CN105092575A - Method and apparatus for evaluating sand duststorm intensity - Google Patents
Method and apparatus for evaluating sand duststorm intensity Download PDFInfo
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Abstract
The present invention provides a method and an apparatus for evaluating the sand duststorm intensity. The method comprises: a) acquiring an image; b) obtaining the reflectivities and the emissivities of each pixel; c) obtaining the brightness temperatures of each pixel; d) taking the pixels corresponding to the sand duststorm region from the multiple pixels; e) calculating the normalization sand-dust indexes and determining whether each normalization sand-dust index is larger than or equal to the threshold, if yes, obtaining the corresponding level visibility and performing a step h), and if not, performing a step f); f) with a parameter inversion method, determining the quantitative inversion relationship, and determining the aerosol optical thickness; g) according to the determined aerosol optical thickness, obtaining the level visibilities corresponding to each pixel; and h) according to the obtained level visibility, determining the sand duststorm intensities of the regions corresponding to each pixel in the sand duststorm region.
Description
Technical field
The present invention relates to technical field of remote sensing image processing, a kind of satellite image that uses is to assess the method and apparatus of Sand specifically, is applicable to environmental analysis and process field.
Background technology
Along with the development of infotech, remote sensing technology is used to obtain and analyze various information more and more.The monitoring utilizing remote sensing technology to carry out sandstorm has become the emphasis of research.
At present, Dust Storm Monitoring assessment technology mainly comprises ground network monitoring and Satellite Remote Sensing.Because satellite remote sensing images can monitor larger continuous print spatial dimension, spectral resolution is also more and more higher, and the information that can extract also gets more and more, and the technical research therefore using satellite remote sensing to carry out monitoring sand-dust storm has even more important meaning.Based on the data of the visible ray that Satellite Remote Sensing mainly uses multispectral sensor or Moderate Imaging Spectroradiomete (MODIS) to obtain and infrared band, analyze regional extent and the intensity of sandstorm in the spectral signature of visible ray and infrared band according to sandstorm." the information comparable method of RS data Sand monitoring " is analyzed the sand and dust feature of the visible ray infrared band of NOAA satellite AVHRR image, and uses the threshold method of far infrared 11 μm and 12 mu m wavebands and differential technique to extract the scope of sandstorm; The reflectivity of 1.6 mu m wavebands is used to build normalization dust intensity index." Dust Storm Monitoring is studied with early warning technology " is same uses NOAA data, extracts sandstorm scope by reflectivity threshold method and differential technique; Use the reflectivity of 1.6 mu m wavebands to build normalization dust intensity index, and use ground monitoring sample data to set up the corresponding relation of normalization dust intensity index and horizontal visibility, then carry out Sand assessment according to horizontal visibility.
Above-mentioned remote sensing images sand and dust analytical approach, can extract sand and dust scope, and analyze dust intensity exponentiate, but for following situation, can not well adapt to.Sandstorm weather is from being less than 50 meters to being greater than 10 kilometers from the scope of the slight measurement index visibility corresponding to severe, and due to normalization Sandy index and the visibility correlativity when being greater than 2 kilometers not high, therefore can not set up corresponding relation in the visibility four corner of required analysis, be only applicable to the situation within visibility 2 kilometer range.
Summary of the invention
Problem that is slight, the analysis of severe various degree dust and sand weather visibility all cannot be adapted to based on remote sensing technology to what exist in the analysis of sandstorm in order to solve, the invention provides a kind of method and apparatus assessing Sand, the method and device are based on remote sensing technology, not only can assess the intensity in severe sandstorm region exactly, and the intensity in slight sandstorm region can be assessed exactly.
The invention provides a kind of method assessing Sand, the method comprises the following steps:
A) satellite sensor is utilized, the image obtaining described earth surface area is taken to earth surface area, described image comprises multiple pixel, each pixel in described multiple pixel has gray-scale value, described each pixel corresponds to multiple optical band, and each optical band in described multiple optical band has multiple parameter;
B) according to described gray-scale value and described multiple parameter, the respective radiance of another part optical band in the respective reflectivity of a part of optical band in described multiple optical band that described each pixel is corresponding and described multiple optical band corresponding to described each pixel is drawn;
C) according to described radiance, the brightness temperature of each optical band in described another part optical band that described each pixel is corresponding is drawn;
D) from described multiple pixel, take out those pixels corresponding with meet predetermined range described reflectivity and described brightness temperature, those pixels described correspond to the sandstorm region in described earth surface area;
E) normalization Sandy index is calculated to each pixel in those pixels described, and judge whether each normalization Sandy index calculated is more than or equal to threshold value, if, then obtain corresponding horizontal visibility according to normalization Sandy index, and enter step h), if not, then step f is entered);
F) parameter inversion method is utilized, the multiple functional relations changed with the brightness temperature of pixel from the aerosol optical depth of pixel, determine that a functional relation is as quantitative inversion relational expression, and the brightness temperature of any one same light wave band according to each pixel in described quantitative inversion relational expression and the pixel corresponding with the normalization Sandy index being less than described threshold value, determine the aerosol optical depth corresponding with this each pixel;
G) according to the aerosol optical depth determined, the horizontal visibility corresponding with this each pixel is obtained;
H) according to the described horizontal visibility obtained, the Sand in the region corresponding with each pixel in described sandstorm region is determined.
Described multiple optical band at least comprises visible light wave range, near-infrared band, middle-infrared band and far infrared band.
Described a part of optical band at least comprises one or more described visible light wave range and one or more described near-infrared band, and described another part optical band at least comprises one or more described middle-infrared band and one or more described far infrared band.
In step e) in, the reflectivity of the same optical band in described one or more described visible light wave range corresponding according to described each pixel and described one or more described near-infrared band, calculates the described normalization Sandy index of described each pixel.
Described same optical band is 1.6 μm of near-infrared bands.
Described sandstorm region comprises severe sandstorm region and slight sandstorm region, described threshold value is for dividing described severe sandstorm region and described slight sandstorm region, wherein, what be more than or equal to described threshold value is described severe sandstorm region, and what be less than described threshold value is described slight sandstorm region.
