CN104567754A - Wheat LAI (leaf area index) estimation method coupled with satellite-ground remote sensing - Google Patents
Wheat LAI (leaf area index) estimation method coupled with satellite-ground remote sensing Download PDFInfo
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Abstract
The invention relates to the field of agricultural vegetation remote sensing, particularly to a wheat LAI (leaf area index) estimation method coupled with satellite-ground remote sensing. The method comprises steps as follows: 1) acquiring two different scales of canopy spectrum information, namely, a wheat ground high-spectrum and an SPOT-5 image; 2) fitting a wheat canopy high-spectrum and a ridge high-spectrum with a satellite sensor wave spectrum response function; 3) extracting a pure satellite pixel spectrum on the basis of a mixed pixel linear decomposition model; 4) meanwhile, verifying the pure satellite pixel spectrum by the aid of a synchronous wheat simulation pixel spectrum; 5) constructing a wheat LAI monitoring model coupled with satellite-ground remote sensing with related statistical methods. The method overcomes defects of existing ground remote sensing point scales and satellite remote sensing mixed pixels and is higher in precision and accuracy of estimation of wheat LALs at different nitrogen application levels in different ecological regions in particular, wheat LAI growth information is acquired in real time, and wide application of remote sensing based crop growth remote sensing monitoring technologies is facilitated.
Description
Technical field
The present invention relates to agricultural vegetation remote sensing fields, relate in particular to a kind of wheat leaf area index evaluation method of the star that is coupled-ground remote sensing.
Background technology
Leaf area index LAI(Leaf Area Index) refer to the ratio of plant all blade one sides area summation and the land area shared by plant, the potential blade area of plant that can be used for luminous energy intercepting and capturing and gas exchanges can be reflected, being describe the most frequently used parameter of crop growing state in remote sensing monitoring, is also the important parameter for crop yield assessment.Leaf area index, as important agronomy, ecology and a meteorological mathematic(al) parameter, is widely used in plant-growth model, energy equilibrium model, climate model and canopy reflectance model.
The method of traditional acquisition Crop leaf area index mainly relies on and samples with destructiveness, and in office analysis measures, although reliable results, has certain hysteresis quality.Remote sensing technology, as the cutting edge technology of modern information technologies, has macroscopic view, the advantage such as quick, objective, accurate, can fast monitored large area field-crop leaf area index, provides important technical support for implementing accurate agricultural.
Utilize spectral remote sensing technology to carry out Crop leaf area Index Monitoring study general to carry out on two yardsticks, i.e. ground population canopy yardstick and satellite remote sensing space scale.The crop remote sensing monitoring of two scale levels respectively has its relative merits, and ground spectrum remote sensing technology wave spectrum is continuous and affected by environment less, and Crop leaf area index is estimated, and Study system deeply, but be confined to point scale scope; Space remote sensing dynamically large area can obtain plant growth indication information, but subjects to the problem such as atmospheric interference and mixed pixel.
Summary of the invention
The object of the invention is to overcome deficiency of the prior art and the wheat leaf area index evaluation method that a kind of star that is coupled-ground remote sensing is provided, for solving the more problem of mixed pixel of space scale plant growth remote sensing monitoring.
The object of the present invention is achieved like this:
Be coupled the wheat leaf area index evaluation method of star-ground remote sensing, specific as follows: the canopy spectrum information 1) obtaining wheat ground high-spectrum and SPOT-5 image two kinds of different scales;
2) satellite sensor spectral response function matching wheat canopy EO-1 hyperion and ridge EO-1 hyperion is utilized;
3) extraction of pure satellite pixel spectrum is realized based on mixed pixel linear unmixed model;
4) utilize synchronous wheat simulation pixel spectrum to verify pure satellite pixel spectrum simultaneously;
5) ASSOCIATE STATISTICS method is utilized to build the wheat leaf area index monitoring model of coupling star-ground remote sensing.
