CN103411846A - Leaf surface dust fall quantity testing method based on hyperspectral technique - Google Patents
Leaf surface dust fall quantity testing method based on hyperspectral technique Download PDFInfo
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Abstract
The invention relates to a leaf surface dust fall quantity testing method based on a hyperspectral technique. The method comprises the following steps: firstly, collecting healthy leaves with different leaf surface dust fall quantities, quickly testing spectral information of a single leaf, putting the leaf subjected to the spectral information testing into a room, acquiring leaf surface dust fall quantity data of the leaf by virtue of a leaf area instrument and an electronic scale, and determining a sensitive spectral band subjected to leaf surface dust fall by analyzing the correlation between hyperspectral information and the leaf surface dust fall quantity data; and carrying out modeling by virtue of the data of the sensitive spectral band subjected to the leaf surface dust fall, selecting a model with a minimal root-mean-square error and maximal errors between a determination coefficient and a predicted root-mean-square error and between a sample standard deviation and the predicted root-mean-square error, and predicting the leaf surface dust fall quantity of the model only by virtue of the hyperspectral information of the leaf. Compared with a traditional determination method, the leaf surface dust fall quantity testing method has the beneficial effects of reducing the tedious experimental steps of indoor leaf area testing, cleaning, weighing and the like, being simple, convenient and rapid, and meanwhile, providing references for monitoring of the sand storm strength and environment quality of a dust fall region by virtue of astronautic hyperspectral remote sensing.
Description
Technical field
The present invention relates to the Environmental Monitoring and Assessment technical field, relate in particular to a kind of quantity measuring method of foliar dust based on high spectral technique.
Background technology
Sandstorm is northern China arid, a kind of common diastrous weather in semiarid zone.During the sandstorm outburst, in air, produce a large amount of floating dust, floating dust forms obvious depositing dust on the plant leaf blade surface after sedimentation, usually be referred to as foliar dust, the foliar dust amount is an important indicator of reflection Sand, also to weigh a regional air quality quality, a good and bad Main Factors of eco-environmental quality, while or the indicator of a Sandstorm Disaster intensity.There are some researches show foliar dust to the obvious damaging effect of having grown of plant, also have the scholar to point out that foliar dust can affect the institutional framework of plant leaf blade simultaneously, cause the blade palisade tissue to present irregular alignment.Therefore, by measuring the foliar dust amount, can effectively reflect the quality of this area's environmental quality, also can basic data is provided and estimate foundation for the extent of injury monitoring and evaluation of sandstorm to plant.
How the Sand of sand and dust Burst Regions carried out to large tracts of land, monitoring fast and accurately, thereby for the Disaster Assessment of sandstorm, monitoring and the evaluation of environmental quality provide basic data and estimate foundation, be a difficult point, be also current numerous scholars' study hotspot always.Traditional sandstorm ground surface monitoring method, because restraining factors are many, can't portray the dynamic process of sandstorm effectively.Along with appearance and the development of remote sensing technology, for the monitoring of sandstorm provides a kind of new advanced means.Research about the remote sensing of dust storm monitoring, start from 20 century 70s the earliest.The monitoring method of Shenk etc. have utilized visible light or infrared channel the data research water surface and land face overhead sandstorm; Griggs utilizes the visible data of ERTS-1 to study the measuring method of water surface overhead atmosphere suspended particle optical thickness; Carlson utilizes the brightness data of moonscope to determine outburst and the corresponding atmospheric disturbance thereof of area, the Sahara sand and dust.But the research on sand-dust storm work before the nineties in 20th century only is confined to processing and the analysis of single channel information.In recent years, developed into the monitoring of hyperchannel remotely-sensed data, even RS data merges monitoring.The people such as Zheng Xinjiang utilize weather satellite multi-channel information monitoring sand-dust storm.The peaceful grade of Luo Jing utilizes NOAA KLM and FY-1C/D satellite remote sensing date to monitor and be studied Sand.Xiao Jidong etc. utilize the EOS/MODIS data, have built the Studying Dust Storm Using Remote Sensing Monitoring Index that extracts sandstorm regional extent and strength grade information.The all-victorious grade for the MODI data in sea set up a stable sand and dust exponential model of sentencing the knowledge dust intensity.The result of study to the remote sensing of dust storm monitoring that forefathers are numerous, for follow-up further further investigation provides good reference and has laid a good foundation.
