CN116148189B - Aerosol layer height acquisition method based on passive polarized satellite data - Google Patents
Aerosol layer height acquisition method based on passive polarized satellite data Download PDFInfo
- Publication number
- CN116148189B CN116148189B CN202310398990.8A CN202310398990A CN116148189B CN 116148189 B CN116148189 B CN 116148189B CN 202310398990 A CN202310398990 A CN 202310398990A CN 116148189 B CN116148189 B CN 116148189B
- Authority
- CN
- China
- Prior art keywords
- aerosol
- layer height
- near infrared
- infrared band
- toa
- 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
- 239000000443 aerosol Substances 0.000 title claims abstract description 174
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000005855 radiation Effects 0.000 claims abstract description 95
- 230000005540 biological transmission Effects 0.000 claims abstract description 53
- 238000002310 reflectometry Methods 0.000 claims abstract description 45
- 230000010287 polarization Effects 0.000 claims abstract description 36
- 238000001228 spectrum Methods 0.000 claims abstract description 19
- 230000003287 optical effect Effects 0.000 claims abstract description 18
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 19
- 239000002245 particle Substances 0.000 claims description 15
- 238000005259 measurement Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 7
- 238000010521 absorption reaction Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000012986 modification Methods 0.000 claims description 5
- 230000004048 modification Effects 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 4
- 239000011248 coating agent Substances 0.000 claims description 3
- 238000000576 coating method Methods 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims description 3
- 239000000428 dust Substances 0.000 claims description 3
- 239000006260 foam Substances 0.000 claims description 3
- 239000010954 inorganic particle Substances 0.000 claims description 3
- 239000000779 smoke Substances 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 238000002834 transmittance Methods 0.000 claims description 3
- 230000017105 transposition Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 230000007547 defect Effects 0.000 description 8
- 238000009826 distribution Methods 0.000 description 8
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000002939 conjugate gradient method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 238000000790 scattering method Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/21—Polarisation-affecting properties
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
-
- 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
Abstract
The invention provides an aerosol layer height acquisition method based on passive polarized satellite data, belongs to the technical field of remote sensing image processing, and solves the problem that an accurate aerosol layer height result is difficult to obtain in the prior art. The method comprises the following steps: acquiring multi-angle polarization spectrum data of a target region acquired by a passive polarization satellite; determining components and optical parameters of the aerosol to be detected, and inputting the set aerosol layer height by combining the multi-angle polarization spectrum dataa p In the forward radiation transmission model of (2), the simulated aerosol layer height is obtaineda p Apparent reflectivity of TOA of corresponding near infrared bandf(a p ) The method comprises the steps of carrying out a first treatment on the surface of the Obtaining the apparent reflectivity of the TOA of the actually measured near infrared band, and simulating the apparent reflectivity of the TOA of the near infrared bandf(a p ) And actually measured apparent reflectancef* Performing consistency fit matching, if the consistency is not consistent, returning to the step S2 to modify the aerosol layer height of the forward radiation transmission modela p Until the matching results are consistent, the currenta p The final aerosol layer is high as the target zone.
Description
Technical Field
The invention relates to the technical field of remote sensing image processing, in particular to an aerosol layer height acquisition method based on passive polarized satellite data.
Background
The acquisition of the layer height distribution of the aerosol, i.e. the vertical distribution, is of great importance for radiation forcing research and atmospheric quality assessment, explaining aerosol scattering in trace gases and marine component inversion. Since aerosols have a cloud-to-cloud effect on the cloud floor height, the vertical distribution of aerosols can affect the aerosol-cloud interaction process.
In order to measure the layer height distribution of aerosols, since the cloud-aerosol polarized laser radar (CALIOP) mounted on the CALIPSO platform in 2006 was provided, there has been a great deal of research in aerosol layer height measurement. Active remote sensing techniques using high spectral resolution laser radar (LIDAR) measurements can accurately measure the vertical distribution of aerosols, but there are limitations in breadth and revisit periods. So far, passive satellites still cannot provide inversion aerosol vertical distribution data as accurate as LIDAR, but their data is an indispensable complement to LIDAR aerosol layer height inversion in terms of spatial extent and revisit period.
