CN107271382A - A kind of different growing rape leaf SPAD value remote sensing estimation methods - Google Patents
A kind of different growing rape leaf SPAD value remote sensing estimation methods Download PDFInfo
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Abstract
The invention discloses a kind of different growing rape leaf SPAD value remote sensing estimation methods, using the Northwest's industrial crops rape as research object, determine each breeding time Leaf reflectance of rape and SPAD values, by the correlation for analyzing 10 kinds of spectral indexes and rape leaf SPAD values, rape leaf SPAD random forest regression estimation models are built based on spectral index, and institute's established model is verified, it is compared with reference to traditional Linear Regression Model in One Unknown and multiple stepwise regression model with it.The modeling of the present invention and checking R2Respectively reach 0.91 and more than 0.74, RMSE is between 1.571 5.004 for checking, RE is rape leaf SPAD maximum likelihood estimation model between 2.66% 13.22%.
Description
Technical field
The invention belongs to agricultural technology field, it is related to a kind of different growing rape leaf SPAD value remote sensing estimation methods.
Background technology
Chlorophyll is plant and the extraneous important substance for carrying out energy exchange, due to being deposited between chlorophyll and Leaf nitrogen concentration
In preferably correlation, therefore the nutrition condition of plant can be characterized.Hyperspectral technique have that spectral region is wide, wave band many and
The advantages of data volume is big, can be for being monitored, in recent years to crops blade and canopy biochemical component, domestic and foreign scholars pair
Numerous studies are done in terms of chlorophyll content of plant is monitored using high-spectral data, Dash.J etc., which is proposed, utilizes MERID numbers
Carry out inverting chlorophyll content according to the MTCI indexes to high chlorophyll density sensitive；Gitelson selects corn and soybean to be research pair
As establishing the estimation models of canopy chlorophyll content using the inverse of canopy reflectance spectrum；Broge etc. is with different nitrogen level
Wheat canopy spectroscopic data, it is indicated that ratio vegetation index (RVI) can effectively predict the content of canopy chlorophyll；Yao Fuqi
Etc. comprehensive analysis correlations and predictability of 10 kinds of vegetation indexs with oriental plane tree chlorophyll, principal component analysis and BP god are utilized
The estimation of oriental plane tree chlorophyll content is carried out through network, it is believed that the pass of normalized differential vegetation index (NDVI) and chlorophyll content
System is the closest；Palace million is peaceful etc. have studied vegetation chlorophyll and " three sides " parameter and the ratio (SR) being made up of spectral reflectivity and
The dependency relation between (ND) spectral index is normalized, the inverse model of chlorophyll content is established.
Random forest (RandomForest) is a kind of Statistical Learning Theory, with very strong capability of fitting, will not be gone out
Existing overfitting phenomenon, modeling speed is fast, very efficient during processing large data sets (high-spectral data), and algorithm is for result
With interpretation, there is unique advantage in terms of inversion problem is solved, one of current best algorithm is described as.With height
Random forests algorithm is applied on vegetation high-spectrum remote-sensing by the development of spectral technique, existing scholar, but using rape as research pair
As, using random forests algorithm build leaf chlorophyll EO-1 hyperion inverse model research there is not been reported；Simultaneously because regional
Difference, crop species are different, and the sensitive band of leaf chlorophyll occurs significant difference, and the applicability of spectral index is not yet
Together.
The content of the invention
It is an object of the invention to provide a kind of different growing rape leaf SPAD value remote sensing estimation methods, this method with
Northwest drought, semiarid zone industrial crops rape are subjects, analysis rape different growing Spectra of The Leaves and chlorophyll
Dependency relation between relative amount SPAD values, builds the difference based on random forest (RF) algorithm raw using 10 kinds of spectral indexes
Educate phase and the time of infertility rape leaf SPAD appraising models, and with traditional Linear Regression Model in One Unknown based on spectral index and
Multiple stepwise regression model carry out ratio of precision compared with, realize hyperspectral technique it is lossless, fast and accurately estimate each breeding time leaf of rape
Piece SPAD values, provide theory and technology and support for northwest drought, the monitoring of semiarid zone growth.
