CN107609695A - 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, the input parameter and primary condition of the crop growth model in the region are set, crop growth model is run multiple times to obtain a plurality of LAI curves of simulation and corresponding crop yield；Choose with a plurality of LAI curves more than 75% of the leaf area index LAI similarities of the multidate based on remote sensing observations data and the LAI curves that are fitted, using the average of each crop yield corresponding to LAI curves of the similarity more than 75% as crop yield corresponding to the LAI curves being fitted；Using corresponding crop yield as area estimation yield.Using the present invention, crop yield estimation precision can be lifted.
Description
Technical field
The present invention relates to the crop yield estimating techniques of agriculture field, more particularly to a kind of work 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 of the crop yield estimation to field management strategy
The loss appraisals such as disaster have important theoretical significance and application value realistic, are the study 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 observation data in large area, so as 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, by choosing the mechanism class model such as rational crop growth model, using remote sensing observations data as input driving crop life
Long model simulates process of crop growth, so as to quantitative estimation crop yield.Crop growth model is used as being capable of comprehensive and quantitative point
The instrument 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, it is necessary 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 verified on regional scale, so as to reduce crop
Efficiency of the growth model in terms of area crops the yield by estimation, also limit the application of model.3rd class model required input parameter compared with
It is few, and each parameter is easier to obtain and checking, it is more efficient in terms of agricultural output assessment.Preceding two class models generally use leaf area refers to
For number as link parameter, the remote sensing inversion accuracy of leaf area index affects the agricultural output assessment essence subsequently based on crop growth model
Degree；3rd class model generally use biomass is affected subsequently based on work as link parameter, the remote sensing inversion accuracy of biomass
The agricultural output assessment precision of thing growth model.However, carried out using remote sensing observations data quantitative inverting leaf area index or biomass
The mode of the yield by estimation, the defects of the yield by estimation efficiency and not high precision be present.
Accordingly, it is considered to crop yield remote sensing quantitative estimation is timely, high-precision application demand, require to carry in the prior art
For a kind of the advantages of can combining each class model, with more the yield by estimation efficiency and accuracy benefits crop yield remote sensing appraising side
Method.
The content of the invention
Embodiments of the invention provide 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, with reference to crop leaf area index and make use of the 3rd class
The advantages of model, carries out crop yield estimation, overcomes defect present in prior art.
According to the present invention, there is provided a kind of crop yield remote sensing estimation method based on adjustable vegetation index, this method bag
Include：
Region for needing estimated crops, the crop in the region is obtained in early growth period, growth medium and life
Multi-source multi-temporal remote sensing observation data in the long maturity period, the work of multidate is determined based on multi-source multi-temporal remote sensing observation data
The adjustable vegetation index of thing, 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, set the plant growth mould in the region
The input parameter and primary condition of type；
Input parameter and primary condition based on setting, crop growth model is run multiple times to obtain a plurality of LAI of simulation
Crop yield corresponding to curve and every LAI curve；LAI curve tables are shown as the LAI of thing and the adjustable vegetation index of different phases
Corresponding relation；
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
A plurality of LAI curve of the product index LAI similarities more than 75%, is fitted what is be fitted by a plurality of LAI curves of selection
LAI curves, and using the average value of each crop yield corresponding to a plurality of LAI curves of the similarity more than 75% as fitting
Crop yield corresponding to LAI curves；
Estimation yield using crop yield corresponding to the LAI curves of fitting as the region.
It is preferred that determine 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 ρNIRWith reflection to red light rate ρred；
Reflection to red light rate and near-infrared of the crop in the crop of early growth period, growth medium and growth and maturity phase are calculated respectively
Reflectivity ratio ρred/ρNIR；
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 averaged, and the average value of acquirement is multiplied by into predetermined weight coefficient δ as Dynamic gene α；
The adjustable vegetation index VDVI of crop is determined according to formula 1：
Wherein, weight coefficient δ is determined previously according to empirical value, and its span is 1.02 to 1.08.
It is preferred that 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 adjustable vegetation index value corresponding to different time in crop growth period.
Wherein, with the conditions of same phase, if LAI values difference corresponding to LAI curves and the crop that determines is adjustable
The leaf area index LAI for the multidate that inverting corresponding to vegetation index obtains ratio is not less than 0.75, then makees the LAI curves
The LAI curves for being similarity more than 75%.