In step f) in, described parameter inversion method is as follows: by the actual measured value of the actual measured value of the respective brightness temperature of any one same light wave band described at least three pixels and described at least three pixels aerosol optical depth separately, substitute into respectively in described multiple functional relation and carry out Inversion Calculation, to determine each coefficient value in each functional relation
The determination of described quantitative inversion relational expression is as follows: the actual measured value of at least three pixels aerosol optical depth separately described in utilization, the actual measured value of the respective brightness temperature of any one same light wave band described of described at least three pixels, according to each aerosol optical depth that the described each functional relation determining each coefficient value described calculates, and the mean value of the actual measured value of described at least three pixels aerosol optical depth separately, calculate the degree of fitting of each functional relation, and maximum for the value of degree of fitting functional relation is determined as described quantitative inversion relational expression.
Described multiple functional relation can comprise linear functional relation formula, polynomial function relational expression, exponential function relation formula, logarithmic function relational expression, power function relationship formula.
Any one same light wave band described is a far infrared band in described one or more far infrared band.
The present invention also provides a kind of device assessing Sand, and this device comprises:
Acquiring unit, described acquiring unit utilizes satellite sensor, the image obtaining described earth surface area is taken to earth surface area, described image comprises multiple pixel, each pixel in described multiple pixel has gray-scale value, described each pixel corresponds to multiple optical band, and each optical band in described multiple optical band has multiple parameter;
Pretreatment unit, comprises reflectivity and radiance calculating part and brightness temperature calculating portion,
Wherein, described reflectivity and radiance calculating part are according to described gray-scale value and described multiple parameter, draw the respective radiance of another part optical band in the respective reflectivity of a part of optical band in described multiple optical band that described each pixel is corresponding and described multiple optical band corresponding to described each pixel
Described brightness temperature calculating portion, according to described radiance, draws the brightness temperature of each optical band in described another part optical band that described each pixel is corresponding;
Data analysis unit, comprises sandstorm region extraction portion, horizontal visibility determination portion, Sand determination portion,
Wherein, described sandstorm region extraction portion, takes out those pixels corresponding with the described reflectivity and described brightness temperature that meet predetermined range from described multiple pixel, and those pixels described correspond to the sandstorm region in described earth surface area;
Described horizontal visibility determination portion calculates normalization Sandy index to each pixel in those pixels described, and judges whether each normalization Sandy index calculated is more than or equal to threshold value,
If be more than or equal to the normalization Sandy index of described threshold value, then obtain corresponding horizontal visibility according to normalization Sandy index,
If be less than described threshold value, then utilize parameter inversion method, the multiple functional relations changed with the brightness temperature of pixel from the aerosol optical depth of pixel, determine that a functional relation is as quantitative inversion relational expression, and the brightness temperature of any one same light wave band according to each pixel in described quantitative inversion relational expression and the pixel corresponding with the normalization Sandy index being less than described threshold value, determine the aerosol optical depth corresponding with this each pixel; Again according to the aerosol optical depth determined, obtain the horizontal visibility corresponding with this each pixel;
Sand determination portion, according to the described horizontal visibility obtained, determines the Sand in the region corresponding with each pixel in described sandstorm region.
For the device of this assessment Sand, described multiple optical band at least comprises visible light wave range, near-infrared band, middle-infrared band and far infrared band.
For the device of this assessment Sand, described a part of optical band at least comprises one or more described visible light wave range and one or more described near-infrared band, and described another part optical band at least comprises one or more described middle-infrared band and one or more described far infrared band.
For the device of this assessment Sand, described horizontal visibility determination portion, according to the reflectivity of the same optical band in described one or more described visible light wave range corresponding to described each pixel and described one or more described near-infrared band, calculates the described normalization Sandy index of described each pixel.
For the device of this assessment Sand, described same optical band is 1.6 μm of near-infrared bands.
For the device of this assessment Sand, described sandstorm region comprises severe sandstorm region and slight sandstorm region, described threshold value is for dividing described severe sandstorm region and described slight sandstorm region, wherein, what be more than or equal to described threshold value is described severe sandstorm region, and what be less than described threshold value is described slight sandstorm region.
For the device of this assessment Sand, described parameter inversion method is as follows: by the actual measured value of the actual measured value of the respective brightness temperature of any one same light wave band described at least three pixels and described at least three pixels aerosol optical depth separately, substitute into respectively in described multiple functional relation and carry out Inversion Calculation, to determine each coefficient value in each functional relation
The determination of described quantitative inversion relational expression is as follows: the actual measured value of at least three pixels aerosol optical depth separately described in utilization, the actual measured value of the respective brightness temperature of any one same light wave band described of described at least three pixels, according to each aerosol optical depth that the described each functional relation determining each coefficient value described calculates, and the mean value of the actual measured value of described at least three pixels aerosol optical depth separately, calculate the degree of fitting of each functional relation, and maximum for the value of degree of fitting functional relation is determined as described quantitative inversion relational expression.
For the device of this assessment Sand, described multiple functional relation comprises linear functional relation formula, polynomial function relational expression, exponential function relation formula, logarithmic function relational expression, power function relationship formula.
For the device of this assessment Sand, any one same light wave band described is a far infrared band in described one or more far infrared band.
By the method and apparatus of assessment Sand of the present invention, can carry out assessing to the intensity of the sandstorm of four corner comprehensively and exactly.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the method for assessment Sand according to the embodiment of the present invention.
Fig. 2 is the block scheme of the device of assessment Sand according to the embodiment of the present invention.
Embodiment
Below in conjunction with embodiment, the present invention is described in detail.
Fig. 1 is the process flow diagram of the method for assessment Sand according to the embodiment of the present invention.In step S11, utilize satellite sensor, the image obtaining this earth surface area is taken to earth surface area, this image comprises multiple pixel, each pixel in multiple pixel has gray-scale value, and each pixel corresponds to multiple optical band, and each optical band in multiple optical band has multiple parameter.In the present embodiment, satellite sensor is MODIS, and this earth surface area is such as positioned at east longitude 88 degree to 120 degree, straight 54 degree of north latitude 31 degree, and shooting time is 12:40 on April 23rd, 2009.The resolution of this image is 1000m, and the size of image is 2277 × 2113 pixels.This earth surface area is primarily of cloud layer, exposed earth's surface (desert or low vegetation coverage earth's surface) and sandstorm composition.
Wherein, multiple optical band at least comprises visible light wave range, near-infrared band, middle-infrared band and far infrared band etc.Obtain multiple parameters of each optical band from this image, in the present embodiment, obtain 0.65 μm of visible light wave range, 1.6 μm of near-infrared bands, 3.75 μm, 8.5 μm middle-infrared bands, and 11 μm, 12 μm far infrared bands multiple parameters separately.As known in the art, 0.65 mu m waveband refers to the light of particular range of wavelengths, and its centre wavelength is 0.65 μm.