Although existing a large amount of report about the remote sensing of dust storm monitoring, but most research all concentrates on the remote sensing monitoring to sandstorm outburst process, as origin, diffusion and the settling process etc. of sandstorm, and be directed to after the sandstorm sedimentation the research of the Quantitative Monitoring aspect of the harm such as negative area environment, vegetation very few.In addition, the leading indicator of current Sand monitoring is optical thickness, although this index can effectively reflect the quantity of floating dust in atmosphere, and airborne dust has obvious movability and diffusivity, therefore can't reflect the actual extent of injury of sandstorm to its range of influence, reflect that sandstorm affects intensity and the actual extent of injury in this zone but this index of foliar dust amount can be authentic and valid.Finally, rarely seen at the report of application aspect this about high-spectrum remote-sensing.
In view of above reason, the present invention be take the South Sinkiang that sandstorm takes place frequently and intensity is large and is survey region, the foliar dust amount data of the high-spectral data that the ground remote sensing of take obtains and indoor measurement are basis, inquire into the feasibility of foliar dust amount high-spectrum remote-sensing Quantitative Monitoring, for high-spectrum remote-sensing provides theoretical foundation and technical support in the application aspect Dust Storm Monitoring, final Disaster Assessment for sandstorm and environmental monitoring and evaluation provide new thinking and method.
Summary of the invention
The present invention be for solve the existing research method be directed to after the sandstorm sedimentation the Quantitative Monitoring aspect of the harm such as negative area environment, vegetation can't reflect sandstorm the affecting the problem such as intensity and the actual extent of injury of its range of influence proposed a kind of by measure the foliar dust amount authentic and valid reflect foliar dust based on the high spectral technique quantity measuring method that affect intensity and the actual extent of injury of sandstorm in this zone.
The present invention is achieved by the following technical solutions:
The above-mentioned quantity measuring method of the foliar dust based on high spectral technique, it comprises the following steps:
(1) selection of test material and collection
The seeds of first selecting the depositing dust district to have stronger regulation of absorbing dust capability, then to every seeds gather 3～5 health, without scab and insect pest and have the blade of certain foliar dust;
(2) spectrum test and processing
First the blade in above-mentioned steps (1) is put on the black cotton, again the probe of spectral radiometer is kept vertically downward and apart from the blade certain distance, then on each blade, gather 5 different parts sampling points and carry out respectively spectroscopic assay, the mean value that 5 sampling points of finally usining are measured is as the spectral reflectivity of this blade;
(3) foliar dust quantitative determination
First will survey the blade of spectrum wipes out petiole and measures its leaf area, after having surveyed leaf area, put into and be preheated to 105 ℃ and completed 30 minutes, then under 80 ℃, dry to constant weight and weigh for the first time, the blade after then weighing is for the first time put into distilled water and was soaked 20～30 minutes, after the blade deliquescing, with the depositing dust that banister brush is positive by it, clean, after distilled water flushing, again dry to constant weight in 80 ℃, weigh for the second time;
(4) removal of foliar dust
Select blade face to have the blade of obvious depositing dust, take and carry out rapidly first the dirt spectroscopic assay is arranged afterwards, first have the dirt spectroscopic assay complete after, with distilled water, its depositing dust is cleaned, be put under sunlight surperficial moisture evaporation, during without obviously visible moisture, carry out dustless spectroscopic assay for the second time until blade surface;
(5) the foliar dust sensitive band determines
Contrast the situation of change of the high spectral signature of same blade defoliation face depositing dust front and back, defoliation face depositing dust back reflection rate is changed to maximum wave band and tentatively be defined as sensitive band; Again the high-spectral data of foliar dust amount data and blade 350～1050nm wave band is carried out to correlation analysis, the wave band that correlativity is reached to the utmost point level of signifiance tentatively is defined as sensitive band; Compare the scope of these two kinds of sensitive bands, the wave band that the two is overlapping finally is defined as the sensitive band of foliar dust;
(6) modeling and checking
Adopt one-variable linear regression and multiple linear regression, partial least squares regression, four kinds of methods of principal component regression to carry out respectively modeling; Select the sample of some to be respectively used to modeling and for the check of model; Choosing root-mean-square error, the coefficient of determination, sample standard deviation, tests to predictive ability and the stability of model than these 3 indexs with predicted root mean square error.
