CN102313526A - Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data - Google Patents
Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data Download PDFInfo
- Publication number
- CN102313526A CN102313526A CN 201010218909 CN201010218909A CN102313526A CN 102313526 A CN102313526 A CN 102313526A CN 201010218909 CN201010218909 CN 201010218909 CN 201010218909 A CN201010218909 A CN 201010218909A CN 102313526 A CN102313526 A CN 102313526A
- Authority
- CN
- China
- Prior art keywords
- data
- leaf area
- area index
- angle
- remote sensing
- 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.)
- Pending
Links
Images
Landscapes
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention provides a method for obtaining a leaf area index based on quantitative fusion and inversion of multi-angle remote sensing data and multi-spectral remote sensing data, which is characterized in that a coefficient of a bidirectional reflectance distribution function (BRDF) of a vegetation type of the best matching pixel level of the multi-angle remote sensing data and a surface reflectivity is adopted, a surface soil reflectivity profile is obtained based on best matching of the multi-spectral data, and a canopy radiation transmission model is driven to obtain the leaf area index with high accuracy and large-scope coverage based on the multi-spectral data. The invention has the advantages that: the ranges of wave bands of the multi-angle data and the multi-spectral data need not to be overlapped, and approximate treatment can be carried out by adopting the similarity of the bidirectional function of the available wave bands; the coefficient of the bidirectional reflectance function and the best matching vegetation type obtained based on the multi-angle data is relatively stable along with changes in the time and the space, time sequence data can be made into a background library to be used as input for inversion of the multi-spectral data, and thus the large-scale leaf area index with high time resolution can be obtained; and the best matching surface soil reflectivity profile obtained based on the multi-spectral data is relatively stable, and historical time sequence data can also be made into a background library. The method can be applied in crop growth monitoring, rapid estimation of crop yields and the like.
Description
Technical field
The invention belongs to process in remote sensing digital image processing and quantitative data and merge the field.The present invention is that a kind of multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data of realizing quantitatively merges the method for inverting leaf area index; Particularly utilize the multi-angle remotely-sensed data to carry out the leaf area index inverting look-up table that optimum matching is automatically selected suitable vegetation pattern, obtain the pixel level the earth surface reflection rate two to reflective function (BRDF) coefficient; Utilize multispectral data to obtain the wider angular observation data of coverage; Obtain information such as surface soil reflectivity section, chlorophyll and leaf water content; Both combine, and drive canopy radiation delivery model, thereby obtain the high precision and the method for the leaf area index of covering on a large scale.The present invention can be used for fields such as plant growth monitoring, the quick estimation of crop yield.
Background technology
Greenery surface area summation is half the on leaf area index (LAI) unit of the being defined as surface area, is important face of land parameter, can be used for fields such as global carbon and climate change research, plant growth monitoring, agricultural output assessment.Remote sensing technology is to obtain the unique channel of leaf area index on a large scale, and consistent leaf area index is to describe canopy reflectance spectrum through inverting to obtain with the radiation delivery model that the vegetation biophysical parameters concerns from remotely-sensed data estimation space.The radiation delivery model utilizes blade radiation delivery model and canopy radiation delivery model to combine based on the photon transmission process of vegetation canopy, the reflectivity of canopy is expressed as the function of canopy, blade and Soil Background characteristic.Through suitable hypothesis vegetation, Soil Background profile and atmospheric condition, from parameters such as the moonscope reflectivity of some and corresponding geometric angles, the implementation model parametric inversion obtains canopy biophysical parameters such as leaf area index.
