CN102426153A - Wheat plant moisture monitoring method based on canopy high spectral index - Google Patents

Wheat plant moisture monitoring method based on canopy high spectral index Download PDF

Info

Publication number
CN102426153A
CN102426153A CN2011103687572A CN201110368757A CN102426153A CN 102426153 A CN102426153 A CN 102426153A CN 2011103687572 A CN2011103687572 A CN 2011103687572A CN 201110368757 A CN201110368757 A CN 201110368757A CN 102426153 A CN102426153 A CN 102426153A
Authority
CN
China
Prior art keywords
spectrum
wheat
moisture
canopy
plant
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN2011103687572A
Other languages
Chinese (zh)
Other versions
CN102426153B (en
Inventor
朱艳
姚霞
韩刚
田永超
刘小军
王薇
倪军
曹卫星
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Agricultural University
Original Assignee
Nanjing Agricultural University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Agricultural University filed Critical Nanjing Agricultural University
Priority to CN201110368757.2A priority Critical patent/CN102426153B/en
Publication of CN102426153A publication Critical patent/CN102426153A/en
Application granted granted Critical
Publication of CN102426153B publication Critical patent/CN102426153B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention relates to a wheat plant moisture monitoring method based on a canopy high spectral index, which uses two-year wheat pool-planted test data of two-year 2 varieties under 4 different moisture treatments, adopts a decrement fine sampling method, and analyzes the quantitative relationship between a high spectral index combined by any two wave bands of an original spectrum and a reciprocal spectrum within the waveband range of 350-2500 nm and the wheat plant moisture content and the leaf layer moisture content; results show that the wheat plant moisture content can be monitored based on spectra of NDVI (R836, R793) and RVI (RC837, RC793), and the wheat leaf layer moisture content moisture content can be monitored based on original spectra of NDVI (R1100, R770) and RVI (R893, R805). The research conclusions of the invention provide new wave band combination and theoretical basis for the rapid lossless monitoring of wheat moisture conditions by using high spectral data.

