CN109187398A - A kind of EO-1 hyperion measuring method of wheat plant nitrogen content - Google Patents
A kind of EO-1 hyperion measuring method of wheat plant nitrogen content Download PDFInfo
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Abstract
The invention discloses a kind of EO-1 hyperion measuring methods of wheat plant nitrogen content, comprising the following steps: chooses test sample, data acquisition, data processing and model discrimination, the most suitable wheat plant nitrogen content monitoring model of building, Optimization of Wheat N content of crop tissue monitoring model, Test And Checkout wheat plant nitrogen content monitoring model.The present invention is using different wheat varieties kind as material, the wheat plant nitrogen content under the conditions of different planting sites, time, growthdevelopmental stage, Fertilization Level, planting density etc. is measured using high spectrum resolution remote sensing technique, high spectrum resolution remote sensing technique overcomes the shortcomings that cumbersome bothersome effort of traditional measuring method, it can quickly and effectively, non-destructively obtain successional a large amount of spectral informations, for real-time monitoring crop growing state, nutrition condition, estimates crop nitrogen content, yield and quality etc. and provide possibility.
Description
Technical field
The invention belongs to agricultural technology field more particularly to a kind of EO-1 hyperion measuring methods of wheat plant nitrogen content.
Background technique
Wheat is to cultivate that most ancient, cultivated area is maximum, total output is most and the maximum crop of volume of trade in the world, at me
The cultivated area of state occupies third position, is important commodity food and main food deposit kind；And nitrogen is Growth of Wheat institute
Required first big mineral nutrient element, the height of nutrient efficiency directly affect the yield and quality of crop, have " life
Element " title.In recent years, China's amount of application of nitrogen fertilizer continues to increase, while promoting crop yield, also because excessively application is led
The effect that causes fat sharply declines, and causes the serious wasting of resources and problem of environmental pollution.Based on economic benefit and ecological environmental protection
Double requirements improve utilization rate of nitrogen fertilizer and have become the advanced problems of agricultural technology renovation and ensure the important need of China's grain security
It asks.Therefore, wheat plant growth real time monitoring is greatly developed, wheat plant Nitrogen Status is carried out real-time, quick and quasi-
Really detecting, accurate management improves fertilizer utilization efficiency to crop yield and QT Quality Target is ensured with regulation amount of application of nitrogen fertilizer,
It reduces environmental pollution and is of great significance, this is also modern agriculture fertilizing management key technology in the urgent need to address.
For a long time, the nitrogen content monitoring of traditional wheat plant is all by the sampling of field destructiveness, lab analysis
It measures and obtains, need to put into great effort and time, analysis cost is high, and the period is longer, poor in timeliness.In recent years, with science
The development of technology, the method for Nondestructive Nitrogen Status propose by many researchers, including leaf color diagnosis, chlorophyll meter,
Chlorophyll fluorescence techniques etc., in contrast, the target sample of crop spectroscopic diagnostics technology is not single sample but a wide range of
Group, can fast and accurately diagnose the growth conditions of crop and the spatial variations of big area crops.With hyperspectral technique
Fast development, being capable of monitoring crop nitrogen content situation real-time, quickly using spectrum lossless detection technology, it has also become current and will
Carry out the important method of field crops growing way and nutrient diagnosis, is accurately managed for crop nitrogen content and reliable technical support is provided.It is small
Most of physiological acoustic signals can cause the variation of certain specific band reflectance spectrums in wheat plant body.It, can based on this principle
With the spectral signature wavelength and vegetation index using wheat plant growth information come the nitrogen content state of inverting wheat plant.However
It is very few to different genetic wheat varieties kind research at present about the monitoring method of nitrogen content mostly using rice, corn as research object,
And using EO-1 hyperion feature construction physiological and biochemical index monitoring model between kind under the conditions of different tests, and then evaluate nitrogen content phase
It is even more few few for closing the research of index.
Summary of the invention
It is an object of the invention to: there is destructive, analysis for the method for above-mentioned traditional measurement plant nitrogen more
At high cost and time-consuming and laborious, the period is longer, poor in timeliness and at present about the monitoring method of nitrogen content mostly with rice, corn be grind
Study carefully object, the problem very few to the research of different wheat varieties kind under the conditions of different tests, the present invention provides a kind of wheat
The EO-1 hyperion measuring method of N content of crop tissue.