Physical model is expressed as the reflectivity of canopy the complex nonlinear function of canopy, blade and Soil Background characteristic.Because the complex nonlinear characteristic of function can't directly use the method for parsing to obtain leaf area index, must use suitable hypothesis to come the parameter of simplified model could calculate leaf area index; Because modeling speed is slower, can't be used for mass data and handles in addition, need to adopt technology such as look-up table to accelerate modeling speed to reach the calculation requirement of real data processing.Following based on the main flow process of Physical Modeling inverting leaf area index: remote sensing input data are confirmed in (1): according to the remotely-sensed data characteristic of input, from remotely-sensed data, select suitable wave band and how much input parameters to carry out remote-sensing inversion; (2) look-up table is set up: according to the physical simulation model; Characteristic in conjunction with input remote sensing wave band data; Non-variable parameter to physical model is suitably supposed; The combination of different canopies, blade biophysics, Soil Background profile and observation situation is imported as model, simulated the canopy reflectance spectrum under the different situations respectively, set up the look-up table of the correlationship of serial input parameter and earth surface reflection rate; (3) Remote Sensing Data Processing: the Remote Sensing Data Processing that satellite is obtained is the earth surface reflection rate, calculates auxiliary datas such as relevant observation angle and locus simultaneously; (4) leaf area index remote-sensing inversion: by the remotely-sensed data and the auxiliary data of input; Find corresponding serial look-up table; Obtain other variable parameter of optimum matching; Finally find the look-up table value of leaf area index and remote sensing wave band reflectivity, and obtain leaf area index through linear interpolation from suitable look-up table.
In leaf area index inverting flow process; Most critical be confirm that suitable model parameter is set up corresponding look-up table and in refutation process by the suitable look-up table of the Information Selection of remote sensing wave band; After confirming in this two step, can be fairly simple obtain the leaf area index of correspondence from look-up table and remotely-sensed data spectral reflectivity.Concrete parameter is confirmed to comprise: the surface cover type of (1) pixel; (2) model parameter such as soil profile; (3) two of wave band to the reflective function correction coefficient.At present existing global leaf area index product, Different products has adopted different strategies to address these problems.
(1) confirming of surface cover type: mainly contain two kinds of methods; A kind of is to utilize existing surface cover data as the model input parameter; Most leaf area index products adopt this method; Global vegetation pattern is divided into several kinds of biotic formation types, thinks that different biotic formation types have different vegetation biophysics characteristic and architectural feature, like intermediate-resolution sensor (MODIS) product
[1], Cook Luo Pusi (CYCLOPES)
[2], global carbon leaf area index (ClobalCarbon LAI)
[3], multi-angle sensor leaf area index (MISR LAI) product
[4]Deng; Another kind method is based on remote sensing observations and utilizes automatic matching method to obtain best surface cover type; The inversion result that can avoid input error to bring is uncertain; Multi-angle sensor leaf area index (MISR LAI) product makes full use of its multi-angle observation information, and inverting need not to import the surface cover type
[4]
(2) the soil profile is confirmed: in most of remote sensing inversion methods, the soil outline line is that hypothesis is fixing.In the intermediate-resolution product sensor, set up the Soil Background cross-sectional data of several quasi-representatives in advance, come to select automatically only soil profile type through the optimum matching of multi light spectrum hands then;
(3) remotely-sensed data two is proofreaied and correct to reflective function: the earth surface reflection rate that remote sensing obtains not only receives the influence of face of land parameter, and different observation angle has different values, the reflectivity difference problem that needs elimination observation angle difference to bring.In the leaf area index inverting, a kind of is that the directional reflectance ratio of remote sensing observations directly is used for the leaf area index inverting; Another kind is the remote sensing canopy reflectance spectrum to be carried out two earlier proofread and correct to reflective function and to be used further to the leaf area index inverting.
The problem that in the leaf area index remote-sensing inversion, exists at present can reduce: But most of algorithms needs soil cover data to do input, has not only introduced the error of soil cover data, also has the inconsistent problem of different product supporting information; The soil profile is selected difficulty; Need to eliminate observation two to the reflective function effect.If use the multi-angle sensor, data volume is huge, and the spatial dimension of covering is narrow.Use single sensor possibly address these problems hardly,, then possibly solve the problem of a plurality of sensors if can two kinds of dissimilar remotely-sensed datas be combined.
List of references:
[1]Myneni?R?B，Hoffman?S，Knyazikhin?Y，et?al.(2002).Global?products?of?vegetation?leaf?area?andfraction?absorbed?PAR?from?year?one?of?MODIS?data.Remote?Sensing?of?Environment，83：214-231.
[2]Baret，F.，et?al.(2007).LAI，fAPAR?and?fCover?CYCLOPES?global?products?derived?fromVEGETATION：Part?1：Principles?ofthe?algorithm，Remote?Sens.Environ.，110，275-286.