Its concrete technical scheme is：
A kind of different growing rape leaf SPAD value remote sensing estimation methods, comprise the following steps：
Step 1, spectral reflectivity are determined
Rape leaf spectral reflectivity is measured using U.S.'s SVC HR-1024i type spectrometers, instrument spectral detection
Scope is 350~2500nm, in wave band 350nm-1000nm spectral resolutions 3.5nm, 1000-1850nm band spectrum resolution ratio
9.5nm, 1850-2500nm band spectrum resolution ratio 6.5nm.Directly determined using automatic light source type Manual blades spectral detector
Spectra of The Leaves, light source is built-in halogen tungsten lamp.Instrument is optimized with reference to version using diffusing reflection before determining every time, will be treated afterwards
Survey blade and be placed directly within detector progress spectral measurement.In order to obtain the spectrum that blade is representative, every leaf measures 3
Position, each two spectrum of position measurement take the average value of six spectrum as the final spectral reflectivity of the sample.
Step 2, SPAD values are determined
The chlorophyll meter Simultaneous Determination rape leafs of SPAD 502 produced using Japanese KONICA MINOLTA companies
SPAD values.To reduce error, every leaf measures 10 points, its average value is then taken as the SPAD values of the blade, during measurement
Avoid vein, at the same according to oil recovery dish blade area difference, suitably increase the SPAD collection points of some leaves.
Step 3, data processing and model construction
Data are handled using SVC HR-1024i PC, Excel2013, Origin2016 and SPSS22.0 softwares
With calculating, selection 400-1000nm wavelength band is studied, and by spectral resampling method to 1nm.To 180 of each issue of collection
Sample is ranked up by SPAD values, and 135 are extracted as modeling sample using the method for stratified sampling, and remaining 45 are used as inspection
Sample.
Further, chosen in step 1 10 kinds of spectral index spectral index RVI, NDVI, mNDVI, TCARI, GRVI, NPCI,
DCNI、MSR705、FDRVI、FDNDVI。
Further, in step 3, model construction is respectively adopted one-variable linear regression, multiple stepwise regression and random forest and returned
Reduction method.Wherein random forest regression algorithm carries out regressive model based on RandomForest software kits in R environment, in model
In building process, the quantity (ntree) of classification tree and the stochastic variable number (mtry) of spliting node are mostly important in the model
Two parameters, through repetition test, according to the predicated error of Random Forest model and its coefficient of determination (R2) determine in the present invention
The quantity of classification tree be 3000, the variable number of spliting node is 3.
Further, in step 3, to verify the precision of model, using test samples by the prediction SPAD values of different models and
Survey SPAD values and carry out regression fit, with the coefficient of determination (R2), root-mean-square error (RMSE) and relative error (RE) evaluate mould
The quality of type.
Compared with prior art, beneficial effects of the present invention：
The present invention combines traditional Linear Regression Model in One Unknown and multiple stepwise regression model is compared with it.As a result table
It is bright：Rape leaf SPAD values show downward trend after first rising within the time of infertility；Each spectral index is in different growing
And the time of infertility and the correlation of SPAD values reach the significantly correlated of 0.01 level；The random forest built based on spectral index
Regression model, which was modeled and predicted the outcome in each breeding time of rape and the time of infertility, is substantially better than the traditional regression models of the same period, its
Modeling and checking R20.91 and more than 0.74 are respectively reached, RMSE is between 1.571-5.004 for checking, and RE is in 2.66%-
It is rape leaf SPAD maximum likelihood estimation model between 13.22%.
Brief description of the drawings
Fig. 1 is different growing rape leaf curve of spectrum feature；
Fig. 2 is different growing rape leaf SPAD and original spectrum correlation.
Embodiment
Technical scheme is described in more detail with specific embodiment below in conjunction with the accompanying drawings.