O provided by passive satellite 2 The a-band (0.76 μm) measurement has the advantage of being immune to thermal radiation, which has proven to be of great value in inverting the aerosol layer height with a large aerosol optical thickness. However, in practical application, ensure that the slave O 2 The consistency of the aerosol height obtained in the a-band with LIDAR is very difficult, depending on the condition assumptions. Currently, there is an urgent need for more practical passive satellite measurement techniques to make up for the deficiencies of LIDAR in inversion of aerosol layer vertical distributions.
Disclosure of Invention
In view of the above analysis, the embodiment of the invention aims to provide an aerosol layer height acquisition method based on passive polarized satellite data, which is used for solving the problem that an accurate aerosol layer height result is difficult to obtain in the prior art.
In one aspect, the embodiment of the invention provides an aerosol layer height acquisition method based on passive polarized satellite data, which comprises the following steps:
s1, acquiring multi-angle polarization spectrum data of a target region acquired by a passive polarization satellite;
s2, determining components of the aerosol to be detected and optical parameters of each component, and inputting the set aerosol layer height a by combining the multi-angle polarization spectrum data p In the forward radiation transmission model of (2), the simulated aerosol layer height a is obtained p Corresponding near infrared band TOA apparent reflectance f (a p )；
S3, acquiring the apparent reflectivity f of the TOA of the actually measured near infrared band acquired by the passive remote sensing satellite, and simulating the apparent reflectivity fThe apparent reflectivity f (a) p ) Performing consistency fit matching with the apparent reflectivity f of the TOA of the actually measured near infrared band, and if the apparent reflectivity f is inconsistent, returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Performing consistency fitting matching again until the matching result is consistent, and then performing the current a p The final aerosol layer as the target zone is high output.
The beneficial effects of the technical scheme are as follows: to date, there is no related art for inverting the aerosol layer height distribution based on passive polarized satellite data. Under the background, the technical scheme provides an aerosol layer height inversion method based on passive satellite near-infrared multi-angle polarization measurement, and aerosol layer height data with larger breadth and shorter revisit period relative to LIDAR can be obtained through a passive polarized satellite, so that the method is a great breakthrough in the field of satellite inversion of atmospheric components. The multi-angle polarization data of the target region acquired by the passive polarization satellite are utilized, the forward radiation transmission model is combined to invert and obtain the aerosol layer height parameter of the air above the target sea area, and the defects of high product width and long revisit period of the aerosol layer facing active remote sensing (CALIOP, LIDAR) are greatly overcome by utilizing the passive remote sensing data.
Based on the further improvement of the method, the components of the aerosol to be measured comprise at least one of smoke aerosol, water-soluble aerosol, marine aerosol and dust aerosol;
the optical parameters of each component include aerosol particle radius, logarithmic standard deviation of aerosol particle radius, complex refractive index.
Further, the forward radiation transmission model adopts one of an OSOAA radiation transmission model and a PCOART-SA radiation transmission model.
Further, after the current radiation transmission model adopts the OSOAA radiation transmission model, step S2 further includes:
s21, determining components of aerosol to be detected in a target region and optical parameters of each component;
according to the components of the aerosol to be tested and the optical parameters of each component, the aerosol is obtained by the following formulaNear infrared band vector radiation field L received by passive polarized satellite on atmosphere roof t (λ)，
L t (λ)＝L r (λ)+L a (λ)+T(λ)L g (λ)+t(λ)L wc (λ)+t(λ)L w (λ)，
L a (λ)＝g(a p ，λ，d i )，
Wherein lambda is the center wavelength of the passive polarized satellite, d i I= … … n, n being the total number of components of the aerosol to be measured, L r (lambda) is the vector radiation contributed by Rayleigh scattering, L a (λ) is the vector radiation contributed by aerosol scattering-absorption including aerosol and rayleigh scattering interactions; l (L) g (lambda) is the vector radiation contributed by solar flare, L g (λ)＝0；L wc (lambda) is the vector radiation contributed by sea surface white foam, near infrared band L wc (λ)＝0；L w (lambda) is the vector water-leaving radiation above the atmosphere bottom, the sea surface, near infrared band L w (λ) =0; t (λ) and T (λ) represent the atmospheric diffusion and direct transmittance, respectively, of vector radiation above the sea surface; g (a) p ，λ，d i ) Calculating a function for the aerosol scattering-absorption contribution;
for the near infrared band vector radiation field L t (lambda) normalized by the following formula to obtain the simulated aerosol layer height a at each observation angle p Corresponding near infrared band TOA apparent reflectance f (a p )，
ρ t (λ)＝π·L t (λ)/F 0 ，
Wherein eta is f I 、ρ f Q 、ρ f U Apparent reflectance ρ at the top of the atmosphere t The first three values of (lambda), F 0 Is the value of the solar irradiance outside the ground in each wave band.