Description

A kind of wheat plant moisture monitoring method based on the high spectrum index of canopy
Technical field
The present invention relates to the wheat plant moisture monitoring, be specifically related to a kind of wheat plant moisture monitoring method based on the high spectrum index of canopy.
Background technology
Wheat is one of most important cereal crops in the world.It is staple food with the wheat that there is 35%～40% population in the whole world.China's wheat annual production is about 100,000,000 tons, accounts for 22% of national total output of grain, accounts for 20% of world wheat total production.Moisture is the element factor of crop vital movement, and the green plants water cut can reach more than 80%～90%.Lack of water all exerts an influence to form generation, the physiology course of crop, finally makes output reduce.Implementing precision irrigation according to the crop water situation is the important channel of improving efficiency of water application and water production efficiency.
In recent years, the spectral remote sensing technology develops rapidly to obtaining the crop water situation and has opened up new way.Research shows, causes when utilizing crop water to wane that blade interior physiological ecological and formalness structure etc. change the response characteristic on high spectrum, can obtain crop water information quickly and accurately.Discover; At 970mm, near 1450mm and the 1940mm wave band, the peak energy of the spectral reflectivity of plants such as wheat, gerbera and soybean reflects the water regime of blade preferably; Therefore, the vegetation index that is made up of visible light and near infrared region wave band can be used for the monitoring of plant moisture situation.Researchs such as Gregory think that moisture is the direct absorbed radiation of hydrone to the elementary influence of spectrum, and secondary influence is that moisture causes the blade interior structure to change, and elementary impact effect is much larger than secondary impact effect.Gao shows that to the influence of moisture NDWI is monitoring index canopy moisture more exactly through analyzing vegetation canopy scattering spectrum.Tian Qing waits so long and discovers, the characteristic absorption peak degree of depth and area near wheat leaf blade relative water content and the 1450mm present the better linearity correlativity.Gu Yanfang etc. have confirmed to utilize spectral reflectivity can accurately predict the feasibility of plant leaf blade moisture with the achievement in research of Wang Jihua etc.The field is superfine forever discovers ratio vegetation index R 810/ R 460Can monitor the water percentage of different growth stage rice plant and blade preferably; Find simultaneously based on crop canopies spectrum vegetation index RVI (610,560)/ NDVI (810,610)Can predict wheat vegetation water regime.Ah not all gas carries me and draws wood etc. to research and propose the vertical dehydration index of the short-wave infrared SPSI that can be used for monitoring large scale vegetation moisture, and the precision of monitoring reaches 74%.
Along with the develop rapidly of high spectrum resolution remote sensing technique, in recent years, the research report of relevant this respect is more and more both at home and abroad.Ceccato etc. have proposed to utilize spherical vegetation humidity index (GVMI) inverting vegetation moisture; Superfine forever R (610, the 560)/ND (810,610) that discovers in field is the good vegetation index of prediction wheat plant water regime; Kakani etc. discover R1689/R1657 and outdoor potted plant cotton leaf flow of water height correlation; Lucky Umihiko etc. has been measured winter wheat blade reflectance spectrum in 1400～1600nm scope, set up the model of moisture and reflectance spectrum with PLS.To sum up, existing research is always carried out based on multispectral data mostly, and spectral resolution is lower; Wavelength band is less; And in the analysis based on high spectrum, the possible high-spectral data of the institute in less analysis 350～2500nm scope, and spectrum does not pass through pre-service; Noise is bigger, and this just possibly cause the sensitive band of some reflection vegetation moisture by abundant excavation or result deviation not to be arranged as yet.Therefore, be necessary to adopt more comprehensive and meticulous high spectrum sample and analytical approach, the sensitive band that exploration discovery is new and high spectrum index thereof.Simultaneously; Reason owing to high spectrum magnanimity information extraction and data preprocessing method; The Quantitative Monitoring model accuracy that makes up based on spectral technique perhaps can only be applied to specific breeding time than low; Perhaps model somewhat complicated on structure and algorithm causes the universality of monitoring model relatively poor, has weakened its application to a certain extent.Explore new core bands, make up the better way spectrum index is emphasis and the difficult point that spectrum monitoring is used in the agricultural remote sensing field always.
In high-spectral data was analyzed, in order to reduce the influence of background noise, people adopted various technical finesse spectroscopic datas, to improve the accuracy of spectral information.Select technology and method deepening continuously about the sensitive band of wheat plant and canopy leaf water content always.The research of Shibayama etc. shows, with near infrared spectrum (1190～1320nm) or the first order derivative of the spectral reflectivity of short-wave infrared wave band (1600nm) can diagnose double cropping of rice canopy water regime.The canopy spectra at discovery 690nm such as Dobrowski and 740nm place can reflect that plant receives the state of water stress; Zhang Jiahua etc. discovers 469nm, 645nm, 700nm and the 710nm wave band of visible region, and 1550nm, 1600nm, 1640nm, 1750nm and the 2130nm wave band in the 760nm of near infrared region, 815nm, 855nm, 930nm, 1075nm and 1100nm wave band and short-wave infrared zone are to survey the sensitive band that leaf water content changes; Graeff etc. handle wheat leaf blade spectroscopic data down to 6 kinds of different in moisture and analyze, find 510 with 780nm, 540 and 780nm, 490 and 1300nm and 540 and 1300nm be that the best of the leaf water situation of expression wheat is indicated wave band.
Summary of the invention
The objective of the invention is deficiency, the wheat plant moisture monitoring method based on the high spectrum index of canopy is provided to the prior art existence.
Utilize high-spectral data analytical approach and technology; In multianalysis 350～2500nm wavelength band by the normalization of original spectrum reflectivity, first order derivative, inverse logarithm and all combinations that constitute reciprocal with compare value index number; Explore the new sensitive band of indication wheat plant moisture; And set up wheat plant moisture content index monitoring model based on high spectrum parameter, its technical scheme is:
A kind of wheat plant moisture monitoring method based on the high spectrum of canopy, the The real time measure soil moisture content, concrete steps are following:
1) high spectral information obtains
Utilize the high spectrum index data of spectrometer measurement wheat canopy, the significant wave segment limit is 350～2500nm, and wherein 350～1050nm spectrum sample is spaced apart 1.4nm, and spectral resolution is 3nm; 1050～2500nm spectrum sample is spaced apart 2nm, and spectrum rate respectively is 10nm, carries out when spectroscopic assay is chosen in ceiling unlimited, calm or gentle breeze, and minute is 10:00-14:00.Sensor probe is vertically downward in the canopy top during measurement, and the spectrometer field angle is 25 °, highly about 1.0m; Ground field range diameter is 0.44m; 10 sampling points of each cell measurement, each sampling point duplicate measurements 5 times (visual field) is with the spectral reflectance value of its mean value as this sub-district;