The described quantity measuring method of foliar dust based on high spectral technique, wherein: the seeds in described step (1) are elm, have gathered respectively sample twice, gather for the first time 50 leaves, gather for the second time 33 leaves, amount to 83 leaves.
The described quantity measuring method of foliar dust based on high spectral technique, wherein: what in described step (2), spectroscopic assay was adopted is hand-held field spectrum radiation gauge, the wave band value of described hand-held field spectrum radiation gauge is 350～1050nm, and spectrum minute rate is 3nm, and spectrum sample is spaced apart 1.4nm; Spectroscopic assay in described step (2) is selected at the weather that ceiling unlimited, wind-force be less than three grades to be carried out, and minute is Beijing time 12:00～16:00; In the mensuration process, demarcated a blank every one hour, simultaneously, the spectroscopic data of 350～399nm wavelength band that noise is larger is removed, and the spectroscopic data after measuring is carried out to smoothing processing and the processing of single order differential.
The described quantity measuring method of foliar dust based on high spectral technique, wherein: the spectroscopic data after described smoothing processing and single order differential are processed will be done correlation analysis with the foliar dust amount, according to the correlation analysis result, build foliar dust difference index, foliar dust Ratio index, foliar dust normalization index.
The described quantity measuring method of foliar dust based on high spectral technique, wherein: in described step (3), be that the blade that will survey spectrum is put in numbered envelope, after taking back laboratory, wipe out petiole and use the leaf area analyzer to measure the leaf area of blade, be designated as S, unit is cm
2, after having surveyed leaf area, be put in the baking oven that is preheated to 105 ℃ and completed 30 minutes, then dry to constant weight under 80 ℃, then with ten thousand/ electronic balance weigh for the first time, be designated as W
1Then the blade after weighing for the first time is put in distilled water and soaks after 30 minutes, cleans with the depositing dust that banister brush is positive by it, after distilled water flushing, is put in corresponding envelope and in baking oven, dries to constant weight, weighs for the second time, is designated as W
2
The computing formula of described foliar dust amount is: FDC (gm
-2)=(W
1-W
2) ÷ S * 10000.
The described quantity measuring method of foliar dust based on high spectral technique, wherein: for modeling described in described step (5) is to adopt described 50 samples that gather for the first time; Described is to adopt described 33 samples that gather for the second time for model testing.
Beneficial effect:
The foliar dust quantity measuring method that the present invention is based on high spectral technique not only simple to operate, step is few, quick, efficiency is high, effective, and in this zone, affect intensity and the actual extent of injury by the sandstorm that reflects of measuring that the foliar dust amount can be authentic and valid, the quality that has namely effectively reflected this area's environmental quality, also can, for the extent of injury monitoring and evaluation of sandstorm to plant provides basic data and estimate foundation, be suitable for propagation and employment.Simultaneously, for high-spectrum remote-sensing provides new thinking and method about obtaining of basic data in the application of environmental quality monitoring and evaluation, significant for the breakthrough of the environmental quality monitoring method of large areas.
Embodiment
The present invention is based on the foliar dust quantity measuring method of high spectral technique, comprise the following steps:
1, the selection of test material and collection
First select the depositing dust district to have the seeds of stronger regulation of absorbing dust capability, green tree species-the elm that the residential block, South Sinkiang generally plants of take in the present embodiment is test material, take Alar City is the sample collection zone, gathered altogether sample twice, acquisition time is on August 15th, 2012 and on September 5th, 2012, wherein gather for the first time 50 leaves, gathered for the second time 33 leaves, amounted to 83 leaves.During collection, select healthy, without the blade of scab and insect pest, 3 leaves of every representational collection of tree.Vaned foliar dust amount (Foliar dustfall content, FDC) situation is in Table 1.
