CN104569952B - Aerosol optical thickness temporal-spatial distribution inversion method without mid-infrared channel sensor - Google Patents
Aerosol optical thickness temporal-spatial distribution inversion method without mid-infrared channel sensor Download PDFInfo
- Publication number
- CN104569952B CN104569952B CN201310505797.6A CN201310505797A CN104569952B CN 104569952 B CN104569952 B CN 104569952B CN 201310505797 A CN201310505797 A CN 201310505797A CN 104569952 B CN104569952 B CN 104569952B
- Authority
- CN
- China
- Prior art keywords
- reflectivity
- zenith
- current
- pixel
- aerosol
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Image Processing (AREA)
Abstract
The invention discloses an aerosol optical thickness temporal-spatial distribution inversion method without a mid-infrared channel sensor. The method comprises the following steps: firstly eliminating cloud pixels, water body pixels and ice and ice and snow pixels in channels, then estimating the surface reflectance of the two channels by using a clear sky zenith reflectance image of a time sequence; meanwhile, establishing a lookup table in an offline manner on the basis of an aerosol mode, the combination of different incidence and observation attitudes and the surface reflectance; finally inverting the aerosol optical thickness temporal-spatial distribution result on the basis of the zenith reflectance data of the time sequence and the determined surface reflectance according to the block adjustment value. According to the method disclosed by the invention, the influences of an observation zenith angle and the length of a sliding time window are simultaneously considered in a multi-day combined observation method, and the precision of the obtained surface reflectance result is particularly better in East Asian regions with serious air pollution.
Description
Technical field
The present invention relates to a kind of aerosol optical depth spatial and temporal distributions inverting side based on the no sensor of mid-infrared passage
Method, particularly to East Asia Region aerosol optical depth spatial and temporal distributions inversion method.
Background technology
The main difficulty of Over-land aerosol inverting come self calibration, cloud remove, ground vapour contribution be precisely separating gentle molten
Rubber moulding formula determination etc..Wherein, it is precisely separating aerosol contribution from sensor signal, need the accurate estimation of Reflectivity for Growing Season.
And the evaluated error of Reflectivity for Growing Season is considered as one of main source of error of aerosol optical depth inverting.
At present, carrying out the popular algorithm of land aerosol optical depth inverting using reflectance spectrum intensity is DT
（Dark Target）Method.Kaufman etc. obtains the intensive secretly table of vegetation in red, Lan He by a large amount of aircraft testing data
There is certain linear relationship in the Reflectivity for Growing Season on three passages of mid-infrared, and it is molten that this relation is applied to MODIS land gas
Glue optical thickness inverting（Kaufman et al., 1997）.Levy et al.（2007）The aerosol inverting of MODIS land is calculated
Method is updated, using the Reflectivity for Growing Season relation of 0.47 μm, 0.66 μm and 2.12 μm three passages（Different passage earth's surfaces are anti-
The relation penetrated between rate changes with angle of scattering and vegetation index）Carry out the inverting of aerosol optical depth, can also obtain simultaneously
Reflectivity for Growing Season and aerosol thickness example ratio.
For the sensor not having the setting of mid-infrared passage, DT method just seems helpless, the CCD that such as environment star carries
The VISSR sensor that sensor, the AVHRR sensor of NOAA series of satellites carrying, FY-2 series fixed statellite carry.Early stage
A kind of solution is several images using areal difference phase, selects the image only existing the impact of background aerosol to work as
Day is as sunny day, and assumes that sunny day is constant with the Reflectivity for Growing Season of pollution day, and the difference inverting gas using two days images is molten
Colloidality matter（Fraser et al. 1984；Kaufman et al. 1990）.Or another way directly assumes dark target
Reflectivity for Growing Season is certain constant value（Soufflet et al. 1997）.Improvement afterwards said method it is assumed that this
In the section time, Reflectivity for Growing Season is constant, selects sunny day based on seasonal effect in time series AVHRR image.Because AVHRR is wide cut sensing
Device, in the revisiting period of 8 days, moonscope attitudes vibration is very big, and therefore, improved method also contemplates observation attitude（Mainly
Satellite zenith angle）The impact that earth's surface emissivity is estimated（Hauser et al. 2005；Riffler et al. 2010）.But
Said method is applied to the East Asia Region of atmosphere pollution, because sunny day is more difficult to find, often leads to inversion error and become big.