Further, step S3 further includes:
s31, acquiring continuous spectrum data in a near infrared band acquired by a water color sensor on a passive remote sensing satellite;
s32, identifying the apparent reflectivity f of the TOA of the actually measured near infrared band of the target region under different observation angles in the continuous spectrum data, wherein the apparent reflectivity f is = [ rho ] I (i),ρ Q (i),ρ U (i)]；
S33, simulating the apparent reflectivity f (a) of the near infrared band TOA p ) Inputting the apparent reflectivity f of the TOA of the near infrared band to the cost function eta in the following formula 2 Is matched by consistency fit,
wherein i is a specific observation wavelength and an observation angle in a near infrared band; ρ f I (i)、ρ f Q (i)、ρ f U (i) For i the observation wavelength and the observation angle, simulating the aerosol layer height a p The apparent reflectivity of the TOA of the corresponding near infrared band; phi I (i)、Φ Q (i)、Φ U (i) A covariance value related to the error in the cost function;
s34, identifying eta 2 If the matching result is within the set range, judging that the matching result is consistent, otherwise, judging that the matching result is inconsistent, and returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Performing consistency fitting matching again until the matching result is consistent, and then performing the current a p The final aerosol layer as the target zone is high output.
Further, step S3 further includes:
s35, further calculating a covariance value phi related to the error in the cost function through the following formula I 、Φ Q 、Φ U ，
In phi, phi f Is background noise phi s Phi is shot noise caused by charge discontinuity c For uncertainty of satellite radiation intensity data, Φ p As uncertainty of polarization degree, θ 0 Is the zenith angle of the sun, ρ I 、ρ Q 、ρ U The apparent reflectivity of TOA in the near infrared band is actually measured;
s36, identifying phi I 、Φ Q 、Φ U Whether the matching results are all in the respective set ranges, if so, judging that the matching results are reliable, otherwise, judging that the matching results are not reliable, and returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Again, consistency fitting and matching are carried out until the matching results are consistent and phi I 、Φ Q 、Φ U All are within the respective setting range, the current a p The final aerosol layer as the target zone is high output.
Further, in step S3, the aerosol layer height a of the forward radiation transmission model is determined by the following substeps p Inversion modification is performed:
s341, coating the aerosol with a p F (a) obtained by the forward radiation transmission model as the target argument x p ) As the dependent variable f (x), an initial value x of the independent variable is set 0 Radius of trust delta 0 Iterative accuracy epsilon>0；
S342, obtaining the k iteration f (x) at x (k) Function value f at k Gradient of the body k Identifying whether:
‖g k ‖≤ε，
if yes, ending the iteration, otherwise, calculating a Hessian matrix H k And deriving the Hessian matrixThe trust domain model is as follows,
wherein, ψ is k (s) def Is a substitution function of f (x), s is the change amount of a target variable, T is a transposition operation, and delta k The radius of the confidence domain for the kth iteration, s.t. represents the boundary;
s343, solving the trust domain model to obtain a change amount optimal solution s of the target variable k ；
S344, according to the optimal solution s k To obtain f (x k +s k )、Ψ k (s) def Further, the ratio ρ of the actual drop amount to the predicted drop amount is obtained by the following formula k ，
S345, identifying the rho k Whether or not ρ is satisfied k >Mu, if so, updating the argument x of the next iteration k+1 ＝x k +Δ k Otherwise, the independent variable of the next iteration is the same as the independent variable of the current iteration, and x k+1 ＝x k ；
S346, identifying the rho k Whether or not ρ is satisfied k >η, if so, update the confidence region radius Δ for the next iteration k+1 ＝2Δ k Otherwise, executing the next step; 0<μ<η<1；
S347. Identifying the above ρ k Whether or not mu is satisfied<ρ k <η, if so, the confidence region radius of the next iteration is the same as the confidence region radius of the current iteration, i.e. Δ k+1 ＝Δ k Otherwise, the confidence region radius Δ for the next iteration is updated according to the following formula k+1 ，
S348, turning to the step S342, continuing the subsequent iteration process until II g k The II is less than or equal to epsilon, the iteration is finished, and the target independent variable x in the last iteration is output and is used as the optimal a p 。
Further, the aerosol layer height obtaining method further comprises the following steps of:
s4, enabling the aerosol layer height a of the forward radiation transmission model p Setting the final aerosol layer obtained in the step S3;
s5, inputting components of the aerosol to be detected and optical parameters of each component into the forward radiation transmission model by combining the multi-angle polarization spectrum data to obtain the apparent reflectivity f (a) of the near infrared band TOA p ) ρ in (b) f I 、ρ f Q 、ρ f U ；
S6, according to the rho f I 、ρ f Q 、ρ f U The concentration IPM of inorganic particles in the water body for reflecting the turbidity of the water body is further obtained by the following formula,
IPM＝1.469ln(DoLP-44.498)+5.957，
further, in step S35, background noise Φ f ＝7×10 -5 Shot noise Φ caused by charge discontinuity s ＝7×10 -8 Uncertainty Φ of satellite radiation intensity data c Uncertainty of polarization degree Φ=0.03 p ＝0.01。
Further, marine aerosols are composed of 5% of water-soluble aerosol particles and 95% of marine aerosol particles.