[3]Deng?F，J.M.Chen，S.Plummer(2006).Algorithm?for?global?leaf?area?index?retrieval?using?satelliteimagery.IEEE?Transactions?on?Geoscience?and?Remote?Sensing，44：2219-2229.
[4]Diner，D.J.，J.V.Martonchik，C.Borel，S.A.W.Gerstl，H.R.Gordon，Y.Knyazikhin，R.Myneni，B.Pinty，and?M.M.Verstraete(2008).MISR?Level?2?Surface?Retrieval?Algorithm?Theoretical?Basis.Jet?PropulsionLaboratory?California?Institute?of?Technology，May，2008.
Summary of the invention
The present invention is directed to the defective that exists in the prior art; Realize that a kind of multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data quantitatively merge the method for inverting leaf area index; Particularly utilize the multi-angle remotely-sensed data to carry out the leaf area index inverting look-up table that optimum matching is automatically selected suitable vegetation pattern, obtain the pixel level the earth surface reflection rate two to reflective function (BRDF) coefficient; Utilize multispectral data to obtain the wider angular observation data of coverage; Obtain information such as surface soil reflectivity section, chlorophyll and blade face water cut; Combine with both, drive canopy radiation delivery model, thereby obtain the high precision and the method for the leaf area index of covering on a large scale.This method makes full use of the multi-angle remotely-sensed data can obtain the information that vegetation structure information and multispectral sensor obtain the surface soil section and cover on a large scale; Can under the situation that does not have other input of outside, maximum possible realize constraint condition, reach the characteristics that not only accurate inverting leaf area index but also high time cover on a large scale the leaf area index inverting.
Technical scheme of the present invention is following:
A kind of multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data of realizing quantitatively merges the method for inverting leaf area index, it is characterized in that comprising following steps: (1) approaches two to the reflective function model through the multi-angle remotely-sensed data, obtains two to the reflective function coefficient; (2) utilize the precalculated look-up table of canopy radiation delivery model, obtain the approximate leaf area index and the vegetation pattern of optimum matching by the multi-angle remotely-sensed data; (3) obtain the reflectivity of substar multi light spectrum hands with two of multi-angle sensor to the multispectral angle-data of reflective function coefficient correction; (4) the optimum matching vegetation pattern that obtains with the multi-angle sensor and multispectral data be as input, from look-up table optimum matching surface soil reflectivity section, and the leaf area index of acquisition optimum matching.
Said band selection is: when the leaf area index inverting, select red and near-infrared band; When soil profile is confirmed, infrared or near-infrared band in the selection;
The vegetative breakdown that said look-up table generates is: meadow and crop, broad-leaf forest, coniferous forest, shrub, broad leaf crop, woods grass mix;
Said two to the reflective function model are: two adopt Luo Si-Li (Ross-Li) model to the reflective function model,
Wherein, f
1Be the Luo Simi nuclear (RossThickkernel) of vertical scattering component, f
2Be the sparse nuclear of Lee (LiSparse Kernel) nuclear of geometrical shadow model; a
0, a
1And a
2Be relevant with kernel in the same way, vertical and geometric direction component;
Saidly approach two approach: select series different leaf area index and vegetation pattern for use to the reflective function model with multi-angle observation; The reflectivity identical that obtains with the radiation delivery modeling with observation angle, these reflectivity are optimum matching with the squared difference difference minimum of observing the reflectivity that obtains;
The approach of the best vegetation pattern of said coupling: the vegetation pattern of two optimum matching of approaching to reflective function is the best vegetation pattern of this pixel;
Said approach from multi-angle two to the reflective function model coefficient that proofread and correct multispectral coefficient of angularity with: multi-angle is proofreaied and correct and is adopted two to the reflective function model; The observed reading of this angle and the modeling typical coefficient of this angle are divided by, can obtain the angle normalization reflectivity of specified angle.Computing formula is:
Wherein,
is sun altitude, satellite altitude angle and relative bearing, and
is normalized geometric angle.With multi-angle two when the reflective function model coefficient is proofreaied and correct multi light spectrum hands, select the most close corresponding wave band coefficient to proofread and correct.