2) plant determination of moisture
Synchronous with step 1), at different growth stage, representative wheat 20 strains are got in each sub-district; By organ separation, again blade is separated by various position leaves earlier, take back the laboratory in the valve bag of weighing of packing into rapidly; Claim its fresh weight with ten thousand/precision electronic balance, after drying to constant weight under 80 ℃, weigh after putting into 105 ℃ of 30min that complete of baking oven then, obtain different parts organ dry weight; Calculate respectively plant water content (Plant water content, PWC), leaf water content (Leafwater content; LWC) and leaf layer water cut (Canopy leaf water content, CLWC), formula is following:
PWC(％)＝(PFW-PDW)/PFW?×100％
LWC(％)＝(LFW-LDW)/LFW×100％
CLWC(％)＝(∑LFW-ΣLDW)/ΣLFW×100％
Wherein, PFW (Plant Fresh Weight) is the plant fresh weight, and PDW (Plant Dry Weight) is the plant dry weight, and LFW (Leaf FreshWeight) is a fresh weight, and LDW (Leaf Dry Weight) is a leaf dry weight;
3) vegetation index makes up
Original spectrum NDSI=(R λ 1-R λ 2)/(R λ 1+ R λ 2) RSI=R λ 1/ R λ 2
Spectrum NDSI=(RC reciprocal λ 1-RC λ 2)/(RC λ 1+ RC λ 2) RSI=RC λ 1/ RC λ 2
R wherein λ 1And R λ 2Be respectively the reflectivity of any two wavelength, the scope of λ 1 and λ 2 is 350～2500nm, RC λ 1And RC λ 2Be its corresponding spectrum reciprocal; To the estimation ability of wheat water content content, filter out the result who behaves oneself best according to normalization spectrum index NDSI (normalized difference spectral index) and ratio spectrum index RSI (ratio spectral index).
4) data analysis and utilization
Utilize the data in the step 3); Analysis-by-synthesis plant water content and leaf water content and the direct quantitative relationship of canopy spectra reflectivity; Adopt the meticulous sampling method of high-spectral data decrement, filter out wavelength band and spectrum parameter, and make up the moisture monitoring model moisture-sensitive.
5) structure of monitoring model and check
Adopt relative root-mean-square deviation (RRMSE) to carry out assay, and draw 1: 1 graph of a relation between experimental observation value and the model predication value, wherein the RRMSE computing formula is following:
RRMSE = 1 n × Σ i = 1 n ( P i - O i ) 2 × 100 O ‾ i
O in the following formula iBe the moisture content value of observing in the test, P iBe the moisture content value of model estimation, n is a model measurement test samples number.
Further preferred, use Germany to produce portable soil soil moisture measure TRIME-EZ The real time measure soil moisture content.
Further preferred, measure the open-air high spectral radiometer of FieldSpec Pro FR2500 type back hanging type that wheat canopy high spectrum index The data U.S. Analytical SpectralDevice (ASD) company produces in the step 1).
Further preferred, in the step 4) based on spectrum NDVI (R 836, R 793), RVI (RC 837, RC 793) the wheat plant moisture monitoring model that makes up, the coefficient of determination (R 2) be respectively 0.851 and 0.852; Based on original spectrum NDVI (R 1100, R 770) and RVI (R 893, R 805) the wheat leaf layer moisture monitoring model that makes up, coefficient of determination R 2Be 0.730 and 0.843.
Further preferred, data analysis is all carried out at Excel2007 and MATLAB7.0 (The Math Works, 2000) with arrangement in the step 4).
Further preferred, the precision (R of model measurement in the step 5) 2) all greater than 0.783, RRMSE is all less than 0.205.
Beneficial effect of the present invention:
The wheat pond that the present invention is based under different year, different cultivars, different in moisture gradient and the different growth stage plants test; Multianalysis in 350～2500nm scope wheat canopy original spectrum reflectivity with reciprocal arbitrarily in twos the normalized differential vegetation index of band combination, ratio vegetation index and with the quantitative relationship of wheat plant water cut, canopy leaf water content, and set up (R respectively based on NDVI 836, R 793) and RVI (RC 837, RC 793) the plant water content monitoring model; Based on NDVI (R 1100, R 770) and RVI (R 893, R 805) leaf layer moisture monitoring model.Utilize the independent experiment data that the assay of model is shown, the coefficient of determination of all models is all greater than 0.783, and RRMSE circle is in 0.148-0.205, and accuracy is better with stability.These results provide reliable estimation model for accurately diagnosing fast of wheat plant and canopy leaf water content; For the exploitation of portable moisture spectromonitor provides crucial band selection, for the relevant high-spectral data monitoring crop water situation of utilizing from now on provides reference.
Description of drawings
Fig. 1 is the wheat plant moisture monitoring method synoptic diagram that the present invention is based on the high spectrum index of canopy;
Fig. 2 wheat plant water cut and original high spectrum index NDVI (R 836, R 793) between graph of a relation (n=141);
Fig. 3 wheat plant water cut and spectrum index RVI (RC reciprocal 837, RC 793) between graph of a relation (n=141);
Fig. 4 wheat leaf layer water cut and original spectrum index NDVI (FD 1100, FD 770) between quantitative relationship figure (n=141);
Fig. 5 wheat leaf layer water cut and original spectrum index RVI (R 893, R 805) between quantitative relationship figure (n=141);
Fig. 6 is based on NDVI (R 836, R 793) and RVI (RC 837, RC 793) wheat plant water cut predicted value and 1: 1 graph of a relation (n=112) of observed reading;
Fig. 7 is based on NDVI (R 1100, R 770) and RVI (R 893, R 805) wheat leaf layer water cut predicted value and 1: 1 graph of a relation (n=112) of observed reading.
Embodiment
Below in conjunction with concrete accompanying drawing and embodiment method of the present invention is done explanation in further detail.
The present invention has carried out 2 field tests altogether, relates to different year, different types of varieties and different soils moisture and handles, and concrete test design is described below.
Embodiment 1: (118 ° of 51 ' E carry out in 32 ° of 1 ' N) rainproof cement pit in Agricultural University Of Nanjing decorated archway testing station in 2008-2009.Experimental cultivar is Huaihe River wheat 25 and Yang Mai 18.Experimental field soil types is a yellowish soil, and soil organic matter content is 1.63%, and total nitrogen content is 0.09%, and available phosphorus contents is 58.3mgkg -1, quick-acting potassium content is 91.64mgkg -1, PH is 7.20.If four moisture are handled: middle drought (W1), light drought (W2), contrast (W3) and heavy irrigation (W4) (be about respectively field capacity 40%～45%, 60%～65%, 75%～80% and 100%).The sub-district area is 6m 2, district's group is arranged at random, repeats 3 times.Adopt the seed drilling, line-spacing is 25cm, and basic seedling is 1,800,000 strain hm -2Nitrogen fertilizer amount is 210kghm -2, base chases after than being 1: 1, and each is handled to join and executes phosphate fertilizer P 2O 5110kghm -2With potash fertilizer K 2O 135kghm -2All as base manure.Other management is with conventional field management.This test figure is used for the check of model.
