CN106290197A - The estimation of rice leaf total nitrogen content EO-1 hyperion and estimation models construction method - Google Patents
The estimation of rice leaf total nitrogen content EO-1 hyperion and estimation models construction method Download PDFInfo
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Abstract
Embodiment of the invention discloses that a kind of rice leaf total nitrogen content EO-1 hyperion estimation models construction method, comprise the following steps: to select multiple experimental plot, select multiple sampling points in each experimental plot；Oryza sativa L. key developmental stages is selected to carry out canopy spectra measurement；Each sampling point records multiple sampling spectrum, the spectra measurement as this sampling point of averaging；The Hyperspectral imaging of experimental plot is obtained by airborne imaging spectrum instrument；The multiple functional leafs gathering different parts at each sample point record rice leaf total nitrogen content；Spectral index or partial least-square regression method is used to build rice leaf total nitrogen content EO-1 hyperion estimation models.Embodiments of the invention also disclose a kind of rice leaf total nitrogen content EO-1 hyperion estimating and measuring method, utilize the model built according to said method to estimate rice leaf total nitrogen content.The present invention can be space inversion and the efficient implementation offer Science and Technology foundation of precision agriculture of regional scale Nitrogen in Rice content.
Description
Technical field
The present invention relates to Oryza sativa L. monitoring field, more particularly it relates to a kind of rice leaf total nitrogen content Gao Guang
Spectrum estimation and estimation models construction method.
Background technology
Nitrogen is the nutrient the closest with crop photosynthesis, yield and quality relation, be also crop demand and
The mineral element that amount of application is maximum.When crop nitrogen stress, not only can affect crop yield and also can reduce its quality.On the contrary, if
Nitrogen nutrition is superfluous, water, air can be caused certain pollution of area source again[1].In farmland production is put into practice, should efficiently utilize nitrogen
Fertilizer, reduces environmental pollution, to realize high grade and yield of crops.Therefore, crop nitrogen situation quickly and is accurately obtained, it is achieved farmland
Precisely efficiently fertilising be agricultural modernization produce in the urgent need to.
Oryza sativa L. is one of staple food crop in the world, and Nitrogen Nutrition of Paddy Rice Plant assessment is conducive to monitoring Oryza sativa L. growing way and field
Precision management.Traditional Nitrogen in Rice diagnostic method mainly carries out chemical analysis by laboratory to plant tissue, and the method is taken
Time, laborious, and there is hysteresis quality.Although there are some researches show SPAD (Soil and Plant Analyzer
Development) value can be as the good index of Nitrogen Status, but crop SPAD measures by crop varieties, other nutrition unit
The impacts such as element shortage, environment and measurement site, have a lot of uncertain factor[2].High spectrum resolution remote sensing technique can realize quickly,
Non-destructive monitoring crop nitrogen situation, easily expands to regional scale, is the key content in crop Remote sensing monitoring study field[3].Crop
In growth course, the change of Nitrogen Nutrition can cause the crop pattern structures such as leaf color, chlorophyll levels, moisture
Change, and then cause the change of canopy spectra, this is the theoretical basis that high-spectrum remote-sensing carries out nitrogen estimation.At present, learned
Nitrogen in Rice situation is estimated by person by remote sensing[4-6].Xue etc.[7]Point out Ratio Spectrum index R810/R560 with
Rice leaf nitrogen accumulation has good linear relationship, and is not affected by fertilising and period of duration.Stroppiana etc.[8]Grind
Study carefully and show that the normalization light spectrum index utilizing R503 and R483 to form and Nitrogen in Rice content have good dependency.Tian
Deng[9]With Chu etc.[10]Have evaluated the different EO-1 hyperion vegetation index predictive ability to Nitrogen in Rice situation.Some scholars are also had to combine
Close and use hyperspectral technique and partial least-square regression method (Partial Least Squares Regression, PLSR) right
Nitrogen in Rice level is estimated[11,12]。
It is located in the irrigation area of Ningxia superior natural conditions of northwest, is one of optimal ecotope of Nation wide fine quality japonica rice, is also
The superior rice base that northwest is important.Its Nitrogen in Rice situation is estimated, thus the good quality and high output realizing rice is significant.
At present, the estimation of domestic Oryza sativa L. nitrogen content is mainly for paddy fields in south china, and the forecast model set up may to the Northwest Oryza sativa L.
And it is inapplicable；Research in the past not for high-spectral data is abundant and successional feature is optimized, and may pass through low latitude
The scope Nitrogen in Rice content inverting of unmanned plane Hyperspectral imaging feasible region also rarely has and relates to.List of references
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Summary of the invention
In consideration of it, the present invention uses hyperspectral technique to the Northwest rice leaf total nitrogen content (Leaf Nitrogen
Content, LNC) to estimate, comprehensive different spectral index and the different modeling method analyzed is in Nitrogen in Rice content is estimated
Application power, and the spatial distribution of the Hyperspectral imaging analyzing rice leaf total nitrogen content obtained by low latitude unmanned plane, to
Determine the optimum estimating and measuring method of the full nitrogen content of the Northwest's rice leaf.