The approach of said acquisition best soil reflectivity section: design different soil reflectivity section input models; The middle-infrared band reflectivity of model output and the middle-infrared band luminance factor of observation, the minimum value of square-error is an optimum matching soil reflectivity section.
The approach of said acquisition optimum matching leaf area index: the pixel vegetation pattern of the optimum matching of confirming with two multiband reflectivity to the reflective function model tuning of multi-angle sensor optimum matching, with the multi-angle sensor, optimum matching soil reflectivity section and multispectral red and near-infrared band are as input; From look-up table, search the leaf area index value of mating most, obtain the leaf area index of optimum matching through linear interpolation.
The said vegetation pattern, two that obtains from the multi-angle remotely-sensed data has reasonable temporal and spatial stability to reflective function model coefficient and the soil reflectivity section that obtains from the Multi-spectral Remote Sensing Data optimum matching, and this can ensure when two data differences are obtained simultaneously and can substitute with the data that history obtains.
Technique effect of the present invention is following:
The different observation angle information that the present invention adopts the multi-angle remotely-sensed data by optimum matching obtain wave band two to the reflective function model coefficient, simultaneously through with the optimum matching of the look-up table of different vegetation types, confirm the best vegetation pattern of pixel.Wave band coupling through the multi-angle remotely-sensed data can obtain preliminary leaf area index, pixel wave band two to reflective function model coefficient, vegetation pattern; Through the coupling of look-up table and different-waveband, can select the soil reflectivity section of optimum matching, and utilize the covering on a large scale of multi-wavelength data, large tracts of land leaf area index that covered, high-precision on a large scale.
The present invention includes multi-angle two to reflective function model coefficient coupling, vegetation pattern coupling, multispectral soil reflectivity section coupling, leaf area inverting four steps.Multi-angle two is from data of multiple angles, to obtain each wave band only two to the reflective function model coefficient to reflective function model coefficient coupling; The vegetation pattern coupling is the pixel vegetation pattern that from data of multiple angles, obtains optimum matching; The soil reflectivity section is the soil reflectivity section that from multispectral data, obtains optimum matching; The leaf area inverting is that multispectral red and near infrared data, vegetation pattern and the soil profile proofreaied and correct with process normalization are input; Obtain nearest a plurality of leaf area index from corresponding look-up table, obtain final leaf area index from these leaf area index neutral line interpolation then.
The present invention has following characteristics with present compared with techniques:
(1) owing to adopted two spectral bands of data of multiple angles, overcome multispectral data and need suppose two errors in advance to the introducing of reflective function model parameter to reflective function model coefficient correction multispectral data;
(2) owing to adopted automatic best vegetation pattern coupling, maximum possible has reduced the error that artificial classification causes;
(3) owing to the infrared characteristic more responsive in having adopted from multispectral data,, overcome the defective that a plurality of soil profiles of former usefulness are averaged at last in order to confirm the soil reflectivity section of soil to the vegetation underlying surface.
Description of drawings
Fig. 1 multi-angle of the present invention and Multi-spectral Remote Sensing Data quantitatively merge the process flow diagram of inverting leaf area index.
Embodiment
Below in conjunction with accompanying drawing the present invention is done further detailed description.
The process flow diagram that multi-angle that the present invention realizes and Multi-spectral Remote Sensing Data quantitatively merge the inverting leaf area index is as shown in Figure 1.Fig. 1 comprises that data of multiple angles processing unit 2, two is to reflective function model best match unit 4, vegetation pattern best match unit 6, multispectral data processing unit 8, soil reflectivity section best match unit 10 and leaf area index best match unit 12.
Unit 2 converts the different multi-angle sensing datas in source into specification consistent data, comprises through coordinate transform the coordinate conversion of data for the coordinate consistent with multispectral data space projection coordinate, convert picture count value or radiation value into earth surface reflection rate, each observation angle data space location matches, sign cloud, noise and clear sky pixel status information; Get into map unit 4 then.
The clear sky pixel of 4 pairs of 2 entering from the unit in unit scans one by one; When the pixel of all observation angles all is the clear sky pixel; To each wave band; Iterative computation two is to the error of the observed reading of the reflectivity of reflective function model modeling and multi-angle, when error reaches minimum or during less than assign thresholds, obtain optimal approximation two to the reflective function model coefficient.Get into unit 6 then.