Compared with the prior art, the invention has at least one of the following beneficial effects:
1. the existing active satellite technology (CALIOP) aiming at the inversion of the aerosol layer height has the defects of narrow width and long revisit period. Therefore, if only active satellite data is relied on, real-time acquisition of aerosol layer height parameters at a ground target area is almost impossible. The scheme adopts the passive satellite, overcomes the defects of the active satellite, and can acquire the aerosol layer height parameters of the ground target area in real time.
2. Existing passive satellite techniques for aerosol layer height inversion (e.g., O-based 2 A band) has defects that the accuracy is insufficient and that it is highly dependent on the condition assumption. Meanwhile, few studies utilize passive polarized satellite data to perform aerosol layer height inversion. The scheme obtains the aerosol layer height data with larger breadth and shorter revisit period relative to the LIDAR through the passive polarized satellite, which is a great breakthrough in the field of satellite inversion of atmospheric components.
The summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the invention, nor is it intended to be used to limit the scope of the invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 shows a schematic diagram of the steps of the aerosol layer height acquisition method of example 1;
FIG. 2 shows a schematic diagram of the aerosol layer inversion principle of the aerosol layer height acquisition method of example 2;
fig. 3 shows a schematic diagram of the forward radiation model frame of example 2.
Reference numerals
a p -aerosol layer height; f (a) p ) -simulating the resulting near infrared band TOA apparent reflectivity; f—measured near infrared band TOA apparent reflectance.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While embodiments of the present invention are illustrated in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The term "comprising" and variations thereof as used herein means open ended, i.e., "including but not limited to. The term "or" means "and/or" unless specifically stated otherwise. The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment. The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
The abbreviations and definitions to which the invention relates are first described below
TOA: at the top of the atmosphere.
BOA: the bottom of the atmosphere and above the sea surface.
Example 1
An embodiment of the invention discloses an aerosol layer height acquisition method based on passive polarized satellite data, as shown in fig. 1, comprising the following steps:
s1, acquiring multi-angle polarization spectrum data of a target region acquired by a passive polarization satellite;
s2, determining components of the aerosol to be detected and optical parameters of each component, and inputting the set aerosol layer height a by combining the multi-angle polarization spectrum data p In the forward radiation transmission model of (2), the simulated aerosol layer height a is obtained p The corresponding near infrared band (0.865 μm) TOA apparent reflectance f (a) p ) The method comprises the steps of carrying out a first treatment on the surface of the The TOA apparent reflectivity is also called as apparent reflectivity at the top of the atmosphere;
the forward radiation transmission model may use a variety of models that support coupling vector radiation transmission simulation, such as the existing OSOAA radiation transmission model, PCOART-SA radiation transmission model.
The OSOAA radiation transmission model is described in the prior art: OSOAA a vector radiative transfer model of coupled atmosphere-ocean system for a rough sea surface application to the estimates of the directional variations of the water leaving reflectance to better process multi-angular satellite sensors data over the ocean [ O ]. Chami Malik, lafrance Bruno, fougnie Bertrand,2015 (OSOAA: a vector radiation transmission model of a coupled marine-marine system, adapted for rough sea applications, for estimating water direction changes and leaving reflectivity to better process multi-angle satellite sensor data on the ocean).