The technical solution adopted by the invention is as follows:
A kind of EO-1 hyperion measuring method of wheat plant nitrogen content, comprising the following steps:
(1) test sample: from different year, different location, different cultivars, different fertilization, different planting densities
With 960 parts of wheat plant samples under the conditions of different growth stage etc.；
(2) data acquire: obtaining wheat plant vertical angle spectral reflectance using ASD Fieldspc FR2500 spectrometer
Rate, and add and survey Soil Background spectrum；
(3) data processing and model discrimination: to the spectroscopic data of test acquisition, with 15 common spectral indexes selecting and
1 spectral index newly constructed carries out data processing using SPSS17.0 and MATLAB software software；
(4) most suitable wheat plant nitrogen content monitoring model is constructed；
(5) Optimization of Wheat leaf N content monitoring model；
(6) Test And Checkout wheat leaf N content monitoring model.
Further, the different year in the step (1) is respectively: 2013,2014,2015,2016,
2017；Different location is respectively: national moisture soil fertility and Application techniques long term monitoring experiment station-Zhengzhou, Kaifeng, Shangshui；No
Be respectively with kind: loose type wheat (Zheng wheat 9694, opens wheat 18 at all wheats 18), erect type wheat (Henan wheat 50, Henan wheat 49-198,
Henan wheat 34)；Different fertilization is respectively: not applying fertilizer, applies nitrogen, phosphorus and potassium fertilizer, organic fertilizer and with applying nitrogen, phosphorus and potassium fertilizer, straw-returning
With match organic fertilizer and nitrogen, phosphorus and potassium fertilizer；Different densities are respectively: 900,000 plants/hectare, 1,800,000 plants/hectare, 3,600,000 plants/hectare；
Different growth stage is respectively: phase of standing up, jointing stage, florescence, maturity period.
Further, the ASD Fieldspc FR2500 spectrometer wavelength band in the step (2) be 350~
2500nm, field angle are 25 °, wherein 350~1000nm wavelength band, spectral resolution 3nm, sampling interval 1.4nm；
1000~2500nm wavelength band spectral resolution is 10nm, sampling interval 2nm.
Further, 15 common spectral indexes are respectively in the step (3): GNDVI, SAVI, NDCI, NPCI,
PRIc、Carter2、mND705、RI-1dB、DDNI、NDRE、NAOC、MTVI1、R705/(R717+R491)、(R780-R710)/
(R780-R680) and (R924-R703+2*R423)/(R924+R703-2*R423), 1 spectral index newly constructed is: vegetation
The Ratio Spectrum index of index NDRE and moisture index FWBI building, i.e. water resistant divide nitrogen index W RNI:NDRE/FWBI.
Further, the wheat plant nitrogen content monitoring model in the step (4) is the linear mould established according to WRNI
Type.
Further, the wave band of screening is during Optimization of Wheat N content of crop tissue monitoring model in the step (5)
725nm and 735nm, the index after optimization are WRNI=[(R735-R720) * R900]/[Rmin (930-980) * (R735+
R720)]。
Further, the reliability that wheat plant nitrogen content monitoring model is examined in the step (6), by square
The coefficient of determination R of root error (RMSE), average relative error (RE), predicted value and actual value linear regression2Carry out evaluation model
Precision and accuracy, RMSE and RE are smaller, then model accuracy is higher.
Using national moisture soil soil fertility and the 30 years long term experiments in Application techniques long term monitoring station as platform, with difference
Time, different location, different cultivars, different fertilization, different growth stage and different planting densities are experimental condition, screening
960 parts of nitrogen utilization efficiencies there were significant differences wheat plant sample out, when crop nitrogen status changes, leaf color etc.
Morphosis changes therewith, so that the absorption of spectrum, reflection and transmission are influenced, and the variation of these spectral signatures is crop
The diagnosis and monitoring of nitrogen content nutrition condition provide theoretical foundation.