All effective pixels of 6 pairs of 4 entering from the unit in unit scan one by one; To each pixel; To the leaf area index model lookup table of all six kinds of vegetation types calculate each wave band two to the reflective function model coefficient; Calculate these two two standard errors to the reflective function model coefficient to reflective function model coefficient and unit 4 inputs, the minimum vegetation pattern of error is optimum matching vegetation pattern.Get into unit 12 then.
Unit 8 is the different multispectral sensor data-switching in source the consistent data of specification, comprises through coordinate transform the coordinate conversion of Multi-spectral Remote Sensing Data for the coordinate consistent with the multi-angle projection coordinate, convert picture count value or radiation value into earth surface reflection rate, sign cloud, noise and clear sky image element information; Get into map unit 10 then.
10 pairs of unit are from the look-up table of Different Soil reflectivity section; Calculate the reflectivity of middle-infrared band; The middle-infrared band luminance factor of these reflectivity and 8 entering from the unit; The minimum soil profile of error mean square difference is the optimum matching soil profile, and this soil reflectivity section enters to unit 12 as input.
12 pairs of unit from the unit 2, the data of pixel one by one of the same position that gets into of unit 8, unit 10; If data of multiple angles disappearance; From look-up table, find the look-up table of specified type,, calculate the reflectivity of each corresponding wave band of different leaf area index again through look-up table then from wherein finding corresponding soil reflectivity section data item; Obtain serial leaf area index value from look-up table, use linear interpolation to obtain the leaf area index value of this pixel then.
Should be pointed out that the above embodiment can make those skilled in the art more fully understand the present invention, but do not limit the present invention in any way.Therefore, although this instructions has carried out detailed explanation with reference to accompanying drawing and embodiment to the present invention,, it will be appreciated by those skilled in the art that still the present invention make amendment or are equal to replacement; And all do not break away from the technical scheme and the improvement thereof of spirit of the present invention and technical spirit, and it all should be encompassed in the middle of the protection domain of patent of the present invention.
Claims (4)
1. realize that multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data quantitatively merge the method for inverting leaf area index for one kind.It is characterized in that utilizing the multi-angle remotely-sensed data to carry out the leaf area index inverting look-up table that optimum matching is selected suitable vegetation pattern automatically, obtain the pixel level the earth surface reflection rate two to reflective function (BRDF) coefficient; Utilize multispectral data to obtain the wider angular observation data of coverage; Obtain information such as surface soil reflectivity section, chlorophyll and leaf water content; Both combine, and drive canopy radiation delivery model, thus the leaf area index that obtains high precision and cover on a large scale; Data of multiple angles and multispectral data wavelength band do not need overlapping, available band two tropism's functional similarity property approximate processing; Space-variantization is relatively stable at any time for BRDF coefficient that obtains from data of multiple angles and optimum matching vegetation pattern, can time series data be made context vault, thereby as the input of multispectral data inverting, obtains the leaf area index on a large scale of high time resolution; The Optimum Matching surface soil reflectivity section that multispectral data obtains is relatively stable, also can the data of historical time sequence be made context vault.It comprises the steps: that specifically (1) approach the BRDF model through the multi-angle remotely-sensed data, obtains the BRDF coefficient; (2) utilize the precalculated look-up table of canopy radiation delivery model, obtain the approximate leaf area index and the vegetation pattern of optimum matching by the multi-angle remotely-sensed data; (3) obtain the reflectivity of substar multi light spectrum hands with the multispectral angle-data of the BRDF coefficient correction of multi-angle sensor; (4) the optimum matching vegetation pattern that obtains with the multi-angle sensor and multispectral data be as input, from look-up table optimum matching surface soil reflectivity section, and the leaf area index of acquisition optimum matching.
2. quantitatively merge the method for inverting leaf area index according to right 1 described a kind of multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data of realizing; It is characterized in that: obtain BRDF coefficient and the optimum matching vegetation pattern of pixel level with data of multiple angles, thus the vegetation pattern look-up table of the best when required best BRDF coefficient of angularity correction and leaf area index inverting being provided for Multi-spectral Remote Sensing Data.