PCOART-SA radiation transmission model see the prior art: effects of Earth curvature on atmospheric correction for ocean color remote sensing [ J ], xianqiang He, knut Stamnnes, yan Bai, wei Li, difeng Wang,2018.Remote Sensing of Environment.
S3, acquiring the actual measurement near infrared band TOA apparent reflectivity f acquired by a passive remote sensing satellite, and simulating the obtained near infrared band TOA apparent reflectivity f (a p ) Performing consistency fit matching with the apparent reflectivity f of the TOA of the actually measured near infrared band, and if the apparent reflectivity f is inconsistent, returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Performing consistency fitting matching again until the matching result is consistent, and then performing the current a p The final aerosol layer as the target zone is high output.
In implementation, as the forward radiation transmission model has higher integration, the method can acquire the height parameter of the aerosol layer height at the target point through operation about 5min after inputting the multi-angle polarization spectrum data of the target sea area. In addition, the direct product obtained by the method can reflect the air quality condition.
In contrast to the prior art, to date, there is no related art for inverting aerosol layer height based on passive polarized satellite data. Under the background, the embodiment provides an aerosol layer height inversion method based on passive satellite near-infrared multi-angle polarization measurement, and aerosol layer height data with larger breadth and shorter revisit period relative to LIDAR can be obtained through a passive polarized satellite, which is a great breakthrough in the field of satellite inversion of atmospheric components. The multi-angle polarization data of the target region acquired by the passive polarization satellite are utilized, the forward radiation transmission model is combined, the aerosol layer height parameter above the target sea area can be obtained through inversion, and the defects of high product width and long revisit period of the aerosol layer facing active remote sensing (CALIOP, LIDAR) are overcome greatly by utilizing the passive remote sensing data.
Example 2
The aerosol to be measured comprises at least one of smoke aerosol, water-soluble aerosol, marine aerosol and dust aerosol by improving the aerosol of the embodiment 1. The optical parameters of each component included aerosol particle radius, logarithmic standard deviation of aerosol particle radius, complex refractive index, see table 1.
At different proportions of aerosol component concentrations, different aerosol types in the atmosphere can be simulated, for example a common marine aerosol type can be represented as (V WS ＝5％,V OC =95%), i.e. consisting of 5% water-soluble aerosol particles+95% marine aerosol particles.
TABLE 1 optical parameters of aerosol components (865 nm)
The forward radiation transmission model can use various models supporting coupling vector radiation transmission simulation, the mathematical framework is shown in fig. 2, and the implementation principle of the whole method is shown in fig. 3.
Preferably, the forward radiation transmission model employs one of an OSOAA radiation transmission model, a PCOART-SA radiation transmission model. The normalized vector radiance at TOA was then simulated using the OSOAA radiation transfer model as an example (see equation (3) below, ρ t (lambda)). The OSOAA uses planar parallel layer assumption and successive scattering methods to handle ocean-atmosphere coupled vector radiation transport.
Preferably, the polarization spectrum data of the target region can be represented by the near infrared band vector radiation field S (Stokes vector) of the coupled marine atmospheric system of the following formula
Wherein I is the radiation intensity received by a water color sensor of a passive polarized satellite, and is also called total radiation brightness, Q is the linear polarization component of radiation in the horizontal or vertical direction, U is the linear polarization component of radiation in the + -45 DEG direction, and V is the elliptical polarization component of radiation; in general, the V component is negligible in marine atmospheric systems, so v=0; e (E) x And E is y The vibration components of the electric field vector signals acquired by the passive polarized satellites along the X, Y direction in the selected coordinate system are respectively shown, delta is the phase difference between the two vibration components of the Q linear polarization component and the U linear polarization component,<>representing the average over time. The subscript I, Q, U of the variables hereinafter is as defined above.