Embodiment 2: (118 ° of 51 ' E carry out in 32 ° 1 ' N) the rainproof cement pit in Agricultural University Of Nanjing decorated archway testing station in 2009-2010.Experimental cultivar is Huaihe River wheat 25 and Yang Mai 18.Experimental field soil types is a yellowish soil, and soil organic matter content is 1.78%, and total nitrogen content is 0.10%, and available phosphorus contents is 52.7mgkg -1, quick-acting potassium content is 95.82mgkg -1, PH 7.25.If four moisture are handled: middle drought (W1), light drought (W2), contrast (W3) and heavy irrigation (W4) (be about respectively field capacity 40%～45%, 60%～65%, 75%～80% and 100%).The sub-district area is 10m 2, district's group is arranged at random, repeats 3 times.Adopt the seed drilling, line-spacing is 25cm, and basic seedling is 1,800,000 strain hm -2Nitrogen fertilizer amount is 210kghm -2, base chases after than being 1: 1, and each is handled to join and executes phosphate fertilizer P 2O 5110kghm -2With potash fertilizer K 2O 135kghm -2All as base manure.Other management is with conventional field management.This test figure is used for the structure of model.
The present invention includes following five steps with reference to Fig. 1:
1) high spectral information obtains
The open-air high spectral radiometer of the FieldSpec Pro FR2500 type back hanging type that the high spectrum index DATA REASONING of wheat canopy adopts U.S. Analytical Spectral Device (ASD) company to produce; The significant wave segment limit is 350～2500nm; Wherein 350～1050nm spectrum sample is spaced apart 1.4nm, and spectral resolution is 3nm; 1050～2500nm spectrum sample is spaced apart 2nm, and spectrum rate respectively is 10nm, carries out when spectroscopic assay is chosen in ceiling unlimited, calm or gentle breeze, and minute is 10:00-14:00.Sensor probe is vertically downward in the canopy top during measurement, and the spectrometer field angle is 25 °, highly about 1.0m; Ground field range diameter is 0.44m; 10 sampling points of each cell measurement, each sampling point duplicate measurements 5 times (visual field) is with the spectral reflectance value of its mean value as this sub-district.
2) plant determination of moisture
Synchronous with step 1), at different growth stage, representative wheat 20 strains are got in each sub-district; By organ separation, again blade is separated by various position leaves earlier, take back the laboratory in the valve bag of weighing of packing into rapidly; Claim its fresh weight with ten thousand/precision electronic balance, after drying to constant weight under 80 ℃, weigh after putting into 105 ℃ of 30min that complete of baking oven then, obtain different parts organ dry weight; Calculate respectively plant water content (Plant water content, PWC), leaf water content (Leafwater content; LWC) and leaf layer water cut (Canopy leafwater content, CLWC), formula is following:
Plant water content PWC (%)=(PFW-PDW)/PFW * 100%
Leaf water content LWC (%)=(LFW-LDW)/LFW * 100%
Leaf layer water cut CLWC (%)=(∑ LFW-Σ LDW)/∑ LFW * 100%
Wherein, PFW is the plant fresh weight, and PDW is the plant dry weight, and LFW is a fresh weight, and LDW is a leaf dry weight.
3) vegetation index makes up
Original spectrum NDSI=(R λ 1-R λ 2)/(R λ 1+ R λ 2) RSI=R λ 1/ R λ 2
Spectrum NDSI=(RC reciprocal λ 1-RC λ 2)/(RC λ 1+ RC λ 2) RSI=RC λ 1/ RC λ 2
R wherein λ 1And R λ 2Be respectively the reflectivity of any two wavelength, the scope of λ 1 and λ 2 is 350～2500nm, RC λ 1And RC λ 2Be its corresponding spectrum reciprocal, to the estimation ability of wheat water content content, filter out the result who behaves oneself best according to normalization spectrum index NDSI and ratio spectrum index RSI.
4) data analysis and utilization
Utilize the data in the step 3); Analysis-by-synthesis plant water content and leaf water content and the direct quantitative relationship of canopy spectra reflectivity; Adopt the meticulous sampling method of decrement, filter out wavelength band and spectrum parameter, and make up the moisture monitoring model moisture-sensitive; Data analysis is all carried out at Excel2007 and MATLAB7.0 (The Math Works, 2000) with arrangement.
5) structure of monitoring model and check
Adopt relative root-mean-square deviation (RRMSE) to carry out assay, and draw 1: 1 graph of a relation between experimental observation value and the model predication value, wherein the RRMSE computing formula is following:
RRMSE = 1 n × Σ i = 1 n ( P i - O i ) 2 × 100 O ‾ i
O in the following formula iBe the moisture content value of observing in the test, Pi is the moisture content value of model estimation, and n is a model measurement test samples number.
Further analyze between wheat canopy original spectrum and first order derivative thereof, inverse, the inverse logarithm correlationship with plant water content and leaf layer water cut.
Structure is based on the plant water content quantitative model of the high spectrum index of wheat canopy
Utilize the testing data of 2009-2010, calculate original spectrum, first derivative spectrum, inverse logarithm spectrum and the NDSI of spectrum reciprocal formation and the coefficient of determination (R of RSI and plant water content PWC of all any two band combinations in the 350-2500nm wavelength band 2).Show referring to Fig. 2, Fig. 3 result, based on original spectrum NDVI (R 836, R 793) and based on spectrum RVI (RC reciprocal 837, RC 793) the model coefficient of determination R that makes up 2Performance better is respectively 0.851 and 0.852.
Structure is based on the leaf layer water cut quantitative model of the high spectrum index of wheat canopy
Original spectrum, first derivative spectrum, inverse logarithm spectrum and the NDVI of spectrum reciprocal formation and the coefficient of determination (R of RVI and CLWC of all any band combinations in twos in the 350-2500nm wavelength band calculated in employing with quadrat method 2).Show referring to Fig. 4, Fig. 5 result, based on NDVI (FD 1100, FD 770) and RVI (FD 893, FD 805) performance all better, coefficient of determination R 2Be respectively 0.730 and 0.882.
The test and the check of wheat plant water cut monitoring model
For reliability and the applicability of checking the wheat water content monitoring model, utilize 2008-2009 year independent experiment data above-mentioned model is tested and to be checked, utilize the coefficient of determination (R between predicted value and the observed reading simultaneously 2), average relative error (RE) and relatively 3 indexs of root-mean-square deviation (RRMSE) come the performance (specifically seeing table 1) of integrated survey model, and select 1: 1 graph of a relation (Fig. 6) that the result makes predicted value and observed reading preferably, the predictive ability of intuitively showing model.Fig. 6 shows, based on NDVI (R 836, R 793), RVI (RC 837, RC 793) the plant moisture model measurement result that makes up shows better the match coefficient of determination (R 2) being respectively 0.830 and 0.834, relative error (RE) is respectively 0.136 and 0.138, and root-mean-square deviation (RRMSE) is respectively 0.151 and 0.152 relatively, has passed through the test and the check of model.
Figure BSA00000615828500071
Table 1
The test and the check of wheat leaf layer water cut monitoring model
Utilize 2008-2009 year independent experiment data that the monitoring equation of above-mentioned different spectrum types modelings is tested respectively, utilize the coefficient of determination (R between predicted value and the observed reading 2), average relative error (RE) and relatively 3 indexs of root-mean-square deviation (RRMSE) come the integrated survey model specifically to see table 2, and 1: 1 graph of a relation making predicted value and observed reading is intuitively showed and is showed the predictive ability of model (Fig. 7) preferably.Fig. 7 shows, with NDVI (R 1100, R 770) and RVI (R 893, R 805) the leaf layer water cut monitoring model set up for variable behaves oneself best coefficient of determination R 2Be respectively 0.783 and 0.812, relative error RE is respectively 0.242 and 0.182, and root-mean-square deviation RRMSE is respectively 0.205 and 0.148 relatively.
Figure BSA00000615828500081
Table 2