In cloudless calm fair weather, minute is 10:00~14:00 for spectroscopic assay selection.Sensor when measurement
Vertical range of the probe at the top of wheat canopy be 1.0m, be repeated 10 times, be averaged, ground visual field is in field range
The diameter range of 0.44m, the spectral reflectance value using its average value as the cell carry out standard white plate school in measurement process in time
Just.Based on EO-1 hyperion monitoring data, the correlativity of different growing wheat canopy reflectance spectrum and nitrogen content is analyzed, is picked out
The spectral band sensitive to nitrogen content.Based on sensitive band and document analysis, 15 common spectral vegetation indexes and 1 are summarized
A spectral index newly constructed, using the high-spectral data of test, by MATLAB software (MathWorks, Inc,
Natick, MA) data processing and correlation analysis, the related coefficient of different EO-1 hyperion parameters and wheat canopy nitrogen content is calculated, and
The higher spectral index of selection related coefficient carries out regression analysis and establishes regression equation.Pass through coefficient of determination R2And independent development
Computer program based on MATLAB software optimizes regression equation.On this basis, using verification test sample to mould
Type is tested, and root-mean-square error (RMSE), the decision of average relative error (RE), predicted value and actual value linear regression are passed through
Coefficients R2Come the precision and accuracy of evaluation model, is based on these standards, determines most suitable model.Wherein, the meter of RMSE, RE
It is as follows to calculate formula:
In formula: PiAnd OiIt is predicted value and measured value respectively, n is number of samples；If RE < 10%, the precision and standard of model
Exactness is higher；It secondly is 10% < RE < 20%；If RE > 30%, the precision of model and accuracy are all poor.
Under the conditions of different tests, in 15 common spectral indexes, 10 spectral indexes are based on two band spectrum information structures
It builds, for example, GNDVI predictive ability with higher, coefficient of determination R2It is 0.781, root-mean-square error RMSE is 0.458；5
A spectral index is constructed by the spectral information of three wave bands, and the correlation between Leaf nitrogen concentration is higher than by two wave bands
The spectral index of building, R2Greater than 0.80, for example, the correlation of R705/ (R717+R491) and mND705 and Leaf nitrogen concentration is most
Height, coefficient of determination R2Respectively 0.832 and 0.818, root-mean-square error RMSE is respectively 0.401 and 0.417.The light newly constructed
Spectrum index WRNI is better than other 15 spectral indexes, coefficient of determination R in terms of precision of prediction and monitoring Leaf nitrogen concentration variation2
It is 0.848, root-mean-square error RMSE is 0.392 (see Fig. 1).3 preferable spectrum of performance are picked out from 16 spectral indexes
Index (mND705, R705/ (R717+R491) and WRNI) building model (see Fig. 2).The result shows that according to mND705 and R705/
(R717+R491) the regression equation fitting precision established is higher, R2>0.818, RMSE<0.417, the model established according to WRNI
Correlation R2Improving 3.6%, RMSE reduces 1.9%.Comparatively, this shows that WRNI is that a performance preferably has potentiality
Estimation wheat plant nitrogen content model.
Further, the preferable spectral index GNDVI of performance, mND705, (R780-R710)/(R780- are picked out
R680), R705/ (R717+R491) and the vegetation index WRNI newly constructed, and study they under different field test conditions with
The stability of wheat canopy Leaf nitrogen concentration relationship.The result shows that in traditional vegetation index R705/ (R717+R491) and
The good relationship of mND705 and Leaf nitrogen concentration, coefficient of determination R2For 0.832 and 0.818, root-mean-square error RMSE is 0.401
With 0.417.The vegetation index WRNI and the correlation of Leaf nitrogen concentration newly constructed is best, coefficient of determination R2It is 0.843, root mean square
Error RMSE is 0.382.Further demonstrate that WRNI characterizes the superiority of wheat plant nitrogen content under the conditions of different tests.
For the stability and accuracy of testing model, model verifying is carried out with the data of independent experiment, Fig. 3 is according to sight
Examine what value and model predication value were established according to the relationship of 1:1.By Fig. 3 it can be found that GNDVI and (R780-R710)/(R780-
R680 it is poor that correlation is simulated between observation and predicted value), Relative Error RE > 20%.MND705 and R705/ (R717+
R419) model obtains ideal inspection result.Its precision of prediction R2For 0.847 and 0.857, RE is 17.6% and 17.4%.