In step S12, according to multiple parameters of gray-scale value and each optical band, draw the respective radiance of another part optical band in the respective reflectivity of a part of optical band in multiple optical bands that each pixel is corresponding and multiple optical bands corresponding to each pixel.Wherein, a part of optical band at least comprises one or more visible light wave range and one or more near-infrared band, and another part optical band at least comprises one or more middle-infrared band and one or more far infrared band.
In the present embodiment, a part of optical band is 0.65 μm of visible light wave range and 1.6 μm of near-infrared bands, and another part optical band is 3.75 μm, 8.5 μm middle-infrared bands, and 11 μm, 12 μm far infrared bands.In order to easy, only use 0.65 μm, 1.6 μm, 3.75 μm, 8.5 μm, 11 μm, 12 μm below and represent each optical band.Visible light wave range and near-infrared band mainly reflect the electromagnetic wave from the sun, and its reflectivity of different atural object is different, to visible light wave range and near-infrared band computational reflect rate.Reflectivity is the ratio of reflected energy and projectile energy, is expressed as a percentage, and codomain is [0,1].In addition, the electromagnetic wave of middle-infrared band and far infrared band mainly atural object its own transmission, that weighs its emissive ability is called radiance.
Show that each pixel corresponds respectively to the reflectivity of 0.65 μm and 1.6 μm according to equation 1:
Ref
i(j)=reflectance_scal (j) × (DN
i-reflectance_offset (j)) (equation 1)
Wherein, Ref
ij () represents the reflectivity of i-th pixel in j optical band, 1≤i≤2277 × 2113 (number of pixels), j optical band is 0.65 μm or 1.6 μm, DN
irepresent the pixel value of i-th pixel, reflectance_scal (j) represents the reflectivity scale factor of j optical band, and reflectance_offset (j) represents the reflectivity side-play amount of j optical band.
Here, reflectance_scal (j) and reflectance_offset (j) is two parameters in multiple parameters of j optical band.That is, multiple parameter comprises reflectivity scale factor and reflectivity side-play amount.
In addition, show that each pixel corresponds respectively to the radiance of 3.75 μm, 8.5 μm, 11 μm, 12 μm according to equation 2:
L
i(d)=radiance_scale (d) × (DN
i-radiance_offset (d)) (equation 2)
Wherein, L
id () represents the radiance of i-th pixel in d optical band, 1≤i≤2277 × 2113 (number of pixels), d optical band represents 3.75 μm or 8.5 μm or 11 μm or 12 μm, DN
irepresent the pixel value of i-th pixel, radiance_scale (d) represents the radiance scale factor of d optical band, and radiance_offset (d) represents the radiance side-play amount of d optical band.
Here, radiance_scale (d) and radiance_offset (d) is two parameters in multiple parameters of d optical band.That is, multiple parameter comprises radiance scale factor and radiance side-play amount.
Next, in step s 13, according to the above-mentioned radiance calculated, the brightness temperature of each optical band in another part optical band that each pixel is corresponding is drawn.In the present embodiment, calculated the brightness temperature of each optical band of each pixel in 3.75 μm, 8.5 μm, 11 μm, 12 μm optical bands by following equation 3.Wherein, temperature when brightness temperature refers to that blackbody radiation goes out the energy equal with observed objects, this brightness temperature weighs an index of object temperature, but be not the true temperature of object.
Here, utilize planck formula, known:
Wherein, h is Planck's constant, value 6.626 × 10
-34js, k are Boltzmann constant, value 1.3806 × 10
-23j/K, c are the light velocity, value 2.998 × 10
8m/s, derives the equation 3 calculating brightness temperature thus:
Wherein, T
id () represents the brightness temperature of i-th pixel in d optical band, λ
drepresent the centre wavelength of d optical band, L
id () represents the radiance of i-th pixel in d optical band as mentioned above.
In step S14, from multiple pixel, take out those pixels corresponding with the reflectivity and brightness temperature that meet predetermined range, those pixels correspond to the sandstorm region in this earth surface area.
In the present embodiment, judge whether each pixel meets this predetermined range the radiance of 3.75 μm, 8.5 μm, 11 μm, 12 μm respectively at the reflectivity of 0.65 μm and each pixel according to following 5 expression formulas.
0.16<Ref
i(0.65 μm) <0.4 (expression formula 1)
305K<T
i(3.75 μm) <325K (expression formula 2)
T
i(3.75 μm)-T
i(8.5 μm) >20K (expression formula 3)
T
i(12 μm)-T
i(11 μm) >0 (expression formula 4)
T
i(11 μm) <290K (expression formula 5)
If i-th pixel meets above-mentioned 5 expression formulas simultaneously, so take out i-th pixel, those pixels of taking-up correspond to the sandstorm region in this earth surface area, thus, and can the scope of sandstorm exactly definitely in table section.
Here, expression formula 1 is for distinguishing sandstorm, earth's surface and cloud significantly, because the luminance factor sandstorm of cloud wants high, be generally all greater than 0.4, and the reflectivity on earth's surface is generally less than 0.16.
Expression formula 2 represents the scope of sandstorm in the brightness temperature of 3.75 mu m wavebands, but similar with low clouds.
Expression formula 3, because sand and dust brightness temperature on 3.75 mu m wavebands is more abnormal than its all band higher, so larger with other object difference.
Expression formula 4, utilizes this difference can distinguish sandstorm and water cloud, ice cloud.
Expression formula 5 is simultaneously for distinguishing water cloud, ice cloud.
In step S15, normalization Sandy index (NDDI) is calculated to each pixel in those pixels, and judge whether each normalization Sandy index calculated is more than or equal to threshold value, if, then obtain corresponding horizontal visibility according to normalization Sandy index, and enter step S18, if not, then enter step S16.
Here, the reflectivity of the same optical band in one or more visible light wave range corresponding according to each pixel and one or more near-infrared band, calculates the normalization Sandy index of each pixel.In the present embodiment, this same optical band is 1.6 μm of near-infrared bands.
The normalization Sandy index of each pixel in those pixels of taking-up is calculated according to equation 4.