Claims (6)

1. wheat plant moisture monitoring method based on the high spectrum index of canopy; It is characterized in that, the moisture processing be period the jointing stage to the maturity stage, the The real time measure soil moisture content; Soil moisture content according to actual measurement is a volumetric(al) moisture content; The soil moisture of each moisture being handled through artificial recharge is adjusted to preset value, controls each sub-district soil moisture content, and concrete steps are following:
1) high spectral information obtains
Measure the high spectrum index data of wheat canopy, the significant wave segment limit is 350～2500nm, and wherein 350～1050nm spectrum sample is spaced apart 1.4nm, and spectral resolution is 3nm; 1050～2500nm spectrum sample is spaced apart 2nm, and spectrum rate respectively is 10nm, carries out when spectroscopic assay is chosen in ceiling unlimited, calm or gentle breeze, and minute is 10:00-14:00.Sensor probe is vertically downward in the canopy top during measurement, and the spectrometer field angle is 25 °, highly about 1.0m; Ground field range diameter is 0.44m; 10 sampling points of each cell measurement, each sampling point duplicate measurements 5 times (visual field) is with the spectral reflectance value of its mean value as this sub-district;
2) plant determination of moisture
Synchronous with step 1), at different growth stage, representative wheat 20 strains are got in each sub-district; By organ separation, again blade is separated by various position leaves earlier, take back the laboratory in the valve bag of weighing of packing into rapidly; Claim its fresh weight with ten thousand/precision electronic balance, after drying to constant weight under 80 ℃, weigh after putting into 105 ℃ of 30min that complete of baking oven then, obtain different parts organ dry weight; Calculate plant water content (PWC) respectively, leaf water content (LWC) and leaf layer water cut (CLWC), formula is following:
PWC(％)＝(PFW-PDW)/PFW×100％
LWC(％)＝(LFW-LDW)/LFW?×100％
CLWC(％)＝(∑LFW-∑LDW)/∑LFW×100％
Wherein, PFW is the plant fresh weight, and PDW is the plant dry weight, and LFW is a fresh weight, and LDW is a leaf dry weight;
3) vegetation index makes up
Original spectrum NDSI=(R λ 1-R λ 2)/(R λ 1+ R λ 2) RSI=R λ 1/ R λ 2
Spectrum NDSI=(RC reciprocal λ 1-RC λ 2)/(RC λ 1+ RC λ 2) RSI=RC λ 1/ RC λ 2
R wherein λ 1And R λ 2Be respectively the reflectivity of any two wavelength, the scope of λ 1 and λ 2 is 350～2500nm, FD λ 1And FD λ 2Be its corresponding first derivative spectrum, AL λ 1And AL λ 2Be its corresponding inverse logarithm spectrum, RC λ 1And RC λ 2Be its corresponding spectrum reciprocal, to the estimation ability of wheat water content content, filter out the result who behaves oneself best according to normalization spectrum index NDSI and ratio spectrum index RSI;
4) data analysis and utilization
Utilize the data in the step 3), analysis-by-synthesis plant water content and leaf water content and the direct quantitative relationship of canopy spectra reflectivity adopt the meticulous sampling method of decrement, filter out wavelength band and spectrum parameter to moisture-sensitive, and make up the moisture monitoring model;
5) structure of monitoring model and check
Adopt relative root-mean-square deviation (RRMSE) to carry out assay, and draw 1: 1 graph of a relation between experimental observation value and the model predication value, wherein the RRMSE computing formula is following:
RRMSE = 1 n × Σ i = 1 n ( P i - O i ) 2 × 100 O ‾ i
O in the following formula iBe the moisture content value of observing in the test, P iBe the moisture content value of model estimation, n is a model measurement test samples number.
2. the wheat plant moisture monitoring method based on the high spectrum index of canopy according to claim 1 is characterized in that, uses Germany to produce portable soil soil moisture measure TRIME-EZ The real time measure soil moisture content.
3. the wheat plant moisture monitoring method based on the high spectrum index of canopy according to claim 1; It is characterized in that, measure the open-air high spectral radiometer of FieldSpec Pro FR2500 type back hanging type that wheat canopy high spectrum index The data U.S. Analytical Spectral Device (ASD) company produces in the step 1).
4. the wheat plant moisture monitoring method based on the high spectrum index of canopy according to claim 1 is characterized in that, in the step 4) based on spectrum NDVI (R 836, R 793), RVI (RC 837, RC 793) the wheat plant moisture monitoring model that makes up, the coefficient of determination (R 2) be respectively 0.851 and 0.852; Based on original spectrum NDVI (R 1100, R 770) and RVI (R 893, R 805) the wheat leaf layer moisture monitoring model that makes up, coefficient of determination R 2Be respectively 0.730 and 0.843.
5. the wheat plant moisture monitoring method based on the high spectrum index of canopy according to claim 1 is characterized in that, data analysis is all carried out at Excel2007 and MATLAB7.0 (The Math Works, 2000) with arrangement in the step 4).
6. the wheat plant moisture monitoring method based on the high spectrum index of canopy according to claim 1 is characterized in that, the precision (R of model measurement in the step 5) 2) all greater than 0.783, RRMSE is all less than 0.205.
CN201110368757.2A 2011-11-21 2011-11-21 A kind of Wheat plant moisture monitoring method based on canopy high spectral index Active CN102426153B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201110368757.2A CN102426153B (en) 2011-11-21 2011-11-21 A kind of Wheat plant moisture monitoring method based on canopy high spectral index