1 materials and methods
1.1 research area's overviews
Study area be located at Xianyang, Shanxi province city Qian County Liangshan Zhen Qinan villages (108 ° 7 ' 6 of east longitude ", 34 ° 38 ' 33 of north latitude "), should
Zone of transition of the area in the southern edge of Loess Plateau of North Shaanxi and the central Shaanxi plain, the semiarid continental monsoon climate in temperate zone, year precipitation
525mm is measured, 12.7 DEG C of average temperature of the whole year, annual rainfall skewness is concentrated mainly on the 6-9 months, and cropping system is essentially 1 year
One is ripe, the crops such as Winter Wheat Planted, rape, corn
1.2 experimental designs and sample collection
In September, 2015 is in May, 2016 in research area plantation winter rape, and experimental cultivar is sweet miscellaneous No. 1.30 examinations are set altogether
Cell is tested, each plot area is 48m2, set nitrogenous fertilizer, phosphate fertilizer and potash fertilizer to handle each 4 level (N：0、120、240、
360kgha-1, P：0th, 60,120,180kgha-1, K：0th, 90,180,270kgha-1), each processing is repeated 3 times, field
Between manage and carried out by local Production of Large Fields mode.Respectively at March 25 (seedling stage) in 2016, April 12 (flower bud a kind of sedge phase), May 3
(florescence) and May 24 days (maturity period) carry out Field observation sampling.Each cell 6 rapeseed plants canopies of random acquisition are total to
The blade of 6 identical leaf positions loads hermetic bag, is placed in blue ice crisper and transports laboratory back immediately, Spectra of The Leaves is carried out in time
Reflectivity and SPAD value Simultaneous Determinations.
1.3 determine project and method
1.3.1 spectral reflectivity is determined
Rape leaf spectral reflectivity is measured using U.S.'s SVC HR-1024i type spectrometers, instrument spectral detection
Scope is 350~2500nm, in wave band 350nm-1000nm spectral resolutions 3.5nm, 1000-1850nm band spectrum resolution ratio
9.5nm, 1850-2500nm band spectrum resolution ratio 6.5nm.Directly determined using automatic light source type Manual blades spectral detector
Spectra of The Leaves, light source is built-in halogen tungsten lamp.Instrument is optimized with reference to version using diffusing reflection before determining every time, will be treated afterwards
Survey blade and be placed directly within detector progress spectral measurement.In order to obtain the spectrum that blade is representative, every leaf measures 3
Position, each two spectrum of position measurement take the average value of six spectrum as the final spectral reflectivity of the sample.
1.3.2 SPAD values are determined
The chlorophyll meter Simultaneous Determination rape leafs of SPAD 502 produced using Japanese KONICA MINOLTA companies
SPAD values.To reduce error, every leaf measures 10 points, its average value is then taken as the SPAD values of the blade, during measurement
Avoid vein, at the same according to oil recovery dish blade area difference, suitably increase the SPAD collection points of some leaves.
1.4 spectral indexes are selected
Spectral index is the parameter for linearly or nonlinearly combining structure using vegetation spectroscopic data, can reflect that vegetation exists
Difference between visible ray, near infrared band reflection and environmental background.The present invention can be used for estimating chlorophyll content a variety of
It has chosen that 10 kinds of degrees of recognition are higher, explicit physical meaning spectral index (table 1) in spectral index.
The spectral index of table 1 and its calculation formula
Tab 1 Spectral Indices and Formulas
1.5 data processings and model construction
Using the softwares such as SVC HR-1024i PC, Excel2013, Origin2016 and SPSS22.0 to data at
Reason because the absorption spectrum of chlorophyll is concentrated mainly in the range of 500-750nm, therefore mainly selects 400- with calculating
1000nm wavelength band is studied, and by spectral resampling method to 1nm.180 samples of each issue of collection are entered by SPAD values
Row sequence, 135 are extracted as modeling sample using the method for stratified sampling, and remaining 45 are used as test samples.
One-variable linear regression, multiple stepwise regression and random forest regression algorithm is respectively adopted in model construction.It is wherein random
Forest regression algorithm carries out regressive model based on RandomForest software kits in R environment, in model construction process, classification
The quantity (ntree) of tree and the stochastic variable number (mtry) of spliting node are mostly important two parameters in the model, through anti-
Retrial is tested, according to the predicated error of Random Forest model and its coefficient of determination (R2) determine that the quantity of classification tree in the present invention is
3000, the variable number of spliting node is 3.
To verify the precision of model, the prediction SPAD values of different models are returned with actual measurement SPAD values using test samples
Return fitting, with the coefficient of determination (R2), root-mean-square error (RMSE) and relative error (RE) carry out the quality of evaluation model.