NDDI
g=a × (exp (b × Ref
g(1.6 μm)-1) equation 4
Wherein, NDDI
grepresent the normalization Sandy index of g pixel, 1≤g≤n, n represents the number of those pixels of taking-up, and g, n are integer, and a, b are regulatory factor, respectively value 10 and 0.8, Ref
g(1.6 μm) represent the reflectivity of g pixel at 1.6 mu m wavebands.1.6 mu m wavebands have good stability in Dust Storm Monitoring, and in multi-satellite data, 1.6 mu m wavebands all have comparatively fixing sand and dust message reflection scope, utilize 1.6 mu m wavebands can solve the standard sameization problem of dust detection well.Therefore, in the present embodiment, utilize each pixel in those pixels at the reflectivity of 1.6 mu m wavebands to calculate the normalization Sandy index of each pixel.
The normalization Sandy index of each pixel in those pixels is judged, that is, judges NDDI
gwhether be more than or equal to threshold value, in the present embodiment, this threshold value is such as 11.Here, sandstorm region generally includes severe sandstorm region and slight sandstorm region, this threshold value for dividing severe sandstorm region and slight sandstorm region, wherein, what be more than or equal to threshold value corresponds to severe sandstorm region, and what be less than threshold value corresponds to slight sandstorm region.
If the NDDI of certain pixel is more than or equal to 11 (being "Yes" in step S15), so determines the horizontal visibility of this pixel according to table 1, and enter step S18.
Table 1
| The scope of NDDI | Horizontal visibility (kilometer) |
| 11≤NDDI＜50 | 1～2 |
| 50≤NDDI＜70 | 0.5～1 |
| 70≤NDDI＜85 | 0.05～0.5 |
| NDDI≥85 | ＜0.05 |
According to table 1, the horizontal visibility corresponding with this pixel can be obtained, so, each pixel determination horizontal visibility in the one part of pixel of 11 can be more than or equal to NDDI, then enter step S19.
If be "No" in step S15, so enter step S16.In step S16, utilize parameter inversion method, the multiple functional relations changed with the brightness temperature of pixel from the aerosol optical depth of pixel, determine that a functional relation is as quantitative inversion relational expression, and the brightness temperature of any one same light wave band according to each pixel in quantitative inversion relational expression and the pixel corresponding with the normalization Sandy index being less than threshold value, determine the aerosol optical depth corresponding with this each pixel.This any one same light wave band is a far infrared band in one or more far infrared band, in the present embodiment, such as, is 11 mu m wavebands.
Here, multiple functional relation can comprise linear functional relation formula, polynomial function relational expression, exponential function relation formula, logarithmic function relational expression, power function relationship formula.Certainly other functional relations can also be had.
In the present embodiment, these functional relations are such as follows:
Linear function: y=a
1x+b
1
Polynomial function: y=a
2x
2+ b
2x+c
2
Exponential function: y=a
3e
x+ b
3
Logarithmic function: y=a
4ln (x)+b
4
Power function:
In step s 16, parameter inversion method is as follows: by the actual measured value of the actual measured value of the respective brightness temperature of any one same light wave band of at least three pixels and at least three pixels aerosol optical depth separately, substitute into respectively in multiple functional relation and carry out Inversion Calculation, to determine each coefficient value in each functional relation.
Concrete, with the actual measured value of at least three pixels in the respective brightness temperature of 11 mu m wavebands, namely, the brightness temperature value of three actual measurements of at least three pixels is as independent variable x, using the aerosol optical depth (AOT) of three of at least three pixels actual measurements as y, substitute into respectively in above-mentioned five functional relations and carry out Inversion Calculation, thus determine each coefficient value in each functional relation.
Here, when actual measured value is greater than 3 groups (that is, the actual measured value of the brightness temperature of 3 pixels and actual measured value of aerosol optical depth), the approximating methods such as calculating can be returned by least square method and power function and ask optimum solution.Actual measured value is more, and the inverting relation obtained is more stable and accurate.Coefficient a, b, the c in each functional relation is obtained by solving equations.
Certainly, during coefficient value in each functional relation of Inversion Calculation, the actual measured value of same pixel can be used, also can use the actual measured value of different pixels.
In the present embodiment, the concrete functional relation calculated is as follows:
Linear function: y=-44.527x+13443
Polynomial function: y=-3.0998x
2+ 1733.1x-241310
Exponential function: y=3.26 × 10
13× e
x
Logarithmic function: y=-12713ln (x)+72613
Power function: y=1.55 × 10
63× x
-24.58
For different areas, aerosol type has larger difference, and such as southern and northern, East Coastal and Northwest inland, aerosol type is all different.For same area, these functional relations can be obtained by using actual measured value (that is, surveying sample data) to carry out inverting.
In addition, the determination of above-mentioned quantitative inversion relational expression is as follows: the actual measured value utilizing at least three pixels aerosol optical depth separately, the actual measured value of the respective brightness temperature of any one same light wave band of at least three pixels, according to each aerosol optical depth that each functional relation determining each coefficient value calculates, and the mean value of the actual measured value of at least three pixels aerosol optical depth separately, calculate the degree of fitting of each functional relation, and maximum for the value of degree of fitting functional relation is determined as quantitative inversion relational expression.
Concrete, utilize following equation 5 to calculate the degree of fitting of each functional relation:
Wherein, R
trepresent the above-mentioned degree of fitting determining t functional relation in 5 functional relations of each coefficient value, 1≤t≤5, m represents the number of above-mentioned at least three pixels, such as m=3, y
srepresent the actual measured value of the aerosol optical depth of s pixel at least three pixels, 1≤s≤m,
represent s pixel to bring into as independent variable x in the actual measured value of the brightness temperature of 11 mu m wavebands and determine according to above-mentioned the value that t functional relation in 5 functional relations of each coefficient value calculates,
represent the mean value of the actual measured value of at least three aerosol optical depths of at least three pixels.S, t, m are integer.
In the present embodiment, according to above-mentioned equation 5, the degree of fitting obtaining 5 functional relations is respectively: the degree of fitting of exponential function is 0.7501, the degree of fitting of linear function is 0.7972, the degree of fitting of logarithmic function is 0.7909, the degree of fitting of polynomial function is 0.9203, and the degree of fitting of power function is 0.7426.
Wherein, the value of the degree of fitting of polynomial function is maximum, so polynomial function is optimum inverting relational expression, and determines this polynomial function as quantitative inversion relational expression.