According to the first aspect of the invention, it is provided that a kind of rice leaf total nitrogen content EO-1 hyperion estimation models structure side
Method, comprises the following steps: to select multiple experimental plot, selects multiple sampling points in each experimental plot；Select the fertility of Oryza sativa L. key
Phase carries out canopy spectra measurement；Each sampling point records multiple sampling spectrum, the spectra measurement as this sampling point of averaging；Logical
Cross airborne imaging spectrum instrument and obtain the Hyperspectral imaging of experimental plot；Multiple functional leafs of different parts are gathered at each sample point
Sheet records rice leaf total nitrogen content；Spectral index or partial least-square regression method is used to build rice leaf total nitrogen content high
Spectrum estimation models.
Preferably, the handling process of described EO-1 hyperion impact comprises the following steps: splicing gray level image；To splicing gray-scale map
As carrying out geometric correction；EO-1 hyperion impact and gray level image are carried out impact merge, obtain fusion evaluation；Gray-scale map by correction
As carrying out Image registration with fusion evaluation, obtain correcting fusion evaluation；Correction fusion evaluation is carried out image mosaic；Splicing is merged
Image；Extract area-of-interest；Final image is obtained after carrying out image smoothing.
Preferably, with total nitrogen content as dependent variable, with normalization light spectrum index NDSI and Ratio Spectrum index RSI it is respectively
Independent variable, sets up total nitrogen content EO-1 hyperion estimation models.
Preferably, use 450～950nm wave bands of non-imaged spectroscopic data, and spectroscopic data carried out smooth and heavily adopt
Sample, the sampling interval is set to 4nm, NDSI and RSI and is defined respectively as:
NDSI (x, y)=(y-x)/(x+y)
RSI (x, y)=x/y
The spectral reflectivity (Ri, Rj) of i nm and j nm or light in formula, x and y represents 450～950nm wavelength band respectively
Spectrum reflectance first derivative (Di, Dj), is constituted by making any two band spectrum reflectance or the combination of reflectance first derivative
The coefficient of determination R of NDSI and RSI and rice leaf total nitrogen content2Isopotential map, searching has the spectrum of higher predictive ability and refers to
Number.
Preferably, PLS is expressed as follows:
Y in formulaiFor target variable；XijFor spectral reflectivity；M is spectral band number；N is hits；eiFor error；βkFor
Regression coefficient；TikFor latent variable；R is latent variable number；CkjFor latent variable coefficient.
Preferably, in order to check the reliability of rice leaf total nitrogen content estimation models, use coefficient of determination R2, root-mean-square
Error RMSE and average relative error RE testing model precision, RMSE and RE is the least, then model accuracy is the highest.
Preferably, described key developmental stages includes: jointing stage, heading stage, milk stage, dough stage.
According to a further aspect of the invention, it is provided that a kind of rice leaf total nitrogen content EO-1 hyperion estimating and measuring method, including utilizing
Rice leaf total nitrogen content is estimated according to the model that said method builds.
Preferably, Ratio Spectrum index RSI (D738, D522) constituted with spectrum first derivative D738 and D522 builds
The Northwest's rice leaf total nitrogen content estimated by model.
According to drawings and Examples described below, these and other aspects of the invention will be apparent from understanding
, and will be elucidated with reference to embodiment described below.
Accompanying drawing explanation
The present invention will be explained more fully below with reference to preferred embodiment illustrated in accompanying drawing.
Fig. 1 shows the trial zone location drawing according to embodiments of the present invention.
Fig. 2 shows the handling process of Hyperspectral imaging according to embodiments of the present invention.
Fig. 3 shows jointing stage different leaves total nitrogen content rice canopy spectral signature according to embodiments of the present invention.
Fig. 4 shows NDSI (Ri, Rj), the RSI (Ri, Rj) that any two band combinations according to embodiments of the present invention are constituted
Dependency coefficient of determination isopotential map with RSI (Di, Dj) Yu LNC.
Fig. 5 shows rice leaf total nitrogen content estimation models based on spectral index according to embodiments of the present invention.
Fig. 6 shows rice leaf total nitrogen content forecast result of model based on spectral index according to embodiments of the present invention
Inspection.
Fig. 7 shows rice leaf total nitrogen content according to embodiments of the present invention and spectrum first derivative D738 and D522's
Relation.
Fig. 8 shows the explanation degree rectangular histogram to independent variable X and dependent variable Y of the main constituent according to embodiments of the present invention.
Fig. 9 shows rice leaf total nitrogen content forecast result of model based on PLSR inspection according to embodiments of the present invention
Test.
Figure 10 shows that spectral index according to embodiments of the present invention and partial least square method predict leaf total nitrogen content
Results contrast.