2 results and analysis
2.1 different growing rape leaf SPAD value changes and bloom spectrum signature
In the different growing of rape, the SPAD values of blade show the parabola trend for first rising and declining afterwards.From seedling stage
To flower bud a kind of sedge phase, rape leaf SPAD values gradually rise, and flower bud a kind of sedge phase reaches the peak of whole breeding time, and average value is by seedling stage
48.98 have risen to the 52.17 of flower bud a kind of sedge phase, and max min also all shows obvious ascendant trend, and maximum reaches
59.5, minimum value 48.15.Growing with rape after flower bud a kind of sedge phase, blade SPAD values are tapered into, in florescence average value
45.39, minimum value 36.05.To the maturity period after florescence, because siliqua of oilseed rape starts maturation, blade gradually withered aging, nutrition
Material is shifted to silique, and blade turns yellow, and the SPAD values of rape leaf drastically decline, maturity period SPAD average value 31.51, maximum
Value 41.9, minimum value as little as 17.1.Meanwhile, with the propulsion of rape breeding time, the SPAD amplitudes of variation in each stage become larger,
Standard deviation increased dramatically after florescence, and the SPAD standard deviations of maturity period rape leaf reach the maximum 7.13 of whole breeding time.
The different growing rape leaf SPAD value changes of table 2
Tab.2 Variation of SPAD Values for Rape at Different Growth Stages
Each breeding time reflection spectrum curve of rape leaf is as shown in Figure 1.The rape leaf spectral signature base of different growing
This is consistent, in 400-500nm royal purple optical band and 600-700nm red spectral band due to the strong absorption formation two of chlorophyll
Individual absorption paddy, while so that forming a relatively strong reflection peak at 550nm；In 680-1000nm near infrared region,
Due to the dominating role by rape leaf internal structure, spectral reflectivity steeply rises, and forms high reflection platform.From seedling stage to
Flower bud a kind of sedge phase, due to rapeseed plants continued propagation so that rape leaf area index and blade gradually increase, on chlorophyll content continues
Rise, photosynthesis is strong, it is seen that light region blade average reflectance is reduced, the curve of spectrum is moved down, and Red edge position occurs
" red shift " phenomenon；After flower bud a kind of sedge phase, rape is transferred to reproductive growth, and chlorophyll content in leaf blades is gradually reduced, it is seen that light region spectrum is anti-
The rate of penetrating starts to increase, and the curve of spectrum is moved up, and Red edge position shows the feature of " blue shift ".
2.2 rape leaf SPAD values are analyzed with spectral correlations
2.2.1 rape leaf original spectrum and SPAD value correlation analysis
The SPAD values and spectral reflectivity to each breeding time blade of rape carry out correlation analysis respectively, as a result such as Fig. 2.From figure
2 as can be seen that the correlative trend basic one in seedling stage, flower bud a kind of sedge phase and florescence rape leaf SPAD and original spectrum reflectivity
Cause, it is related near red spectral band 700nm near the blue wave band 520nm in extremely significantly negatively correlated between wavelength 500-730nm
Coefficient reaches two peak values, wherein the most notable with the correlation near 700nm；After 700nm, each breeding time rape leaf
Reduced rapidly with the coefficient correlation of original spectrum reflectivity, florescence is -0.1 or so, and other breeding times tend to 0 substantially；It is ripe
Coefficient correlation change between phase rape leaf SPAD values and original spectrum reflectivity is slightly different with first three period,
Correlation is relatively low at 551nm, and peak value reaches maximum correlation coefficient at 630nm and 696nm at wherein 696nm.
2.2.2 the correlation analysis of rape leaf SPAD values and spectral index
The correlation of each breeding time actual measurement SPAD values and spectral index is analyzed, as a result such as table 3.Can by the data of analytical table 3
To draw：Each breeding time blade SPAD of rape reaches significantly correlated with spectral index, and wherein TCARI, GRVI and NPCI are negative
Correlation, other spectral indexes are notable positive correlation.10 spectral indexes in seedling stage in addition to NPCI with SPAD in extremely significantly correlated,
Wherein correlation is best for RVI, coefficient correlation 0.64；The correlation of flower bud a kind of sedge phase SPAD values and spectral index all reaches extremely notable
The coefficient correlation of correlation, RVI, FDRVI and FDNDVI and SPAD values has reached more than 0.8；Florescence SPAD value and spectral index
Correlation be it is best in rape whole breeding time, more than half spectral index coefficient correlation more than 0.8, wherein phase
Closing property is best for FDNDVI, coefficient correlation 0.86, and correlation is relatively poor for NPCI, but has also reached 0.70；Maturity period
The coefficient correlation of the SPAD values and spectral index of rape leaf differs greatly, wherein RVI, NDVI, mNDVI, TCARI and MSR705
Coefficient correlation reach more than 0.8, GRVI and NPCI coefficient correlation less than 0.4.Four lifes during comprehensive growth of rape
The original spectral data and SPAD values in the phase are educated, time of infertility rape leaf SPAD values and each spectral index correlation point is carried out
Analysis, as a result shows, in the time of infertility rape leaf SPAD values and each spectral index be in it is extremely significantly correlated, wherein RVI, NDVI,
The coefficient correlation of mNDVI and rape leaf SPAD values is more than 0.9, and correlation is very high；DCNI, MSR705, FDRVI and
The coefficient correlation of the SPAD values of FDNDVI and rape leaf is also all more than 0.8.