Then, according to this polynomial function: y=-3.0998x
2+ 1733.1x – 241310, is less than each pixel in these pixels of 11 and substitutes into this polynomial function in the brightness temperature of 11 mu m wavebands as independent variable x, calculate these pixels aerosol optical depth separately using NDDI.Each pixel in these pixels is the brightness temperatures calculated in step s 13 in the brightness temperature of 11 mu m wavebands.
Then step S17 is entered.In step S17, according to the aerosol optical depth determined, obtain the horizontal visibility corresponding with this each pixel.Here, Peterson model (equation 6) is utilized to calculate the horizontal visibility of this each pixel.
V
z=3.0/ (y
z/ H+0.0146) equation 6
V
zrepresent that NDDI is less than the horizontal visibility of z pixel in these pixels of 11, z is the integer being more than or equal to 1 and being less than or equal to the number (that is, NDDI is less than the number of these pixels of 11) of these pixels, V
zunit be kilometer, y
zrepresent the aerosol optical depth of z the pixel calculated according to this polynomial function as mentioned above, H is atmospheric scale height, because difference and changing in season, four seasons spring, summer, autumn and winter respectively value be 1.25km, 1.79km, 0.79km and 0.78km.
Then step S19 is entered.In step S19, according to the horizontal visibility obtained in step S18, S17, determine the Sand in the region corresponding with each pixel in this sandstorm region.
Here, the Sand in the region corresponding with each pixel in sandstorm region is determined according to horizontal visibility and national standard " Sand grade classification " (as shown in table 2).
Table 2
| Sand (grade) | Horizontal visibility (kilometer, km) |
| Floating dust | >＝10km |
| Sand | 1～10km |
| Sandstorm | 0.5～1km |
| Strong chromatic number | <0.5km |
| Severe Sand-Dust Storms | <50m |
In step S15 of the present invention, the NDDI of each pixel is judged, and enter step S16 or S18 according to judged result, so, the Sand of each pixel in this image can be determined.
See Fig. 2, the present invention also provides a kind of device 2 assessing Sand, and this device 2 comprises acquiring unit 21, pretreatment unit 22 and data analysis unit 23.
Acquiring unit 21 utilizes satellite sensor, take to earth surface area the image obtaining this earth surface area, this image comprises multiple pixel, and each pixel in multiple pixel has gray-scale value, each pixel corresponds to multiple optical band, and each optical band in multiple optical band has multiple parameter.In the present embodiment, satellite sensor is MODIS, and this earth surface area is such as positioned at east longitude 88 degree to 120 degree, straight 54 degree of north latitude 31 degree, and shooting time is 12:40 on April 23rd, 2009.The resolution of this image is 1000m, and the size of image is 2277 × 2113 pixels.This earth surface area is primarily of cloud layer, exposed earth's surface (desert or low vegetation coverage earth's surface) and sandstorm composition.
Wherein, multiple optical band at least comprises visible light wave range, near-infrared band, middle-infrared band and far infrared band etc.Obtain multiple parameters of each optical band from this image, in the present embodiment, obtain 0.65 μm of visible light wave range, 1.6 μm of near-infrared bands, 3.75 μm, 8.5 μm middle-infrared bands, and 11 μm, 12 μm far infrared bands multiple parameters separately.As known in the art, 0.65 mu m waveband refers to the light of particular range of wavelengths, and its centre wavelength is 0.65 μm.
Pretreatment unit 22 comprises reflectivity and radiance calculating part 221 and brightness temperature calculating portion 222, this reflectivity and radiance calculating part 221, according to multiple parameters of gray-scale value and each optical band, draw the respective radiance of another part optical band in the respective reflectivity of a part of optical band in multiple optical bands that each pixel is corresponding and multiple optical bands corresponding to each pixel.Wherein, a part of optical band at least comprises one or more visible light wave range and one or more near-infrared band, and another part optical band at least comprises one or more middle-infrared band and one or more far infrared band.
In the present embodiment, a part of optical band is 0.65 μm of visible light wave range and 1.6 μm of near-infrared bands, and another part optical band is 3.75 μm, 8.5 μm middle-infrared bands, and 11 μm, 12 μm far infrared bands.In order to easy, only use 0.65 μm, 1.6 μm, 3.75 μm, 8.5 μm, 11 μm, 12 μm below and represent each optical band.Visible light wave range and near-infrared band mainly reflect the electromagnetic wave from the sun, and its reflectivity of different atural object is different, to visible light wave range and near-infrared band computational reflect rate.Reflectivity is the ratio of reflected energy and projectile energy, is expressed as a percentage, and codomain is [0,1].In addition, the electromagnetic wave of middle-infrared band and far infrared band mainly atural object its own transmission, that weighs its emissive ability is called radiance.
Show that each pixel corresponds respectively to the reflectivity of 0.65 μm and 1.6 μm according to equation 1:
Ref
i(j)=reflectance_scal (j) × (DN
i-reflectance_offset (j)) (equation 1)
Wherein, Ref
ij () represents the reflectivity of i-th pixel in j optical band, 1≤i≤2277 × 2113 (number of pixels), j optical band is 0.65 μm or 1.6 μm, DN
irepresent the pixel value of i-th pixel, reflectance_scal (j) represents the reflectivity scale factor of j optical band, and reflectance_offset (j) represents the reflectivity side-play amount of j optical band.
Here, reflectance_scal (j) and reflectance_offset (j) is two parameters in multiple parameters of j optical band.That is, multiple parameter comprises reflectivity scale factor and reflectivity side-play amount.
In addition, show that each pixel corresponds respectively to the radiance of 3.75 μm, 8.5 μm, 11 μm, 12 μm according to equation 2:
L
i(d)=radiance_scale (d) × (DN
i-radiance_offset (d)) (equation 2)
Wherein, L
id () represents the radiance of i-th pixel in d optical band, 1≤i≤2277 × 2113 (number of pixels), d optical band represents 3.75 μm or 8.5 μm or 11 μm or 12 μm, DN
irepresent the pixel value of i-th pixel, radiance_scale (d) represents the radiance scale factor of d optical band, and radiance_offset (d) represents the radiance side-play amount of d optical band.
Here, radiance_scale (d) and radiance_offset (d) is two parameters in multiple parameters of d optical band.That is, multiple parameter comprises radiance scale factor and radiance side-play amount.