Figure 11 shows rice milky ripe stage land for growing field crops LNC spatial distribution according to embodiments of the present invention.
Detailed description of the invention
The preferred embodiments of the present invention are described in detail below in conjunction with accompanying drawing.
0 materials and methods
0.1 experimental design
This test includes that plot experiment and field experiment, rice varieties used are peaceful round-grained rice 43.Plot experiment is in Ningxia
Autonomous region of Hui ethnic group Qingtongxia City Ye Sheng town Oryza sativa L. Demonstration Base is carried out, and field experiment is positioned at osmanthus, Shili shop township, Helan County, Yinchuan City four
Wen Cun, trial zone is distributed as shown in Figure 1.0,240,300kg hm plot experiment sets 3 and executes nitrogen (pure N) level:-2, it is designated as respectively
N0、N1、N2.Divide 12 communities, each plot area 60m altogether2If 4 are repeated (Fig. 1 c).Nitrogenous fertilizer divides 3 times and applies, respectively base
Fertilizer 60%, tillering fertilizer 20%, ear manuer 20%, artificially cause nothing fertilizer, nitrogenous fertilizer moderate and the excessive 3 kinds of situations of nitrogenous fertilizer.Each community phosphorus, potassium
Amount of application is identical, all makees base manure.The fertilizer that test uses is carbamide, double superhosphate and potassium chloride.Rice field includes 6 altogether
Block (Fig. 1 b), fertilising and field management are all carried out by local normal level.
Sampling time is 2014, selects Oryza sativa L. key developmental stages to sample: jointing stage (July 12), vegetation characteristics
Show as colony less, in field, have water, the most exposed soil.Heading stage (August 12) vegetation cover degree close to 90%, substantially without
Soil bareness.Milk stage (August 31), blade starts to turn Huang, and rice paddy seed full grains, seed is green, with normal grain
Size is identical, the liquid Han white " milky " in grain, and anhydrous in rice field, vegetation cover degree is close to 90%, and partial blade starts withered and yellow, de-
Fall.Dough stage (JIUYUE 17 days), grain is by green flavescence, tiller and leaf senile, anhydrous in field.
0.2 high-spectral data obtains
0.2.1 non-imaged spectroscopic data
The HR-1024i portable field spectroradiometer that rice canopy spectrum uses SVC company of the U.S. to produce measures.Spectrogrph
Wavelength band is 350～2500nm, and wherein 350～1000nm spectrum sample are spaced apart 1.5nm, and spectral resolution is 3.5nm；
1000～1890nm spectrum sample are spaced apart 3.8nm, and spectral resolution is 9.5nm；1890～2500nm spectrum sample are spaced apart
2.5nm, spectral resolution is 6.5nm.
Canopy spectra measures and selects fine, calm or carry out time wind speed is less, the time be 10:00～14:00 (too
Sun elevation angle is more than 45 °).The spectrogrph angle of visual field 25 ° during measurement, sensor probe vertically downward, away from rice canopy vertical height
About 0.80m.All carry out reference plate correction, each sampling point record 5 sampling spectrum every time before and after gathering target optical spectrum, average
Spectra measurement as this sampling point.
0.2.2 unmanned plane Hyperspectral imaging
The UHD185 airborne imaging spectrum instrument that Hyperspectral imaging is produced by Cubert company of Germany obtains.UHD185 is one
Money silent frame, non-scanning type, realtime imaging spectrogrph, spectral region 450～950nm, sampling interval 4nm, spectral resolution
8nm, comprises 125 passages.UHD185 can obtain simultaneously the Hyperspectral imaging of 50 × 50 pixels and one 1000 ×
The grayscale image of 1000 pixels.
Flight test of unmanned aerial vehicle was carried out in overhead, land for growing field crops on August 20th, 2015 (milk stage).UHD185 high light spectrum image-forming
The onboard flight platform of system is eight rotor wing unmanned aerial vehicles.Flying height is set to 100m, ship's control 80%, sidelapping degree
60%.Spectrogrph camera lens selects focal length 25mm, the corresponding angle of visual field to be about 13 °, at the EO-1 hyperion shadow that 100m flying height obtains
The ground resolution of picture about 32cm, the ground resolution of grayscale image about 1.6cm, the fabric width about 16m of every width image.EO-1 hyperion shadow
The handling process of picture is shown in Fig. 2.
0.3 leaf total nitrogen assay
Each sample point gathers the functional leaf about 30 of different parts, loads valve bag immediately, takes back laboratory.First
First blade is completed half an hour under the conditions of 105 DEG C, then dry to constant weight under the conditions of 80 DEG C, adopt after taking out and grinding
The total nitrogen content of rice leaf is recorded with Kjeldahl's method.