Particularly in view of the air pollution of East Asia Region is than Europe and serious many in North America, the probability finding sunny day will
Little it is therefore desirable to broadening select sunny day sliding time window length.But the length of time window is longer, earth surface reflection
The constant hypothesis of rate is just more difficult to meet, and the amplitude of variation of Reflectivity for Growing Season may be bigger.
Content of the invention
The problem existing for above-mentioned prior art, the present invention is firstly introduced survey field block adjustment theory, and will
Space scale expands to time scale, based on time series remotely-sensed data, using spatial and temporal scales block adjustment theory inverting east
Subregion aerosol optical depth spatial and temporal distributions result.Consider satellite zenith angle and sliding time window length to determination simultaneously
The impact of Reflectivity for Growing Season.
For achieving the above object, aerosol optical depth spatial and temporal distributions inversion method of the present invention, using no mid-infrared passage
When sensor determines Reflectivity for Growing Season, satellite sliding time window length can not be oversize, in order to avoid Reflectivity for Growing Season change, also can not
Too short, in order to avoid can not find sunny day, therefore sliding time window length is set to 30-61 days, and current pixel is set to currently processed
Pixel, present satellites zenith angle is the satellite zenith angle of current pixel, comprises the following steps that：
（1）Present satellites zenith angle is deducted 10 as angle current lower limit, plus 10 as angle current upper bound；
（2）The current bound of angle is compared with ± 90 respectively, when beyond when, then by the current upper bound exceeding or under
Limit is set to 90 or -90；
（3）Current sliding time window length is initially set the shortest 30 days；
（4）Select satellite zenith angle in current sliding time window anti-between the zenith of the passage 1 of current angular bound
Penetrate rate, and the zenith reflectivity number selected must be more than 7；
（5）When meeting condition：
1）Secondary dark reflectivity is not equal to the zenith reflectivity when the day before yesterday；Or
2）Secondary dark reflectivity is equal to the zenith reflectivity when the day before yesterday, and current slot is equal to 61 days,
Using the secondary secretly reflectivity of the zenith selected reflectivity as the background reflectivity of current pixel；
（6）When not obtaining the background reflectivity of current pixel, current slot increases by 2 days, constant when the day before yesterday；Follow
Ring step（4）、（5）With（6）Until obtaining the background reflectivity of current pixel or current slot more than 61 days；
（7）If not obtaining the background reflectivity of current pixel yet, skip to step（1）, change the bound of angle, from
Step（1）To step（7）Circulation is until obtaining the background reflectivity of current pixel, or the upper limit is more than 90, and lower limit exceed-
90º；
（8）If finding the background reflectivity of the passage 1 when the day before yesterday, the background reflection of the passage 2 of corresponding sunny day
Rate is i.e. when the background reflectivity of the passage 2 of the day before yesterday；
（9）Due to still there being aerosol to affect background day, the background aerosol effect of obtained image need to be removed it is assumed that carrying on the back
Scape day, the aerosol optical depth value at 550nm was 0.05, and aerosol model is continent type, and background aerosol effect is carried out
Correction, obtains passage 1 and the Reflectivity for Growing Season of passage 2.
Further, aerosol optical depth inversion method, comprises the following steps that：
1) reject described passage 1 and passage 2 medium cloud pixel, water body pixel and ice and snow pixel, only retain sunny pixel, then
Sunny pixel is filtered with ocean water body, retains land and Inland Water pixel；For Spring/winter, retain pixel and enter one
Step filters ice and snow pixel；
2) look-up table is set up offline based on aerosol model, different incidence and observation attitude integration and Reflectivity for Growing Season；
3) Reflectivity for Growing Season based on time series zenith reflectivity data and determination, according to block adjustment value, inverting
Aerosol optical depth spatial and temporal distributions result；Look-up table is carried out with linear interpolation input to practical inversion, during interpolation, first look for
Given input corresponding zenith reflectivity near the geometry attitude angle of practical inversion input, Reflectivity for Growing Season, then according to
Reflectivity for Growing Season, relative bearing, satellite zenith angle, the order of solar zenith angle, successively to often grading row interpolation, and by this level
Interpolation result return upper level and continue interpolation, until obtaining corresponding aerosol light when the observation of actual geometry and Reflectivity for Growing Season
Learn thickness.