Coefficient correlation between the different growing rape leaf SPAD values of table 3 and spectral index
Tab.3 Correlation Coefficients between SPAD Values and Spectral
Indices at Different Growth Stages of Rape Leaves
Note：* represents significantly correlated in 0.001 level；* represent significantly correlated in 0.01 level.
Note：**indicates significant correlation at 0.001level；*indicates
significant correlation at 0.01 level.
2.3 each breeding time rape leaf SPAD value EO-1 hyperions inverse models are built
Rape leaf SPAD EO-1 hyperion appraising models are built in such a way：1. each breeding time and rape leaf are chosen
The spectral index that SPAD values are significantly correlated and coefficient correlation is maximum is independent variable, builds the SPAD value unitary lines of rape each breeding time
Property regression model (VI-LR)；2. each breeding time and rape leaf SPAD values is chosen in 0.001 level extremely significantly correlated spectrum to refer to
Number builds SPAD value multiple stepwise regression models, is designated as VI-MSR；3. all spectral indexes structures of each breeding time are chosen random gloomy
Woods regression model, institute's established model is designated as VI-RF.As a result it is as shown in table 4.
From table 4, the fit equation of all models has all reached 0.01 level of signifiance, in each breeding time of rape
In, the precision of the Linear Regression Model in One Unknown built based on single spectral index is all minimum；Based on extremely significantly correlated institute
There are multiple stepwise regression model and Random Forest model that spectral index is built to increase substantially the precision of appraising model, its
In with the coefficient of determination highest of Random Forest model, more than 0.9 can be reached within whole breeding time, is 0.974 to the maximum.
Different bearing is interim, and the precision of seedling stage SPAD appraising model will be significantly less than other breeding times；Maturity period appraising model precision is most
Height, the coefficient of determination R of each model2More than 0.74 can be reached, is VI-RF models, the coefficient of determination 0.942 to the maximum.Full fertility
The rape leaf SPAD appraising models of phase will be substantially better than the appraising model built point breeding time, the coefficient of determination R of three kinds of models2
All more than 0.85, the estimation precision of VI-RF models is still highest, coefficient of determination R2Reach 0.974.As random forest is built
The raising of mould precision, model variable also gradually rises for SPAD explanation rate, and wherein seedling stage solution to model releases variation percentage
Minimum 39.18%, and time of infertility solution to model releases variation percentage and reaches 85.57%.Comprehensive modeling result can draw,
The rape leaf SPAD appraising models effect built based on random forest regression algorithm is best, can be widely used in rape whole
The SPAD estimations of breeding time.
The rape different growing blade SPAD regressive prediction models of table 4
Tab4 SPAD Estimation Models at Different Growth Stages of Rape
2.4 model accuracies compare
The estimation precision of different models is tested using test samples, as a result as shown in table 5.As can be seen from Table 5,
The R of each breeding time difference checking model2All more than 0.57, the pole level of signifiance has been reached, can be used in the prediction of rape SPAD values.