0.4 research method
0.4.1 spectral index method
Owing to the sensitive band of Nitrogen in Rice is normally between 400～1000nm[13], in order to make non-imaged spectrogrph obtain
The wave band of high-spectral data and low latitude unmanned plane airborne-remote sensing unify mutually, the present invention uses non-imaged spectroscopic data
450～950nm wave bands, and spectroscopic data is carried out smooth and resampling, the sampling interval is set to 4nm.The spectrum used refers to
Number includes normalization light spectrum index (Normalized Difference Spectral Index, NDSI) and Ratio Spectrum index
(Ratio Spectral Index, RSI), is defined respectively as:
NDSI (x, y)=(y-x)/(x+y) (1)
RSI (x, y)=x/y (2)
The spectral reflectivity (Ri, Rj) of i nm and j nm or light in formula, x and y represents 450～950nm wavelength band respectively
Spectrum reflectance first derivative (Di, Dj).
By making any two band spectrum reflectance or NDSI and RSI of reflectance first derivative combination composition and Oryza sativa L.
The coefficient of determination R of LNC2Isopotential map, finds the spectral index with higher predictive ability.And the proposition of other document of comparative evaluation
The predictive ability of 16 spectral indexes.
0.4.2 partial least-square regression method
Partial least-square regression method integrates principal component analysis, canonical correlation analysis and multiple linear regression analysis,
Can well solve multiple conllinear problem, PLS is expressed as follows:
Y in formulaiFor target variable (dependent variable)；XijFor spectral reflectivity (independent variable)；M is spectral band number；N is sampling
Number；eiFor error；βkFor regression coefficient；TikFor latent variable；R is latent variable number；CkjFor latent variable coefficient.
0.4.3 model evaluation standard
In order to check the reliability of rice leaf total nitrogen content estimation models, according to the most conventional model evaluation side
Method, the present invention uses the coefficient of determination (Coefficient of Determination, R2), root-mean-square error (Root Mean
Square Error, RMSE) and 3 index test model accuracies of average relative error (Relative Error, RE), RMSE and
RE more mini Mod precision is the highest.
1 result and analysis
Relation between 1.1 oryza sativa l. NC and canopy spectra
Fig. 3 is the rice canopy curve of spectrum that jointing stage difference LNC is corresponding.As seen from the figure, visible light wave range 450～
740nm, rice canopy spectral reflectivity reduces with the increase of leaf total nitrogen content, and reason is that nitrogen level is with chlorophyll just
Relevant, nitrogen level raising causes chlorophyll content to raise, and Rice Photosynthesis strengthens, and increases absorption red, blue light, accordingly
Reflection then reduce.At near infrared band, spectral reflectivity increases with total nitrogen content and increases, and reason is that nitrogen level affects
Rice canopy structure and leaf area index, nitrogen content increase, rice biological amount, leaf area index and canopy moisture also with
Increase, cause canopy spectra to raise the most accordingly at the reflectance of near infrared band.Carter etc.[14]Research shows near-infrared ripple
Section reflectance and nitrogen level positive correlation, consistent with result of the present invention.
1.2 oryza sativa l. NC forecast models based on spectral index
1.2.1 the optimum spectral index of oryza sativa l. NC is estimated
Fig. 4 is NDSI (Ri, Rj), RSI (Ri, Rj) and the phase of RSI (Di, Dj) and LNC that any two band combinations are constituted
Closing property coefficient of determination R2Isopotential map.Spectral index is the highest with the dependency of LNC, and corresponding isopotential map color is the reddest, the most then face
Color is the most blue.From R2Isopotential map can be seen that optimal bands composite and the waveband width of estimation oryza sativa l. NC spectral index.For
For NDSI (Ri, Rj) (Fig. 4 a), coefficient of determination R2The region of ＞ 0.5 is 800～860nm and the band combination of 528～560nm
And 748～860nm and 708～748nm combinations of wave band.The best band combination of dependency is NDSI (R826, R730), R2Reach
To 0.679.
Fig. 4 b is the R of RSI (Ri, Rj) and the LNC that two band combinations are constituted2Isopotential map.R2The region of ＞ 0.5 comprise 724～
748nm and 748～852nm band combination, 752～852nm and 712～744nm band combinations and 760～852nm and 528～
The band combination of 568nm.Wherein the dependency of RSI (R830, R726) and LNC is best, R2It is 0.685.Generally speaking, RSI with
The band combination scope relatively NDSI width (R that LNC dependency is higher2＞ 0.5).NDSI (R826, R730) and RSI (R830, R726)
All comprising a wave band being positioned at red edge regions (680～760nm), respectively R730, R726, the two wave band is estimated with some
The spectral index wave band of chlorophyll content of plant is close, such as red limit chlorophyll index CIred edge [15].Reason is, green plants
Nitrogen level and chlorophyll there is the strongest dependency, for rice leaf, in whole period of duration, total nitrogen content
75%～85% is all present in the chloroplast of blade.Although most scholars often using 720nm as Red edge position, but position, red limit
Put and can be moved with the change of plant biological physicochemical property (such as chlorophyll and moisture)[16]。
Due to the impact of first derivative spectrum part that can be removed Soil Background, and then enhanced spectrum variable and target variable
Dependency.By calculating the coefficient of determination of NDSI (Di, Dj) and RSI (Di, Dj) and LNC, find NDSI (Di, Dj) and LNC's
Dependency and RSI (Di, Dj) compare relatively low, and the present invention only lists RSI (Di, Dj) and LNC coefficient of determination isopotential map (Fig. 4 c).