The foliar dust amount statistics of table 1 test elm blade
2, spectrum test and processing
Hand-held field spectrum radiation gauge is adopted in spectroscopic assay, and the wave band value is 350～1050nm, and spectrum minute rate is 3nm, and spectrum sample is spaced apart 1.4nm.The weather of selecting ceiling unlimited, wind-force to be less than three grades carries out, and minute is Beijing time 12:00～16:00, in the mensuration process, every one hour, demarcates a blank.During mensuration, blade is put on the black cotton, probe keeps vertically downward, and from be 50cm, 5 sampling points of every blade collection, using the spectral reflectivity of its mean value as this blade apart from blade pitch.Consider the reason that 350～399nm band noise is larger, therefore, directly the spectroscopic data of this wavelength band is removed.
Because noise of instrument has a certain impact to spectroscopic data quality tool, the spectroscopic data after measuring has been carried out to smoothing processing and the processing of single order differential.Utilize spectroscopic data and foliar dust amount after smoothing processing and single order differential are processed to do correlation analysis, according to the correlation analysis result, foliar dust difference index (FDCDI), foliar dust Ratio index (FDCRI), foliar dust normalization index (FDCNI) have been built.
FDCDI=R
852-R
673 (1)
FDCRI=R
852÷R
673 (2)
FDCNI=(R
852-R
673)÷(R
852-R
673) (3)
R in formula
852, R
673Be respectively the reflectivity of 852nm and 673nm wave band.
3, foliar dust quantitative determination
The blade of having surveyed spectrum is put in the envelope of numbering, after taking back laboratory, wipes out petiole, with the leaf area analyzer, measure the leaf area of blade, be designated as S, unit is cm
2, after having surveyed leaf area, be put in the baking oven that is preheated to 105 ℃ and completed 30 minutes, then dry to constant weight in 80 ℃, with ten thousand/ electronic balance weigh for the first time, be designated as W
1, the blade after weighing is put in distilled water and soaks 20～30 minutes, after the blade deliquescing, cleans with the depositing dust that banister brush is positive by it, after distilled water flushing, is put in corresponding envelope in baking oven 80 ℃ and dries to constant weight, weighs for the second time, is designated as W
2.The computing formula of foliar dust amount is:
FDC(g·m
-2)=(W
1-W
2)÷S×10000 (4)
4, the removal of foliar dust
Select blade face to have the blade of obvious depositing dust, take the rear dirt spectroscopic assay that has for the first time rapidly, after having the dirt spectroscopic assay complete, with distilled water, its depositing dust is cleaned, be put under sunlight surperficial moisture evaporation, during without obviously visible moisture, carry out dustless spectroscopic assay for the second time until blade surface.
5, the foliar dust sensitive band determines
Contrast the situation of change of the high spectral signature of same blade defoliation face depositing dust front and back, defoliation face depositing dust back reflection rate is changed to maximum wave band and tentatively be defined as sensitive band; Again the high-spectral data of foliar dust amount data and blade 350～1050nm wave band is carried out to correlation analysis, the wave band that correlativity is reached to the utmost point level of signifiance tentatively is defined as sensitive band, compare the scope of these two kinds of sensitive bands, the wave band that the two is overlapping finally is defined as the sensitive band of foliar dust.
6, modeling and checking
Adopt one-variable linear regression and multiple linear regression (MLR), partial least squares regression (PLSR), four kinds of modeling methods of principal component regression (PCR).In all 83 samples, for modeling, 33 samples that gather for the second time are for the check of model with 50 samples gathering for the first time.Choose root-mean-square error (RMSE), the coefficient of determination (R
2), sample standard deviation, tests to predictive ability and the stability of model than (RPD) these 3 indexs with predicted root mean square error.The coefficient of determination, sample standard deviation are with predicted root mean square error than larger, and root-mean-square error is less, illustrates that the predictive ability of model and stability are stronger.For sample standard deviation and predicted root mean square error ratio, when its value is greater than 2, illustrate that model has good predictive ability, in the time of between 1.4 to 2, illustrate that model can do just slightly estimation to sample, and be less than at 1.4 o'clock, illustrate that model can't predict sample.Data are processed with modeling and all in software Unscrambler X10.1, are completed.
The present invention not only simple to operate, step is few, efficiency is high, effective, and in this zone, affect intensity and the actual extent of injury by the sandstorm that reflects of measuring that the foliar dust amount can be authentic and valid, the quality that has namely effectively reflected this area's environmental quality, also can, for the extent of injury monitoring and evaluation of sandstorm to plant provides basic data and estimate foundation, be suitable for propagation and employment.