3. quantitatively merge the method for inverting leaf area index according to right 1 described a kind of multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data of realizing, it is characterized in that: data of multiple angles confirms that the BRDF coefficient can confirm through estimation LAI optimal approximation.
4. quantitatively merge the method for inverting leaf area index according to right 1 described a kind of multi-angle remotely-sensed data and Multi-spectral Remote Sensing Data of realizing; It is characterized in that: under the constraint of BRDF coefficient that data of multiple angles obtains and vegetation pattern, can from multispectral data, obtain optimum matching surface soil reflectivity section.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010218909 CN102313526A (en) | 2010-07-07 | 2010-07-07 | Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010218909 CN102313526A (en) | 2010-07-07 | 2010-07-07 | Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102313526A true CN102313526A (en) | 2012-01-11 |
Family
ID=45426910
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201010218909 Pending CN102313526A (en) | 2010-07-07 | 2010-07-07 | Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102313526A (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103197303A (en) * | 2013-04-08 | 2013-07-10 | 中国科学院遥感与数字地球研究所 | Earth surface two-direction reflection characteristic retrieval method and earth surface two-direction reflection characteristic retrieval system based on multiple sensors |
| CN103278112A (en) * | 2013-05-17 | 2013-09-04 | 中国科学院对地观测与数字地球科学中心 | Leaf area index inversion method and device based on remote sensing images |
| CN103632040A (en) * | 2013-11-14 | 2014-03-12 | 北京航空航天大学 | Universal aquatic vegetation radiation transmission model |
| CN104568772A (en) * | 2014-12-21 | 2015-04-29 | 华东交通大学 | Device and method for detecting content of glutamic acid of tomato leaves by multi-angle in-situ spectrum |
| CN104899464A (en) * | 2015-06-19 | 2015-09-09 | 中国地质大学（武汉） | Sampling learning machine remote sensing quantitative inversion method applicable to noise condition |
| CN104899394A (en) * | 2015-06-23 | 2015-09-09 | 中国科学院遥感与数字地球研究所 | Practical leaf area index remote sensing inversion method |
| CN105158168A (en) * | 2015-04-29 | 2015-12-16 | 新疆农垦科学院 | Digital image technology-based cotton photosynthetically active radiation fraction determination method |
| CN107014763A (en) * | 2017-04-07 | 2017-08-04 | 山东理工大学 | Chlorophyll remote-sensing inversion device and method |
| CN108416297A (en) * | 2018-03-09 | 2018-08-17 | 河北省科学院地理科学研究所 | A kind of vegetation information method for quickly identifying based on chlorophyll fluorescence |
| CN108693154A (en) * | 2018-04-25 | 2018-10-23 | 南京大学 | A kind of method of multi-angle observation exact inversion vegetation negative and positive leaf sunlight-induced chlorophyll fluorescence |
| CN109063660A (en) * | 2018-08-09 | 2018-12-21 | 成都天地量子科技有限公司 | A kind of crop recognition methods based on multispectral satellite image |
| CN109521437A (en) * | 2018-12-05 | 2019-03-26 | 武汉大学 | Multiplex physicotherapy laser radar wavelength selection method towards the detection of vegetation biochemical parameter |
| CN109614763A (en) * | 2019-01-30 | 2019-04-12 | 北京师范大学 | A kind of area crops yield estimation method correcting crop modeling based on multi-source information substep |
| 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 |
| CN111552004A (en) * | 2020-04-24 | 2020-08-18 | 中国地质科学院矿产资源研究所 | Method and system for extracting angle abnormal information of remote sensing data |
| CN113192117A (en) * | 2021-02-08 | 2021-07-30 | 南京农业大学 | Method for improving estimation of leaf area index of early growth stage of wheat based on Sentinel-2 satellite image red edge band |
| CN113345004A (en) * | 2021-08-05 | 2021-09-03 | 航天宏图信息技术股份有限公司 | Estimation method and device for vegetation leaf area index |