As seen from the figure, RSI (Di, Dj) and the region (R of LNC good relationship2＞ 0.6) and NDSI (Ri, Rj) and RSI (Ri, Rj) phase
The most, the respectively band combination near D522, D650, D706 and D738, and the band combination near D738 and D518.
Wherein RSI (D738, D522) and the coefficient of determination R of rice leaf total nitrogen content2Maximum, reaches 0.763.Visible, it is in red limit
The D738 in region plays very important effect in estimation oryza sativa l. NC, and reason is that LNC is closely related with chlorophyll content,
And chlorophyll 670～680nm maximum absorption band red limit reflectance is had a great impact[17]。
1.2.2 oryza sativa l. NC model construction based on spectral index and inspection
With LNC as dependent variable, with NDSI (R826, R730), RSI (R830, R726) and RSI (D738, D522) it is respectively
Independent variable, sets up LNC EO-1 hyperion estimation models (Fig. 5).From 3 models it can be seen that almost all of sample point is all 95%
In confidence interval, the prediction R of 3 models2All more than 0.65, the wherein prediction R of RSI (D738, D522)2The highest, it is 0.763,
RSI (R830, R726) takes second place (R2=0.685), NDSI (R826, R730) is minimum, R2It is 0.679.RSI in 3 models (D738,
The RMSE of RMSE minimum 0.369 D522), RSI (R830, R726) and NDSI (R826, R730) is more or less the same, and is respectively
0.383 and 0.387.In sum, RSI (D738, D522) is the highest to the precision of prediction of oryza sativa l. NC.
3 kinds of model accuracies are tested by the land for growing field crops independent sample data using the observation same period, and result is shown in Fig. 6.3 models
Checking precision R2All more than 0.6, illustrate that LNC all can preferably be predicted by 3 models.For different growing, 3
Individual model is all best to jointing stage LNC prediction effect, and along with the propelling of period of duration, LNC gradually decreases, and the predictive ability of model is also
It is gradually reduced.Dough stage LNC can be caused too high estimation by model based on NDSI (R826, R730) and RSI (R830, R726),
Most test samples values are positioned on 1:1 line.It is the lowest that reason is probably dough stage oryza sativa l. NC, and mixes in rice canopy spectrum
The information of a large amount of spikes of rice, reduces the precision of prediction of model to a certain extent.
For the further analyzing rice LNC impact on two wave band first derivative spectrums of 738nm and 522nm, point difference
Oryza sativa l. NC gradient makes the scatterplot between D522 and D738, sees Fig. 7.Along with the change of LNC gradient, D738 and D522 shows
Go out different spectral responses.For same gradient LNC, D738 Yu D522 approximates proportional.From different gradients
From the point of view of, D738 increases along with the increase of LNC, and D522 then has the trend of reduction.It is said that in general, red side wave section and near infrared band
More sensitive to leaf area index and Biomass, green wave band is then the most more sensitive to leaf color[18].D738 may be right by it
Different leaf area index is extremely sensitive responds leaf total nitrogen content, and D522 is then to the nitrogen content in unit blade area more
Sensitive.The model RMSE built by NDSI (R826, R730) and two spectral indexes of RSI (R830, R726) is respectively 0.353 He
0.351, RE is respectively 13.7% and 13.4%, RMSE and RE is the biggest (Fig. 6), can be attributed to Rice Population structure (such as blade face
Long-pending index and canopy structure) difference, these architectural differences derive from the difference of planting area and control measures.And this structure
The response of LNC can be standardized by the spectrum that difference causes by the ratio between D740 and D522.In 3 models, RSI
The RMSE (0.329) and RE (12.7%) of (D738, D522) model is the most minimum (Fig. 6), therefore, with RSI (D738, D522) spectrum
Index is that the oryza sativa l. NC estimation models that variable is set up is more sane.
The multivariate linear model of 1.3 oryza sativa l. NC estimations
In order to inquire into the multiple linear regression model estimation precision to LNC, the present invention by partial least-square regression method,
Use the modeling sample identical with spectral index method, build the Partial Least-Squares Regression Model of LNC, and use identical checking
Model accuracy is tested by sample.