It is preferred that a plurality of LAI curves of selection are fitted to the LAI curves being fitted to be included：
For each phase of crop growth period, it is assumed that a plurality of LAI curves of selection are N bars, and N is the integer more than 2,
If LAI1, LAI2..., LAINThe respectively corresponding LAI values of N bars LAI curves, 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.
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
Storehouse；The priori storehouse is used for as the input parameter of the crop growth model and the basis of design of primary condition；According to
The priori storehouse, set 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, the leaf area for determining the multidate based on remote sensing observations data using adjustable vegetation index inverting refers to
Number LAI, and with the multidate LAI curve ratios based on operation crop growth model acquisition compared with selection similarity is more than 75%
LAI curves are fitted, and the LAI curves obtained using fitting carry out crop yield estimation.The crop yield estimation of the present invention
Method can make use of plant page index and the model usually using crop biomass so that crop yield is estimated due to taking into account
Many factors and it is more accurate.In addition, in determination for adjusting plant index, due to make use of 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 saturation, so that more accurate come the page index LAI values of inverting based on adjustable vegetation index.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described.It should be evident that the accompanying drawing in following description is only this
Some embodiments of invention, for those of ordinary skills, can also be obtained according to these accompanying drawing illustrated embodiments
Other embodiments and its accompanying drawing.
Fig. 1 is the crop yield remote sensing estimation method schematic flow sheet based on adjustable vegetation index of the embodiment of the present invention.
Embodiment
Clear, complete description is carried out to the technical scheme of various embodiments of the present invention below with reference to accompanying drawing, it is clear that retouched
The embodiment stated is only the part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention,
Those of ordinary skill in the art's all other embodiment resulting on the premise of creative work is not made, belongs to this
The protected scope of invention.
Fig. 1 shows the signal of the crop yield remote sensing estimation method flow based on adjustable vegetation index of the embodiment of the present invention
Figure.As shown in figure 1, in step 10, the region for needing estimated crops, obtain the crop in the region early growth period,
Multi-source multi-temporal remote sensing observation data in growth medium and growth and maturity phase, data are observed based on the multi-source multi-temporal remote sensing
The adjustable vegetation index of the crop of multidate is determined, and is obtained according to adjustable vegetation index inverting based on the more of remote sensing observations data
The leaf area index LAI of phase.
It is preferred that the adjustable vegetation index that crop is determined 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
ρNIRWith reflection to red light rate ρred.Then, respectively calculate crop early growth period, growth medium and growth and maturity phase crop it is red
Light reflectivity and near infrared reflectivity ratio ρred/ρNIR.Afterwards, by crop in early growth period, growth medium and growth and maturity phase
Crop reflection to red light rate and near infrared reflectivity ratio sum and average, the average value of acquirement is multiplied by predetermined
Weight coefficient δ as Dynamic gene α.Finally, the adjustable vegetation index VDVI of crop is determined according to formula 1：
Wherein, weight coefficient δ is determined previously according to empirical value, and its span is 1.02 to 1.08.
It will be appreciated by those skilled in the art that normalized site attenuation is usually used in the prior art.However, it is determined that returning
During one change difference vegetation index, if the near infrared reflectivity ρ of cropNIRMuch larger than reflection to red light rate ρred, then difference is normalized
Vegetation index shows " saturation " phenomenon, causes identified normalized site attenuation truly to reflect vegetation state,
The problem of thus using normalized differential vegetation index to also result in estimated value inaccuracy during estimated crops.In the reality of the present invention
Apply in example, using 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 simultaneously combines the weight coefficient that determines based on experience value to determine vegetation index so that no matter crop is in early growth period, life
How is difference between the reflection to red light rate and near infrared reflectivity of the crop of long mid-term and growth and maturity phase, can to plant
Delicately changed with the change of reflection to red light rate and near infrared reflectivity value by index, it is entirely avoided near due to crop
Infrared reflectivity ρNIRMuch larger than reflection to red light rate ρredDifference caused by normalized site attenuation show it is " full
With " phenomenon.
According to practical experience, weight coefficient δ value 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 adjustable vegetation index value corresponding to 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, the area is set
The input parameter and primary condition of the crop growth model in domain.