Brightness temperature calculating portion 222, according to the above-mentioned radiance calculated, draws the brightness temperature of each optical band in another part optical band that each pixel is corresponding.In the present embodiment, calculated the brightness temperature of each optical band of each pixel in 3.75 μm, 8.5 μm, 11 μm, 12 μm optical bands by following equation 3.Wherein, temperature when brightness temperature refers to that blackbody radiation goes out the energy equal with observed objects, this brightness temperature weighs an index of object temperature, but be not the true temperature of object.
Here, utilize planck formula, known:
Wherein, h is Planck's constant, value 6.626 × 10
-34js, k are Boltzmann constant, value 1.3806 × 10
-23j/K, c are the light velocity, value 2.998 × 10
8m/s, derives the equation 3 calculating brightness temperature thus:
Wherein, T
id () represents the brightness temperature of i-th pixel in d optical band, λ
drepresent the centre wavelength of d optical band, L
id () represents the radiance of i-th pixel in d optical band as mentioned above.
Data analysis unit 23 comprises sandstorm region extraction portion 231, horizontal visibility determination portion 232 and Sand determination portion 233, wherein, sandstorm region extraction portion 231 takes out those pixels corresponding with the reflectivity and brightness temperature that meet predetermined range from multiple pixel, and those pixels correspond to the sandstorm region in this earth surface area.
In the present embodiment, judge whether each pixel meets this predetermined range the radiance of 3.75 μm, 8.5 μm, 11 μm, 12 μm respectively at the reflectivity of 0.65 μm and each pixel according to following 5 expression formulas.
0.16<Ref
i(0.65 μm) <0.4 (expression formula 1)
305K<T
i(3.75 μm) <325K (expression formula 2)
T
i(3.75 μm)-T
i(8.5 μm) >20K (expression formula 3)
T
i(12 μm)-T
i(11 μm) >0 (expression formula 4)
T
i(11 μm) <290K (expression formula 5)
If i-th pixel meets above-mentioned 5 expression formulas simultaneously, so just take out i-th pixel.Those pixels of taking out correspond to the sandstorm region in this earth surface area, thus, and can the scope of sandstorm exactly definitely in table section.
Here, expression formula 1 is for distinguishing sandstorm, earth's surface and cloud significantly, because the luminance factor sandstorm of cloud wants high, be generally all greater than 0.4, and the reflectivity on earth's surface is generally less than 0.16.
Expression formula 2 represents the scope of sandstorm in the brightness temperature of 3.75 mu m wavebands, but similar with low clouds.
Expression formula 3, because sand and dust brightness temperature on 3.75 mu m wavebands is more abnormal than its all band higher, so larger with other object difference.
Expression formula 4, utilizes this difference can distinguish sandstorm and water cloud, ice cloud.
Expression formula 5 is simultaneously for distinguishing water cloud, ice cloud.
Horizontal visibility determination portion 232 calculates normalization Sandy index (NDDI) to each pixel in those pixels, and judges whether each normalization Sandy index calculated is more than or equal to threshold value.
Here, horizontal visibility determination portion 232, according to the reflectivity of the same optical band in one or more visible light wave range corresponding to each pixel and one or more near-infrared band, calculates the normalization Sandy index of each pixel.In the present embodiment, this same optical band is 1.6 μm of near-infrared bands.
The normalization Sandy index of each pixel in those pixels of taking-up is calculated according to equation 4.
NDDI
g=a × (exp (b × Ref
g(1.6 μm)-1) equation 4
Wherein, NDDI
grepresent the normalization Sandy index of g pixel, 1≤g≤n, n represents the number of those pixels of taking-up, and g, n are integer, and a, b are regulatory factor, respectively value 10 and 0.8, Ref
g(1.6 μm) represent the reflectivity of g pixel at 1.6 mu m wavebands.1.6 mu m wavebands have good stability in Dust Storm Monitoring, and in multi-satellite data, 1.6 mu m wavebands all have comparatively fixing sand and dust message reflection scope, utilize 1.6 mu m wavebands can solve the standard sameization problem of dust detection well.Therefore, in the present embodiment, utilize each pixel in those pixels at the reflectivity of 1.6 mu m wavebands to calculate the normalization Sandy index of each pixel.
The normalization Sandy index of each pixel in those pixels is judged, that is, judges NDDI
gwhether be more than or equal to threshold value, in the present embodiment, this threshold value is such as 11.Here, sandstorm region generally includes severe sandstorm region and slight sandstorm region, this threshold value for dividing severe sandstorm region and slight sandstorm region, wherein, what be more than or equal to threshold value corresponds to severe sandstorm region, and what be less than threshold value corresponds to slight sandstorm region.
If the NDDI of certain pixel is more than or equal to 11, so determine the horizontal visibility of this pixel according to table 1.
Table 1
| The scope of NDDI | Horizontal visibility (kilometer) |
| 11≤NDDI＜50 | 1～2 |
| 50≤NDDI＜70 | 0.5～1 |
| 70≤NDDI＜85 | 0.05～0.5 |
| NDDI≥85 | ＜0.05 |
According to table 1, the horizontal visibility corresponding with this pixel can be obtained, so, each pixel determination horizontal visibility in the one part of pixel of 11 can be more than or equal to NDDI.
If the normalization Sandy index of certain pixel is less than 11, so horizontal visibility determination portion 232 utilizes parameter inversion method, the multiple functional relations changed with the brightness temperature of pixel from the aerosol optical depth of pixel, determine that a functional relation is as quantitative inversion relational expression, and the brightness temperature of any one same light wave band according to each pixel in quantitative inversion relational expression and the pixel corresponding with the normalization Sandy index being less than threshold value, determine the aerosol optical depth corresponding with this each pixel.This any one same light wave band is a far infrared band in one or more far infrared band, in the present embodiment, such as, is 11 mu m wavebands.
Here, multiple functional relation comprises linear functional relation formula, polynomial function relational expression, exponential function relation formula, logarithmic function relational expression, power function relationship formula.Certainly other functional relations can also be had.
In the present embodiment, these functional relations are such as follows:
Linear function: y=a
1x+b
1
Polynomial function: y=a
2x
2+ b
2x+c
2
Exponential function: y=a
3e
x+ b
3
Logarithmic function: y=a
4ln (x)+b
4
Power function:
Wherein, parameter inversion method is as follows: by the actual measured value of the actual measured value of the respective brightness temperature of any one same light wave band of at least three pixels and at least three pixels aerosol optical depth separately, substitute into respectively in multiple functional relation and carry out Inversion Calculation, to determine each coefficient value in each functional relation.