Fig. 8 is the main constituent explanation degree rectangular histogram to independent variable X and dependent variable Y.As seen from the figure, estimate oryza sativa l. NC's
Optimal number of principal components number is 2.1st main constituent is the strongest to the interpretability of independent variable and dependent variable, contains two changes respectively
The information of amount 94.1% and 66.4%.From main constituent for the accumulative explanation degree of variable, 2 main constituents add up to explain
The independent variable information of 99.8% and the dependent variable information of 86.4%.Show that the main constituent that partial least square method extracts can maximum journey
The expression original spectrum reflectance of degree and leaf total nitrogen content information.
After main constituent number determines, the PLSR model of LNC can be set up, by land for growing field crops sample, the PLSR model set up be entered
Performing check, assay is shown in Fig. 9.It can be seen that the checking R of PLSR model2Being 0.654, RMES is 0.336, and RE is
12.9%.PLSR model is more slightly higher than NDSI (R826, R730) and two model accuracies of RSI (R830, R726), and compares RSI
The precision of (D738, D522) model is lower slightly.Possible reason is, although PLSR has used whole spectral band, but some wave band
Owing to signal to noise ratio is low, the information comprising target variable is less, even can disturb the relation between other wave band and target variable.Grind
The person of studying carefully also show the limitation of PLSR in the result of study of laboratory chemical quantitative analysis[19,20].Although PLSR can use
All wave bands modelings, and preferable precision of prediction can be obtained, but it not in the monitoring of crop physiology and ecology parameter remote
Good method.Accordingly, it is considered to the impact of Confounding Factor many in monitoring of environmental, waveband selection is most important.
1.4 various spectral indexes prediction oryza sativa l. NC precision comparison
In recent years, researcher proposes the spectral index of multiple estimation crop biology physical and chemical parameter, and the present invention chooses 16
Spectral index and result of the present invention carry out Integrated comparative.These spectral indexes are based on respective independent trials data, in theory
With foundation on the basis of experience, it is used for estimating crop chlorophyll content and nitrogen content etc..Although some spectral index is not
Propose for nitrogen, but owing to nitrogen is closely related with chlorophyll, therefore can estimate nitrogen with estimating chlorophyllous index
Content.It is used for estimating oryza sativa l. NC by 16 spectral indexes selected, it was predicted that precision R2Figure 10 is seen with RMSE.Although multiple indexes
All employ Red-edge parameter, only CIred edgeStronger predictive ability is had with MTCI.And the spectrum utilizing red edge parameters to build refers to
Number λ rep predicts R2Less, RMSE is the biggest, this may owing to the spendable scope in red limit narrower (680～760nm), and
Generally comprising some extreme points, this limits the range of Red-edge parameter to a certain extent.D735 wave band carries with the present invention
D738 wave band in the RSI (D738, D522) gone out is close, but refers to by selecting an optimum wave band D522 to constitute Ratio Spectrum
Number can be greatly improved precision of prediction.Some Ratio Spectrum indexes proposed for paddy fields in south china[7,9,10,28], at the Northwest's water
Rice LNC estimation does not show preferable predictive ability.In sum, for the Northwest oryza sativa l. NC, spectral index
The prediction effect of RSI (D738, D522) is optimal.
1.5 oryza sativa l. NC spatial distributions
Spectral index RSI (D738, D522) that each for Hyperspectral imaging pixel extracts is brought into the LNC EO-1 hyperion of foundation
Estimation models: LNC=0.201* (D738/D522)+1.185, obtains the spatial distribution of study area oryza sativa l. NC, sees Figure 11.By scheming
Understand, the distribution of study area LNC of Hyperspectral imaging prediction between 1.28%～2.56%, whole study area average
Value is 1.86%, and the LNC value in the 1st piece of land for growing field crops relatively other field is high, and this is consistent with earth's surface actual state.Ground with synchronous acquisition
Measured data carries out accuracy test: with the estimated value of same place on LNC spatial distribution map, Land Surface Temperatures is carried out regression fit.
Result shows, oryza sativa l. NC predictive value based on Hyperspectral imaging and fit equation R of measured value2Being 0.614, RMSE is
0.386, RE is 16.3%.Visible, carry out Oryza sativa L. LNC space, region in conjunction with spectral index and low latitude unmanned plane Hyperspectral imaging and divide
Cloth inverting has degree of precision.The geographic space distribution figure of oryza sativa l. NC not only contributes to accurate field fertilization and manages, and for
The prediction of rice yield and quality is the most significant.