Preferably, the current forward radiation transmission model employs an OSOAA radiation transmission model, and step S2 further includes:
s21, determining components of aerosol to be detected in a target region and optical parameters of each component;
s22, acquiring a near infrared band vector radiation field L received by a passive polarized satellite on the atmosphere roof according to components of aerosol to be detected and optical parameters of each component through the following formula t (λ)，
L t (λ)＝L r (λ)+L a (λ)+T(λ)L g (λ)+t(λ)L wc (λ)+t(λ)L w (λ)
L a (λ)＝g(a p ，λ，d i ) (2)
Where λ is the center wavelength (observation band) of the passive polarized satellite, d i I= … … n, n being the total number of components of the aerosol to be measured, L r (lambda) is the vector radiation contributed by Rayleigh (molecular) scattering (obtainable by looking up Rayleigh scattering tables or computational steps looking up existing OSOAA models), L a (λ) is the vector radiation contributed by aerosol scattering-absorption including aerosol and rayleigh scattering interactions; l (L) g (lambda) is the vector radiation contributed by solar flare, L g (λ)＝0；L wc (lambda) is the vector radiation contributed by sea surface white foam, near infrared band L wc (λ)＝0；L w (lambda) is the vector water-leaving radiation above the atmosphere bottom, the sea surface, near infrared band L w (λ) =0; t (λ) and T (λ) represent the atmospheric diffusion and direct transmittance, respectively, of vector radiation above the sea surface; g (a) p ，λ，d i ) Calculating a function for the aerosol scattering-absorption contribution (the specific calculation formula can be obtained by referring to the existing OSOAA model, or by fitting);
s23, for the near infrared band vector radiation field L t (lambda) normalized by the following formula (i.e., stokes vector at TOA normalized to the value of the solar irradiance outside the earth) to obtain simulated aerosol layer height a at each observation angle p Corresponding near infrared band TOA apparent reflectance f (a p )，
Wherein ρ is f I 、ρ f Q 、p f U Apparent reflectance ρ at the top of the atmosphere t The first three values of (lambda), F 0 Is the value of the solar irradiance outside the ground in each wave band.
Referring to equation (2), since the total reflectance and polarization reflectance of the water-leaving contribution approach 0 in both the ultraviolet and near infrared bands, the ocean is set to be fully absorbed in the near infrared band (ρ w ＝0，ρ wc =0), i.e. dark picture elements. At the same time, the radiance approximation contributed by solar flare is colloquially excluded from the atmospheric correction flow (ρ g =0). Furthermore, the reflectivity (ρ) of the molecular scattering contribution can be easily obtained by means of a pre-computed rayleigh scattering look-up table r ). Based on the prior calculation, the apparent reflectivity f (a) of TOA is uniquely determined through aerosol parameters of near-red wave band p ) Then utilizeAnd (3) fitting the simulated TOA apparent reflectivity and the actually measured apparent reflectivity by an aerosol layer height inversion algorithm in the step (S3) to finally determine the aerosol layer height parameters.
Preferably, referring to fig. 3, step S3 further comprises the step of combining the f-x, a of satellite data under a series of prior conditions to achieve p Function of performing nonlinear optimization:
s31, acquiring continuous spectrum data in a near infrared band acquired by a water color sensor on a passive remote sensing satellite;
s32, identifying the apparent reflectivity f of the TOA of the actually measured near infrared band of the target region under different observation angles in the continuous spectrum data, wherein the apparent reflectivity f is = [ rho ] I (i)、ρ Q (i)、ρ U (i)]；
S33, simulating the apparent reflectivity f (a) of the near infrared band TOA p ) Inputting the apparent reflectivity f of the TOA of the near infrared band to the cost function eta in the following formula 2 A consistency fit match is made in (also known as a nonlinear optimization algorithm cost function),
wherein i is a certain specific observation wavelength and observation angle in the near infrared band, namely each specific observation wavelength and observation angle form a group of data, i= … … N, and N is the number of data groups; ρ f I (i)、ρ f Q (i)、ρ f U (i) For i the observation wavelength and the observation angle, simulating the aerosol layer height a p The apparent reflectivity of the TOA of the corresponding near infrared band; phi I (i)、Φ Q (i)、Φ U (i) For the above-mentioned error-related covariance value in the cost function (more than one calculation method, see the prior patent CN201911043261.0 in addition to the formula in the following step S35);
s34, identifying eta 2 If the matching result is within the set range, judging that the matching result is consistent, otherwise, judging that the matching result is inconsistent, and returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Performing consistency fitting matching again until the matching result is consistent, and then performing the current a p The final aerosol layer as the target zone is high output.
