CN109030301A - Aerosol optical depth inversion method based on remotely-sensed data - Google Patents
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Abstract
The present invention relates to a kind of aerosol optical depth inversion method based on remotely-sensed data, using dark pixel method and 6S radiative transfer model inverting aerosol optical depth, include: data acquisition, Data Synthesis, angle-data synthesis, cloud detection, aerosol inverting, aerosol type determining, determination of Reflectivity for Growing Season, distribution map made according to the AOD of inverting, and establishes the correlativity of AOD and PM2.5.The MODIS AOD data obtained according to the present invention can be used as effective means of supplementing out economy of PM2.5 concentration ground monitoring.
Description
Technical field
The present invention relates to a kind of aerosol optical depth inversion method based on remotely-sensed data.
Background technique
With the development of society, harm of the gray haze to environment and human health, causes more and more extensive concern, to gray haze
Prevention and treatment and correlative study it is more more and more urgent.Forest plays vital role in atmosphere benign cycle.In forest
The branches and leaves of trees can reduce wind speed, facilitate the sedimentation of dust in air, and can adsorb the small floating dust substance of air.
In addition, forest cover has interception, absorption, delay to act on PM2.5, forest can mitigate endangering to the mankind for gray haze to a certain extent
Evil.Remote sensing technology can monitor forest cover and gray haze on a large scale, therefore, probe into forest cover to gray haze using remotely-sensed data
Response, specify the quantitative and qualitative relationship of forest cover and gray haze, prevention and treatment for gray haze and formulate corresponding control measures
There is very great meaning.
U.S. NASA and China's national weather satellite center have all issued the product data of aerosol optical depth.The U.S.
NASA uses dark pixel method according to MODIS data, the aerosol optical depth in the inverting whole world, and to whole world Free distribution.
Its product entitled MOD04, MOD08, MOD04 is day product, and MOD08 is moon product.China's national weather satellite center is according to wind
Cloud No. 3 weather satellite datas are using the also inverting distribution of global aerosol optical depth of dark pixel method, but its data distribution master
It will be towards the member user of registration.These two types product has various deficiencies at present, and as the product of NASA divides, spatial resolution is low, counts
According to missing；The more difficult acquisition of data of China's national weather satellite center publication, and due to data format issues, it is also difficult to it utilizes.
For this status, a kind of method of inverting aerosol optical depth is needed, available higher resolution takes for correlative study
Business.
Summary of the invention
Purpose according to the present invention provides a kind of aerosol optical depth inversion method based on remotely-sensed data, feature
It is, this method uses dark pixel method and 6S radiative transfer model inverting aerosol optical depth, comprising:
(1) data acquisition: the MODIS L1B data on date needed for obtaining, it is ensured that nothing within the scope of the wanted survey region of covering
Cloud.Then radiant correction and geometric correction are carried out to MODIS L1B data respectively；
(2) Data Synthesis: to the 1st in the MODIS data for meeting aerosol inverting, 3, the data of 7 wave bands pre-process,
It is a file by the emissivity and reflectivity file synthesis of radiant correction and geometric correction；
(3) angle-data synthesizes
Open satellite zenith angle, satellite aximuth, solar zenith angle and the solar azimuth file of MODIS data, synthesis
For resampling is at an angle of file again after a file, the GCP file correction angle data files of geometric correction are finally utilized, and
The emissivity synthesized, reflectivity data and angle-data are cut out into area data by region vector boundary；
(4) cloud detection
Cloud amount detection is carried out to the radiative and reflective coefficient composite document cut and saves testing result file；
(5) aerosol inverting selects blue wave band, red band and the short infrared wave band of MODIS data as inverting
Model wave band；
(6) aerosol type determines
Determine the aerosol type in wanted inverting area；
(7) determination of Reflectivity for Growing Season
Under pure atmospheric conditions, the corresponding vegetation-covered area of aerosol is under red and blue wave band, earth surface reflection
Rate can be calculated by 2.1 μm of channel reflection rates；
(8) distribution map is made according to the AOD of inverting.
Further, the invention also includes the AOD data according to inverting, the relationship of AOD and PM2.5 are established.