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201110368757.2A CN102426153B (en) 2011-11-21 2011-11-21 A kind of Wheat plant moisture monitoring method based on canopy high spectral index

Publications (2)

Publication Number Publication Date
CN102426153A true CN102426153A (en) 2012-04-25
CN102426153B CN102426153B (en) 2015-09-16

Family

ID=45960158

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201110368757.2A Active CN102426153B (en) 2011-11-21 2011-11-21 A kind of Wheat plant moisture monitoring method based on canopy high spectral index

Country Status (1)

Country Link
CN (1) CN102426153B (en)

Cited By (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103293111A (en) * 2013-06-07 2013-09-11 南京农业大学 Wheat leaf layer nitrogen content spectral monitoring mode under soil background interference and modeling method
CN103323404A (en) * 2013-05-30 2013-09-25 中国农业科学院北京畜牧兽医研究所 Method for supplying nitrogenous fertilizer for cool-season gramineous pasture community
CN103472009A (en) * 2013-09-16 2013-12-25 南京农业大学 Method for monitoring wheat plant water content under different plant nitrogen content levels
CN103528965A (en) * 2013-08-28 2014-01-22 南京农业大学 Wheat leaf equivalent water thickness hyperspectral monitoring method
CN103900976A (en) * 2012-12-28 2014-07-02 中国科学院沈阳应用生态研究所 Extraction method for ozone stress plant leaf pigment and spectral information
CN104330322A (en) * 2014-09-05 2015-02-04 江苏大学 Method for determining irrigation time point of crop based on reconstituted water utilization efficiency
CN105823736A (en) * 2016-05-23 2016-08-03 塔里木大学 Detection method for content of carotenoid of jujube crown layer
CN106033052A (en) * 2015-03-13 2016-10-19 中国人民解放军***司令部工程科研设计所 A vegetation information extracting method based on hyperspectral data sensitive waveband
CN106323880A (en) * 2016-07-29 2017-01-11 河南科技大学 Plant leaf anthocyanin content estimation method and device based on SOC hyperspectral index
CN106772427A (en) * 2016-12-07 2017-05-31 南京农业大学 A kind of method that wheat leaf dry weight quantitative model is set up based on continuous wavelet analysis
CN107219226A (en) * 2017-07-24 2017-09-29 中国科学院遥感与数字地球研究所 Image collecting device and enhancing vegetation index monitoring system
CN107576618A (en) * 2017-07-20 2018-01-12 华南理工大学 Pyricularia Oryzae detection method and system based on depth convolutional neural networks
CN109765247A (en) * 2018-11-15 2019-05-17 中国农业科学院农业资源与农业区划研究所 A kind of different bearing stage wheat crops overlay area Soil Moisture Inversion method
CN110082300A (en) * 2019-05-20 2019-08-02 中国水利水电科学研究院 Canopy of winter wheat moisture content monitoring model method for building up based on spectral parameter
CN110222475A (en) * 2019-07-03 2019-09-10 中国水利水电科学研究院 A method of based on unmanned plane multispectral remote sensing inverting winter wheat plant moisture content
CN110779875A (en) * 2019-11-01 2020-02-11 北华航天工业学院 Method for detecting moisture content of winter wheat ear based on hyperspectral technology
CN110852475A (en) * 2019-09-24 2020-02-28 广州地理研究所 Extreme gradient lifting algorithm-based vegetation index prediction method, system and equipment
CN110929222A (en) * 2019-10-24 2020-03-27 中山大学 Irrigation farmland identification method based on remote sensing vegetation canopy moisture index
CN111175784A (en) * 2019-12-31 2020-05-19 塔里木大学 Satellite remote sensing monitoring method for cotton canopy moisture content
CN111289441A (en) * 2020-02-21 2020-06-16 中国农业大学 Multispectral field crop water content determination method, system and equipment
CN112649392A (en) * 2020-12-15 2021-04-13 中国农业大学 Method for rapidly identifying water-saving drought resistance of wheat
CN112945882A (en) * 2021-03-11 2021-06-11 内蒙古农业大学 Hyperspectrum-based potato plant moisture monitoring irrigation method
CN112945881A (en) * 2021-03-11 2021-06-11 内蒙古农业大学 Method for monitoring water content of potato leaves based on hyperspectral characteristic parameters
CN113504186A (en) * 2021-08-25 2021-10-15 河南农业大学 Method for estimating wheat nitrogen fertilizer utilization rate by remote sensing
CN115144346A (en) * 2022-06-23 2022-10-04 勐海曼香云天农业发展有限公司 Method and system for determining field maturity of fragrant rice
US11895953B2 (en) 2019-05-31 2024-02-13 Argus Control Systems Ltd. Plant health monitoring apparatus and methods