Further, used sensor is AVHRR sensor, ccd sensor, VISSR sensor.
Brief description
Fig. 1 is Beijing Station Reflectivity for Growing Season comparison diagram；
Fig. 2 is prosperous station Reflectivity for Growing Season comparison diagram；
Fig. 3 is that the present invention carries out comparison diagram in East Asia Region with AERONET website ground based observa tion；
Fig. 4 is the contrast scatterplot density map of East Asia Region result of the present invention and MYD04 aerosol optical depth product；
The AVHRR aerosol optical depth figure of Fig. 5 a the inventive method inverting；
Fig. 5 b is MODIS dark target aerosol optical depth figure；
Fig. 5 c is 1km MYD02 data True color synthesis figure；
Fig. 5 d is AVHRR aerosol optical depth value, MODIS dark target aerosol optical depth value and respective transit time
The AERONET aerosol optical depth observation contrast of coupling.
Specific embodiment
Below in conjunction with the accompanying drawings the specific embodiment of the present invention is illustrated.
The present embodiment, describes earth's surface in aerosol optical depth inverting flow process and flow process anti-taking AVHRR sensor as a example
Penetrate the determination method of rate, and the contrast verification of the Reflectivity for Growing Season that obtains and aerosol optical depth.
Reflectivity for Growing Season determines method
For the determination of Reflectivity for Growing Season, because sliding time window length is too short, can lead to can not find sunny day, time
Length of window is long, is difficult to meet the constant hypothesis of Reflectivity for Growing Season.This algorithm supposition sliding time window length is the shortest to be 30
My god, no longer than 61 days.Current pixel is currently processed pixel, and present satellites zenith angle is the satellite zenith of current pixel
Angle.Comprise the following steps that：
（1）Present satellites zenith angle is deducted 10 as angle current lower limit, plus 10 as angle current upper bound；
（2）The current bound of angle is compared with ± 90 respectively, if it was exceeded, by exceed current on（Under）Limit
It is set to 90（-90º）；
（3）Current sliding time window length is initially set the shortest 30 days；
（4）Select satellite zenith angle in current sliding time window anti-between the zenith of the passage 1 of current angular bound
Penetrate rate, and the zenith reflectivity number selected must be more than 7；
（5）When meeting one of condition, using time dark reflectivity of the zenith selected reflectivity as current picture
The background reflectivity of unit：1）Secondary dark reflectivity is not equal to the zenith reflectivity when the day before yesterday；2）Secondary dark reflectivity was equal to when the day before yesterday
Zenith reflectivity, and current slot was equal to 61 days；
（6）Without the background reflectivity obtaining current pixel, current slot increases by 2 days, constant when the day before yesterday, circulation
Step（4）、（5）With（6）Until obtaining the background reflectivity of current pixel or current slot more than 61 days；
（7）If not obtaining the background reflectivity of current pixel yet, skip to step（1）, change the bound of angle, from step
Suddenly（1）Extremely（7）Circulation is until obtaining the background reflectivity of current pixel, or the upper limit is more than 90, and lower limit exceedes -90；
（8）If finding the background reflectivity of the passage 1 when the day before yesterday, the background reflection of the passage 2 of corresponding sunny day
Rate is i.e. when the background reflectivity of the passage 2 of the day before yesterday；
（9）The background aerosol effect of image obtained by removal.It is assumed that aerosol optical depth at 550nm for the background day
It is worth for 0.05, aerosol model is continent type, background aerosol effect is corrected, obtains passage 1 and the earth's surface of passage 2 is anti-
Penetrate rate.
Different from other algorithms, this algorithm considers sliding time window length simultaneously and defends when determining Reflectivity for Growing Season
The impact of star zenith angle.