Based on the above, the spectral response function utilized in described step (2), specific algorithm utilizes subdivided spectral section, spectrally covering the source data treating analog sensor spectral range, come to calculate the image quantitative simulation treating analog sensor of known spectra performance, ground high-spectrum reflectivity simulation broadband satellite data calculates according to following formula:
, in formula, R is the simulated reflectivity of broadband satellite, and i is that the interior response of broadband of spectral response functions is counted, S (λ
i) be the spectral response functions value of i-th response point of different satellite sensor, R (λ
i) be the wheat canopy hyper spectral reflectance of i-th response point of spectrophotometer, the wave band step-length between △ λ spectral response point.
Based on the above, mixed pixel linear unmixed model in described step (3), think the reflectivity of arbitrary pixel at certain wave band, by the common reflex at this wave band of atural object each in this pixel, size and the area of atural object of effect are linear, and mixed pixel linear unmixed model is expressed as:
In formula, n is the total number of end member in mixed pixel, and m is wave band total number.
r i for the reflectivity of mixed pixel i-th wave band,
f j for the area ratio that each end member is shared in mixed pixel,
r eij be the reflectivity of an i-th wave band jth end member, i represents wave band, and j represents the end member in mixed pixel, is residual error.
Based on the above, pure satellite pixel spectrum in described step 3) refers to: ridge width, EO-1 hyperion instrument ground field range calculate, in test site, pure wheatland area accounts for 91.6%, ridge spectrum area accounts for 8.4%, carry out Decomposition of Mixed Pixels according to this area ratio to wheat canopy SPOT-5 pixel wave spectrum, the spectrum after decomposition is called Pure pixel spectrum.
Based on the above, set up monitoring model in described step (5) and set up monitoring model by multiple spectrum indexes of adding up in certain hour, selected regression model form comprises linear function, exponential function, power function and logarithmic function, and assesses monitoring model.
Based on the above, the assessment of described monitoring model adopts monitoring decision systems and standard error assessment models degree of fitting.
The present invention has following advantage:
The wheat leaf area index evaluation method of a kind of star that is coupled-ground remote sensing of the present invention overcomes the defect of existing ground remote sensing point scale and satellite remote sensing mixed pixel, especially to the wheat leaf area index estimation under planted in different ecological areas, different nitrogen amount applied, there is higher precision and accuracy, realize the Real-time Obtaining of wheat leaf area index growth information, facilitate the widespread use of the crop growing state remote sensing monitoring technology based on remote sensing.
Accompanying drawing explanation
Fig. 1 is the method flow diagram that the present invention estimates wheat leaf area index.
Fig. 2 is wheat canopy of the present invention and ridge area ratio schematic diagram.
Fig. 3 is for adopting Pure pixel spectrum optimization model y=2.685 e
2.907 xcharting contrast is carried out to two test great Qu.
Embodiment
Below in conjunction with specific embodiment, the invention will be further described.
Be coupled the wheat leaf area index evaluation method of star-ground remote sensing, specific as follows:
1) canopy spectrum information of wheat ground high-spectrum and SPOT-5 image two kinds of different scales is obtained;
2) satellite sensor spectral response function matching wheat canopy EO-1 hyperion and ridge EO-1 hyperion is utilized;
3) extraction of pure satellite pixel spectrum is realized based on mixed pixel linear unmixed model;
4) utilize synchronous wheat simulation pixel spectrum to verify pure satellite pixel spectrum simultaneously;
5) ASSOCIATE STATISTICS method is utilized to build the wheat leaf area index monitoring model of coupling star-ground remote sensing.
Based on the above, the spectral response function utilized in described step (2), specific algorithm utilizes subdivided spectral section, spectrally covering the source data treating analog sensor spectral range, come to calculate the image quantitative simulation treating analog sensor of known spectra performance, ground high-spectrum reflectivity simulation broadband satellite data calculates according to following formula:
, in formula, R is the simulated reflectivity of broadband satellite, and i is that the interior response of broadband of spectral response functions is counted, S (λ
i) be the spectral response functions value of i-th response point of different satellite sensor, R (λ
i) be the wheat canopy hyper spectral reflectance of i-th response point of spectrophotometer, the wave band step-length between △ λ spectral response point.