Claims (6)
1. the quantity measuring method of the foliar dust based on high spectral technique, is characterized in that, comprises the following steps:
(1) selection of test material and collection
The seeds of first selecting the depositing dust district to have stronger regulation of absorbing dust capability, then to every seeds gather 3～5 health, without scab and insect pest and have the blade of certain foliar dust;
(2) spectrum test and processing
First the blade in above-mentioned steps (1) is put on the black cotton, again the probe of spectral radiometer is kept vertically downward and apart from the blade certain distance, then on each blade, gather 5 different parts sampling points and carry out respectively spectroscopic assay, the mean value that 5 sampling points of finally usining are measured is as the spectral reflectivity of this blade;
(3) foliar dust quantitative determination
First will survey the blade of spectrum wipes out petiole and measures its leaf area, after having surveyed leaf area, put into and be preheated to 105 ℃ and completed 30 minutes, then under 80 ℃, dry to constant weight and weigh for the first time, the blade after then weighing is for the first time put into distilled water and was soaked 20～30 minutes, after the blade deliquescing, with the depositing dust that banister brush is positive by it, clean, after distilled water flushing, again dry to constant weight in 80 ℃, weigh for the second time;
(4) removal of foliar dust
Select blade face to have the blade of obvious depositing dust, take and carry out rapidly first the dirt spectroscopic assay is arranged afterwards, first have the dirt spectroscopic assay complete after, with distilled water, its depositing dust is cleaned, be put under sunlight surperficial moisture evaporation, during without obviously visible moisture, carry out dustless spectroscopic assay for the second time until blade surface;
(5) the foliar dust sensitive band determines
Contrast the situation of change of the high spectral signature of same blade defoliation face depositing dust front and back, defoliation face depositing dust back reflection rate is changed to maximum wave band and tentatively be defined as sensitive band; Again the high-spectral data of foliar dust amount data and blade 350～1050nm wave band is carried out to correlation analysis, the wave band that correlativity is reached to the utmost point level of signifiance tentatively is defined as sensitive band; Compare the scope of these two kinds of sensitive bands, the wave band that the two is overlapping finally is defined as the sensitive band of foliar dust;
(6) modeling and checking
Adopt one-variable linear regression and multiple linear regression, partial least squares regression, four kinds of methods of principal component regression to carry out respectively modeling; Select the sample of some to be respectively used to modeling and for the check of model; Choosing root-mean-square error, the coefficient of determination, sample standard deviation, tests to predictive ability and the stability of model than these 3 indexs with predicted root mean square error.
2. the quantity measuring method of the foliar dust based on high spectral technique as claimed in claim 1, it is characterized in that: the seeds in described step (1) are elm, have gathered respectively sample twice, gather for the first time 50 leaves, gather for the second time 33 leaves, amount to 83 leaves.
3. the quantity measuring method of the foliar dust based on high spectral technique as claimed in claim 1, it is characterized in that: what in described step (2), spectroscopic assay was adopted is hand-held field spectrum radiation gauge, the wave band value of described hand-held field spectrum radiation gauge is 350～1050nm, spectrum minute rate is 3nm, and spectrum sample is spaced apart 1.4nm;
Spectroscopic assay in described step (2) is selected at the weather that ceiling unlimited, wind-force be less than three grades to be carried out, and minute is Beijing time 12:00～16:00; In the mensuration process, demarcated a blank every one hour, simultaneously, the spectroscopic data of 350～399nm wavelength band that noise is larger is removed, and the spectroscopic data after measuring is carried out to smoothing processing and the processing of single order differential.
4. the quantity measuring method of the foliar dust based on high spectral technique as claimed in claim 3, it is characterized in that: the spectroscopic data after described smoothing processing and single order differential are processed will be done correlation analysis with the foliar dust amount, according to the correlation analysis result, build foliar dust difference index, foliar dust Ratio index, foliar dust normalization index.