In embodiments of the 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 can include but
It is not limited to crop sowing time, seeding method, application rate, crop irrigation time, irrigation method, irrigation volume, Crop growing stage length
The information such as gesture situation, crop yield.Breeding time of the crop physiology performance data including 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 storehouse, and constructed priori storehouse is used for as biological growth model AquaCrop's
The basis of design of input parameter and primary condition.Afterwards, in the input parameter based on priori storehouse setting AquaCrop models
During with primary condition, the information of setting comprises at least：
(1) 8 input file：Weather file, temperature file, the file that evapotranspires, precipitation file, carbon dioxide file, irrigation
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 number 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, input parameter and primary condition based on setting, crop growth model is run multiple times to be simulated
A plurality of LAI curves and every LAI curve corresponding to crop yield；LAI curve tables are shown as the adjustable of the LAI of thing and different phases
The corresponding relation of vegetation index.
On the basis of the input parameter and primary condition of setting AquaCrop models, by setting 3 adjustable inputs
The different numerical value of parameter, an AquaCrop model is run based on setting each time, a plurality of LAI for obtaining corresponding crop is bent
Crop yield corresponding to line and every LAI curve.It will be appreciated by those skilled in the art that above-mentioned song can be replaced using look-up table
Line scheme.
Next, in step 40, according to multidate LAI data, 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 more than 75%, a plurality of LAI curves of selection are entered
The LAI curves that are fitted of row, and by each crop yield corresponding to a plurality of LAI curves of the similarity more than 75%
Average value is as crop yield corresponding to the LAI curves of fitting.
Wherein, a plurality of LAI curves of selection are fitted to the LAI curves being fitted to be included：
For each phase of crop growth period, it is assumed that a plurality of LAI curves of selection are N bars, and N is the integer more than 2,
If LAI1, LAI2..., LAINThe respectively corresponding LAI values of N bars LAI curves, 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.
Wherein, with the conditions of same phase, if LAI values difference corresponding to LAI curves and the crop that determines is adjustable
The leaf area index LAI for the multidate that inverting corresponding to vegetation index obtains ratio is not less than 0.75, then makees the LAI curves
The LAI curves for being similarity more than 75%.
In the present invention, by choosing in a plurality of LAI curves obtained through running AquaCrop models with being based on adjustable vegetation
Curve of the similarity for the LAI curves that index VDVI invertings obtain more than 75%, 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, the estimation yield using crop yield corresponding to the LAI curves of fitting as the region, step 50.
The method according to the 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 the weighting that determines based on experience value
Coefficient, so that adjustable vegetation index is not by the near infrared reflectivity ρ due to vegetationNIRMuch larger than reflection to red light rate ρredShi Cheng
The influence of " saturation " phenomenon revealed, so that more accurate according to the LAI curves of adjustable vegetation index inverting vegetation.Enter one
Step, be in a plurality of LAI curves obtained due to selection through running AquaCrop models with it is based on adjustable vegetation index VDVI anti-
Curve of the similarity for the LAI curves for drilling to obtain more than 75%, hence in so that being fitted to obtain by the LAI curves of selection
The LAI curves of fitting more precisely react actual crop yield, are advantageous to improve the estimation precision of crop yield.
Obviously, those skilled in the art can carry out the spirit of various changes and modification without departing from the present invention to the present invention
And scope.So, if belonging to the scope of the claims in the present invention and its equivalent technologies to these modification and variation of the present invention
Within, then the present invention also comprising these change and modification including.
Claims (8)
1. a kind of crop yield remote sensing estimation method based on adjustable vegetation index, this method include：
Region for needing estimated crops, the crop in the region is obtained in early growth period, growth medium and is grown into
Multi-source multi-temporal remote sensing observation data in the ripe phase, 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；
Input parameter and primary condition based on setting, crop growth model is run multiple times to obtain a plurality of LAI curves of simulation
With every LAI curve corresponding to crop yield；LAI curve tables are shown as the LAI of thing and the corresponding relation of different phases；
According to multidate LAI data, choose from a plurality of LAI curves and refer to the leaf area of the multidate based on remote sensing observations data
A plurality of LAI curve of the number LAI similarities more than 75%, it is bent a plurality of LAI curves of selection to be fitted the LAI being fitted
Line, and the average value of each crop yield corresponding to a plurality of LAI curves of the similarity more than 75% is bent as the LAI of fitting
Crop yield corresponding to line；
Estimation yield using crop yield corresponding to the LAI curves of fitting as the region.
