crop the crop of early growth period, growth medium and growth and maturity phase reflection to red light rate and near-infrared reflection
Rate ratio；
Seek crop the reflection to red light rate of the crop of early growth period, growth medium and growth and maturity phase average value ρredWith it is close
The average value ρ of infrared reflectivityNIR；By crop the crop of early growth period, growth medium and growth and maturity phase reflection to red light rate
It sums and is averaged near infrared reflectivity ratio, the average value of acquirement is multiplied by predetermined weighting coefficient δ as tune
Integral divisor α；
The adjustable vegetation index VDVI of crop is determined according to formula 1：
Formula 1,
In formula 1, weighting coefficient δ is determined previously according to empirical value, and value range is 1.02 to 1.08.
2. crop yield remote sensing estimation method according to claim 1, wherein under the conditions of same phase, if LAI
The leaf area index for the multidate that the inverting corresponding with the adjustable vegetation index of crop determined of the corresponding LAI values of curve obtains
The ratio of LAI is not less than 0.75, then the LAI curves using the LAI curves as similarity 75% or more.
3. crop yield remote sensing estimation method according to claim 2, wherein intend a plurality of LAI curves of selection
Closing the LAI curves being fitted includes：
For each phase of crop growth period, it is assumed that a plurality of LAI curves of selection are N items, and N is the integer more than 2, if
LAI1,LAI2,...,LAINThe corresponding LAI values of respectively N LAI curve, by LAI1,LAI2,...,LAINAverage value make
For the LAI values of the fitting of corresponding phase；
The fitting LAI values of each phase are depicted as to the LAI curves of fitting.
4. crop yield remote sensing estimation method according to claim 1, wherein the crop growth model drives for moisture
AquaCrop。
5. crop yield remote sensing estimation method according to claim 1, wherein it is more that multi-source multi-temporal remote sensing, which observes data,
Source multi-temporal remote sensing observes image data.
6. crop yield remote sensing estimation method according to claim 1, wherein the growth model priori data includes：
Profile data, crop historical data and the crop physiology performance data in the region, it is described based on the area being obtained ahead of time
The growth model priori data of the crop in domain, the input parameter and primary condition for setting the crop growth model in the region include：
Priori library is built according to the profile data in the region, crop historical data and crop physiology performance data；Institute
Priori library is stated for as the input parameter of the crop growth model and the basis of design of primary condition；
According to the priori library, the input parameter and primary condition of crop growth model are set.
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| CN103839184A (en) * | 2014-02-21 | 2014-06-04 | 三峡大学 | Multi-space-scale self-adaptive monitoring method and device for population growth of crops |
| CN106845428A (en) * | 2017-01-26 | 2017-06-13 | 中国科学院遥感与数字地球研究所 | A kind of crop yield remote sensing estimation method and system |
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| CN103839184A (en) * | 2014-02-21 | 2014-06-04 | 三峡大学 | Multi-space-scale self-adaptive monitoring method and device for population growth of crops |
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Address after: Room 101, 5/F, Room 101, F11, Building 17, 18th District, No. 188, South 4th Ring West Road, Fengtai District, Beijing, 100160 Patentee after: AEROSPACE XINDE ZHITU (BEIJING) SCIENCE AND TECHNOLOGY Co.,Ltd. Address before: 100083 No.6, room 207, 2 / F, Kemao building, no.5-38, 35 Qinghua East Road, Haidian District, Beijing Patentee before: BEIJING XINDEZHITU TECHNOLOGY CO.,LTD. |
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