5. the quantity measuring method of the foliar dust based on high spectral technique as claimed in claim 1, it is characterized in that: in described step (3), be that the blade that will survey spectrum is put in numbered envelope, after taking back laboratory, wipe out petiole and use the leaf area analyzer to measure the leaf area of blade, be designated as S, unit is cm
2, after having surveyed leaf area, be put in the baking oven that is preheated to 105 ℃ and completed 30 minutes, then dry to constant weight under 80 ℃, then with ten thousand/ electronic balance weigh for the first time, be designated as W
1Then the blade after weighing for the first time is put in distilled water and soaks after 30 minutes, cleans with the depositing dust that banister brush is positive by it, after distilled water flushing, is put in corresponding envelope and in baking oven, dries to constant weight, weighs for the second time, is designated as W
2
The computing formula of described foliar dust amount is: FDC (gm
-2)=(W
1-W
2) ÷ S * 10000.
6. the quantity measuring method of the foliar dust based on high spectral technique as claimed in claim 1 or 2 is characterized in that: for modeling described in described step (5) is to adopt described 50 samples that gather for the first time; Described is to adopt described 33 samples that gather for the second time for model testing.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744777A (en) * | 2013-12-26 | 2014-04-23 | 浙江大学 | Detection method and purpose for detecting water content of tea by same |
| CN103954567A (en) * | 2014-04-15 | 2014-07-30 | 塔里木大学 | Soil salinity determining method based on continuum removal method |
| CN104268892A (en) * | 2014-10-13 | 2015-01-07 | 中国林业科学研究院资源信息研究所 | Leaf surface dust amount image determination method and system |
| CN104793269A (en) * | 2015-04-24 | 2015-07-22 | 北京师范大学 | Method for monitoring ranges and strength of sand covering regions |
| CN105954206A (en) * | 2016-04-29 | 2016-09-21 | 北京市农林科学院 | Method for measuring anthocyanin content of purple corn blades, and system thereof |
| CN104374711B (en) * | 2014-10-08 | 2016-09-28 | 中国林业科学研究院资源信息研究所 | A kind of trees blade face dust method for determination of amount and system |
| CN106124697A (en) * | 2016-06-12 | 2016-11-16 | 苏州大学 | The appraisal procedure of a kind of plant regulation of absorbing dust capability and application thereof |
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| CN109270011A (en) * | 2018-11-15 | 2019-01-25 | 广州地理研究所 | A kind of plant regulation of absorbing dust capability detection method based on machine learning algorithm |
| CN109270012A (en) * | 2018-11-15 | 2019-01-25 | 广州地理研究所 | A kind of plant regulation of absorbing dust capability detection method based on relative coefficient |
| CN112964641A (en) * | 2021-04-08 | 2021-06-15 | 北京市园林科学研究院 | Vane dust retention measuring system and method based on hyperspectral technology |
| CN113203696A (en) * | 2021-05-06 | 2021-08-03 | 塔里木大学 | Hyperspectrum-based method for detecting change of cyperus esculentus leaf area under saline water irrigation |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103217312A (en) * | 2012-01-20 | 2013-07-24 | 中国科学院沈阳应用生态研究所 | Simulation device for receiving surficial airborne dust |
-
2013
- 2013-08-16 CN CN201310359336.2A patent/CN103411846B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103217312A (en) * | 2012-01-20 | 2013-07-24 | 中国科学院沈阳应用生态研究所 | Simulation device for receiving surficial airborne dust |
Non-Patent Citations (1)
| Title |
|---|
| 徐金鸿等: "大气降尘对小叶榕叶子光谱的影响", 《重庆市测绘学会第三届优秀论文评选获奖论文暨2005-2006年度学术交流会论文选编》, 31 December 2008 (2008-12-31), pages 21 - 24 * |
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| CN103954567A (en) * | 2014-04-15 | 2014-07-30 | 塔里木大学 | Soil salinity determining method based on continuum removal method |
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| CN109270011A (en) * | 2018-11-15 | 2019-01-25 | 广州地理研究所 | A kind of plant regulation of absorbing dust capability detection method based on machine learning algorithm |
| CN109270012A (en) * | 2018-11-15 | 2019-01-25 | 广州地理研究所 | A kind of plant regulation of absorbing dust capability detection method based on relative coefficient |
| CN112964641A (en) * | 2021-04-08 | 2021-06-15 | 北京市园林科学研究院 | Vane dust retention measuring system and method based on hyperspectral technology |
| CN113203696A (en) * | 2021-05-06 | 2021-08-03 | 塔里木大学 | Hyperspectrum-based method for detecting change of cyperus esculentus leaf area under saline water irrigation |
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