Preferably, particulate matter quality concentration is converted by AOD, it is also necessary to carry out humidity and correct, by the delustring of wet particle
Coefficient is converted into dried particle extinction coefficient, to reduce uncertainty caused by particle moisture-absorption characteristics；
The humidity, which is corrected, corrects factor empirical model using relative humidity, specifically:
F (PM2.5)=PM2.5 × f (RH)
When AOD data that the present invention obtains and PM2.5 is fitted, humidity is carried out to PM2.5 and is corrected, then with aerosol
Thickness is fitted, it is found that the correlation of the two significantly improves, humidity factor is introduced in PM2.5 monitoring process, and effect is preferable,
Directly PM2.5 can be monitored using aerosol optical depth, be conducive to analyze response of the forest cover to gray haze, also have
Conducive to the prevention and improvement of pollutant.
Detailed description of the invention
Fig. 1 is aerosol optical depth inversion process figure；
Fig. 2 is distribution map made by the AOD of inverting；
Fig. 3 is 10 air quality monitoring station's point distributions of Changsha；
Fig. 4 is each website AOD and PM2.5 fitted figure；
Fig. 5 is AOD and PM2.5 four seasons fitted figure.
Specific embodiment
The present invention uses dark pixel method according to MODIS data, and inverting aerosol optical depth has more high score to obtain
The product of resolution is correlative study service.
When land surface is uniform Lambertion surface, when SEQUENCING VERTICAL even variation, satellite measurement can use apparent reflectance
It indicates, when Reflectivity for Growing Season very little, moonscope reflectivity depends primarily on atmospheric contribution item.When Reflectivity for Growing Season is very big
When, the contribution of earth's surface becomes main contributions item.In this way it can be concluded that the basis of inverting: if oneself knows Reflectivity for Growing Season, determining big
Gas aerosol model can be obtained by aerosol optical depth.
Dark pixel method
Earth's surface object is different, and reflectivity also can be different.Therefore outbound path radiation is separated from the radiation value of remote sensing images
Item is highly difficult, therefrom to obtain aerosol information, just must can achieve the degree ignored in surface radiation value or have radiation
But it is smaller and can accurately obtain under such a case.Reflectivity for Growing Season could only in this way be eliminated to the shadow of aerosol thickness
It rings.For dense vegetation, their reflectivity is all very low (about 0.01-0.02), therefore many with forest or a wide range of dense
The place of vegetation can obtain aerosol information with the method, and thus carry out atmospheric correction, this method, that is, dark pixel method
(Dense DarkVegetation) method can be used for the sensor such as (MODIS) of intermediate-resolution.
It is needed in conjunction with practical study of the present invention, mainly studies forest cover to the response investigations of gray haze, therefore the present invention adopts
Dark pixel method inverting aerosol optical depth is taken.
Radiative transfer model
Aerosol thickness inverting of the present invention is based primarily upon dark pixel method and 6S radiative transfer model.6S model (SECOND
SIMULATION OF THE SATELLITE SIGNAL IN THE SOLAR SPECTRMM) it is that Lille, France University of Science and Technology is big
Gas optical laboratory and Department of Geography of Univ Maryland-Coll Park USA are based on atmospheric radiative transfer equation, in 5S (Simulation
OfSatellite Signalin the Solar Spectrum) second generation solar spectrum that grows up on the basis of model
Band satellite signal imitation program.For simulating spaceborne or aerial remote sensing instrument it is assumed that atmosphere is cloudless atmosphere, consideration steam
Scattering process with other atmospheric molecules to the absorption of solar radiation, aerosol and atmospheric molecule to solar radiation, and
Earth's surface is non-uniform earth's surface, in the case where considering bidirectional reflectance etc., a length of 0.25 μm -4.0 μm of ground vapour system medium wave
The radiance that the transmission process and satellite sensor of solar radiation receive.The present invention is mainly by 6S radiative transfer model needle
Aerosol optical depth inverting is carried out to test block and surrounding area.