The wherein coefficient of determination R of seedling stage VI-RF models2Reach that 0.77, RMSE and RE is less than other models, respectively 1.571 Hes
2.66%, but regression equation slope is slightly less than VI-MSR models；The R of flower bud a kind of sedge phase VI-RF models2, regression equation slope, RMSE and
RE be the breeding time it is optimal；The coefficient of determination and regression equation slope of florescence VI-RF model be it is optimal in three models,
But its RMSE and RE will be slightly larger than VI-MSR models；Maturity period VI-RF model is equally better than other models, its coefficient of determination R2
Reached 0.75, but the RMSE and RE of each model have compared with other breeding times and significantly risen, its root-mean-square error reached 5 with
On, relative error has reached more than 10%.In the time of infertility still with the precision of prediction highest of VI-RF models, R2For 0.88.It is comprehensive
From the point of view of, VI-RF models all show most strong study and predictive ability, coefficient of determination R in point breeding time and the time of infertility2Exist
More than 0.75.But with reference to modeling with predicting the outcome, relative to modeling accuracy, the predictive ability of VI-RF models is relatively weak, determine
Coefficients R2There is decline by a relatively large margin compared with modeling, because random forests algorithm is when classification tree is generated, it is possible to meeting
Diversity factor very small tree is produced, this can produce influence for ultimately generating correct decision-making, the result of study with Wang Liai etc.
It is consistent.
The different appraising model the results of table 5
Tab 5 Testing Results under Different Estimation Models
3 discuss
The crops spectral information obtained by high-spectrum remote-sensing can preferably reflect its physical and chemical parameter, monitor growing way shape
Condition], but the acquisition of vegetation spectrum is vulnerable to the factors such as noise, ambient noise and the blade interior physiological structure of spectrometer in itself
Influence.Numerous studies show that SPAD values and its total chlorophyll content of blade have stronger correlation, can be in certain journey
Reflect the nutrient and its growing way situation of crop leaf on degree, the monitoring model for building vegetation blade SPAD using spectral index can be with
Ambient noise is eliminated, crop growth condition monitoring is realized.
(1) in maturity period and the time of infertility, NDVI and rape leaf SPAD correlation highest, and in chlorophyll content phase
To higher seedling stage and flower bud a kind of sedge phase, the correlation of NDVI and SPAD values is then poor, and this is probably because higher in chlorophyll content
When, easily there is supersaturation in NDVI.Simultaneously in each breeding time and the time of infertility, SPAD and GRVI and NPCI correlation are all
Relatively poor, this is primarily due to the most sensitive wave band of rape leaf chlorophyll and is concentrated mainly at 690nm to 710nm, and
Structure all not participations of the wave band of both spectral indexes.
(2) estimate crops SPAD values using spectral index, although can be good at eliminating ambient noise, improve estimation essence
Degree, but the mostly less applicability for considering spectral index for crop different growing of research in the past, and most researchs only base
SPAD estimation models are built in single spectral index.And single spectral index often only includes the partial information of crop spectrum, easily
There is supersaturation, and the precision and stability of model is all difficult to be guaranteed.The present invention uses for reference the same of multiple spectral indexes
When, different growing and the time of infertility for growth of rape, respectively in connection with least-squares algorithm, multiple stepwise regression algorithm and
Random forests algorithm builds SPAD appraising models, as a result finds compared with single spectrum to refer to based on the model accuracy that multiple spectral indexes are built
Exponential model precision improves a lot, also stronger in the applicability of different growing, effectively prevent because leaf chlorophyll contains
Influence of the spectral index saturated phenomenon for model caused by amount is higher.
(3) in the building process of vegetation blade SPAD appraising models, different modeling methods is smart for the prediction of model
Degree influence is also larger.Random Forest model is good in the performance of each breeding time, and model accuracy improves a lot, R20.91 with
On, wherein seedling stage is improved up to 23% compared with linear model precision, and flower bud a kind of sedge phase improves 17%, and florescence improves 9%, and the maturity period carries
It is high by 10%, and the time of infertility then improves 8%, model modeling R2Up to 0.974.Random forests algorithm is compared with least-squares algorithm
And multiple stepwise regression algorithm has very high forecasting accuracy, reason is that Random Forest model can be good at tolerance modeling
During the exceptional value that occurs and noise, and be not easy overfitting occur, it is insensitive to multicollinearity.
4 conclusions
The present invention builds blade SPAD EO-1 hyperion using spectral index using northwest industrial crops rape as research object
Appraising model, and each model inversion precision is tested, obtain drawing a conclusion：
1) significant difference is there is in rape different growing its blade SPAD values, SPAD shows first to raise to be declined afterwards
Trend；As the rise of SPAD values is in the reduction of visible light wave range spectral reflectivity, there is " red shift " phenomenon in Red edge position.