New building spectral index WRNI model performance is preferable, precision of prediction R2It is 0.861, root-mean-square error RMSE is 0.384, relatively
Error RE is 15.6%.The above result shows that the spectral index newly constructed has played red side wave section to the instruction advantage of nitrogen, and most
Reduce to limits the influence of moisture.Therefore, WRNI is a monitoring wheat plant nitrogen well under the conditions of different tests
The spectral target of content.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
1. high-spectrum remote-sensing of the present invention overcomes, the cumbersome bothersome effort of traditional measuring method, analysis cost be high, the period compared with
The shortcomings that length, poor in timeliness, fast and effective, non-destructively real-time, inexpensive can obtain successional a large amount of spectral informations,
For real-time monitoring crop growing state, nutrition condition, estimates crop nitrogen content, yield and quality etc. and provide possibility；
2. the present invention is different from traditional monitoring method for studying nitrogen content as research object using rice, corn, but
Under the conditions of different cultivars, different year, different location, different fertilization conditions, different growth stage and different planting densities etc.
Wheat plant be research object, provide a kind of monitoring method of wheat nitrogen content；
3. the vegetation index WRNI and the correlation of wheat plant nitrogen content that the present invention newly constructs are best, can be in different examinations
The nitrogen content of wheat plant is characterized under the conditions of testing；
4. the vegetation index WRNI that the present invention newly constructs can reduce the influence that water limitation monitors nitrogen content；
It is China master 5. the present invention constructs preferably to go out can precisely estimate the model of nitrogen content using high spectrum resolution remote sensing technique
Want the efficient screening varieties of crops nitrogen to provide theoretical foundation and technical support, and this for improve crops fertilizer utilization efficiency,
It reduces environmental pollution and ensures that national food security has great importance.
Detailed description of the invention
Examples of the present invention will be described by way of reference to the accompanying drawings, in which:
Fig. 1 is that the precision of spectral index estimation canopy leaves nitrogen content compares；
Fig. 2 is Leaf nitrogen concentration and mND705 (a), R705/ (R717+R491) between (b) and WRNI (c) relationship quantify
Analysis；
Fig. 3 is based on spectral index WRNI (a), GNDVI (b), mND705 (c), (R780-R710)/(R780-R680)
(e) and the wheat leaf blade nitrogen content predicted value of R705/ (R717+R491) (f) is compared with measured value.
Specific embodiment
All features disclosed in this specification or disclosed all methods or in the process the step of, in addition to mutually exclusive
Feature and/or step other than, can combine in any way.
It elaborates below with reference to Fig. 1, Fig. 2, Fig. 3 to the present invention.
Embodiment 1
A kind of EO-1 hyperion measuring method of wheat plant nitrogen content, comprising the following steps:
(1) test sample: the sampling time is 2013~2014 years, and place is that national moisture soil fertility and Application techniques are supervised for a long time
Experiment station-Zhengzhou, Kaifeng, Shangshui are surveyed, wheat breed is loose type (Zheng wheat 9694, opens wheat 18 at all wheats 18) and erect type (Henan wheat
50, Henan wheat 49-198, Henan wheat 34), fertilizer treatment is not apply fertilizer, apply nitrogen, phosphorus and potassium fertilizer, organic fertilizer and nitrogen, phosphorus and potassium fertilizer, straw-returning
With with nitrogen, phosphorus and potassium fertilizer is applied, planting density is 900,000 plants/hectare, 1,800,000 plants/hectare, 3,600,000 plants/hectare respectively, and wheat is selected to close
Key breeding time is sampled: phase of standing up, jointing stage, florescence, maturity period, amounts to 960 parts of wheat plants；
(2) data acquire: obtaining wheat plant vertical angle spectral reflectance using ASD Fieldspc FR2500 spectrometer
Rate, and add and survey Soil Background spectrum, spectrometer wavelength band is 350~2500nm, and field angle is 25 °, wherein 350~
1000nm wavelength band, spectral resolution 3nm, sampling interval 1.4nm；1000~2500nm wavelength band spectral resolution
For 10nm, sampling interval 2nm；
(3) data processing and model discrimination: to the spectroscopic data of test acquisition, with 15 common spectral indexes selecting and
1 spectral index newly constructed carries out data processing using SPSS17.0 and MATLAB software software, and 15 common
Spectral index is respectively: GNDVI, SAVI, NDCI, NPCI, PRIc, Carter2, mND705, RI-1dB, DDNI, NDRE,
NAOC, MTVI1, R705/ (R717+R491), (R780-R710)/(R780-R680) and (R924-R703+2*R423)/(R924
+ R703-2*R423), 1 spectral index newly constructed is: the Ratio Spectrum of vegetation index NDRE and moisture index FWBI building
Index, i.e. water resistant divide nitrogen index W RNI:NDRE/FWBI；
(4) wheat plant nitrogen content monitoring model is constructed according to WRNI；
(5) Optimization of Wheat N content of crop tissue monitoring model, best band are 725nm and 735nm, and the index after optimization is
WRNI=[(R735-R720) * R900]/[Rmin (930-980) * (R735+R720)]；
(6) Test And Checkout wheat plant nitrogen content monitoring model passes through root-mean-square error (RMSE), average relative error
(RE), the coefficient of determination R of predicted value and actual value linear regression2Coming the precision and accuracy of evaluation model, RMSE and RE are smaller,
Then model accuracy is higher.