Concrete, with the actual measured value of at least three pixels in the respective brightness temperature of 11 mu m wavebands, namely, the brightness temperature value of three actual measurements of at least three pixels is as independent variable x, using the aerosol optical depth (AOT) of three of at least three pixels actual measurements as y, substitute into respectively in above-mentioned five functional relations and carry out Inversion Calculation, thus determine each coefficient value in each functional relation.
Here, when actual measured value is greater than 3 groups (that is, the actual measured value of the brightness temperature of 3 pixels and actual measured value of aerosol optical depth), the approximating methods such as calculating can be returned by least square method and power function and ask optimum solution.Actual measured value is more, and the inverting relation obtained is more stable and accurate.Coefficient a, b, the c in each functional relation is obtained by solving equations.
Certainly, during coefficient value in each functional relation of Inversion Calculation, the actual measured value of same pixel can be used, also can use the actual measured value of different pixels.
In the present embodiment, the concrete functional relation calculated is as follows:
Linear function: y=-44.527x+13443
Polynomial function: y=-3.0998x
2+ 1733.1x-241310
Exponential function: y=3.26 × 10
13× e
x
Logarithmic function: y=-12713ln (x)+72613
Power function: y=1.55 × 10
63× x
-24.58
For different areas, aerosol type has larger difference, and such as southern and northern, East Coastal and Northwest inland, aerosol type is all different.For same area, these functional relations can be obtained by using actual measured value (that is, surveying sample data) to carry out inverting.
In addition, the determination of above-mentioned quantitative inversion relational expression is as follows: the actual measured value utilizing at least three pixels aerosol optical depth separately, the actual measured value of the respective brightness temperature of any one same light wave band of at least three pixels, according to each aerosol optical depth that each functional relation determining each coefficient value calculates, and the mean value of the actual measured value of at least three pixels aerosol optical depth separately, calculate the degree of fitting of each functional relation, and maximum for the value of degree of fitting functional relation is determined as quantitative inversion relational expression.
Concrete, utilize following equation 5 to calculate the degree of fitting of each functional relation:
Wherein, Rt represents the above-mentioned degree of fitting determining t functional relation in 5 functional relations of each coefficient value, and 1≤t≤5, m represents the number of above-mentioned at least three pixels, such as m=3, y
srepresent the actual measured value of the aerosol optical depth of s pixel at least three pixels, 1≤s≤m,
represent s pixel to bring into as independent variable x in the actual measured value of the brightness temperature of 11 mu m wavebands and determine according to above-mentioned the value that t functional relation in 5 functional relations of each coefficient value calculates,
represent the mean value of the actual measured value of at least three aerosol optical depths of at least three pixels.S, t, m are integer.
In the present embodiment, according to above-mentioned equation 5, the degree of fitting obtaining 5 functional relations is respectively: the degree of fitting of exponential function is 0.7501, the degree of fitting of linear function is 0.7972, the degree of fitting of logarithmic function is 0.7909, the degree of fitting of polynomial function is 0.9203, and the degree of fitting of power function is 0.7426.
Wherein, the value of the degree of fitting of polynomial function is maximum, so polynomial function is optimum inverting relational expression, and determines this polynomial function as quantitative inversion relational expression.
Then, according to this polynomial function: y=-3.0998x
2+ 1733.1x – 241310, is less than each pixel in these pixels of 11 and substitutes into this polynomial function in the brightness temperature of 11 mu m wavebands as independent variable x, calculate these pixels aerosol optical depth separately using NDDI.Each pixel in these pixels is brightness temperatures that brightness temperature calculating portion 222 calculates in the brightness temperature of 11 mu m wavebands.
Then, horizontal visibility determination portion 232, according to the aerosol optical depth calculated, obtains the horizontal visibility corresponding with this each pixel.Here, Peterson model (equation 6) is utilized to calculate the horizontal visibility of this each pixel.
V
z=3.0/ (y
z/ H+0.0146) equation 6
V
zrepresent that NDDI is less than the horizontal visibility of z pixel in these pixels of 11, z is the integer being more than or equal to 1 and being less than or equal to the number (that is, NDDI is less than the number of these pixels of 11) of these pixels, V
zunit be kilometer, y
zrepresent the aerosol optical depth of z the pixel calculated according to this polynomial function as mentioned above, H is atmospheric scale height, because difference and changing in season, four seasons spring, summer, autumn and winter respectively value be 1.25km, 1.79km, 0.79km and 0.78km.
The horizontal visibility that Sand determination portion 233 obtains according to horizontal visibility determination portion 232, determines the Sand in the region corresponding with each pixel in this sandstorm region.
Here, the Sand in the region corresponding with each pixel in sandstorm region is determined according to horizontal visibility and national standard " Sand grade classification " (as shown in table 2).
Table 2
| Sand (grade) | Horizontal visibility (kilometer, km) |
| Floating dust | >＝10km |
| Sand | 1～10km |
| Sandstorm | 0.5～1km |
| Strong chromatic number | <0.5km |
| Severe Sand-Dust Storms | <50m |
The NDDI of horizontal visibility determination portion 232 to each pixel judges, and obtains corresponding horizontal visibility according to judged result, so, can determine the Sand of each pixel in this image.
After the Sand determining each pixel in image, the Sand of each pixel can export by the device 2 of this assessment Sand, for display or preservation.
The present invention is after the scope determining sandstorm region, not only can assess exactly the sand within 2km in this region, sandstorm, strong chromatic number, Severe Sand-Dust Storms, equally also can assess exactly the sand beyond 2km in this region and floating dust.Therefore, corresponding relation can be set up in the four corner of the horizontal visibility of required analysis, that is, can assess exactly all sidedly the intensity of the sandstorm of this four corner.
The present invention is a kind of dust detection analytical approach based on normalization Sandy index and gasoloid-horizontal visibility model newly.Sand and dust, atural object, cloud layer is identified by multiple spectral band, and the horizontal visibility of sand and dust in analyse atmos layer.The present invention carries out threshold decision in conjunction with multiple wave band, be more suitable for the sand and dust cloud cluster identification in the different area of surface condition, in addition, more weak for dust intensity, the region that horizontal visibility is higher, uses the present invention can make up normalization Sandy index and the not high and situation that cannot process of horizontal visibility correlativity.
Although specific embodiment of the present invention is described, these embodiments are only stated by the mode of example, are not intended to limit scope of the present invention.In fact, innovative approach described herein can be implemented by other forms various; In addition, also can carry out the various omissions to method and system described herein, substitute and change and do not deviate from spirit of the present invention.Attached claim and the object of equivalents thereof contain to fall into such various forms in scope and spirit of the present invention or amendment.