Preferably, step S3 further comprises the steps of:
s35, further calculating a covariance value phi related to the error in the cost function through the following formula I 、Φ Q 、Φ U ，
In phi, phi f Is background noise phi s Phi is shot noise caused by charge discontinuity c For uncertainty of satellite radiation intensity data, Φ p As uncertainty of polarization degree, θ 0 Is the zenith angle of the sun, ρ I 、ρ Q 、ρ U The apparent reflectivity of TOA in the near infrared band is measured.
Preferably, in step S35, the background noise Φ f ＝7×10 -5 Shot noise Φ caused by charge discontinuity s ＝7×10 -8 Uncertainty Φ of satellite radiation intensity data c Uncertainty of polarization degree Φ=0.03 p ＝0.01。
S36, identifying phi I 、Φ Q 、Φ U Whether the matching results are all in the respective set ranges, if so, judging that the matching results are reliable, otherwise, judging that the matching results are not reliable, and returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Again, consistency fitting and matching are carried out until the matching results are consistent and phi I 、Φ Q 、Φ U All are within the respective setting range, the current a p The final aerosol layer as the target zone is high output.
Preferably, the aerosol layer height a of the forward radiation transmission model is determined in step S3 by the following substeps p Inversion modification is performed:
s341, coating the aerosol with a p As a target argument x, forward radiation transmissionModel-derived f (a) p ) As the dependent variable f (x), an initial value x of the independent variable is set 0 Radius of trust delta 0 Iterative accuracy epsilon>0；
S342, obtaining the k iteration f (x) at x (k) Function value f at k Gradient of the body k Identify whether or not to meet
‖g k If the II is less than or equal to epsilon (6), ending the iteration, otherwise, calculating a Hessian matrix H k For calculation of the Hessian matrix reference can be made to the following documents: M.A.Branch, T.F.Coleman, and Y.Li, "A subspace, interor, and conjugate gradient method for large-scale bound-constrained minimization problems," SIAM Journal on Scientific Computing 21,1-23 (1999)]And a trust domain model of the Hessian matrix is derived as follows,
wherein, ψ is k (s) def Is a substitution function of f (x), s is the change amount of a target variable, T is a transposition operation, and delta k The radius of the confidence domain for the kth iteration, s.t. represents the boundary;
s343, solving the trust domain model to obtain a change amount optimal solution s of the target variable k (displacement, the amount of change in the target variable during the kth iteration);
s344, according to the optimal solution s k To obtain f (x k +s k )、Ψ k (s) def Further, the ratio ρ of the actual drop amount to the predicted drop amount is obtained by the following formula k ，
S345, identifying the rho k Whether or not ρ is satisfied k >Mu, if so, updating the argument x of the next iteration k+1 ＝x k +Δ k Otherwise, the independent variable of the next iteration and the independent variable of the current iterationIdentical, x k+1 ＝x k ；
S346, identifying the rho k Whether or not ρ is satisfied k >η, if so, update the confidence region radius Δ for the next iteration k+1 ＝2Δ k Otherwise, executing the next step; 0<μ<η<1, (e.g. μ=1/4, η=3/4)
S347. Identifying the above ρ k Whether or not mu is satisfied<ρ k <η, if so, the confidence region radius of the next iteration is the same as the confidence region radius of the current iteration, i.e. Δ k+1 ＝Δ k Otherwise, the confidence domain radius for the next iteration is updated according to the following formula,
s348, turning to the step S342, continuing the subsequent iteration process until II g k The II is less than or equal to epsilon, the iteration is finished, and the target independent variable x in the last iteration is output and is used as the optimal a p 。
The aerosol layer height a p The optimization method for performing inversion modification adopts a trusted region reflection algorithm, and the algorithm is suitable for large-scale minimization of bounded constraint on variables, and can remarkably reduce iteration times.
In addition, the aerosol layer height acquisition method can simulate and acquire the polarized water-leaving radiance value rho above the water surface by inputting accurate aerosol layer height parameters in the forward radiation transmission model f I ,ρ f Q ,ρ f U The water color component parameters may be further calculated. Illustratively, nI calculated at the polarizer angle (Brewster angle) can be utilized w ,nQ w ,nU w The degree of linear polarization was further obtained (see Chami, m.,&McKee, d. (2007) Determination of biogeochemical properties of marine particles using above water measurements of the degree of polarization at the Brewster angle. Optics Express,15,9494-9509), and further obtains the inorganic particulate concentration IPM in the body of water.
