CN116561709A - Method for directly inverting surface normalization vegetation index by atmospheric top reflectivity - Google Patents
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Abstract
The invention discloses a method for directly inverting an earth surface normalized vegetation index by apparent reflectivity, which utilizes a Ross-Li nuclear driving model to obtain the earth surface reflectivity of a target optical sensor so as to calculate the earth surface normalized vegetation index of the target optical sensor, and utilizes a 6S radiation transmission model to simulate the atmospheric layer top reflectivity of the target optical sensor under different preset aerosol optical thickness parameters, and then establishes a multiple linear regression model coefficient lookup table for converting the atmospheric layer top reflectivity into the earth surface normalized vegetation index so as to realize the direct inverse performance of the apparent reflectivity by real observation. Compared with the traditional method, the method is simpler and more convenient, the ground surface normalization vegetation index which can meet the follow-up application precision can be obtained without atmospheric correction, and meanwhile, the reliability of the inversion result is ensured. In addition, the method has strong expansibility, and can realize direct inversion of the surface normalized vegetation indexes of other optical satellite sensors.
Description
Technical Field
The invention belongs to the field of satellite remote sensing, and particularly relates to a method for directly inverting a surface normalization vegetation index by using an atmospheric top reflectivity. The method is suitable for directly inverting the normalized vegetation index of the earth surface by the atmospheric layer top reflectivity obtained through satellite observation.
Background
The vegetation index effectively describes the surface vegetation coverage, distribution and growth. The normalized vegetation index (Normalized Different Vegetation Index, NDVI) is widely used in vegetation monitoring research as a simple, sensitive vegetation index. The calculation formula of NDVI is:
wherein ρ is NIR ，ρ Red The atmospheric top reflectivities of near infrared and red bands, respectively, are observed by satellites.
The normalization characteristic of the NDVI can eliminate the influence of certain atmospheric conditions, and the initial NDVI is directly calculated by the calculation formula of the NDVI by utilizing the top reflectivity of the atmospheric layer which is not subjected to atmospheric correction. As the research on NDVI is continued, there are studies showing that NDVI before and after atmospheric correction is greatly different in different terrains and spaces. The NDVI value calculated by using the band reflectance without atmospheric correction is low overall and has serious deviation under complex terrain and atmospheric conditions. Therefore, it is necessary to consider the atmospheric influence in the process of calculating the NDVI, which also results in a single current method of obtaining the NDVI, that is, the NDVI is obtained by performing the band operation after performing the atmospheric correction on the atmospheric top reflectivity product.
The accuracy of the NDVI obtained by inversion of the band reflectivity after the atmospheric correction is interfered by atmospheric conditions, so that the accurate atmospheric correction is the key for calculating the NDVI by the traditional method. The current atmospheric correction algorithm mainly comprises: radiation transmission methods, relative correction methods, empirical regression modeling methods, composite modeling methods, and the like. The above methods have problems in terms of accuracy or computational efficiency. Taking the most widely used atmospheric radiation transmission model method as an example, the method has large calculated amount and more input parameters, and is easy to suffer from error accumulation in the calculation process, so that the correction effect is poor. Because the imaging process of the remote sensing image is complex and uncertain factors in the process are numerous, under the complex atmospheric condition, various atmospheric correction methods are difficult to achieve better effects. Meanwhile, the performance of the sensors is different, and the applicability of the unified algorithm to different sensors is also different. Therefore, a method for directly estimating the surface NDVI independent of the process of atmospheric correction of the remote sensing image is needed, so as to avoid the influence of the sensor observation geometry, the band setting, the atmospheric and the surface condition factors and obtain a reliable and effective NDVI inversion result.
Disclosure of Invention
Aiming at the technical problems existing in the estimation of the surface NDVI in the prior art, the invention provides a method for directly estimating the surface NDVI from the top reflectivity of the atmosphere layer obtained from satellite observation without atmospheric correction, and the traditional NDVI estimation flow is simplified.
The technical problems of the invention are mainly solved by the following technical proposal:
a method for directly inverting a surface normalized vegetation index from atmospheric top reflectivity, comprising the steps of:
step 1: inquiring the kernel function coefficients of the MODIS of the medium-resolution imaging spectrometer, wherein the kernel function coefficients of the MODIS comprise isotropic kernel function coefficients of the MODIS, bulk scattering kernel function coefficients of the MODIS and geometric optical kernel function coefficients of the MODIS;
step 2: converting the kernel function coefficients of the selected MODIS into kernel function coefficients of the target optical sensor;
step 3: obtaining the earth surface reflectivity of the target optical sensor at a preset observation angle and a preset wave band by using a Ross-Li nuclear driving model and a nuclear function coefficient simulation of the target optical sensor;
step 4: calculating the surface normalization vegetation index of the target optical sensor by using the surface reflectivities of the near infrared band and the red band of the target optical sensor obtained by the simulation in the step 3;
step 5: simulating the top atmospheric layer reflectivity of the target optical sensor under different preset observation angle parameters and different preset aerosol optical thickness parameters by using a 6S radiation transmission model, wherein the top atmospheric layer reflectivity comprises the combination of the top atmospheric layer reflectivity of different preset wave bands and the top atmospheric layer reflectivity of different preset wave bands;
step 6: establishing a multiple linear regression model between the earth surface normalized vegetation index obtained in the step 4 and the simulated atmospheric top reflectivity in the step 5, fitting the multiple linear regression model based on the atmospheric top reflectivities corresponding to different aerosol optical thicknesses, determining coefficients of the multiple linear regression model, establishing a multiple linear regression model coefficient lookup table for converting the atmospheric top reflectivities into the earth surface normalized vegetation index under different observation angles,
normalizing vegetation index for the target optical sensor surface obtained in step 4, < >>And->For multiple linear regression model coefficients, +.>For the top atmospheric reflectivity obtained in step 5, n is the index of the top atmospheric reflectivity, θ s 、θ v And->The AOD is the optical thickness of the aerosol;
step 7: solving the target optical sensor pixel at the real observation angle (delta) s ,δ v Sigma) the true surface normalized vegetation index,
wherein NDVI (delta) s ,δ v Sigma) is the actual observation angle (delta) of the target optical sensor s ,δ v True surface normalized vegetation index, delta, sigma) at sigma s Is the real zenith angle delta of the sun v For a true observation zenith angle, σ is the relative azimuth of the true satellite,for the top reflectivity of the atmosphere layer in real observation, the top reflectivity of the real observation atmosphere layer comprises the combination of the top reflectivity of the real observation atmosphere layer with the same preset wave band as the step 5 and the top reflectivity of the real observation atmosphere layer with the same preset wave band as the step 5; searching for a multiple linear regression model coefficient corresponding to the real observation angle in the multiple linear regression model coefficient tableAnd->
In step 2, the kernel function coefficient conversion of the MODIS to the kernel function coefficient relation of the target optical sensor is as follows:
wherein F is iso,i For the isotropic kernel function coefficient corresponding to the i band of the target optical sensor, F vol,i For the volume scattering kernel function coefficient corresponding to the i band of the target optical sensor, F geo,i For the geometrical optical kernel function coefficient corresponding to the target optical sensor in the i band, f iso,j For the corresponding isotropic kernel function coefficient of MODIS in the j band, f vol,j For the volume scattering kernel function coefficient corresponding to MODIS in the j band, f geo,j For the geometric optical kernel function coefficient corresponding to MODIS in the J band, i and J are the band indexes of the target optical sensor and the MODIS, respectively, and J represents the target lightThe number of wave bands of MODIS required by wave band conversion of the optical sensor, a j 、b j 、c j Conversion coefficients of isotropic kernel function coefficients, conversion coefficients of bulk scattering kernel function coefficients, and conversion coefficients of geometric optical kernel function coefficients of the j-th band of the MODIS to the target optical sensor, respectively.
And (2) the value of i in the step (2) is 1-6, and the i corresponds to the red wave band, the near infrared wave band, the green wave band, the blue wave band, the first short wave infrared wave band and the second short wave infrared wave band in sequence.
In step 6 as described above:
atmospheric top reflectivity +.>
Atmospheric top reflectance for near infrared band +.> Atmospheric top reflectivity for green band +.>
Atmospheric layer top reflectivity for blue band +.>
Atmospheric top reflectivity for the first short-wave infrared band +.> Atmospheric top reflectivity for the second short-wave infrared band +.> Is->
Is->
Is->
Is-> Is that
Is->
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the traditional method, the method omits the steps of remote sensing image atmospheric correction and the like, and has the characteristics of simplicity and convenience when the NDVI meeting the precision requirement is obtained.
2. The calculation method provided by the algorithm has better generalization, and can be used for directly estimating the NDVI according to the characteristics of any optical sensor (such as Landsat-5, landsat-7, sentien-2 and the like).
Drawings
Fig. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be further described in detail below in conjunction with the following examples, for the purpose of facilitating understanding and practicing the present invention by those of ordinary skill in the art, it being understood that the examples described herein are for the purpose of illustration and explanation only and are not intended to limit the invention.
Example 1:
for the purpose of making the technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and examples.
A method for directly inverting a surface normalized vegetation index from atmospheric top reflectivity, comprising the steps of:
step 1, nuclear function coefficients of a resolution imaging spectrometer (Moderate Resolution Imaging Spectroradiometer, MODIS) in query, wherein the nuclear function coefficients comprise isotropic nuclear function coefficients, bulk scattering nuclear function coefficients and geometric optical nuclear function coefficients. In this embodiment, the surface bidirectional reflectance parameter product MCD43A1 of the MODIS is selected, and the surface bidirectional reflectance parameter product MCD43A1 is mainly used to simulate the surface reflectances of different observation angles through a Ross-Li core driving model, where the basic form of the Ross-Li core driving model is as follows:
wherein the method comprises the steps ofFor the simulated MODIS surface reflectance, θ s 、θ v And->Solar Zenith Angle (SZA), observation zenith angle (VZA), and Relative Azimuth Angle (RAA) of sun and satellite, respectively. />And->Bulk scattering kernel function and geometrical optics kernel function in Ross-Li kernel driven model, f iso Is the isotropy kernel function coefficient of MODIS, f vol Is the volume scattering kernel function coefficient of MODIS, f geo For the geometric optical kernel function coefficients of MODIS, different bands possess different kernel function coefficients and are provided by MCD43A 1.
Step 2, converting the kernel function coefficient of the selected MODIS into the kernel function coefficient of the target optical sensor, wherein Landsat-8 is selected as the target optical sensor in the embodiment. Because the kernel function coefficients of different wave bands are different, the kernel function coefficients of the MODIS are converted into the kernel function coefficients of Landsat-8 by adopting a weighted summation mode:
wherein F is iso,i 、F vol,i And F geo,i Is the kernel function coefficient of the transformed Landsat-8, which has the meaning as in step 1Kernel function coefficients are similar, F iso,i For the corresponding isotropy kernel function coefficient of Landsat-8 in the i band, F vol,i For the corresponding volume scattering kernel function coefficient of Landsat-8 in the i band, F geo,i Is the geometrical optical kernel function coefficient corresponding to Landsat-8 in the i band, f iso,j For the corresponding isotropic kernel function coefficient of MODIS in the j band, f vol,j For the volume scattering kernel function coefficient corresponding to MODIS in the j band, f geo,j For the geometric optical kernel function coefficient corresponding to the MODIS in the j band, i and j are the band indexes of Landsat-8 and MODIS respectively, the target optical sensor needs to correspond to a plurality of MODIS bands for one band conversion, in the embodiment, one band (i) of Landsat-8 needs to be regressed by 7 bands (j=1, 2 … … 7) of MODIS, a j 、b j 、c j Conversion coefficients of isotropic kernel function coefficients, conversion coefficients of bulk scattering kernel function coefficients, and conversion coefficients of geometric optical kernel function coefficients of the j-th band of MODIS to Landsat-8, respectively. In this embodiment, i has a value of (i=1, 2,3 …, 6) and corresponds to red band, near infrared band, green band, blue band, first short-wave infrared band (wavelength range: 1.56 μm-1.66 μm), and second short-wave infrared band (wavelength range: 2.1 μm-2.3 μm) in order.
And 3, substituting the preset observation angle parameter and the kernel function coefficient of the Landsat-8 obtained by calculation in the step 2 into a Ross-Li kernel driving model, and simulating the obtained ground surface reflectivity of the Landsat-8 at the preset observation angle and the preset wave band in the formula (3). The preset observation angle parameters include Solar Zenith Angle (SZA), observation zenith angle (VZA), and Relative Azimuth Angle (RAA) of sun and satellite. Step 2 yields the kernel function coefficients of Landsat-8 (i.e., F in equation 2 iso,i 、F vol,i And F geo,i ) In addition, according to the preset observation angle parameters, the method can solveAnd->At a known F iso,i 、F vol,i 、F geo,i 、/>And->And then, the ground surface reflectivity of Landsat-8 corresponding to different preset observation angles and different preset wave bands can be calculated through a formula (3).
Wherein, the liquid crystal display device comprises a liquid crystal display device,the ith band of Landsat-8, modeled as using Ross-Li core driven model, is at the zenith solar angle θ s Observing zenith angle theta v And the relative azimuth angle of the sun and the satellite +.>The reflectivity of the subsurface, i is step 2, which is the index of the preset wave band of Landsat-8, theta s 、θ v And->The preset values are shown in table 1.
Table 1 table for explaining the settings of various parameters for simulating the surface reflectance and the top reflectance of the atmosphere
Step 4, calculating a surface normalized vegetation index according to the surface reflectivity of Landsat-8 red wave band and the surface reflectivity of near infrared wave band obtained by simulation in the step 3 through a formula (4)
Wherein, the liquid crystal display device comprises a liquid crystal display device,a normalized vegetation index calculated by simulating surface reflectance, whereinAnd->And (3) respectively simulating the ground surface reflectivities of the Landsat-8 near infrared band and the red band obtained in the step (3).
Step 5, simulating Landsat-8 different wave bands in different aerosol optical thicknesses (Aerosol Optical Depth, AOD) and solar zenith angles theta by using a 6S radiation transmission model at different preset observation angle parameters and different preset aerosol optical thickness parameters (see Table 1) s Observing zenith angle theta v And the relative azimuth angle of the sun and the satelliteThe lower corresponding atmospheric layer top reflectivity includes combinations of atmospheric layer top reflectivities of different preset wavelength bands and atmospheric layer top reflectivities of different preset wavelength bands (see table 2). The 6S radiation transmission model is mainly used for simulating the top reflectivity of the atmosphere by the surface reflectivity obtained in the step 3 and simulating the top reflectivity of the atmosphere in different wave bands under the condition of different aerosol optical thickness AOD.
Table 2 table of simulated combinations of atmospheric layer top reflectivities of different preset bands and atmospheric layer top reflectivities of different preset bands
In the table 2 of the description of the present invention,
is the atmospheric top reflectivity at a preset band, wherein:atmospheric top reflectivity +.> Atmospheric top reflectance for near infrared band +.> Atmospheric top reflectivity for green band +.> Atmospheric layer top reflectance for blue band Atmospheric top reflectivity for the first short wave infrared band
Atmospheric top reflectivity for the second short wave infrared band
The combinations of the top reflectivity of the atmosphere at each preset wave band are shown in table 2.
Step 6, establishingMultiple linear regression model between the top reflectance of the atmosphere and AOD based on different aerosol optical thickness +.>Fitting to obtain different observation angles (i.e. the zenith angles theta of each sun s Observing zenith angle theta v And the relative azimuth angle of the sun and the satellite +.>Combining) coefficients of the corresponding multiple linear regression models, and establishing a multiple linear regression model coefficient lookup table for converting the top reflectivity of the atmosphere into the surface normalized vegetation index under different observation angles;
wherein, the liquid crystal display device comprises a liquid crystal display device,and->(wherein n=1, 2,3 … … 12) is a multiple linear regression model coefficient, ++>The atmospheric top reflectivity is obtained for step 5. n is an index of the atmospheric top reflectivity, corresponding to table 2. After the multiple linear regression model coefficients are determined, different preset observation angles are established>I.e. a group of solar zenith angles theta s Observing zenith angle theta v And the relative azimuth angle of the sun and the satellite +.>And converting the top reflectivity of the lower atmosphere into a multiple linear regression model coefficient table of the surface normalized vegetation index.
Step 7, setting a real observation angle (delta) of a certain real Landsat-8 pixel imaging s ,δ v Sigma), i.e. a set of real solar zenith angles delta s Real observation zenith angle delta v And solving a true earth surface normalized vegetation index by a relative azimuth angle sigma of the true sun and the satellite. Calculating the true observed atmosphere top reflectivity of the pixel, which is recorded asThe real observed atmosphere top reflectivity of the pixel comprises the combination of the real observed atmosphere top reflectivity of the same preset wave band as the step 5 and the real observed atmosphere top reflectivity of the same preset wave band as the step 5, and then the real observed angle (delta) is searched in a multiple linear regression model coefficient table obtained in the step 6 s ,δ v Sigma) corresponding preset observation angle ++in the lookup table>The coefficients of the lower multiple linear regression model +.>And->The true surface normalized vegetation index NDVI (delta) of the pixel can be solved through the formula (6) s ,δ v ,σ)。
In the look-up table process, the true observation angle (delta s ,δ v Sigma) corresponds to a preset observation angleThere are generally two methods: method 1, using linear interpolation, uses the real observation angle (delta s ,δ v Sigma) to a preset observation angleCarrying out coefficient encryption; and 2, looking up a table according to the interval, namely, taking the value of the endpoint when the table falls in the interval.
An electronic device, comprising:
a memory for storing one or more programs;
a processor; the steps of steps 1-7 are implemented when the one or more programs are executed by the processor.
The present invention is not limited to the above-described embodiments, which are merely preferred embodiments of the present invention and are not intended to limit the concept of the present invention, and the embodiments of the above-described embodiments may be further combined or replaced, and various changes and modifications of the technical solution of the present invention will be within the scope of the present invention by those skilled in the art.
Claims (4)
1. A method for directly inverting a surface normalized vegetation index from an atmospheric top reflectivity, comprising the steps of:
step 1: inquiring the kernel function coefficients of the MODIS of the medium-resolution imaging spectrometer, wherein the kernel function coefficients of the MODIS comprise isotropic kernel function coefficients of the MODIS, bulk scattering kernel function coefficients of the MODIS and geometric optical kernel function coefficients of the MODIS;
step 2: converting the kernel function coefficients of the selected MODIS into kernel function coefficients of the target optical sensor;
step 3: obtaining the earth surface reflectivity of the target optical sensor at a preset observation angle and a preset wave band by using a Ross-Li nuclear driving model and a nuclear function coefficient simulation of the target optical sensor;
step 4: calculating the surface normalization vegetation index of the target optical sensor by using the surface reflectivities of the near infrared band and the red band of the target optical sensor obtained by the simulation in the step 3;
step 5: simulating the top atmospheric layer reflectivity of the target optical sensor under different preset observation angle parameters and different preset aerosol optical thickness parameters by using a 6S radiation transmission model, wherein the top atmospheric layer reflectivity comprises the combination of the top atmospheric layer reflectivity of different preset wave bands and the top atmospheric layer reflectivity of different preset wave bands;
step 6: establishing a multiple linear regression model between the earth surface normalized vegetation index obtained in the step 4 and the simulated atmospheric top reflectivity in the step 5, fitting the multiple linear regression model based on the atmospheric top reflectivities corresponding to different aerosol optical thicknesses, determining coefficients of the multiple linear regression model, establishing a multiple linear regression model coefficient lookup table for converting the atmospheric top reflectivities into the earth surface normalized vegetation index under different observation angles,
normalizing vegetation index for the target optical sensor surface obtained in step 4, < >>Andfor multiple linear regression model coefficients, +.>For the top atmospheric reflectivity obtained in step 5, n is the index of the top atmospheric reflectivity, θ s 、θ v And->The AOD is the optical thickness of the aerosol;
step 7: solving the target optical sensor pixel at the real observation angle (delta) s ,δ v Sigma) the true surface normalized vegetation index,
wherein NDVI (delta) s ,δ v Sigma) is the actual observation angle (delta) of the target optical sensor s ,δ v True surface normalized vegetation index, delta, sigma) at sigma s Is the real zenith angle delta of the sun v For a true observation zenith angle, σ is the relative azimuth of the true satellite,for the top reflectivity of the atmosphere layer in real observation, the top reflectivity of the real observation atmosphere layer comprises the combination of the top reflectivity of the real observation atmosphere layer with the same preset wave band as the step 5 and the top reflectivity of the real observation atmosphere layer with the same preset wave band as the step 5; searching for a multiple linear regression model coefficient corresponding to the real observation angle in the multiple linear regression model coefficient tableAnd->
2. The method for directly inverting the surface normalized vegetation index from the top reflectivity of the atmosphere according to claim 1, wherein in the step 2, the conversion of the MODIS kernel function coefficient into the kernel function coefficient relation of the target optical sensor is:
wherein F is iso,i For the isotropic kernel function coefficient corresponding to the i band of the target optical sensor, F vol,i For the volume scattering kernel function coefficient corresponding to the i band of the target optical sensor, F geo,i For the geometrical optical kernel function coefficient corresponding to the target optical sensor in the i band, f iso,j For the corresponding isotropic kernel function coefficient of MODIS in the j band, f vol,j For the volume scattering kernel function coefficient corresponding to MODIS in the j band, f geo,j For the geometric optical kernel function coefficient corresponding to MODIS in the J band, i and J are the band indexes of the target optical sensor and the MODIS, J represents the number of the bands of the MODIS required by the conversion of one band of the target optical sensor, and a j 、b j 、c j Conversion coefficients of isotropic kernel function coefficients, conversion coefficients of bulk scattering kernel function coefficients, and conversion coefficients of geometric optical kernel function coefficients of the j-th band of the MODIS to the target optical sensor, respectively.
3. The method for directly inverting the surface normalized vegetation index by the atmospheric top reflectivity according to claim 2, wherein the value of i in the step 2 is 1-6, and the values correspond to a red band, a near infrared band, a green band, a blue band, a first short wave infrared band and a second short wave infrared band in sequence.
4. A method for directly inverting a surface normalized vegetation index from atmospheric top reflectivity according to claim 3, wherein in step 6:
atmospheric top reflectivity +.>
Atmospheric top reflectance for near infrared band +.> Atmospheric top reflectivity for green band +.> Atmospheric layer top reflectivity for blue band +.> Atmospheric top reflectivity for the first short-wave infrared band +.> Atmospheric top reflectivity for the second short-wave infrared band +.> Is->
Is->
Is->
Is->
Is->
Is->
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106407656A (en) * | 2016-08-29 | 2017-02-15 | 中国科学院遥感与数字地球研究所 | Retrieval method for aerosol optical thickness based on high resolution satellite image data |
| CN109974665A (en) * | 2019-04-04 | 2019-07-05 | 东北师范大学 | It is a kind of for the aerosol remote sensing inversion method and system that lack short-wave infrared data |
| CN113066057A (en) * | 2021-03-17 | 2021-07-02 | 云南电网有限责任公司电力科学研究院 | Aerosol optical thickness monitoring method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106407656A (en) * | 2016-08-29 | 2017-02-15 | 中国科学院遥感与数字地球研究所 | Retrieval method for aerosol optical thickness based on high resolution satellite image data |
| CN109974665A (en) * | 2019-04-04 | 2019-07-05 | 东北师范大学 | It is a kind of for the aerosol remote sensing inversion method and system that lack short-wave infrared data |
| CN113066057A (en) * | 2021-03-17 | 2021-07-02 | 云南电网有限责任公司电力科学研究院 | Aerosol optical thickness monitoring method |
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