2 discuss
Nitrogen content is the good indicator of Rice Photosynthesis utilization rate, carries out the distant of nitrogen level at Oryza sativa L. key developmental stages
Sense monitoring is favorably improved rice yield and quality.Near infrared spectrum region is usually directed to Red-edge parameter, owing to chlorophyll is can
Seeing that optical band is sensitive, the spectral reflectivity of red edge regions can be caused impact in various degree by the absorption of pigment, and then impact is permitted
Many ratios and normalization index.Therefore, researcher proposes many spectral indexes selected based on Red edge position and specific band
Come monitoring crop chlorophyll content, nitrogen content etc.[9,10,30].In the present invention by red edge regions and green band spectrum reflectance
Estimation more single-range estimation effect of leaf total nitrogen is had by Ratio Spectrum index RSI (D738, D522) that first derivative builds
Significantly improve.Carry out the estimation of biological physical and chemical parameter by single band parameter, usually can be affected by contextual factors such as soil,
Often there is saturated phenomenon when nitrogen and chlorophyll concentration are higher in red spectral band[31].And green wave band is by blade strong reflection, it is
Good wave band for inverting rice biological physical and chemical parameter[32].Optimal two band indexs of the estimation oryza sativa l. NC that the present invention proposes
For near infrared band and the combination of green wave band, also demonstrate this theoretical.The most base of optimal bands composite that previous scholars proposes
In original spectrum reflectance, and the optimal bands composite RSI that the present invention screens by spectral reflectivity first derivative (D738,
D522) the estimation effect of oryza sativa l. NC to be got well than RSI (R830, R726), this is because first derivative spectrum can well subtract
Weak Soil Background and the interference of air.
Although PLS utilizes whole spectral information to be modeled, but its estimate oryza sativa l. NC time precision relatively
RSI (D738, D522) is poor.And PLSR comprises principal component analysis, during modeling, data are carried out dimensionality reduction, the physics meaning of selected variable
Justice is more difficult, and the model of foundation is relative complex, is unfavorable for popularization and the application of model.Comparatively speaking, build based on spectral index
Vertical model explicit physical meaning, and model structure is simple, precision is the highest.By screening characteristic wave bands composition spectral index,
Uncorrelated or non-linear variable can be rejected, and then obtain predictive ability and the preferable estimation models of robustness, and this model is just
It it is the target pursued of crop biology physics and chemistry parameter EO-1 hyperion inverting.Visible by spectral index method carry out regression modeling and its
Its method is compared has certain superiority, is in particular in: first, can simple and effective removal sensor and environmental background
Impact.Second, although only employing several spectral band, but data user rate is higher.3rd, the precision of remotely-sensed data is wanted
Ask and do not have radiative transfer model and PLSR method the strictest.
High light spectrum image-forming technology becomes the diagram technology can be with feasible region scope crop biology physical and chemical parameter spatial distribution with parameter
Situation inverting, the enforcement for precision agriculture provides reliable foundation.At present, domestic by the monitoring of UAV flight's imaging spectrometer
Crop biology physical and chemical parameter is still in the starting stage, and the method still has some limitations.First, parameter becomes figure to rely on
In the forecast model of biological physical and chemical parameter, and these forecast models are set up often through semi-empirical relationship, and model itself has one
Fixed region and timeliness.Therefore find more stable model, and try to explore the structure on Hyperspectral imaging and spectrum
Information, and organically combine the two to adapt to the problem in science that the development of precision agriculture is needs further investigation from now on.Second, logical
Cross UAV flight's imaging spectrometer and obtain Hyperspectral imaging, unavoidably there will be image joint problem in large area, connect
Spectral reflectivity at limit can change, thus impact predicts the outcome.3rd, due to the restriction of UAV flight's load, need
The spatial resolution of weighing sensor and spectral resolution, although flying height can be reduced to obtain more high spatial resolution
Image, but ground coverage can be caused to reduce and the increase of flight time.Therefore, Hyperspectral imaging is obtained at application unmanned plane
Time face the multiple challenges of spectral resolution, spatial resolution and coverage, need to make rationally choice.The most just
Being actively developed, there is high light spectral resolution, spatial resolution and the EO-1 hyperion satellite of very fast revisiting period, such as the U.S.
HyspIRI, the HISUI of Japan, the EnMAP of Germany, the PRISMA of Italy, the HYPRIXM etc. of France, the transmitting of these satellites
Excellent opportunity will be brought to the development of precision agriculture, thus realize " ground-air-star " integration study on monitoring.
3 conclusions
Real-time assessment rice leaf total nitrogen content, to monitoring Oryza sativa L. growing way and field Precision management, to realize rice high yield
Stable yields maximized reduction are most important to the destruction of environment.The present invention analyzes high-spectral data and leaf total nitrogen by comprehensive
Content data, have evaluated two simple optical spectrum indexs of NDSI and RSI and PLSR multiple linear regression analysis method is complete at rice leaf
Predictive ability in nitrogen content estimation.Result shows that rice canopy spectral reflectivity is negative correlation at visible light wave range and LNC,
Near infrared band, is proportionate with LNC.Comprehensive Correlation spectral index model and PLSR model, with spectrum first derivative D738 and
Ratio Spectrum index RSI (D738, D522) that D522 is constituted behaves oneself best, more at aspects such as model accuracy, property easy to use
It is suitable for the estimation of the Northwest's rice leaf total nitrogen content.The oryza sativa l. NC space made based on low latitude unmanned plane Hyperspectral imaging
Scattergram can be that field yardstick fertilizing management provides certain reference.