6S model assumes first that the cloudless situation of fine day, simulates because of oxygen, ozone, carbon monoxide, nitrogen dioxide and water
Vapour etc. is on influence caused by the absorption of spectrum.Using statistical model, to absorption gas in atmosphere, ground, sensor path
Total transmitance is calculated.As long as 6S model inputs visual field geometric parameter, atmospheric parameter, aerosol model, earth's surface characterisitic parameter,
The observation pixel apparent reflectance and radiance of satellite sensor acquisition can be calculated.Mainly comprising following in 6S model
Several parts:
(1) the triangle geometry site solar zenith angle between the sun, sensor and ground target object, solar azimuth
Angle, view zenith angle, observed azimuth this four angle variables describe；
(2) basis in atmosphere is defined, and establishes 7 kinds of Real-Time Atmospheric models.It can also be artificial defeated
Enter atmospheric model, real simulation atmospheric environment is on influence caused by spectral illumination；
(3) it aerosol model: is made of aerosol type and aerosol concentration two parts.Have in aerosol type mode more
Kind aerosol type is for user's selection.It is wherein aerosol background without aerosol, continent type, ocean type, urban type, desert type
This five seed type be mode standard；
(4) spectral response functions of various kinds of sensors are described in different radiation channels, oneself is through having preset the whole world in 6S model
The spectral response functions of main sensors, such as MODIS, MSS, TM and POLDER etc., mainly with visible light near infrared band
Corresponding to channel；
(5) Reflectivity for Growing Season: having determined the Reflectivity Model of earth's surface, the model be divided into again uniform reflection earth's surface with it is non-uniform
Two kinds of situations of earth's surface are reflected, wherein it is contemplated that whether there is or not directional reflection problematic factors in uniform reflection terrain model.
6S model is the optimization to 5S model, it has mainly made the improvement of the following aspects: first is that by spectrum integral from
The step-length of 5nm is improved to 2.5nm；Second is that interpretable face side effect, the airborne remote sensing observations of simulation, define observed object absolute elevation
And explain BRDF effect, in addition it can calculate the absorption of both gases of CO, N2O；Third is that using SOS
(successive order ofscattering) continuous order algorithm calculates scattering process, improves computational accuracy.
The realization of aerosol thickness inverting
Using the dark pixel method of 6S models coupling come the aerosol optical depth in inverting Hunan region, basic ideas are to utilize
The look-up table that 6S mold cycle generates obtains the atmospheric parameters such as atmospheric path radiation, the total transmitance of atmosphere, Planetary albedo；Using dark
Pixel method obtains the Reflectivity for Growing Season data of 1,3 wave bands by formula；Then the theoretical table that satellite sensor receives is calculated
It sees reflectivity and then corresponds to atmospheric parameter item when theoretical apparent reflectance and identical or close 1, the 3 practical apparent reflectances in channel
Aerosol optical depth value under part, as wants the aerosol optical depth of inverting, and specific inverting thinking is shown in techniqueflow chart
Figure Fig. 1.
Specific step is as follows:
(1) data acquisition: the MODIS L1B data on date needed for being obtained from the official website NASA, it is ensured that Hunan Province's coverage area
It is interior cloudless.Then radiant correction and geometric correction are carried out to MODIS L1B data respectively.
(2) Data Synthesis: according to dark pixel method it is found that meeting the MODIS wave band of aerosol inverting are as follows: the 1st, 3,7 waves
Section, on this basis, using ENVI software by the emissivity and reflectivity file synthesis of radiant correction and geometric correction be one
A file.
(3) angle-data synthesizes
Open satellite zenith angle, satellite aximuth, solar zenith angle and the solar azimuth file of MODIS data, synthesis
For resampling is at an angle of file again after a file, the GCP file correction angle data files of geometric correction are finally utilized.Due to
Angle data files expand 100 times in HDF file, need that angle file is reduced 100 with Band Math tool herein
Times, the emissivity synthesized, reflectivity data and angle-data are cut out into Hunan area data by Hunan Province vector boundary.
(4) cloud detection
Cloud amount detection is carried out to the radiative and reflective coefficient composite document cut using the modis_cloud tool of ENVI
And save testing result file.
(5) aerosol inverting, model selection, selects blue wave band (0.47 μm), red band (0.65 μ of MODIS data
And short infrared wave band (2.13~3.8 μm) m).
(6) aerosol type determines
Aerosol type is most important in aerosol inverting, and aerosol is divided into 4 seed types in the world: sand and dust gas is molten
Glue, water-soluble aerosol, maritime aerosol and bituminous coal aerosol.
(7) determination of Reflectivity for Growing Season
According to the research of Kaufman, under pure atmospheric conditions, the corresponding vegetation-covered area of aerosol is in red and blue
Under wave band, Reflectivity for Growing Season can be calculated by 2.1 μm of channel reflection rates.