| CN114711010A (en) * | 2022-06-09 | 2022-07-08 | 苏州农业职业技术学院 | Water-soil fertilizer management method, system and medium in Chinese rose cultivation |
| CN116071644A (en) * | 2022-12-20 | 2023-05-05 | 中化现代农业有限公司 | Method, device, equipment and storage medium for inversion of sun leaf area index data |
-
2010
- 2010-07-07 CN CN 201010218909 patent/CN102313526A/en active Pending
Cited By (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103197303A (en) * | 2013-04-08 | 2013-07-10 | 中国科学院遥感与数字地球研究所 | Earth surface two-direction reflection characteristic retrieval method and earth surface two-direction reflection characteristic retrieval system based on multiple sensors |
| CN103197303B (en) * | 2013-04-08 | 2015-07-15 | 中国科学院遥感与数字地球研究所 | Earth surface two-direction reflection characteristic retrieval method and earth surface two-direction reflection characteristic retrieval system based on multiple sensors |
| CN103278112A (en) * | 2013-05-17 | 2013-09-04 | 中国科学院对地观测与数字地球科学中心 | Leaf area index inversion method and device based on remote sensing images |
| CN103632040A (en) * | 2013-11-14 | 2014-03-12 | 北京航空航天大学 | Universal aquatic vegetation radiation transmission model |
| CN103632040B (en) * | 2013-11-14 | 2016-05-04 | 北京航空航天大学 | A kind of general aquatic vegetation radiative transfer model |
| CN104568772A (en) * | 2014-12-21 | 2015-04-29 | 华东交通大学 | Device and method for detecting content of glutamic acid of tomato leaves by multi-angle in-situ spectrum |
| CN105158168A (en) * | 2015-04-29 | 2015-12-16 | 新疆农垦科学院 | Digital image technology-based cotton photosynthetically active radiation fraction determination method |
| CN104899464A (en) * | 2015-06-19 | 2015-09-09 | 中国地质大学（武汉） | Sampling learning machine remote sensing quantitative inversion method applicable to noise condition |
| CN104899464B (en) * | 2015-06-19 | 2017-12-29 | 中国地质大学（武汉） | A kind of sampling study machine remote sensing quantitative inversion method under adaptation noise conditions |
| CN104899394A (en) * | 2015-06-23 | 2015-09-09 | 中国科学院遥感与数字地球研究所 | Practical leaf area index remote sensing inversion method |
| CN104899394B (en) * | 2015-06-23 | 2017-10-10 | 中国科学院遥感与数字地球研究所 | A kind of practical leaf area index remote sensing inversion method |
| CN107014763A (en) * | 2017-04-07 | 2017-08-04 | 山东理工大学 | Chlorophyll remote-sensing inversion device and method |
| CN108416297B (en) * | 2018-03-09 | 2018-11-27 | 河北省科学院地理科学研究所 | A kind of vegetation information method for quickly identifying based on chlorophyll fluorescence |
| CN108416297A (en) * | 2018-03-09 | 2018-08-17 | 河北省科学院地理科学研究所 | A kind of vegetation information method for quickly identifying based on chlorophyll fluorescence |
| CN108693154A (en) * | 2018-04-25 | 2018-10-23 | 南京大学 | A kind of method of multi-angle observation exact inversion vegetation negative and positive leaf sunlight-induced chlorophyll fluorescence |
| CN109063660B (en) * | 2018-08-09 | 2021-09-21 | 成都天地量子科技有限公司 | Crop identification method based on multispectral satellite image |
| CN109063660A (en) * | 2018-08-09 | 2018-12-21 | 成都天地量子科技有限公司 | A kind of crop recognition methods based on multispectral satellite image |
| CN109521437A (en) * | 2018-12-05 | 2019-03-26 | 武汉大学 | Multiplex physicotherapy laser radar wavelength selection method towards the detection of vegetation biochemical parameter |
| CN109521437B (en) * | 2018-12-05 | 2023-07-21 | 武汉大学 | Multispectral laser radar wavelength selection method for vegetation biochemical parameter detection |
| CN109614763A (en) * | 2019-01-30 | 2019-04-12 | 北京师范大学 | A kind of area crops yield estimation method correcting crop modeling based on multi-source information substep |
| 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 |
| CN111552004A (en) * | 2020-04-24 | 2020-08-18 | 中国地质科学院矿产资源研究所 | Method and system for extracting angle abnormal information of remote sensing data |
| CN111552004B (en) * | 2020-04-24 | 2023-04-18 | 中国地质科学院矿产资源研究所 | Remote sensing data angle anomaly information extraction method and system |
| CN113192117A (en) * | 2021-02-08 | 2021-07-30 | 南京农业大学 | Method for improving estimation of leaf area index of early growth stage of wheat based on Sentinel-2 satellite image red edge band |
| CN113192117B (en) * | 2021-02-08 | 2022-03-22 | 南京农业大学 | Method for improving estimation of leaf area index of early growth stage of wheat based on Sentinel-2 satellite image red edge band |
| CN113345004A (en) * | 2021-08-05 | 2021-09-03 | 航天宏图信息技术股份有限公司 | Estimation method and device for vegetation leaf area index |
| CN114711010A (en) * | 2022-06-09 | 2022-07-08 | 苏州农业职业技术学院 | Water-soil fertilizer management method, system and medium in Chinese rose cultivation |
| CN116071644A (en) * | 2022-12-20 | 2023-05-05 | 中化现代农业有限公司 | Method, device, equipment and storage medium for inversion of sun leaf area index data |
| CN116071644B (en) * | 2022-12-20 | 2023-08-08 | 中化现代农业有限公司 | Method, device, equipment and storage medium for inversion of sun leaf area index data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102313526A (en) | Method for obtaining leaf area index based on quantitative fusion and inversion of multi-angle and multi-spectral remote sensing data | |
| Jacob et al. | Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors | |
| Richter et al. | Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction | |
| Dial et al. | IKONOS satellite, imagery, and products | |
| Kuusk | Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model | |
| Schlerf et al. | Vegetation structure retrieval in beech and spruce forests using spectrodirectional satellite data | |
| CN112884672B (en) | Multi-frame unmanned aerial vehicle image relative radiation correction method based on contemporaneous satellite images | |
| Franch et al. | Analysis of directional effects on atmospheric correction | |
| CN104483663A (en) | Atmospheric correction method and system for hyperspectral remote sensing image | |
| Tagle Casapia | Study of radiometric variations in Unmanned Aerial Vehicle remote sensing imagery for vegetation mapping | |
| Kavvadias et al. | Precision Agriculture-Comparison and Evaluation of Innovative Very High Resolution (UAV) and LandSat Data. | |
| CN109543654A (en) | A kind of construction method for the modified vegetation index reflecting crop growth situation | |
| Villa et al. | Spectro-temporal reflectance surfaces: a new conceptual framework for the integration of remote-sensing data from multiple different sensors | |
| Pan et al. | Vicarious calibration correction of large FOV sensor using BRDF model based on UAV angular spectrum measurements | |
| Gu et al. | An intensity-independent stereo registration method of push-broom hyperspectral scanner and LiDAR on UAV platforms | |
| Krauss et al. | Fusing stereo and multispectral data from WorldView-2 for urban modeling | |
| Näsi et al. | Optimizing radiometric processing and feature extraction of drone based hyperspectral frame format imagery for estimation of yield quantity and quality of a grass sward | |
| Sola et al. | Inter-comparison of atmospheric correction methods on Sentinel-2 images applied to croplands | |
| Lee et al. | Analysis on the applicability of simulated image from SPOT 4 HRVIR image | |
| Gastellu-Etchegorry et al. | Correction of Directional Effects in Sentinel-2 and-3 Images with Sentinel-3 Time Series and Dart 3D Radiative Transfer Model | |
| Chávez et al. | Evaluation of multispectral unmanned aerial systems for irrigation management | |
| Pavlova | The application of remote sensing data for wheat yield prediction | |
| Chen et al. | High-resolution BRDF and albedo parameters inversion from sentinel-2 multispectral instrument data | |
| Ronchetti | Uav Surveys For Crop Monitoring And Management In Precision Agriculture | |
| Cihlar et al. | National Scale Forest Information Extraction from Coarse Resolution Satellite Data, Part 1: Data Processing and Mapping Land Cover Types |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20120111 |