Based on the above, mixed pixel linear unmixed model in described step (3), think the reflectivity of arbitrary pixel at certain wave band, by the common reflex at this wave band of atural object each in this pixel, size and the area of atural object of effect are linear, and mixed pixel linear unmixed model is expressed as:
In formula, n is the total number of end member in mixed pixel, and m is wave band total number.
r i for the reflectivity of mixed pixel i-th wave band,
f j for the area ratio that each end member is shared in mixed pixel,
r eij be the reflectivity of an i-th wave band jth end member, i represents wave band, and j represents the end member in mixed pixel, is residual error.
Based on the above, pure satellite pixel spectrum in described step 3) refers to: ridge width, EO-1 hyperion instrument ground field range calculate, in test site, pure wheatland area accounts for 91.6%, ridge spectrum area accounts for 8.4%, carry out Decomposition of Mixed Pixels according to this area ratio to wheat canopy SPOT-5 pixel wave spectrum, the spectrum after decomposition is called Pure pixel spectrum.
Based on the above, set up monitoring model in described step (5) and set up monitoring model by multiple spectrum indexes of adding up in certain hour, selected regression model form comprises linear function, exponential function, power function and logarithmic function, and assesses monitoring model.
Based on the above, the assessment of described monitoring model adopts monitoring decision systems and standard error assessment models degree of fitting.
Sample information, from implementing wide field trial between 4 wheat paddocks altogether, relates to planted in different ecological areas, time, wheat breed, nitrogen amount applied and density process.
Test 1: in November, 2008-2009 year June farm, the Changjiang river town, Rugao City of Jiangsu Province (120 ° 35 ' 16 " E, 32 ° 3 ' 9 " N) and Qu Tang town, Haian County (120 ° 20 ' 34 " E, 32 ° 32 ' 20 " N) carry out, hereinafter referred to as test site, Rugao.Two places are respectively for examination wheat breed and raise wheat 15 and Ning Mai 13, all establish 3 nitrogen amount applied: 150,210,270 kghm
-2, wherein base manure is made in test site, Rugao 40%, and 40% makes seed manure, and 20% makes jointing fertilizer, if 2 density process, Basic Seedling is respectively 1.6 × 10
6with 1.8 × 10
6strain hm
-2, the soil texture is sandy loam, and front stubble is paddy rice, and 0 ~ 20 cm topsoil soils content of organic matter is 18.0 gkg
-1, full nitrogen 0.96 gkg
-1, available potassium 113.1 mgkg
-1, rapid available phosphorus 13.6 mgkg
-1; Base manure is made in test site, Hai'an 50%, and 50% makes jointing fertilizer, and Basic Seedling is 67.5 kghm
-2, the soil texture is heavy loam, and front stubble is paddy rice, and 0 ~ 20 cm topsoil soils content of organic matter is 22.6 gkg
-1, full nitrogen 2.15 gkg
-1, available potassium 188.9 mgkg
-1, rapid available phosphorus 15.2 mgkg
-1.Ru Gao great district area about 150 m × 80 m, Hai'an about 100 m × 80 m, ensure that each great Qu has 3 × 3 pixels at least on SPOT-5 image.Equal 2 repetitions of two places test, RANDOMIZED BLOCK DESIGN.
Test 2: carry out year June in November, 2009-2010, test site and process are with test 1, and Labile soil organic carbon and Basic Seedling do not have significant change.