The present invention is total to inverting in January, 2016 in July, 2017 and 4 days 2014 aerosol thickness.It is shown in Fig. 2
It is the distribution map as made by the AOD of the inverting on July 8th, 2014.
The present invention utilizes aerosol optical depth inversion program, according to the data information actually grasped, inverting Hunan Province
Aerosol optical depth, give the inverting example in the four seasons, with regard to its inversion result come precision from the point of view of, meet Kaufman etc.
The absolute error of inverting when aerosol optical depth is larger that people proposes reaches 20%~30% conclusion.Inversion result phase
To relatively accurately, inversion error is also within zone of reasonableness.
The correlation of AOD and PM2.5
Monitoring station and PM2.5 concentration data
PM2.5 mass concentration observes the data information that data use National urban air quality real-time release platform.Data
The air quality monitoring point (Fig. 4) of Chinese Changsha city 10 comprising the period (in January, 2016 in July, 2017) is by hourly average
PM2.5 concentration ground observation value.Based on this data, generates TERRA satellite and pass by the 2 hours PM2.5 average value in front and back.
In order to accurately react the corresponding relationship of AOD and PM2.5, it is in place that monitoring point institute is extracted from the aerosol data of inverting
Set the AOD data of range.It weeds out because AOD caused by the reasons such as cloud block is lacked.For PM2.5 mass concentration data,
After rejecting missing data, matched with the same day data of AOD.Below the data available of acquisition:
1 10 websites of table are effectively matched data logarithm
Humidity is corrected
The hygroscopic nature of atmospheric aerosol is to contact one of microfluidic aerosol physics, the bridge of chemical parameters and tie, even more
One of decisive parameter of optical properties of aerosol, therefore aerosol hygroscopic nature is in entire atmospheric aerosol scientific research
In fundamental position.After hydrophily dried particle moisture absorption, particle diameter has apparent growth, and refractive index dullness is smaller.In this way
Result in same concentrations aerosol showing different Extinction Characteristics before and after moisture absorption or in the case that component is different.It is relatively wet
Degree the factor can particle diameter distribution, complex refractivity index and form etc. to PM2.5 particle have an impact, especially in the higher feelings of relative humidity
Under condition, AOD can be had a huge impact.In this case, we have carried out humidity to result and have corrected.And ground monitoring can
Sucking particulate matter usually carries out under the dry condition, and what is obtained is the mass particle concentration under dry environment.So wanting
Aerosol optical depth is turned into particulate matter quality concentration, it is also necessary to carry out humidity and correct, the extinction coefficient of wet particle is converted
For dried particle extinction coefficient, to reduce uncertainty caused by particle moisture-absorption characteristics.
Of the present invention is that relative humidity corrects factor empirical model:
F (PM2.5)=PM2.5 × f (RH)
Humidity is corrected to be calculated using above formula, obtains the calculated result of 10 websites in Changsha.We select traditional line
Property fitting and by comparing to obtain the preferable power function of effect and exponential function is fitted, as a result See Figure.Utilize inverting
AOD value carries out correlation analysis with the small hourly value of the PM2.5 concentration of corresponding period, obtains the two correlation and corresponding line
Property fitting result.The correlation of entire time span (in January, 2016~2017 year July) data be it is preferable, with certain phase
Guan Xing, fit equation is as shown in table 2, R2Be worth it is lower, only 0.4 or so, linear fit effect is general.
As shown in figure 4, AOD and the PM2.5 concentration value to 10, Changsha monitoring station carry out correlation analysis, its phase is found
The apparent space and time difference of Guan Xingyou, suburb are significantly better than urban district.Monitoring station positioned at Xiang River west is significantly better than the east of a river.It is wherein outstanding
The correlation of its 5 ridge AOD and PM2.5 for being located at the east of a river is minimum, is 0.266 respectively.Sand flat website positioned at town and country junction
Related coefficient is 0.45.
The inversion method that the present invention uses is dark pixel method, the high region of vegetation coverage, and efficiency of inverse process is obviously better than
The urban district that vegetation coverage is low, bright target is more.Influence of the vegetation coverage to dark pixel inversion algorithm, is AOD and PM2.5
There is a key factor of larger difference in urban district and suburb in correlation.
The model of fit and R of 10 websites2Value is as shown in table 2 below.