Using national moisture soil soil fertility and the 30 years long term experiments in Application techniques long term monitoring station as platform, with difference
Time, different location, different cultivars, different fertilization, different growth stage and different planting densities are experimental condition, screening
960 parts of nitrogen utilization efficiencies there were significant differences wheat plant sample out, when crop nitrogen status changes, leaf color etc.
Morphosis changes therewith, so that the absorption of spectrum, reflection and transmission are influenced, and the variation of these spectral signatures is crop
The diagnosis and monitoring of nitrogen content nutrition condition provide theoretical foundation.
In cloudless calm fair weather, minute is 10:00~14:00 for spectroscopic assay selection.Sensor when measurement
Vertical range of the probe at the top of wheat canopy be 1.0m, be repeated 10 times, be averaged, ground visual field is in field range
The diameter range of 0.44m, the spectral reflectance value using its average value as the cell carry out standard white plate school in measurement process in time
Just.Based on EO-1 hyperion monitoring data, the correlativity of different growing wheat canopy reflectance spectrum and nitrogen content is analyzed, is picked out
The spectral band sensitive to nitrogen content.Based on sensitive band and document analysis, 15 common spectral vegetation indexes and 1 are summarized
A spectral index newly constructed, using the high-spectral data of test, by MATLAB software (MathWorks, Inc,
Natick, MA) data processing and correlation analysis, the related coefficient of different EO-1 hyperion parameters and wheat canopy nitrogen content is calculated, and
The higher spectral index of selection related coefficient carries out regression analysis and establishes regression equation.Pass through coefficient of determination R2And independent development
Computer program based on MATLAB software optimizes regression equation.On this basis, using verifying sample to model into
Performing check passes through the coefficient of determination of root-mean-square error (RMSE), average relative error (RE), predicted value and actual value linear regression
R2Come the precision and accuracy of evaluation model, is based on these standards, determines most suitable model.Wherein, the calculating of RMSE, RE are public
Formula is as follows:
In formula: PiAnd OiIt is predicted value and measured value respectively, n is number of samples；If RE < 10%, the precision and standard of model
Exactness is higher；It secondly is 10% < RE < 20%；If RE > 30%, the precision of model and accuracy are all poor.
Under the conditions of different tests, in 15 common spectral indexes, 10 spectral indexes are based on two band spectrum information structures
It builds, for example, GNDVI predictive ability with higher, coefficient of determination R2It is 0.781, root-mean-square error RMSE is 0.458；5
A spectral index is constructed by the spectral information of three wave bands, and the correlation between Leaf nitrogen concentration is higher than by two wave bands
The spectral index of building, R2Greater than 0.80, for example, the correlation of R705/ (R717+R491) and mND705 and Leaf nitrogen concentration is most
Height, coefficient of determination R2Respectively 0.832 and 0.818, root-mean-square error RMSE is respectively 0.401 and 0.417.The light newly constructed
Spectrum index WRNI is better than other 15 spectral indexes, coefficient of determination R in terms of precision of prediction and monitoring Leaf nitrogen concentration variation2
It is 0.848, root-mean-square error RMSE is 0.392 (see Fig. 1).3 preferable spectrum of performance are picked out from 16 spectral indexes
Index (mND705, R705/ (R717+R491) and WRNI) building model (see Fig. 2).The result shows that according to mND705 and R705/
(R717+R491) the regression equation fitting precision established is higher, R2>0.818, RMSE<0.417, the model established according to WRNI
Correlation R2Improving 3.6%, RMSE reduces 1.9%.Comparatively, this shows that WRNI is that a performance preferably has potentiality
Estimation wheat leaf blade nitrogen content model.