Test 3: in October, 2008-2009 year June the institute of agricultural sciences of Anyang in Henan province county experimental field (114 ° 18 ' 45 " E, 36 ° 11 ' 47 " N) carry out, hereinafter referred to as test site, Anyang.Front stubble is corn, and the soil texture is clay loam, and 0 ~ 20 cm topsoil soils content of organic matter is 18.6 gkg
-1, full nitrogen 1.12 gkg
-1, available potassium 188.9 mgkg
-1, rapid available phosphorus 13.2 mgkg
-1.Examination wheat breed is supplied to be all wheats 16, if 4 nitrogen amount applied: 0,100,200,300 kghm
-2, wherein 40% make base manure, 40% makes seed manure, and 20% makes jointing fertilizer, and Basic Seedling is 2.3 × 10
6strain hm
-2. plot area about 60 m × 60 m.RANDOMIZED BLOCK DESIGN, nitrogen clear area repeats 2 times, other process repetition 4 times.
Test 4: carry out year June in November, 2009-2010, test site and process are with test 3, and Labile soil organic carbon and Basic Seedling do not have significant change.
Synchronous or accurate with spectral measurement and remote sensing image transit time synchronous, representative wheat plant sample 4 strain × 5 point is got from each great Qu and sampling point, green blade is pressed leaf position to be separated, the portable leaf area instrument of LI-3000C is utilized to measure sampling plant leaf area, 30 min at 105 DEG C, complete and weigh after oven dry at 80 DEG C, then dry and weigh, obtain the gross dry weight (LW) of each leaf position leaf dry weight and green blade, and investigate planting density, utilize dry weight method to calculate leaf area index (LAI).
The FieldSpec Pro FR2500 type back hanging type field ground feature spectrometer utilizing Analytical Spectral Device (ASD) company of the U.S. to produce, ceiling unlimited weather is selected to measure wheat canopy spectrum, minute is between 10:00 ~ 14:00. during measurement, sensor probe is vertically downward, spectrometer field angle is 25 °, apart from Target scalar vertical height about 1.0 m, ground field number is that 0.44 m. duplicate measurements in field range is averaged for 10 times, diagonally evenly 5 spectrum sample points are set in each community, average as wheat canopy spectra measurement in this community. in addition, measure 3 ridge visual fields, average as this community ridge spectra measurement. before and after each spectroscopic assay, carry out standard white plate correction (standard white plate reflectivity is 1).
SPOT-5 multi-spectrum remote sensing image adopts ENVI 4.7 software] carry out remote sensing image pre-service, first 40 ground GPS dominating pair of vertices images are utilized to carry out geometric accurate correction, geometric correction model is quadratic polynomial, the method of contiguous pixel resampling is adopted to carry out projective transformation, projective parameter selects the Albers projection under Krasovsky reference ellipsoid, ensure that correction error controls within 0.5 pixel, the FLAASH module that the Atmospheric radiation correction of SPOT-5 image adopts ENVI to carry is carried out.
According to spectral response function simulation formula, simulate the wheat canopy and ridge image spectrum that obtain corresponding SPOT-5 sensor respectively with ground wheat canopy and ridge hyper spectral reflectance.Ground high-spectrum reflectivity simulation broadband satellite data calculates according to following formula:
, in formula, R is the simulated reflectivity of broadband satellite, and i is that the interior response of broadband of spectral response functions is counted, S (λ
i) be the spectral response functions value of i-th response point of different satellite sensor, R (λ
i) be the wheat canopy hyper spectral reflectance of i-th response point of spectrophotometer, the wave band step-length between △ λ spectral response point.
Realize the extraction of pure satellite pixel spectrum based on mixed pixel linear unmixed model, mixed pixel linear unmixed model can be expressed as:
, in formula:
nfor the total number of end member in mixed pixel;
mfor wave band total number;
r i for mixed pixel
ithe reflectivity of wave band;
f j be
jthe area ratio that individual end member is shared in mixed pixel;
r eij be
iwave band
jthe reflectivity of individual end member;
for residual error.
According to SPOT-5 wave band feature, calculate the spectrum index that the normalization index (NDVI) of SPOT-5 pure spectra its two band combination any, Ratio index (RVI), difference index (DVI) and soil regulate vegetation index (SAVI) 4 kinds common.
Analyze the relation between 5 kinds of type function such as linear, index, logarithm, polynomial expression and the power function of wheat leaf area index and above-mentioned spectrum index, according to the higher coefficient of determination (R
2) and lower root-mean-square error (RMSE), relative error (RE), determine optimum spectrum index and regression model.Table 1 lists spectrum index and the regression model of planted in different ecological areas optimum.