Each website AOD of table 2 and PM2.5 model of fit
By each website AOD of table 2 and PM2.5 model of fit we it can be found that after humidity is corrected, three kinds of fitting moulds
The R of type2It is substantially all higher.The wherein R of power function2It is higher and highly stable, there is preferable fitting effect in each website.
Linear fit function is lower than power function and exponential function, and especially low value as in 5 ridge websites occurring 0.0777.
The AOD and PM2.5 of Changsha District corrected through humidity after value between there is certain statistical relationships, and power function effect is most
It is good.Meanwhile the PM2.5 distribution of the monitoring station on Xiang River north and south both sides also shows certain comparison, the Nan Gaobei of Changsha District
It is low that accumulation is easy to cause to form secondary aerosol species, demonstrate the accuracy of data.When haze weather, wind speed is smaller, is substantially at
Quiet wind state, air bleed capability is weak, and gray haze is not easy to dispel, and AOD concentration is bigger than normal, and PM2.5 is not easy to spread, dilutes and even remove,
Environmental significantly aggravates.As it can be seen that the haze weather in city is very strong on Atmospheric particulates influence.MODIS AOD data can
Using effective means of supplementing out economy as PM2.5 concentration ground monitoring.
Season correlation
The season correlation of table 3 AOD and PM2.5
Within the period in January, 2016 in July, 2017, it may be seen that PM2.5 is rendered obvious by summer in low winter
High feature.Its reason may be in urban district Precipitation in Winter rareness cause PM2.5 to be detained for a long time, and summer rainfall is more multipair
PM2.5 particulate matter plays the role of instant flushing and cleans with water.
After AOD and PM2.5 four seasons linear fit, effect is as shown in Figure 5.
From Fig. 5 it was determined that R2Each season is relatively high.Show on season scale, AOD is revised with humidity
PM2.5 also has preferable fitting effect.Wherein summer effect is best, and, with dark pixel method in high brightness, the few area of vegetation is anti-for this
It is related to drill low precision.
The present invention utilizes the aerosol optical depth of MODIS data inversion and the PM2.5 number from ground monitoring station for acquiring
According to, and Spatial And Temporal Characteristics are carried out by the AOD and PM2.5 to Changsha in January, 2016 in July, 2017.It was found that both
Change significantly feature.On different monitoring stations, the two difference in correlation is obvious, and suburb website is significantly better than urban district station
Point.Function Fitting is carried out to the two, in common function model, the fitting effect of power function is better than other functions, but R2Still
It is relatively low.In view of the landform of Changsha District, the factors such as precipitation find that the concentration of PM2.5 and AOD are closely related.It is opposite introducing
After the factor, it is found that the correlation of the two is obviously improved, and have apparent Seasonal variation: high summer in winter is low.Gray haze
During weather, air becomes static, poor fluidity, and PM2.5 concentration can rise with the rising of AOD.After study, it utilizes
MODIS aerosol data, which monitors PM2.5, has feasibility, and inversion algorithm and the meteorological condition for studying area are needed in research process
An important factor for considering.Therefore, MODIS AOD data can be used as effective means of supplementing out economy of PM2.5 concentration ground monitoring.
Only several embodiments of the present invention are expressed for example described above, and the description thereof is more specific and detailed, but not
Limitations on the scope of the patent of the present invention therefore can be interpreted as.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (3)
1. a kind of aerosol optical depth inversion method based on remotely-sensed data, which is characterized in that this method uses dark pixel method
With 6S radiative transfer model inverting aerosol optical depth, comprising:
(1) data acquisition: the MODIS L1B data on date needed for obtaining, it is ensured that cloudless within the scope of the wanted survey region of covering；So
Radiant correction and geometric correction are carried out to MODIS L1B data respectively afterwards；
(2) Data Synthesis: to the 1st in the MODIS data for meeting aerosol inverting, 3, the data of 7 wave bands pre-process, will
The emissivity and reflectivity file synthesis of radiant correction and geometric correction are a file；
(3) angle-data synthesizes
Satellite zenith angle, satellite aximuth, solar zenith angle and the solar azimuth file for opening MODIS data, synthesize one
Resampling is at an angle of file again after a file, finally utilizes the GCP file correction angle data files of geometric correction, and will
Emissivity, reflectivity data and the angle-data of synthesis cut out area data by region vector boundary；
(4) cloud detection
Cloud amount detection is carried out to the radiative and reflective coefficient composite document cut and saves testing result file；
(5) aerosol inverting selects blue wave band, red band and the short infrared wave band of MODIS data as inverse model
Wave band；
(6) aerosol type determines
Determine the aerosol type in wanted inverting area；
(7) determination of Reflectivity for Growing Season
Under pure atmospheric conditions, for the corresponding vegetation-covered area of aerosol red under blue wave band, Reflectivity for Growing Season can
It is calculated by 2.1 μm of channel reflection rates；
(8) distribution map is made according to the AOD of inverting.