Further, performance preferable spectral index GNDVI, mND705 are picked out, (R780-R710)/
(R780-R680), R705/ (R717+R491) and the vegetation index WRNI newly constructed, and them are studied in different field experiment items
Under part with the stability (such as table 1) of wheat canopy Leaf nitrogen concentration relationship.
Embodiment 2
In addition to the sampling time is 2014~2015 years in step (1), other conditions are identical.
Embodiment 3
In addition to the sampling time is 2015~2016 years in step (1), other conditions are identical.
Embodiment 4
In addition to the sampling time is 2016~2017 years in step (1), other conditions are identical.
1 Leaf nitrogen concentration of table and spectrum parameter GNDVI, mND705, (R780-R710)/(R780-R680), R705/
(R717+R491) correlation analysis between WRNI:
The result shows that according to R705/ (R717+R491) and mND705 in the traditional vegetation index of table 1 and Leaf nitrogen concentration
Good relationship, coefficient of determination R2For 0.832 and 0.818, root-mean-square error RMSE is 0.401 and 0.417.The vegetation newly constructed
Index W RNI and the correlation of Leaf nitrogen concentration are best, coefficient of determination R2It is 0.843, root-mean-square error RMSE is 0.382.Into one
Step shows the superiority that WRNI characterizes Leaf nitrogen concentration under the conditions of different tests.
For the stability and accuracy of testing model, model verifying is carried out with the data of independent experiment, Fig. 3 is according to sight
Examine what value and model predication value were established according to the relationship of 1:1.By Fig. 3 it can be found that GNDVI and (R780-R710)/(R780-
R680 it is poor that correlation is simulated between observation and predicted value), Relative Error RE > 20%.MND705 and R705/ (R717+
R419) model obtains ideal inspection result.Its precision of prediction R2For 0.847 and 0.857, RE is 17.6% and 17.4%.
New building spectral index WRNI model performance is preferable, precision of prediction R2It is 0.861, root-mean-square error RMSE is 0.384, relatively
Error RE is 15.6%.The above result shows that the spectral index newly constructed has played red side wave section to the instruction advantage of nitrogen, and most
Reduce to limits the influence of moisture.Therefore, WRNI is a monitoring wheat plant nitrogen well under the conditions of different tests
The spectral target of content.
Claims (7)
1. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content, which comprises the following steps:
(1) test sample: the wheat from different year, place, variety type, fertilizer treatment, planting density, growthdevelopmental stage is planted
Strain sample；
(2) data acquire: wheat plant vertical angle spectral reflectivity is obtained using ASD Fieldspc FR2500 spectrometer,
And adds and survey Soil Background spectrum；
(3) data processing and model discrimination: to the spectroscopic data of test acquisition, with the 15 common spectral indexes selected and 1
The spectral index newly constructed carries out data processing using SPSS17.0 and MATLAB software software；
(4) most suitable wheat plant nitrogen content monitoring model is constructed；
(5) Optimization of Wheat N content of crop tissue monitoring model；
(6) Test And Checkout wheat plant nitrogen content monitoring model.
2. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content according to claim 1, which is characterized in that the step
Suddenly the different year in (1) is respectively: 2013,2014,2015,2016,2017；Different location is respectively: complete
State's moisture soil fertility and Application techniques long term monitoring experiment station-Zhengzhou, Kaifeng, Shangshui；Different cultivars is respectively: loose type wheat
(Zheng wheat 9694, opens wheat 18 at all wheats 18), erect type wheat (Henan wheat 50, Henan wheat 49-198, Henan wheat 34)；Different fertilization point
It is not: does not apply fertilizer, applies nitrogen, phosphorus and potassium fertilizer, organic fertilizer and nitrogen, phosphorus and potassium fertilizer, straw-returning and with applying nitrogen, phosphorus and potassium fertilizer；Different densities difference
It is: 900,000 plants/hectare, 1,800,000 plants/hectare, 3,600,000 plants/hectare；Different growth stage is respectively: the phase of standing up, opens at the jointing stage
Florescence, maturity period.
3. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content according to claim 1, which is characterized in that the step
Suddenly the ASD Fieldspc FR2500 spectrometer wavelength band in (2) is 350~2500nm, and field angle is 25 °, wherein 350
~1000nm wavelength band, spectral resolution 3nm, sampling interval 1.4nm；1000~2500nm wavelength band is spectrally resolved
Rate is 10nm, sampling interval 2nm.
4. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content according to claim 1, which is characterized in that the step
Suddenly 15 common spectral indexes are respectively in (3): GNDVI, SAVI, NDCI, NPCI, PRIc, Carter2, mND705, RI-
1dB, DDNI, NDRE, NAOC, MTVI1, R705/ (R717+R491), (R780-R710)/(R780-R680) and (R924-R703
+ 2*R423)/(R924+R703-2*R423), 1 spectral index newly constructed is: vegetation index NDRE and moisture index FWBI
The Ratio Spectrum index of building, i.e. water resistant divide nitrogen index W RNI:NDRE/FWBI.
5. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content according to claim 1, which is characterized in that the step
Suddenly wheat N content of crop tissue monitoring model is the linear model established according to WRNI in (4).
6. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content according to claim 1, which is characterized in that the step
Suddenly the wave band screened during Optimization of Wheat N content of crop tissue monitoring model in (5) is 725nm and 735nm, the index after optimization
For WRNI=[(R735-R720) * R900]/[Rmin (930-980) * (R735+R720)].
7. a kind of EO-1 hyperion measuring method of wheat plant nitrogen content according to claim 1, which is characterized in that the step
Suddenly the reliability that wheat plant nitrogen content monitoring model is examined in (6), by root-mean-square error, average relative error, pre-
The coefficient of determination R of measured value and actual value linear regression2Come the precision and accuracy of evaluation model, root-mean-square error peace is opposite
Error is smaller, then model accuracy is higher.
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| CN110189793A (en) * | 2019-06-04 | 2019-08-30 | 河南农业大学 | The building of wheat nitrogenous fertilizer physiological use efficiency estimation models and wheat varieties with different N efficiency classification based on EO-1 hyperion |
| CN111426645A (en) * | 2020-05-20 | 2020-07-17 | 中国农业大学 | Method for rapidly determining nitrogen content of different organs of plant |
| CN111721738A (en) * | 2020-06-23 | 2020-09-29 | 陕西理工大学 | Hyperspectrum-based analysis method for relationship between plant growth state and soil nitrogen content |
| CN112154646A (en) * | 2019-03-26 | 2020-12-29 | 深圳市大疆创新科技有限公司 | Specifying device, imaging system, and moving object |
| CN112362803A (en) * | 2020-09-22 | 2021-02-12 | 武汉大学 | Application of LY9348 in high-throughput screening of high NUE rice varieties |
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| CN113640230A (en) * | 2021-08-31 | 2021-11-12 | 河南农业大学 | Rapid detection method and system for field wheat moisture utilization rate |
| CN113640230B (en) * | 2021-08-31 | 2022-06-21 | 河南农业大学 | Rapid detection method and system for field wheat moisture utilization rate |
| CN114252406A (en) * | 2021-12-30 | 2022-03-29 | 黑龙江八一农垦大学 | Method for detecting nutrient information in growth period of wheat |
| CN116026772A (en) * | 2023-03-30 | 2023-04-28 | 黑龙江省农业科学院农业遥感与信息研究所 | Corn leaf nitrogen content prediction method based on hyperspectral remote sensing |
| CN116026772B (en) * | 2023-03-30 | 2023-06-02 | 黑龙江省农业科学院农业遥感与信息研究所 | Corn leaf nitrogen content prediction method based on hyperspectral remote sensing |
| CN116735538A (en) * | 2023-08-15 | 2023-09-12 | 山东科技大学 | Crop nitrogen accumulation remote sensing prediction method based on nitrogen distribution model |
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