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106226246A (en) * 2016-07-26 2016-12-14 新疆农垦科学院 A kind of live monitoring method building garden jujube tree plant nitrogen content

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1677085A (en) * 2004-03-29 2005-10-05 中国科学院遥感应用研究所 Agricultural application integrating system for earth observation technique and its method
JP2006250827A (en) * 2005-03-11 2006-09-21 Pasuko:Kk Analytical method for growth condition of crop
US7491944B1 (en) * 2005-07-14 2009-02-17 Sandia Corporation Method to analyze remotely sensed spectral data
CN101614651A (en) * 2009-07-29 2009-12-30 北京大学 A kind of data assimilation method for monitoring soil moisture
CN101986139A (en) * 2010-08-25 2011-03-16 浙江大学 Method for inverting vegetation parameters by remote sensing based on reflection spectrum wavelet transform
CN102175618A (en) * 2011-01-31 2011-09-07 南京农业大学 Method for modeling rice and wheat leaf nitrogen content spectrum monitoring model

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1677085A (en) * 2004-03-29 2005-10-05 中国科学院遥感应用研究所 Agricultural application integrating system for earth observation technique and its method
JP2006250827A (en) * 2005-03-11 2006-09-21 Pasuko:Kk Analytical method for growth condition of crop
US7491944B1 (en) * 2005-07-14 2009-02-17 Sandia Corporation Method to analyze remotely sensed spectral data
CN101614651A (en) * 2009-07-29 2009-12-30 北京大学 A kind of data assimilation method for monitoring soil moisture
CN101986139A (en) * 2010-08-25 2011-03-16 浙江大学 Method for inverting vegetation parameters by remote sensing based on reflection spectrum wavelet transform
CN102175618A (en) * 2011-01-31 2011-09-07 南京农业大学 Method for modeling rice and wheat leaf nitrogen content spectrum monitoring model

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
田永超，等: "不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系", 《植物生态学报》 *
田永超,等: "小麦冠层反射光谱与植株水分状况的关系", 《应用生态学报》 *

Cited By (40)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103900976A (en) * 2012-12-28 2014-07-02 中国科学院沈阳应用生态研究所 Extraction method for ozone stress plant leaf pigment and spectral information
CN103323404A (en) * 2013-05-30 2013-09-25 中国农业科学院北京畜牧兽医研究所 Method for supplying nitrogenous fertilizer for cool-season gramineous pasture community
CN103323404B (en) * 2013-05-30 2015-09-16 中国农业科学院北京畜牧兽医研究所 A kind ofly obtain the method that cold-season-type graminous pasture colony supplements amount of nitrogenous fertilizer
CN103293111B (en) * 2013-06-07 2016-01-13 南京农业大学 The lower wheat leaf layer nitrogen content spectrum monitoring model of a kind of Soil Background interference and modeling method
CN103293111A (en) * 2013-06-07 2013-09-11 南京农业大学 Wheat leaf layer nitrogen content spectral monitoring mode under soil background interference and modeling method
CN103528965B (en) * 2013-08-28 2016-03-02 南京农业大学 A kind of wheat leaf equivalent water thickness EO-1 hyperion monitoring method
CN103528965A (en) * 2013-08-28 2014-01-22 南京农业大学 Wheat leaf equivalent water thickness hyperspectral monitoring method
CN103472009A (en) * 2013-09-16 2013-12-25 南京农业大学 Method for monitoring wheat plant water content under different plant nitrogen content levels
CN103472009B (en) * 2013-09-16 2015-11-18 南京农业大学 The monitoring method of wheat plant water percentage under a kind of different plants nitrogen content level
CN104330322A (en) * 2014-09-05 2015-02-04 江苏大学 Method for determining irrigation time point of crop based on reconstituted water utilization efficiency
CN106033052A (en) * 2015-03-13 2016-10-19 中国人民解放军***司令部工程科研设计所 A vegetation information extracting method based on hyperspectral data sensitive waveband
CN105823736A (en) * 2016-05-23 2016-08-03 塔里木大学 Detection method for content of carotenoid of jujube crown layer
CN106323880A (en) * 2016-07-29 2017-01-11 河南科技大学 Plant leaf anthocyanin content estimation method and device based on SOC hyperspectral index
CN106772427A (en) * 2016-12-07 2017-05-31 南京农业大学 A kind of method that wheat leaf dry weight quantitative model is set up based on continuous wavelet analysis
CN106772427B (en) * 2016-12-07 2019-07-16 南京农业大学 A method of wheat leaf dry weight quantitative model is established based on continuous wavelet analysis
CN107576618A (en) * 2017-07-20 2018-01-12 华南理工大学 Pyricularia Oryzae detection method and system based on depth convolutional neural networks
CN107576618B (en) * 2017-07-20 2020-04-28 华南理工大学 Rice panicle blast detection method and system based on deep convolutional neural network
CN107219226A (en) * 2017-07-24 2017-09-29 中国科学院遥感与数字地球研究所 Image collecting device and enhancing vegetation index monitoring system
CN109765247B (en) * 2018-11-15 2021-06-08 中国农业科学院农业资源与农业区划研究所 Soil moisture inversion method for wheat crop coverage areas at different growth stages
CN109765247A (en) * 2018-11-15 2019-05-17 中国农业科学院农业资源与农业区划研究所 A kind of different bearing stage wheat crops overlay area Soil Moisture Inversion method
US10845243B1 (en) 2019-05-20 2020-11-24 China Institute Of Water Resources And Hydropower Research Method for establishing content monitoring model of canopy water of winter wheat based on spectral parameters
CN110082300B (en) * 2019-05-20 2020-03-03 中国水利水电科学研究院 Method for establishing winter wheat canopy water content monitoring model based on spectral parameters
CN110082300A (en) * 2019-05-20 2019-08-02 中国水利水电科学研究院 Canopy of winter wheat moisture content monitoring model method for building up based on spectral parameter
US11895953B2 (en) 2019-05-31 2024-02-13 Argus Control Systems Ltd. Plant health monitoring apparatus and methods
CN110222475B (en) * 2019-07-03 2021-09-07 中国水利水电科学研究院 Method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicle
CN110222475A (en) * 2019-07-03 2019-09-10 中国水利水电科学研究院 A method of based on unmanned plane multispectral remote sensing inverting winter wheat plant moisture content
CN110852475A (en) * 2019-09-24 2020-02-28 广州地理研究所 Extreme gradient lifting algorithm-based vegetation index prediction method, system and equipment
CN110852475B (en) * 2019-09-24 2020-10-23 广州地理研究所 Extreme gradient lifting algorithm-based vegetation index prediction method, system and equipment
CN110929222A (en) * 2019-10-24 2020-03-27 中山大学 Irrigation farmland identification method based on remote sensing vegetation canopy moisture index
CN110779875A (en) * 2019-11-01 2020-02-11 北华航天工业学院 Method for detecting moisture content of winter wheat ear based on hyperspectral technology
CN110779875B (en) * 2019-11-01 2022-03-01 北华航天工业学院 Method for detecting moisture content of winter wheat ear based on hyperspectral technology
CN111175784A (en) * 2019-12-31 2020-05-19 塔里木大学 Satellite remote sensing monitoring method for cotton canopy moisture content
CN111289441A (en) * 2020-02-21 2020-06-16 中国农业大学 Multispectral field crop water content determination method, system and equipment
CN112649392A (en) * 2020-12-15 2021-04-13 中国农业大学 Method for rapidly identifying water-saving drought resistance of wheat
CN112945881A (en) * 2021-03-11 2021-06-11 内蒙古农业大学 Method for monitoring water content of potato leaves based on hyperspectral characteristic parameters
CN112945882B (en) * 2021-03-11 2022-10-25 内蒙古农业大学 Hyperspectrum-based potato plant moisture monitoring irrigation method
CN112945882A (en) * 2021-03-11 2021-06-11 内蒙古农业大学 Hyperspectrum-based potato plant moisture monitoring irrigation method
CN113504186A (en) * 2021-08-25 2021-10-15 河南农业大学 Method for estimating wheat nitrogen fertilizer utilization rate by remote sensing
CN113504186B (en) * 2021-08-25 2022-07-29 河南农业大学 Method for estimating utilization rate of nitrogen fertilizer in wheat by remote sensing
CN115144346A (en) * 2022-06-23 2022-10-04 勐海曼香云天农业发展有限公司 Method and system for determining field maturity of fragrant rice