Crop nitrogen remote-sensing inversion remains in a lot of uncertain factors, the biochemical component of some complexity such as lignin, shallow lake
Powder etc. are closely related with leaf-nitrogen, and these biochemistry group branches its spectral absorption characteristics ripple occur when the state of crop own changes
Section, these characteristic wave bands are closer to the most overlapping with the characteristic wave bands of nitrogen, thus affect the estimation of nitrogen content[33].From now on
Research in, be considered as the influence factor such as lignin, starch, improve further nitrogen inversion accuracy.
Above in conjunction with drawings and Examples, the present invention is described in detail.It is understood, however, that the enforcement of the present invention
Example is not limited to disclosed specific embodiment, and the amendment and other embodiments to this embodiment is also intended to be comprised in institute
In the range of attached claims.Although being used here particular term, but they only make in descriptive sense general
With, rather than for the purpose limited.
Claims (9)
1. a rice leaf total nitrogen content EO-1 hyperion estimation models construction method, it is characterised in that comprise the following steps:
Select multiple experimental plot, select multiple sampling points in each experimental plot；
Oryza sativa L. key developmental stages is selected to carry out canopy spectra measurement；
Each sampling point records multiple sampling spectrum, the spectra measurement as this sampling point of averaging；
The Hyperspectral imaging of experimental plot is obtained by airborne imaging spectrum instrument；
The multiple functional leafs gathering different parts at each sample point record rice leaf total nitrogen content；
Spectral index or partial least-square regression method is used to build rice leaf total nitrogen content EO-1 hyperion estimation models.
Rice leaf total nitrogen content EO-1 hyperion estimation models construction method the most according to claim 1, it is characterised in that institute
The handling process stating EO-1 hyperion impact comprises the following steps:
Splicing gray level image；
Splicing gray level image is carried out geometric correction；
EO-1 hyperion impact and gray level image are carried out impact merge, obtain fusion evaluation；
The gray level image of correction and fusion evaluation are carried out Image registration, obtains correcting fusion evaluation；
Correction fusion evaluation is carried out image mosaic；
Splicing fusion evaluation；
Extract area-of-interest；
Final image is obtained after carrying out image smoothing.
Rice leaf total nitrogen content EO-1 hyperion estimation models construction method the most according to claim 1, it is characterised in that with
Total nitrogen content is dependent variable, respectively with normalization light spectrum index NDSI and Ratio Spectrum index RSI as independent variable, sets up full nitrogen and contains
Amount EO-1 hyperion estimation models.
Rice leaf total nitrogen content EO-1 hyperion estimation models construction method the most according to claim 3, it is characterised in that make
With 450～950nm wave bands of non-imaged spectroscopic data, and spectroscopic data carrying out smooth and resampling, the sampling interval is set to
4nm, NDSI and RSI are defined respectively as:
NDSI (x, y)=(y-x)/(x+y)
RSI (x, y)=x/y
In in formula, x and y represents 450～950nm wavelength band respectively, spectral reflectivity (Ri, Rj) or the spectrum of i nm and j nm are anti-
Penetrate rate first derivative (Di, Dj), combine composition by making any two band spectrum reflectance or reflectance first derivative
NDSI and RSI and the coefficient of determination R of rice leaf total nitrogen content2Isopotential map, finds the spectral index with higher predictive ability.
Rice leaf total nitrogen content EO-1 hyperion estimation models construction method the most according to claim 1, it is characterised in that partially
Least square regression is expressed as follows:
Y in formulaiFor target variable；XijFor spectral reflectivity；M is spectral band number；N is hits；eiFor error；βkFor returning
Coefficient；TikFor latent variable；R is latent variable number；CkjFor latent variable coefficient.
6. according to the rice leaf total nitrogen content EO-1 hyperion estimation models structure side that any one in the claims is described
Method, it is characterised in that in order to check the reliability of rice leaf total nitrogen content estimation models, uses coefficient of determination R2, root-mean-square
Error RMSE and average relative error RE testing model precision, RMSE and RE is the least, then model accuracy is the highest.
7. according to the rice leaf total nitrogen content EO-1 hyperion estimation models construction method that in claim 1-5, any one is described,
It is characterized in that, described key developmental stages includes: jointing stage, heading stage, milk stage, dough stage.
8. a rice leaf total nitrogen content EO-1 hyperion estimating and measuring method, it is characterised in that include utilizing according to claim 1-5
In the model that builds of any one described method estimate rice leaf total nitrogen content.
Rice leaf total nitrogen content EO-1 hyperion estimating and measuring method the most according to claim 8, it is characterised in that with spectrum single order
The model that Ratio Spectrum index RSI (D738, D522) that derivative D738 and D522 is constituted builds is to estimate the Northwest's Rice Leaf
Sheet total nitrogen content.
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