2. aerosol optical depth inversion method
First reject cloud pixel, water body pixel and the ice and snow pixel of 630nm and 850nm passage, then utilize seasonal effect in time series
Clear sky zenith reflectivity image, estimates the Reflectivity for Growing Season of this two passages.Meanwhile, based on aerosol model, different incidence
Set up look-up table with observation attitude integration and Reflectivity for Growing Season offline.It is finally based on time series zenith reflectivity data and determination
Reflectivity for Growing Season, according to block adjustment value, inverting aerosol optical depth spatial and temporal distributions result.Comprise the following steps that：
1) cloud pixel, water body pixel and ice and snow pixel are rejected.The CLAVR cloud result being carried according to AVHRR data, by cloud picture
Unit, part cloud pixel, partly sunny pixel filter, only retain sunny pixel；Then ocean water body is filtered to sunny pixel, retain
Land and Inland Water pixel；Particularly with Spring/winter, retain pixel and need to further filter out ice and snow pixel.
Wherein, ice and snow mask is derived from the product of NOAA exploitation（ftp://www.orbit.nesdis.noaa.gov/pub/
smcd/emb/snow/global_mult_snow_ice/）.
2) determine that method determines Reflectivity for Growing Season according to above-mentioned Reflectivity for Growing Season.
3) look-up table is set up offline based on aerosol model, different incidence and observation attitude integration and Reflectivity for Growing Season.
Based on 6S（Second Simulation of a Satellite Signal in the Solar Spectrum-
Vector）Software, the look-up table according to needed for the concrete setting of parameters in table 1 is set up.Although the calculating of look-up table is compared
Time-consuming, but can be compensated from last fast inversion aerosol optical depth.
The setting of each parameter of look-up table in table 1. aerosol inverting
| Name variable | Input number | Input |
| Wavelength | 2 | 0.63 μm, 0.85 μm |
| Solar zenith angle（Degree） | 8 | 0, 12, 24, 36, 48, 54, 60, 66 |
| Satellite zenith angle（Degree） | 11 | 0, 8, 14, 20, 24, 30, 36, 42, 48, 54, 60 |
| Relative bearing（Degree） | 15 | 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120,132, 144, 160, 180 |
| Aerosol optical depth | 5 | 0.001,0.2, 0.5, 1.0, 2.0 |
| Reflectivity for Growing Season | 4 | 0.01, 0.12, 0.23, 0.34 |
| Atmospheric model | 4 | Middle latitude summer, middle latitude winter, sub- polar region summer, the torrid zone |
4) aerosol optical depth inverting：Look-up table has been calculated given input aerosol optical depth, earth surface reflection
Zenith reflectivity under rate and geometry observation condition, but in practical inversion, the geometry observation condition of pixel and the earth's surface that obtains are anti-
The input setting that the rate of penetrating is frequently not given inputs it is therefore desirable to look-up table is entered with row interpolation to practical inversion.This method adopts
Be linear interpolation mode.When being aerosol optical depth, therefore interpolation due to required unknown quantity, practical inversion need to be first looked for
Given input corresponding zenith reflectivity near the geometry attitude angle of input, Reflectivity for Growing Season, then according to earth surface reflection
Rate, relative bearing, satellite zenith angle, the order of solar zenith angle, successively to often grading row interpolation, and the interpolation knot by this grade
Fruit returns upper level and continues interpolation, until obtaining corresponding aerosol optical depth when actual geometry observation and Reflectivity for Growing Season.
The zenith reflectance value of known two passages, different from other methods, according to adjusted value, rather than least square method determines that gas is molten
Glue optical thickness.
3. Reflectivity for Growing Season Comparative result
Reflectivity for Growing Season result is in identical incident and observation attitude condition Reflectivity for Growing Season result with other three kinds
Contrasted, and these four results are employed the same AVHRR data set through pretreatment and gas absorption correction.1）Profit
With the ground AERONET aerosol optical depth product in AVHRR zenith reflectivity, AVHRR transit time ± 30 minute,
Ground AERONET Spectral structure in AVHRR transit time ± 3 hour and the ground of complex refractive index product inverting AERONET website
Table reflectivity；2）MODIS BRDF/ albedo product（MCD43C2）Process the incidence to AVHRR and observation attitude again, and due to
It has seasonal effect in time series as a result, it is possible to provide the result reference of time consistency.3）By European Riffler et al.（2010）
Algorithm be applied to same AVHRR data set, this algorithm only considered the impact of satellite zenith angle.
Beijing Station and the surveyed Reflectivity for Growing Season in prosperous station shown in Fig. 2 contrast as shown in Figure 1, and shown time series is 2009
On May 31, on January 31, to 2010, the wherein red result for inversion method of the present invention；Blue is European arithmetic result；Green
Color is the Reflectivity for Growing Season after MCD43C2 process；Purple is the Reflectivity for Growing Season after the process of AERONET website.Stood by two
The contrast of point finds, the Reflectivity for Growing Season after the Reflectivity for Growing Season inversion result of the present invention and AERONET and MCD43 process is more
Close to it is shown that this algorithm and the Europe advantage compared of algorithm.
4. aerosol optical depth inversion result and contrast verification
As shown in figure 3, this algorithm is applied to East Asia Region（18°N-54°N, 100°E-136°E）, inverse time section is
On May 31,31 days to 2010 January in 2009, and carry out contrast verification with AERONET website ground based observa tion.
The inventive method aerosol optical depth inversion result in figure 550nm, European algorithm aerosol optical depth
Inversion result is contrasted with MODIS aerosol optical depth product.Fine line is 1:1 line, heavy line, dotted line and dotted line are respectively
The fitting a straight line of MODIS, European algorithm and this algorithm.In figure text shows fitting formula, coefficient correlation（R）With RMSE by mistake
Difference.
As shown in figure 4, on 2 27th, 2009 this arithmetic result of East Asia Region and MYD04 aerosol optical depth product
Contrast scatterplot density map.On 2 27th, 2009 East Asia Region the inventive method results and MYD04 aerosol optical depth product
Contrast scatterplot density map.Data is sorted and is referred in 0.03 × 0.03 grid.Fine line and heavy line are 1 respectively:1 line
With Linear Quasi zygonema.In figure text representation linear fit formula, coefficient correlation（R）, RMSE error and participate in matching points
（N）.
As shown in Fig. 5 a-5d, on March 26th, 2009 East Asia Region aerosol optical depth becomes figure and contrast：Fig. 5 a this
The AVHRR aerosol optical depth figure of bright inversion method；Fig. 5 b is MODIS dark target aerosol optical depth figure；Fig. 5 c is
1km MYD02 data True color synthesis figure；Fig. 5 d is AVHRR aerosol optical depth value, MODIS dark target aerosol optical is thick
The AERONET aerosol optical depth observation contrast that angle value is mated with respective transit time（Because AVHRR, MODIS are through same
The transit time in area is different）.
One of method innovation point proposed by the present invention is to consider observation zenith at many days in joint observation method simultaneously
Angle and the impact of sliding time window length, especially for the serious East Asia Region of air pollution, the Reflectivity for Growing Season obtaining
Result precision is more preferable.The two of innovative point are that employing block adjustment value replaces least square method, thick as aerosol optical
The criterion of degree refutation process.Through contrasting with AERONET ground based observa tion, the aerosol optical depth result ratio that discovery inverting obtains is not
Consider that the arithmetic accuracy of above-mentioned factor is higher.
The present invention be directed to the method for the no aerosol optical depth spatial and temporal distributions inverting of the sensor of mid-infrared passage, tool
There is the technical characteristic following the course of nature, this method solve the technical problem of inverting aerosol optical depth, and be not belonging to specially
The rules and methods of sharp 25 intellections described in 2 sections of method.
Claims (3)
1. the aerosol optical depth spatial and temporal distributions inversion method of no mid-infrared channel sensor is it is characterised in that using in no
When infrared channel sensor determines Reflectivity for Growing Season, sliding time window length is 30-61 days, and current pixel is currently processed picture
Unit, present satellites zenith angle is the satellite zenith angle of current pixel, comprises the following steps that：
(1) present satellites zenith angle is deducted 10 ° of current lower limits as angle, plus 10 ° of current upper bound as angle；
(2) the current bound of angle is compared with ± 90 ° respectively, if it was exceeded, the current upper bound exceeding or lower limit are set
For 90 ° or -90 °；
(3) current sliding time window length is initially set the shortest 30 days；
(4) select current sliding time window in satellite zenith angle between the passage 1 of current angular bound zenith reflectivity,
And the zenith reflectivity number selected must be more than 7；
(5) when meeting condition：
1) secondary dark reflectivity is not equal to the zenith reflectivity when the day before yesterday；Or
2) secondary dark reflectivity is equal to the zenith reflectivity when the day before yesterday, and current slot is equal to 61 days, will be anti-for the zenith selected
Penetrate the background reflectivity as current pixel for time dark reflectivity of rate；
(6) without the background reflectivity obtaining current pixel, current slot increases by 2 days, constant when the day before yesterday；Circulation step
(4), (5) and (6) are until obtaining the background reflectivity of current pixel or current slot more than 61 days；
(7) if not obtaining the background reflectivity of current pixel yet, skipping to step (1), changing the bound of angle, from step
(1) to step (7) circulation until obtaining the background reflectivity of current pixel, or the upper limit is more than 90 °, and lower limit exceedes -90 °；
(8) if finding the background reflectivity of the passage 1 when the day before yesterday, the background reflectivity of the passage 2 of corresponding sunny day is
Background reflectivity when the passage 2 of the day before yesterday；
(9) obtained by removing, the background aerosol effect of image is it is assumed that aerosol optical depth value at 550nm for the background day is
0.05, aerosol model is continent type, background aerosol effect is corrected, obtains passage 1 and the earth surface reflection of passage 2
Rate.
2. aerosol optical depth spatial and temporal distributions inversion method as claimed in claim 1 is it is characterised in that aerosol optical depth
Inversion method comprises the following steps that：
1) reject described passage 1 and passage 2 medium cloud pixel, water body pixel and ice and snow pixel, only retain sunny pixel, then to fine
Bright pixel filters ocean water body, retains land and Inland Water pixel；For winter in spring, retain pixel and further filter out ice and snow picture
Unit；
2) look-up table is set up offline based on aerosol model, different incidence and observation attitude integration and Reflectivity for Growing Season；
3) Reflectivity for Growing Season based on time series zenith reflectivity data and determination, according to block adjustment value, inverting gas is molten
Glue optical thickness spatial and temporal distributions result；Look-up table is carried out with linear interpolation input to practical inversion, during interpolation, first look for reality
Given input corresponding zenith reflectivity near the geometry attitude angle of inverting input, Reflectivity for Growing Season, then according to earth's surface
Reflectivity, relative bearing, satellite zenith angle, the order of solar zenith angle, successively to often grading row interpolation, and inserting this grade
Value result returns upper level and continues interpolation, until it is thick to obtain corresponding aerosol optical when actual geometry observation and Reflectivity for Growing Season
Degree.
3. aerosol optical depth spatial and temporal distributions inversion method as claimed in claim 1 or 2 is it is characterised in that red in described nothing
Outer tunnel sensor is AVHRR sensor, ccd sensor, VISSR sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310505797.6A CN104569952B (en) | 2013-10-24 | 2013-10-24 | Aerosol optical thickness temporal-spatial distribution inversion method without mid-infrared channel sensor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310505797.6A CN104569952B (en) | 2013-10-24 | 2013-10-24 | Aerosol optical thickness temporal-spatial distribution inversion method without mid-infrared channel sensor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104569952A CN104569952A (en) | 2015-04-29 |
| CN104569952B true CN104569952B (en) | 2017-02-15 |
Family
ID=53086471
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310505797.6A Active CN104569952B (en) | 2013-10-24 | 2013-10-24 | Aerosol optical thickness temporal-spatial distribution inversion method without mid-infrared channel sensor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104569952B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105953921B (en) * | 2016-04-15 | 2018-11-06 | 北京航空航天大学 | The rapid simulation method of earth observation radiation image under the conditions of aerosol parameters difference |
| CN110411919B (en) * | 2019-08-02 | 2021-04-16 | 中国科学院遥感与数字地球研究所 | PM2.5 concentration remote sensing estimation method based on satellite multispectral technology |
| CN112748444B (en) * | 2020-12-30 | 2023-04-07 | 中国科学院空天信息创新研究院 | Aerosol optical thickness inversion method without mid-infrared channel sensor |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103204A (en) * | 2011-01-26 | 2011-06-22 | 环境保护部卫星环境应用中心 | Inversion method for land aerosols optical thickness based on environment satellite 1 |
| CN102338871A (en) * | 2010-07-22 | 2012-02-01 | 曹春香 | Method and device for calculating reflectivity of earth surface |
-
2013
- 2013-10-24 CN CN201310505797.6A patent/CN104569952B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102338871A (en) * | 2010-07-22 | 2012-02-01 | 曹春香 | Method and device for calculating reflectivity of earth surface |
| CN102103204A (en) * | 2011-01-26 | 2011-06-22 | 环境保护部卫星环境应用中心 | Inversion method for land aerosols optical thickness based on environment satellite 1 |
Non-Patent Citations (1)
| Title |
|---|
| 基于AVHRR和VIRR数据的改进型Becker"分裂窗"地表温度反演算法;权维俊1 等;《气象学报》;20121231;第70卷(第6期);第1356-1366页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104569952A (en) | 2015-04-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110186823B (en) | Aerosol optical thickness inversion method | |
| De Keukelaere et al. | Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters | |
| Verhoef et al. | Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models | |
| Sadavoy et al. | The Herschel and JCMT Gould belt surveys: constraining dust properties in the perseus B1 clump with PACS, SPIRE, and SCUBA-2 | |
| CN104483663B (en) | A kind of target in hyperspectral remotely sensed image Atmospheric Correction method and system | |
| CN100362318C (en) | Atmosphere correction method of airosol optical thickness of aeronautical high-spectrum remote-sensing inversion boundary layer | |
| CN102540166B (en) | Cross radiation calibration method based on optimization algorithm of hyper-spectral sensor | |
| EP2637040B1 (en) | Adaptive method for estimating the electronic content of the ionosphere | |
| CA2853546A1 (en) | Identification and analysis of aircraft landing sites | |
| CN104880702A (en) | Method and device for on-orbit absolute radiation calibration | |
| CN103293522A (en) | Intermediate infrared two-channel remote sensing data surface temperature inversion method and device | |
| CN104535979A (en) | Remote sensing inversion method and system for land cloud optical thickness | |
| CN104569952B (en) | Aerosol optical thickness temporal-spatial distribution inversion method without mid-infrared channel sensor | |
| CN108256186A (en) | A kind of pixel-by-pixel atmospheric correction method in line computation look-up table | |
| CN113744249B (en) | Marine ecological environment damage investigation method | |
| CN115308386B (en) | Soil salinity inversion method and system based on CYGNSS satellite data | |
| Nag et al. | Simulation of multiangular remote sensing products using small satellite formations | |
| CN110672640A (en) | Soil humidity estimation method and device for vegetation coverage area | |
| Ceamanos et al. | Improved retrieval of direct and diffuse downwelling surface shortwave flux in cloudless atmosphere using dynamic estimates of aerosol content and type: application to the LSA-SAF project | |
| CN103824260A (en) | High-resolution remote sensing image mist rapid removal technology | |
| Qu et al. | Retrieval of 30-m-resolution leaf area index from China HJ-1 CCD data and MODIS products through a dynamic Bayesian network | |
| Gourrion et al. | Toward an optimal estimation of the SMOS antenna-frame systematic errors | |
| US11416968B2 (en) | Method for increasing the spatial resolution of a multispectral image from a panchromatic image | |
| Joshi et al. | Optical Properties Using Adaptive Selection of NIR/SWIR Reflectance Correction and Quasi-Analytic Algorithms for the MODIS-Aqua in Estuarine-Ocean Continuum: Application to the Northern Gulf of Mexico | |
| Yakunin | A hybrid method for evaluating absorbed solar energy at the surface level using remote sensing data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220715 Address after: 100190 No. 19 West North Fourth Ring Road, Haidian District, Beijing Patentee after: Research Institute of aerospace information innovation, Chinese Academy of Sciences Address before: Institute of remote sensing applications, Chinese Academy of Sciences, north, No. 20, Datun Road, Chaoyang District, Beijing 100101 Patentee before: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences |
|
| TR01 | Transfer of patent right |