2. the method according to claim 1, wherein establishing the pass of AOD and PM2.5 according to the AOD data of inverting
System.
3. according to the method described in claim 2, it is characterized in that, converting particulate matter quality concentration for AOD, it is also necessary to right
Particulate mass concentration carries out humidity and corrects, and converts dried particle extinction coefficient for the extinction coefficient of wet particle, to reduce particle suction
Uncertainty caused by moisture performance；
The humidity, which is corrected, corrects factor empirical model using relative humidity, specifically:
F (PM2.5)=PM2.5 × f (RH)
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109632106A (en) * | 2019-01-03 | 2019-04-16 | 北京师范大学 | A kind of Remote Sensing temperature angles of product effect correction method |
| CN109781593A (en) * | 2019-01-17 | 2019-05-21 | 南京泛在地理信息产业研究院有限公司 | A kind of aerosol quadratic inversion method based on PARASOL multi-angle polarization data |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102539336A (en) * | 2011-02-01 | 2012-07-04 | 环境保护部卫星环境应用中心 | Method and system for estimating inhalable particles based on HJ-1 satellite |
| CN103901420A (en) * | 2014-04-18 | 2014-07-02 | 山东科技大学 | Method for dynamic threshold method remote sensing data cloud identification supported by prior surface reflectance |
| CN103954974A (en) * | 2014-04-21 | 2014-07-30 | 山东科技大学 | Particulate matter optical thickness remote sensing monitoring method used in urban area |
| CN106407656A (en) * | 2016-08-29 | 2017-02-15 | 中国科学院遥感与数字地球研究所 | Retrieval method for aerosol optical thickness based on high resolution satellite image data |
| CN106979911A (en) * | 2017-03-07 | 2017-07-25 | 南京航空航天大学 | The method that PM 2.5 and PM 10 is estimated is carried out using satellite multispectral image data |
-
2018
- 2018-06-05 CN CN201810583104.8A patent/CN109030301A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102539336A (en) * | 2011-02-01 | 2012-07-04 | 环境保护部卫星环境应用中心 | Method and system for estimating inhalable particles based on HJ-1 satellite |
| CN103901420A (en) * | 2014-04-18 | 2014-07-02 | 山东科技大学 | Method for dynamic threshold method remote sensing data cloud identification supported by prior surface reflectance |
| CN103954974A (en) * | 2014-04-21 | 2014-07-30 | 山东科技大学 | Particulate matter optical thickness remote sensing monitoring method used in urban area |
| CN106407656A (en) * | 2016-08-29 | 2017-02-15 | 中国科学院遥感与数字地球研究所 | Retrieval method for aerosol optical thickness based on high resolution satellite image data |
| CN106979911A (en) * | 2017-03-07 | 2017-07-25 | 南京航空航天大学 | The method that PM 2.5 and PM 10 is estimated is carried out using satellite multispectral image data |
Non-Patent Citations (3)
| Title |
|---|
| 周鹏: "基于ＭＯＤＩＳ数据的大气气溶胶光学厚度反演——以皖苏沪地区为例", 《河北北方学院学报(自然科学版)》 * |
| 夏志业,等: "MODIS气溶胶光学厚度的PM2.5质量浓度遥感反演研究", 《高原气象》 * |
| 陈良富,等: "卫星数据和地面观测结合的珠三角地区颗粒物质量浓度统计估算方法", 《热带地理》 * |
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| CN113740263B (en) * | 2021-03-10 | 2023-06-16 | 仲恺农业工程学院 | Aerosol optical thickness inversion method and atmospheric particulate remote sensing inversion method |
| CN112712219A (en) * | 2021-03-11 | 2021-04-27 | 北京英视睿达科技有限公司 | Method, system, electronic device and medium for estimating concentration of atmospheric pollutants |
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