Also Published As

Publication number Publication date
CN102426153B (en) 2015-09-16

Similar Documents

Publication Publication Date Title
CN102426153B (en) A kind of Wheat plant moisture monitoring method based on canopy high spectral index
CN101762463B (en) Method for measuring chlorophyll content of fresh tobacco leaf of flue-cured tobacco based on canopy multi-spectra
CN107796764B (en) Method for constructing wheat leaf area index estimation model based on three-band vegetation index
Yi et al. Estimation of leaf water content in cotton by means of hyperspectral indices
Wang et al. New vegetation index and its application in estimating leaf area index of rice
CN103293111B (en) The lower wheat leaf layer nitrogen content spectrum monitoring model of a kind of Soil Background interference and modeling method
CN110376167A (en) Rice leaf nitrogen content monitoring method based on unmanned plane EO-1 hyperion
CN102435564B (en) Method for estimating plant nitrogen content based on three-band spectral index
CN102313699B (en) Estimation method of total nitrogen content in crop canopy leaf
CN109187398A (en) A kind of EO-1 hyperion measuring method of wheat plant nitrogen content
CN103472009B (en) The monitoring method of wheat plant water percentage under a kind of different plants nitrogen content level
CN103528965B (en) A kind of wheat leaf equivalent water thickness EO-1 hyperion monitoring method
CN107271382A (en) A kind of different growing rape leaf SPAD value remote sensing estimation methods
CN103868880A (en) Wheat leaf nitrogen content monitoring method based on spectrum double-peak index and method for establishing monitoring model
CN102175618A (en) Method for modeling rice and wheat leaf nitrogen content spectrum monitoring model
CN104502283A (en) Two-band hyperspectral index and prediction model for estimating yield and shoot dry weight of soybean
CN103185695A (en) Spectrum-based flue-cured tobacco maturity field quick judgment method
Aasen et al. Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor
Huang et al. Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm
CN107132190A (en) A kind of soil organism spectra inversion model calibration samples collection construction method
CN110987830A (en) Model, method and application for rapidly determining chlorophyll content of plant canopy leaves
CN106990056A (en) A kind of total soil nitrogen spectrum appraising model calibration samples collection construction method
CN110220852A (en) The monitoring method of tobacco leaf equivalent water thickness based on high-spectrum remote-sensing
CN102207453A (en) Method for determining aboveground fresh biomass of flue-cured tobacco based on canopy multi-spectra
CN114169165A (en) Model method for estimating rice chlorophyll under cadmium stress by using three-band vegetation index

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant