CN113672847A - Snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data - Google Patents

Snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data Download PDF

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CN113672847A
CN113672847A CN202110946364.9A CN202110946364A CN113672847A CN 113672847 A CN113672847 A CN 113672847A CN 202110946364 A CN202110946364 A CN 202110946364A CN 113672847 A CN113672847 A CN 113672847A
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叶李灶
肖鹏峰
张学良
冯学智
陈冬花
李虎
刘玉峰
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Chuzhou University
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Abstract

The invention discloses a snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data, which comprises the following steps of: step 1: collecting remote sensing data, and preprocessing to obtain single-angle two-way reflectivity of the snow surface in different wave bands; step 2: eliminating the topographic effect of the two-way reflectivity calculated and obtained according to the step 1 by utilizing a C correction model; and step 3: according to the calculation result of the step 2, constructing a snow-accumulated white space albedo inversion model based on a progressive radiation transmission model; and 4, step 4: constructing a multi-angle two-way reflectivity inversion model of the accumulated snow according to the result of the step 3; and 5: and (3) inverting the snow accumulation white-space albedo by using the snow accumulation multi-angle two-way reflectivity inversion model, and then inputting the relevant angle value to invert the snow accumulation multi-angle two-way reflectivity. The method can be used for continuously carrying out multi-angle bidirectional reflectivity inversion on the accumulated snow in the large-scale mountainous area, does not need multi-day data, and has the advantages of high efficiency and small influence by angle effect.

Description

Snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data
Technical Field
The invention relates to a snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data.
Background
The snow reflectivity reflects inherent physicochemical characteristics of snow, is the basis for accurately acquiring the snow reflectivity, is the inversion of the snow albedo, the snow equivalent optical particle size and the snow pollutant concentration, and has important functions and significance in various fields such as snow resources, environment, regional climate change and the like. Actually measured multi-angle reflectivity of the accumulated snow is the earliest and most direct means for obtaining the reflectivity of the accumulated snow, verification data can be provided for research and development of an accumulated snow bidirectional reflectivity model, and optimal waveband design is provided for sensor design of satellite remote sensing. However, the cost for measuring the snow reflectivity is high, the cost is high, and particularly in a severe cold mountain area with rare human tracks, the method for obtaining the snow reflectivity in a large range through actual measurement is difficult to realize.
Remote sensing data such as medium and high resolution satellite images acquired by the optical remote sensing satellite can be applied to continuously acquiring the snow reflectivity in a large-range and alpine mountain area. However, the optical remote sensing satellite is used for acquiring the snow reflectivity information of the regional scale, and a certain topographic effect exists; meanwhile, the anisotropy of the accumulated snow is remarkable, and the ground reflected radiation received by the sensors at different zenith angles is inconsistent; particularly in the accumulation period or the ablation period of the rapid change of the snow state, the rapid change of the snow state causes the obvious change difference of the snow BRDF (bidirectional reflectance distribution function), a conventional semi-empirical BRDF model needs to fit model parameters by using the remote sensing satellite data of a single angle for multiple days, and the rapid change of the snow state causes the fitting result to have larger errors.
Physical models such as DISORT and sub-continuous medium theoretical models need more input parameters, the required operation time is longer, and much inconvenience is brought to practical application. The inversion process of The existing reflectivity product is rarely directed to snow, and The prior ART, such as "The semi-analytical snow reflectance estimated and its application to MODIS data" (Remote Sensing of environmental, 2007, 111(2-3): 228-. Therefore, the existing few products aiming at the snow reflectivity have imperfect production process, and lack the weakening treatment of the terrain effect or the inversion process of the bidirectional reflectivity. The prior art needs to develop a complete inversion method of multi-angle bidirectional reflectivity of equivalent flat ground in mountainous areas, and also needs to overcome the defects that the prior art needs to collect multi-day single-angle remote sensing satellite data, the inversion speed is slow, and the continuity is poor.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data, which is used for solving the problems that a method capable of continuously and multi-angle two-way reflectivity inversion of snow in a large-range mountain area is lacked in the prior art, the existing method for acquiring the snow two-way reflectivity needs multi-day single-angle remote sensing satellite data or field measurement parameters, the data acquisition time is long, the efficiency is low, and the influence of an angle effect is large.
The snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data comprises the following steps:
step 1: collecting remote sensing data, and preprocessing to obtain single-angle two-way reflectivity of the snow surface in different wave bands;
step 2: eliminating the topographic effect of the two-way reflectivity calculated and obtained according to the step 1 by utilizing a C correction model;
and step 3: constructing a snow-accumulated blank space albedo inversion model based on a progressive radiation transmission model;
and 4, step 4: constructing an accumulated snow multi-angle bidirectional reflectivity inversion model:
Figure BDA0003216825650000021
wherein R (theta)s，θv，
Figure BDA0003216825650000022
) Is the bi-directional reflectance of snow, A is the white-space albedo, R0(θs，θv，
Figure BDA0003216825650000023
) Is the reflectance, K, of a snow medium assumed to be weakly semi-infinitely absorbing0(θv) As escape function of the exit direction, K0(θs) An escape function that is the direction of incidence;
and 5: and (3) firstly inverting the snow accumulation white-space albedo according to the result of the step (2) by using the snow accumulation multi-angle two-way reflectivity inversion model constructed before, and then inputting different observation zenith angles, sun zenith angles and relative azimuth angles to invert the snow accumulation multi-angle two-way reflectivity.
Preferably, in step 3, the progressive radiation transmission model is based on the assumption that the snow layer is a semi-infinite weak absorption medium, and the model expression is as follows:
Figure BDA0003216825650000024
wherein R (theta)s，θv，
Figure BDA0003216825650000025
) Is the bi-directional reflectivity of snow, γ is the absorption coefficient, and the expression is: gamma is 4 pi (chi + M)/lambda, where chi is the imaginary part of complex refractive index of ice, lambda is wavelength, M is the concentration parameter of pollutant in snow, and the parameter a related to particle sizeefApproximately equal to 13d, where d is the equivalent optical diameter of the snow;
escape function K of the emission direction0(θv) And escape function K of incident direction0(θs) Tong (Chinese character of 'tong')Is expressed by the following formula:
Figure BDA0003216825650000031
Figure BDA0003216825650000032
R0(θs，θv，
Figure BDA0003216825650000033
) The reflectivity of the snow medium assumed to be semi-infinite weak absorption is calculated as follows:
Figure BDA0003216825650000034
p(Ω)＝11.1e(-0.087Ω)+1.1e(-0.014Ω)
Figure BDA0003216825650000035
wherein cos θsAnd cos θvThe cosine values of the sun zenith angle and the observed zenith angle,
Figure BDA0003216825650000036
the cosine value of the relative azimuth angle is obtained, in the calculation of p (omega), the unit of omega is degree, and the value range is 0-180 degrees.
Preferably, in step 3, the white space albedo a is inverted by the following model:
Figure BDA0003216825650000037
wherein R (theta)s，θv，
Figure BDA0003216825650000038
)rsThe single-waveband reflectivity is obtained after single-angle remote sensing data processing.
Preferably, in the step 4, the white-space albedo a can be further calculated by the following formula:
Figure BDA0003216825650000039
the arithmetic expression is combined with a progressive radiation transmission model and applied to a model expression of the snow reflectivity to obtain a snow multi-angle bidirectional reflectivity inversion model, and the model eliminates two parameters of equivalent optical snow particle size and pollutant concentration and simplifies the parameters into a parameter of snow accumulation white-space albedo A.
Preferably, in step 4, a vertical direction reflectivity inversion model can be obtained:
Figure BDA00032168256500000310
preferably, in step 2, a linear relationship exists between the reflectivity of each band of the remote sensing image of the mountain terrain and the cosine value of the solar incident angle, and a semi-empirical coefficient C is provided to correct the overcorrection problem existing in the cosine correction, where the C correction model is as follows:
fH＝a+bcos(θ1)
fT＝a+bcos(θv)
Figure BDA0003216825650000041
Figure BDA0003216825650000042
wherein c is a/b, fHIs the reflectivity of the horizontal ground surface, fTIs the reflectivity of an inclined ground surface, θvIs the observed zenith angle, theta, of the inclined earth surface1Is the observed zenith angle under horizontal conditions.
Preferably, in the step 5, the observation zenith angle has a value range of 0 to 90 °, the sun zenith angle has a value range of 0 to 90 °, and the relative azimuth angle has a value range of 0 to 360 °.
Preferably, the method further comprises step 6: and carrying out comparative analysis and evaluation on the inversion result by utilizing the measured data.
The invention has the following advantages: the method utilizes a progressive radiation transmission model to treat snow particles as irregular particles and considers the influence of pollutants in the snow on the optical characteristics of the snow particles, so that the inversion result is reliable, and considers the relation between the white space albedo and absorption coefficients (related to the concentration of the pollutants in the snow) and related parameters of particle size, so that the inversion of the multi-angle two-way reflectivity of the accumulated snow is realized by utilizing the white space albedo. The inversion process does not need remote sensing data of more days or a plurality of angles, does not need to collect accumulated snow samples in an area on site, mainly depends on the remote sensing data in a short time, the angles can be input as required in the inversion process, the influence of angle effect on inversion is avoided, the inversion efficiency is high, inversion can be continuously carried out, the C correction model is utilized to effectively eliminate the advantage of terrain effect, and the defects of the prior art are effectively overcome.
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FIG. 1 is a flow chart of a snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data.
FIG. 2 is a comparative analysis chart of inversion results of the two-way reflectivity on the main plane and the vertical main plane and the change of the actually measured data along with the observed zenith angle after the invention is adopted.
FIG. 3 is a graph showing the variation of the reflectivity with the observed zenith angle for the principal plane of the sun (relative azimuth angles of 0 and 180) and the principal plane of the sun perpendicular to the principal plane of the sun (relative azimuth angles of 90 and 270) for a zenith angle of 0-70 degrees after the present invention is used. On the main plane of the sun, the positive observation zenith angle is the forward direction, and the negative observation zenith angle is the backward direction. The positive and negative of the observed zenith angle perpendicular to the main plane of the sun indicate different relative azimuth angles, positive values of the relative azimuth angle are 90 degrees, and negative values of the relative azimuth angle are 270 degrees.
Detailed Description
The following detailed description of the embodiments of the present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
As shown in FIGS. 1-3, the invention provides a multi-angle bi-directional reflectivity inversion method of snow accumulation based on satellite remote sensing data, which comprises the following steps:
step 1: for given middle-high resolution satellite data (Sentinel-2A multispectral image L1C level data), preprocessing (such as atmospheric correction processing) is carried out to obtain single-angle dichroic reflectivity of the snow surface in different wave bands under the assumption of Lambert.
Step 2: and (3) eliminating the topographic effect of the dichroic reflectivity obtained by the calculation in the step 1 by using a C correction model.
The C correction model is a terrain radiation correction model which is relatively widely applied at present. A semi-empirical coefficient C is mainly proposed to correct the overcorrection problem existing in cosine correction. A linear relation exists between the reflectivity (or radiation value) of each wave band of the remote sensing image for analyzing the mountainous terrain and the cosine value of the solar incident angle:
fH＝a+bcos(θ1) (1)
fT＝a+bcos(θv) (2)
Figure BDA0003216825650000051
Figure BDA0003216825650000052
wherein c is a/b, fHIs the reflectivity of the horizontal ground surface, fTIs the reflectivity of an inclined ground surface, θvIs the observed zenith angle, theta, of the inclined earth surface1Is the observed zenith angle under the horizontal condition, and a and b are the empirical coefficients of the corresponding wave bands.
The C correction model is combined with Digital Elevation Model (DEM) data, so that the reflectivity or radiance of all pixels calculated in the step 1 can be corrected to a certain reference plane (usually a horizontal plane), and the change of the radiance of the pixels caused by the undulating terrain is eliminated.
And step 3: and constructing a snow-accumulated blank-space albedo inversion model based on the progressive radiation transmission model.
The principle of constructing the model is as follows: the progressive radiation transfer (ART) model is based on the assumption that the snow layer is a semi-infinite weak absorbing medium. The single scattering characteristic of the snow particles is calculated by a geometrical optics method, and an approximate solution is obtained by a progressive radiation transmission theory. The ART model has two parameters, namely the size of the particle diameter of the snow and the concentration of pollutants in the snow, and the expression of the model is as follows:
Figure BDA0003216825650000061
wherein R (theta)s，θv，
Figure BDA0003216825650000062
) Is the bi-directional reflectivity of snow, γ is the absorption coefficient, and the expression is: γ is 4 π (χ + M)/λ, where χ is the imaginary component of the complex refractive index of ice, λ is the wavelength, and M is a contaminant concentration parameter in the snow. The absorption coefficient can be represented by a function in direct proportion to the imaginary part of the complex refractive index of ice under the condition that the accumulated snow is not polluted by the outside, and certain errors can be brought when different wavelengths are different; parameter a related to particle sizeefApproximately equal to 13d, where d is the equivalent optical diameter of the snow, another parameter of the ART model, which can be determined by the factAnd the measured near infrared band reflectivity is obtained by inversion or direct measurement.
Escape function K of the emission direction0(θv) And escape function K of incident direction0(θs) Expressed by the following equation:
Figure BDA0003216825650000063
Figure BDA0003216825650000064
R0(θs，θv，
Figure BDA0003216825650000065
) The reflectivity of the snow medium assumed to be semi-infinite weak absorption is calculated as follows:
Figure BDA0003216825650000066
p(Ω)＝11.1e(-0.087Ω)+1.1e(-0.014Ω) (9)
Figure BDA0003216825650000067
wherein cos θsAnd cos θvThe cosine values of the sun zenith angle and the observed zenith angle,
Figure BDA0003216825650000068
the cosine value of the relative azimuth angle is obtained, in the calculation of p (omega), the unit of omega is degree, and the value range is 0-180 degrees.
It can be known from the above that if the bidirectional reflectivity is to be inverted through the remote sensing data, besides the solar zenith angle, the observation zenith angle and the relative azimuth angle which are easily obtained in the remote sensing data, the parameters related to the concentration of pollutants in snow and the particle size are also needed, the latter two parameters need to carry out experimental measurement on the actual snow sample in the area, compared with the parameters which can be directly obtained from the remote sensing data, the two parameters need to collect the snow on site, so that the two parameters are difficult to obtain, and after the two parameters are obtained, the two parameters are substituted into the formula (5), so that the bidirectional reflectivity of the snow in each direction can be obtained through inversion.
According to the prior art, the white-space albedo a is inverted by the following model:
Figure BDA0003216825650000069
wherein R (theta)s，θv，
Figure BDA00032168256500000610
)rsThe single-wave-band reflectivity is obtained after single-angle remote sensing data are processed through the previous steps. Therefore, the white space albedo A can be obtained by inversion based on the acquired remote sensing data. The white-air albedo A is a key parameter input in the inversion of the two-way reflectivity, so that the equivalent optical particle size and the pollutant concentration are not required to be input in the inversion of the two-way reflectivity of the snow. Therefore, white-space albedo of a blue wave band, a near infrared wave band and a short wave infrared wave band of Sentinel-2A can be obtained through inversion by utilizing the data obtained by the processing of the previous step 2.
And 4, step 4: and constructing an inversion model for inverting the snow accumulation white-space albedo and then inverting the snow accumulation multi-angle two-way reflectivity.
According to the prior art, the white-to-empty albedo a can also be calculated by:
Figure BDA0003216825650000071
substituting the formulas (11) and (12) into the formula (5), two parameters of equivalent optical snow particle size and pollutant concentration can be eliminated, the two parameters are simplified into a parameter of snow accumulation white-air albedo A, and A can be obtained by inversion of the formula (11), so that an inversion model for inverting the snow accumulation white-air albedo and then inverting the snow accumulation bi-directional reflectivity is obtained:
Figure BDA0003216825650000072
a vertical-direction reflectivity inversion model can also be obtained:
Figure BDA0003216825650000073
the step 3 and the step 4 are independent of the step 1 and the step 2 and are not in sequence.
And 5: and (3) utilizing the previously constructed snow multi-angle two-way reflectivity inversion model, firstly inverting the snow white-air albedo according to the result of the step (2), and then inputting different observation zenith angles (0-90 degrees), sun zenith angles (0-90 degrees) and relative azimuth angles (0-360 degrees) to invert the snow multi-angle two-way reflectivity. According to each piece of white space albedo data obtained by the white space albedo inversion model constructed in the step 3, the snow multi-angle bidirectional reflectivity of a blue wave band, a near infrared wave band and a short wave infrared wave band of Sentiel-2A can be obtained through inversion according to the inversion model established in the step 4, and the reflectivity changes along with the observation zenith angle on a sun main plane (the relative azimuth angle is 0 degree and 180 degrees) and a vertical sun main plane (the relative azimuth angle is 90 degrees and 270 degrees) under the condition that the sun zenith angle is 0-70 degrees. On the main plane of the sun, the positive observation zenith angle is the forward direction, and the negative observation zenith angle is the backward direction. The positive and negative of the observed zenith angle perpendicular to the main plane of the sun indicate different relative azimuth angles, positive values of the relative azimuth angle are 90 degrees, and negative values of the relative azimuth angle are 270 degrees.
Step 6: and carrying out comparative analysis and evaluation on the inversion result by utilizing the measured data.
By adopting the method, remote sensing data (a high-resolution satellite image, such as Sentinel-2A multispectral image L1C level data) can be used for efficiently inverting the equivalent flat multi-angle bidirectional reflectivity of the snow in the mountainous area. The method and the device avoid the situation that in the prior art, the snow reflectivity in a large-scale and high-cold mountain area needs to be calculated by acquiring corresponding parameters through actual measurement of collected snow samples, so that the scheme of the invention not only provides an effective way for continuously acquiring the snow reflectivity in the large-scale and high-cold mountain area, but also has quick and efficient inversion calculation, and simultaneously, because the topographic effect is eliminated, the influence of pollutant concentration and the influence of related parameters of the particle size of the snow are considered, and the reliability of the inversion result is ensured.
The invention is described above with reference to the accompanying drawings, it is obvious that the specific implementation of the invention is not limited by the above-mentioned manner, and it is within the scope of the invention to adopt various insubstantial modifications of the inventive concept and solution of the invention, or to apply the inventive concept and solution directly to other applications without modification.

Claims (8)

1. A snow multi-angle two-way reflectivity inversion method based on satellite remote sensing data is characterized by comprising the following steps: comprises the following steps:
step 1: collecting remote sensing data, and preprocessing to obtain single-angle two-way reflectivity of the snow surface in different wave bands;
step 2: eliminating the topographic effect of the two-way reflectivity calculated and obtained according to the step 1 by utilizing a C correction model;
and step 3: constructing a snow-accumulated blank space albedo inversion model based on a progressive radiation transmission model;
and 4, step 4: constructing an accumulated snow multi-angle bidirectional reflectivity inversion model:
Figure FDA0003216825640000011
in the formula
Figure FDA0003216825640000018
Is the bi-directional reflectivity of snow, A is the white-space albedo,
Figure FDA0003216825640000012
is the reflectance, K, of a snow medium assumed to be weakly semi-infinitely absorbing0(θv) As escape function of the exit direction, K0(θs) In the direction of incidenceAn escape function;
and 5: and (3) firstly inverting the snow accumulation white-space albedo according to the result of the step (2) by using the snow accumulation multi-angle two-way reflectivity inversion model constructed before, and then inputting different observation zenith angles, sun zenith angles and relative azimuth angles to invert the snow accumulation multi-angle two-way reflectivity.