The quantitative relationship of table 1 wheat leaf area index (y) and Pure pixel spectrum index (x)
Note: I: test site, Rugao; II: test site, Anyang; III: test site, two places
Result shows, adapts to optimum estimation models y=2.685 e of wheat from jointing stage to pustulation period of Henan and two places, Jiangsu
2.907 x, the estimation coefficient of determination 0.56.
Independent time testing data wheat leaf area index is utilized to carry out test and check to appraising model, root-mean-square error 1.245, relative error 18.6%.
Adopt Pure pixel spectrum optimization model y=2.685 e
2.907 xcharting (Fig. 3) is carried out to two test great Qu.Result shows, two test site leaves of winter wheat area index estimation results and measured result have certain consistance in space distribution, and the inverting value average of each great Qu and measured value gap not quite, also demonstrate the reliability of optimization model from the side.
The above is only the preferred embodiment of the present invention; be noted that for those skilled in the art; not departing under the original prerequisite of the present invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (6)
1. be coupled the wheat leaf area index evaluation method of star-ground remote sensing, it is characterized in that: specific as follows: the canopy spectrum information 1) obtaining wheat ground high-spectrum and SPOT-5 image two kinds of different scales;
2) satellite sensor spectral response function matching wheat canopy EO-1 hyperion and ridge EO-1 hyperion is utilized;
3) extraction of pure satellite pixel spectrum is realized based on mixed pixel linear unmixed model;
4) utilize synchronous wheat simulation pixel spectrum to verify pure satellite pixel spectrum simultaneously;
5) ASSOCIATE STATISTICS method is utilized to build the wheat leaf area index monitoring model of coupling star-ground remote sensing.
2. the wheat leaf area index evaluation method of a kind of star that is coupled-ground remote sensing according to claim 1, is characterized in that:
The spectral response function utilized in described step (2), specific algorithm utilizes subdivided spectral section, spectrally covering the source data treating analog sensor spectral range, come to calculate the image quantitative simulation treating analog sensor of known spectra performance, ground high-spectrum reflectivity simulation broadband satellite data calculates according to following formula:
in formula, R is the simulated reflectivity of broadband satellite, and i is that the interior response of broadband of spectral response functions is counted, S (λ
i) be the spectral response functions value of i-th response point of different satellite sensor, R (λ
i) be the wheat canopy hyper spectral reflectance of i-th response point of spectrophotometer, the wave band step-length between △ λ spectral response point.
3. the wheat leaf area index evaluation method of a kind of star that is coupled-ground remote sensing according to claim 1, is characterized in that:
Mixed pixel linear unmixed model in described step (3), think the reflectivity of arbitrary pixel at certain wave band, by the common reflex at this wave band of atural object each in this pixel, the size of effect and the area of atural object linear, mixed pixel linear unmixed model is expressed as:
In formula, n is the total number of end member in mixed pixel, and m is wave band total number,
r i for the reflectivity of mixed pixel i-th wave band,
f j for the area ratio that each end member is shared in mixed pixel,
r eij be the reflectivity of an i-th wave band jth end member, i represents wave band, and j represents the end member in mixed pixel, is residual error.
4. the wheat leaf area index evaluation method of a kind of star that is coupled-ground remote sensing according to claim 1, it is characterized in that: the pure satellite pixel spectrum in described step 3) refers to: ridge width, EO-1 hyperion instrument ground field range calculate, in test site, pure wheatland area accounts for 91.6%, ridge spectrum area accounts for 8.4%, carry out Decomposition of Mixed Pixels according to this area ratio to wheat canopy SPOT-5 pixel wave spectrum, the spectrum after decomposition is called Pure pixel spectrum.