2) each spectral index in different growing with rape leaf SPAD values in significantly correlated, wherein TCARI, GRVI and
NPCI is with rape leaf SPAD values in notable negative correlation, and remaining spectral index is in notable positive correlation with rape leaf SPAD values.Into
The ripe phase and the time of infertility NDVI, with blade SPAD values correlation preferably seedling stage RVI was best with blade SPAD values correlation, in flower bud a kind of sedge
Phase and florescence, based on it is red while and it is blue while the position vegetation index that builds with blade SPAD values have maximum correlation coefficient.
3) point breeding time built based on spectral index and time of infertility rape leaf SPAD values appraising model are by aobvious
Work property is examined, and wherein random forest regression model shows best modeling and checking precision in each breeding time, and it models R2
More than 0.91, R is verified2More than 0.74 is reached, is the optimal models for carrying out the estimation of rape leaf SPAD values.
The foregoing is only a preferred embodiment of the present invention, protection scope of the present invention not limited to this, any ripe
Those skilled in the art are known in the technical scope of present disclosure, the letter for the technical scheme that can be become apparent to
Altered or equivalence replacement are each fallen within protection scope of the present invention.
Claims (4)
1. a kind of different growing rape leaf SPAD value remote sensing estimation methods, it is characterised in that comprise the following steps：
Step 1, spectral reflectivity are determined
Rape leaf spectral reflectivity is measured using U.S.'s SVC HR-1024i type spectrometers, the instrument spectral investigative range
For 350~2500nm, in wave band 350nm-1000nm spectral resolutions 3.5nm, 1000-1850nm band spectrum resolution ratio
9.5nm, 1850-2500nm band spectrum resolution ratio 6.5nm；Directly determined using automatic light source type Manual blades spectral detector
Spectra of The Leaves, light source is built-in halogen tungsten lamp；Instrument is optimized with reference to version using diffusing reflection before determining every time, will be treated afterwards
Survey blade and be placed directly within detector progress spectral measurement；In order to obtain the spectrum that blade is representative, every leaf measures 3
Position, each two spectrum of position measurement take the average value of six spectrum as the final spectral reflectivity of the sample；
Step 2, SPAD values are determined
The SPAD values of the chlorophyll meter Simultaneous Determination rape leafs of SPAD 502 produced using Japanese KONICA MINOLTA companies；
To reduce error, every leaf measures 10 points, then takes its average value as the SPAD values of the blade, leaf is avoided during measurement
Arteries and veins, at the same according to oil recovery dish blade area difference, suitably increase the SPAD collection points of some leaves；
Step 3, data processing and model construction
Data are handled and counted using SVC HR-1024i PC, Excel2013, Origin2016 and SPSS22.0 softwares
Calculate, selection 400-1000nm wavelength band is studied, and by spectral resampling method to 1nm；To 180 samples of each issue of collection
It is ranked up by SPAD values, 135 is extracted as modeling sample using the method for stratified sampling, remaining 45 is used as inspection sample
This.
2. different growing rape leaf SPAD value remote sensing estimation methods according to claim 1, it is characterised in that step
Chosen in 1 10 kinds of spectral index spectral index RVI, NDVI, mNDVI, TCARI, GRVI, NPCI, DCNI, MSR705, FDRVI,
FDNDVI。
3. different growing rape leaf SPAD value remote sensing estimation methods according to claim 1, it is characterised in that step
In 3, one-variable linear regression, multiple stepwise regression and random forest regression algorithm is respectively adopted in model construction；Wherein random forest
Regression algorithm carries out regressive model, in model construction process, classification tree based on RandomForest software kits in R environment
The stochastic variable number of quantity and spliting node is mostly important two parameters in the model, through repetition test, according to random gloomy
The predicated error and its coefficient of determination R of woods model2It is determined that the quantity of the classification tree in the present invention is 3000, the variable of spliting node
Number is 3.
4. different growing rape leaf SPAD value remote sensing estimation methods according to claim 1, it is characterised in that step
In 3, to verify the precision of model, the prediction SPAD values of different models are subjected to recurrence plan with actual measurement SPAD values using test samples
Close, with coefficient of determination R2, root-mean-square error RMS and relative error RE carry out the quality of evaluation model.
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