2. crop yield remote sensing estimation method according to claim 1, true based on multi-source multi-temporal remote sensing observation data
The adjustable vegetation index of ordered goods includes：
Obtain the near infrared reflectivity of crop of the crop in the region in early growth period, growth medium and growth and maturity phase
ρNIRWith reflection to red light rate ρred；
Reflection to red light rate and near-infrared reflection of the crop in the crop of early growth period, growth medium and growth and maturity phase are calculated respectively
Rate ratio ρred/ρNIR；
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
Sum and average, the average value of acquirement is multiplied by predetermined weight coefficient δ as Dynamic gene α；
The adjustable vegetation index VDVI of crop is determined according to formula 1：
Wherein, weight coefficient δ is determined previously according to empirical value, and its span is 1.02 to 1.08.
3. crop yield remote sensing estimation method according to claim 2, wherein, and obtained according to adjustable vegetation index inverting
The leaf area index LAI of multidate based on remote sensing observations data 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 adjustable vegetation index value corresponding to different time in crop growth period.
4. the crop yield remote sensing estimation method according to claim 1 or 3, wherein, with the conditions of same phase, if
The blade face for the multidate that inverting corresponding to the adjustable vegetation index of LAI values difference and the crop determined corresponding to LAI curves obtains
Product index LAI ratio is not less than 0.75, then using LAI curve of the LAI curves as similarity more than 75%.
5. crop yield remote sensing estimation method according to claim 4, wherein, a plurality of LAI curves of selection are intended
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 bars, and N is the integer more than 2, if
LAI1, LAI2..., LAINThe respectively corresponding LAI values of N bars LAI curves, by LAI1, LAI2..., 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.
6. crop yield remote sensing estimation method according to claim 1, wherein, the crop growth model drives for moisture
AquaCrop。
7. crop yield remote sensing estimation method according to claim 1, wherein, multi-source multi-temporal remote sensing observation data are more
Source multi-temporal remote sensing observation image data.
8. 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 storehouse is built according to the profile data in the region, crop historical data and crop physiology performance data；Institute
Priori storehouse is stated to be used for as the input parameter of the crop growth model and the basis of design of primary condition；
According to the priori storehouse, the input parameter and primary condition of crop growth model are set.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108363983A (en) * | 2018-03-06 | 2018-08-03 | 河南理工大学 | A kind of Urban vegetation classification method based on unmanned plane image Yu reconstruction point cloud |
| CN110428107A (en) * | 2019-08-06 | 2019-11-08 | 吉林大学 | A kind of corn yield remote sensing prediction method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050165552A1 (en) * | 2001-02-28 | 2005-07-28 | The Mosaic Company | Method for prescribing site-specific fertilizer application in agricultural fields |
| CN103278112A (en) * | 2013-05-17 | 2013-09-04 | 中国科学院对地观测与数字地球科学中心 | Leaf area index inversion method and device based on remote sensing images |
| 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 |
-
2017
- 2017-09-04 CN CN201710786639.0A patent/CN107609695B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050165552A1 (en) * | 2001-02-28 | 2005-07-28 | The Mosaic Company | Method for prescribing site-specific fertilizer application in agricultural fields |
| CN103278112A (en) * | 2013-05-17 | 2013-09-04 | 中国科学院对地观测与数字地球科学中心 | Leaf area index inversion method and device based on remote sensing images |
| 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 |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108363983A (en) * | 2018-03-06 | 2018-08-03 | 河南理工大学 | A kind of Urban vegetation classification method based on unmanned plane image Yu reconstruction point cloud |
| CN108363983B (en) * | 2018-03-06 | 2021-05-18 | 河南理工大学 | Urban vegetation classification method based on unmanned aerial vehicle image and reconstructed point cloud |
| CN110428107A (en) * | 2019-08-06 | 2019-11-08 | 吉林大学 | A kind of corn yield remote sensing prediction method and system |
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