Claims (10)
1. assess a method for Sand, it is characterized in that, said method comprising the steps of:
A) satellite sensor is utilized, the image obtaining described earth surface area is taken to earth surface area, described image comprises multiple pixel, each pixel in described multiple pixel has gray-scale value, described each pixel corresponds to multiple optical band, and each optical band in described multiple optical band has multiple parameter;
B) according to described gray-scale value and described multiple parameter, the respective radiance of another part optical band in the respective reflectivity of a part of optical band in described multiple optical band that described each pixel is corresponding and described multiple optical band corresponding to described each pixel is drawn;
C) according to described radiance, the brightness temperature of each optical band in described another part optical band that described each pixel is corresponding is drawn;
D) from described multiple pixel, take out those pixels corresponding with meet predetermined range described reflectivity and described brightness temperature, those pixels described correspond to the sandstorm region in described earth surface area;
E) normalization Sandy index is calculated to each pixel in those pixels described, and judge whether each normalization Sandy index calculated is more than or equal to threshold value, if, then obtain corresponding horizontal visibility according to normalization Sandy index, and enter step h), if not, then step f is entered);
F) parameter inversion method is utilized, the multiple functional relations changed with the brightness temperature of pixel from the aerosol optical depth of pixel, determine that a functional relation is as quantitative inversion relational expression, and the brightness temperature of any one same light wave band according to each pixel in described quantitative inversion relational expression and the pixel corresponding with the normalization Sandy index being less than described threshold value, determine the aerosol optical depth corresponding with this each pixel;
G) according to the aerosol optical depth determined, the horizontal visibility corresponding with this each pixel is obtained;
H) according to the described horizontal visibility obtained, the Sand in the region corresponding with each pixel in described sandstorm region is determined.
2. the method for assessment Sand as claimed in claim 1, it is characterized in that, described multiple optical band at least comprises visible light wave range, near-infrared band, middle-infrared band and far infrared band.
3. the method for assessment Sand as claimed in claim 2, it is characterized in that, described a part of optical band at least comprises one or more described visible light wave range and one or more described near-infrared band, and described another part optical band at least comprises one or more described middle-infrared band and one or more described far infrared band.
4. the method for assessment Sand as claimed in claim 3, it is characterized in that, in step e) in, the reflectivity of the same optical band in described one or more described visible light wave range corresponding according to described each pixel and described one or more described near-infrared band, calculates the described normalization Sandy index of described each pixel.
5. the method for assessment Sand as claimed in claim 4, it is characterized in that, described same optical band is 1.6 μm of near-infrared bands.
6. the method for assessment Sand as claimed in claim 1, it is characterized in that, described sandstorm region comprises severe sandstorm region and slight sandstorm region, described threshold value is for dividing described severe sandstorm region and described slight sandstorm region, wherein, what be more than or equal to described threshold value is described severe sandstorm region, and what be less than described threshold value is described slight sandstorm region.
7. the method for assessment Sand as claimed in claim 3, it is characterized in that, in step f) in, described parameter inversion method is as follows: by the actual measured value of the actual measured value of the respective brightness temperature of any one same light wave band described at least three pixels and described at least three pixels aerosol optical depth separately, substitute into respectively in described multiple functional relation and carry out Inversion Calculation, to determine each coefficient value in each functional relation
The determination of described quantitative inversion relational expression is as follows: the actual measured value of at least three pixels aerosol optical depth separately described in utilization, the actual measured value of the respective brightness temperature of any one same light wave band described of described at least three pixels, according to each aerosol optical depth that the described each functional relation determining each coefficient value described calculates, and the mean value of the actual measured value of described at least three pixels aerosol optical depth separately, calculate the degree of fitting of each functional relation, and maximum for the value of degree of fitting functional relation is determined as described quantitative inversion relational expression.
8. the method for assessment Sand as claimed in claim 7, it is characterized in that, described multiple functional relation comprises linear functional relation formula, polynomial function relational expression, exponential function relation formula, logarithmic function relational expression, power function relationship formula.
9. the method for assessment Sand as claimed in claim 7, it is characterized in that, any one same light wave band described is a far infrared band in described one or more far infrared band.
10. assess a device for Sand, it is characterized in that, described device comprises:
Acquiring unit, described acquiring unit utilizes satellite sensor, the image obtaining described earth surface area is taken to earth surface area, described image comprises multiple pixel, each pixel in described multiple pixel has gray-scale value, described each pixel corresponds to multiple optical band, and each optical band in described multiple optical band has multiple parameter;
Pretreatment unit, comprises reflectivity and radiance calculating part and brightness temperature calculating portion,
Wherein, described reflectivity and radiance calculating part are according to described gray-scale value and described multiple parameter, draw the respective radiance of another part optical band in the respective reflectivity of a part of optical band in described multiple optical band that described each pixel is corresponding and described multiple optical band corresponding to described each pixel
Described brightness temperature calculating portion, according to described radiance, draws the brightness temperature of each optical band in described another part optical band that described each pixel is corresponding;
Data analysis unit, comprises sandstorm region extraction portion, horizontal visibility determination portion, Sand determination portion,
Wherein, described sandstorm region extraction portion, takes out those pixels corresponding with the described reflectivity and described brightness temperature that meet predetermined range from described multiple pixel, and those pixels described correspond to the sandstorm region in described earth surface area;
Described horizontal visibility determination portion calculates normalization Sandy index to each pixel in those pixels described, and judges whether each normalization Sandy index calculated is more than or equal to threshold value,
If be more than or equal to the normalization Sandy index of described threshold value, then obtain corresponding horizontal visibility according to normalization Sandy index,
If be less than described threshold value, then utilize parameter inversion method, the multiple functional relations changed with the brightness temperature of pixel from the aerosol optical depth of pixel, determine that a functional relation is as quantitative inversion relational expression, and the brightness temperature of any one same light wave band according to each pixel in described quantitative inversion relational expression and the pixel corresponding with the normalization Sandy index being less than described threshold value, determine the aerosol optical depth corresponding with this each pixel, again according to the aerosol optical depth determined, obtain the horizontal visibility corresponding with this each pixel,
Described Sand determination portion, according to the described horizontal visibility obtained, determines the Sand in the region corresponding with each pixel in described sandstorm region.
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