2. The method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim 1, wherein the method comprises the following steps: in the step 3, the progressive radiation transmission model is established on the basis of assuming that the snow layer is a semi-infinite weak absorption medium, and the model expression is as follows:
Figure FDA0003216825640000013
in the formula
Figure FDA0003216825640000014
Is the bi-directional reflectivity of snow, γ is the absorption coefficient, and the expression is: gamma is 4 pi (chi + M)/lambda, where chi is the imaginary part of complex refractive index of ice, lambda is wavelength, M is the concentration parameter of pollutant in snow, and the parameter a related to particle sizeefApproximately equal to 13d, where d is the equivalent optical diameter of the snow;
escape function K of the emission direction0(θv) And escape function K of incident direction0(θs) Expressed by the following equation:
Figure FDA0003216825640000015
Figure FDA0003216825640000016
Figure FDA0003216825640000019
the reflectivity of the snow medium assumed to be semi-infinite weak absorption is calculated as follows:
Figure FDA0003216825640000017
Figure FDA0003216825640000021
Figure FDA0003216825640000022
wherein cos θsAnd cos θvThe cosine values of the sun zenith angle and the observed zenith angle,
Figure FDA0003216825640000023
the cosine value of the relative azimuth angle is obtained, in the calculation of p (omega), the unit of omega is degree, and the value range is 0-180 degrees.
3. The method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim 2, wherein the method comprises the following steps: in the step 3, the white space albedo A is inverted by the following model:
Figure FDA0003216825640000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003216825640000025
the single-waveband reflectivity is obtained after single-angle remote sensing data processing.
4. The method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim 3, wherein the method comprises the following steps: in the step 4, the white-space albedo a can be further calculated by the following formula:
Figure FDA0003216825640000026
the arithmetic expression is combined with a progressive radiation transmission model and applied to a model expression of the snow reflectivity to obtain a snow multi-angle bidirectional reflectivity inversion model, and the model eliminates two parameters of equivalent optical snow particle size and pollutant concentration and simplifies the parameters into a parameter of snow accumulation white-space albedo A.
5. The method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim 4, wherein the method comprises the following steps: in the step 4, a vertical direction reflectivity inversion model can be obtained:
Figure FDA0003216825640000027
6. the method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim I, wherein the method comprises the following steps: in the step 2, a linear relation exists between the reflectivity of each wave band of the remote sensing image of the mountainous terrain and the cosine value of the solar incident angle, a semi-empirical coefficient C is provided to correct the overcorrection problem existing in cosine correction, and a C correction model is as follows:
fH＝a+b cos(θ1)
fT＝a+b cos(θv)
Figure FDA0003216825640000028
Figure FDA0003216825640000029
wherein c is a/b, fHIs the reflectivity of the horizontal ground surface, fTIs the reflectivity of an inclined ground surface, θvIs the observed zenith angle, theta, of the inclined earth surface1Is the observed zenith angle under horizontal conditions.
7. The method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim 1, wherein the method comprises the following steps: in the step 5, the value interval of the observation zenith angle is 0-90 degrees, the value interval of the sun zenith angle is 0-90 degrees, and the value interval of the relative azimuth angle is 0-360 degrees.
8. The method for inverting the multi-angle bi-directional reflectivity of the accumulated snow based on the satellite remote sensing data as claimed in claim 1, wherein the method comprises the following steps: further comprising the step 6: and carrying out comparative analysis and evaluation on the inversion result by utilizing the measured data.
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