5. the wheat leaf area index evaluation method of a kind of star that is coupled-ground remote sensing according to claim 1, it is characterized in that: set up monitoring model in described step (5) and set up monitoring model by multiple spectrum indexes of adding up in certain hour, selected regression model form comprises linear function, exponential function, power function and logarithmic function, and assesses monitoring model.
6. the wheat leaf area index evaluation method of a kind of star that is coupled-ground remote sensing according to claim 1, is characterized in that: the assessment of described monitoring model adopts monitoring decision systems and standard error assessment models degree of fitting.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN108303044A (en) * | 2018-02-01 | 2018-07-20 | 苏州市农业科学院 | A kind of leaf area index acquisition methods and system |
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| CN113063740A (en) * | 2021-02-25 | 2021-07-02 | 北京麦飞科技有限公司 | Wheat canopy nitrogen content monitoring method based on multi-source remote sensing data |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101916438A (en) * | 2010-07-20 | 2010-12-15 | 浙江大学 | Method for obtaining leaf area index and average leaf inclination of rice canopy by using hemisphere photographic process |
| CN102269576A (en) * | 2010-06-03 | 2011-12-07 | 曹春香 | Active and passive joint inversion method for forest coverage and effective leaf area index |
| CN102778212A (en) * | 2012-08-02 | 2012-11-14 | 浙江大学 | Method and device for utilizing digital camera to obtain rice canopy leaf area index |
| CN102829739A (en) * | 2012-08-21 | 2012-12-19 | 北京农业信息技术研究中心 | Object-oriented remote sensing inversion method of leaf area index of crop |
| CN102878957A (en) * | 2012-09-26 | 2013-01-16 | 安徽大学 | Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters |
-
2014
- 2014-12-26 CN CN201410830825.6A patent/CN104567754A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102269576A (en) * | 2010-06-03 | 2011-12-07 | 曹春香 | Active and passive joint inversion method for forest coverage and effective leaf area index |
| CN101916438A (en) * | 2010-07-20 | 2010-12-15 | 浙江大学 | Method for obtaining leaf area index and average leaf inclination of rice canopy by using hemisphere photographic process |
| CN102778212A (en) * | 2012-08-02 | 2012-11-14 | 浙江大学 | Method and device for utilizing digital camera to obtain rice canopy leaf area index |
| CN102829739A (en) * | 2012-08-21 | 2012-12-19 | 北京农业信息技术研究中心 | Object-oriented remote sensing inversion method of leaf area index of crop |
| CN102878957A (en) * | 2012-09-26 | 2013-01-16 | 安徽大学 | Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters |
Non-Patent Citations (1)
| Title |
|---|
| 王来刚: "基于多源遥感信息融合的小麦生长监测研究", 《中国博士学位论文电子期刊网》 * |
Cited By (14)
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| CN107907075A (en) * | 2017-12-28 | 2018-04-13 | 山东省农业可持续发展研究所 | Remote sensing monitoring method for peanut leaf area index |
| CN108303044A (en) * | 2018-02-01 | 2018-07-20 | 苏州市农业科学院 | A kind of leaf area index acquisition methods and system |
| CN110544277B (en) * | 2019-08-12 | 2023-01-10 | 蔡建楠 | Method for inverting subtropical vegetation leaf area index by unmanned aerial vehicle-mounted hyperspectral imager |
| CN110544277A (en) * | 2019-08-12 | 2019-12-06 | 蔡建楠 | Method for inverting subtropical vegetation leaf area index by unmanned aerial vehicle-mounted hyperspectral imager |
| CN110514597A (en) * | 2019-09-04 | 2019-11-29 | 北京麦飞科技有限公司 | The diseases and pests of agronomic crop monitoring method of based on star remotely-sensed data collaboration |
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| CN113063740A (en) * | 2021-02-25 | 2021-07-02 | 北京麦飞科技有限公司 | Wheat canopy nitrogen content monitoring method based on multi-source remote sensing data |
| 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 |
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| CN114747349A (en) * | 2022-04-28 | 2022-07-15 | 河南科技大学 | Robust wheat population cultivation method based on population growth remote sensing detection and grading |
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