Preferably, the aerosol layer height obtaining method further includes the following steps to achieve the function of reflecting the turbidity of the water body by utilizing the final aerosol layer height of the target region:
s4, enabling the aerosol layer height a of the forward radiation transmission model p Setting the final aerosol layer obtained in the step S3;
s5, inputting components of the aerosol to be detected and optical parameters of each component into the forward radiation transmission model by combining the multi-angle polarization spectrum data to obtain the apparent reflectivity f (a) of the near infrared band TOA p ) ρ in (b) f I 、ρ f Q 、ρ f U ；
S6, according to the rho f I 、ρ f Q 、ρ f U The concentration IPM of inorganic particles in the water body for reflecting the turbidity of the water body is further obtained by the following formula,
IPM＝1.469ln(DoLP-44.498)+5.957，
preferably, the marine aerosols are composed of 5% water-soluble aerosol particles and 95% marine aerosol particles.
Compared with the prior art, the aerosol layer height acquisition method provided by the embodiment has the following beneficial effects:
1. the existing active satellite technology (CALIOP) aiming at the inversion of the aerosol layer height has the defects of narrow width and long revisit period. Therefore, if only active satellite data is relied on, real-time acquisition of aerosol layer height parameters at a ground target area is almost impossible. The scheme adopts the passive satellite, overcomes the defects of the active satellite, and can acquire the aerosol layer height parameters of the ground target area in real time.
2. Existing passive satellite techniques for aerosol layer height inversion (e.g., O-based 2 A band) has defects that the accuracy is insufficient and that it is highly dependent on the condition assumption. Meanwhile, few studies on high reflection of an aerosol layer by utilizing passive polarized satellite dataAnd (5) performing. The scheme obtains the aerosol layer height data with larger breadth and shorter revisit period relative to the LIDAR through the passive polarized satellite, which is a great breakthrough in the field of satellite inversion of atmospheric components.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of the prior art, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. The aerosol layer height acquisition method based on the passive polarized satellite data is characterized by comprising the following steps of:
s1, acquiring multi-angle polarization spectrum data of a target region acquired by a passive polarization satellite;
s2, determining components of the aerosol to be detected and optical parameters of each component, and inputting the set aerosol layer height a by combining the multi-angle polarization spectrum data p In the forward radiation transmission model of (2), the simulated aerosol layer height a is obtained p Corresponding near infrared band TOA apparent reflectance f (a p )；
S3, acquiring the actual measurement near infrared band TOA apparent reflectivity f acquired by a passive remote sensing satellite, and simulating the obtained near infrared band TOA apparent reflectivity f (a p ) Performing consistency fit matching with the apparent reflectivity f of the TOA of the actually measured near infrared band, and if the apparent reflectivity f is inconsistent, returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Performing consistency fitting matching again until the matching result is consistent, and then performing the current a p The final aerosol layer as the target zone is high output.
2. The method for acquiring the aerosol layer height based on the passive polarized satellite data according to claim 1, wherein the components of the aerosol to be detected comprise at least one of smoke aerosol, water-soluble aerosol, marine aerosol and dust aerosol;
the optical parameters of each component include aerosol particle radius, logarithmic standard deviation of aerosol particle radius, complex refractive index.
3. The method of claim 1 or 2, wherein the forward radiation transmission model is one of an OSOAA radiation transmission model and a PCOART-SA radiation transmission model.
4. The method for obtaining an aerosol layer height based on passive polarized satellite data according to claim 3, wherein after the current radiation transmission model adopts the OSOAA radiation transmission model, step S2 further comprises:
s21, determining components of aerosol to be detected in a target region and optical parameters of each component;
s22, acquiring a near infrared band vector radiation field L received by a passive polarized satellite on the atmosphere roof according to components of aerosol to be detected and optical parameters of each component through the following formula t (λ)，
L t (λ)＝L r (λ)+ a (λ)+(λ)L g (λ)+(λ)L wc (λ)+(λ)L w (λ)
L a (λ)＝(a p ,,d i )
Wherein lambda is the center wavelength of the passive polarized satellite, d i I= … … n, n being the total number of components of the aerosol to be measured, L r (lambda) is the vector radiation contributed by Rayleigh scattering, L a (λ) is the vector radiation contributed by aerosol scattering-absorption including aerosol and rayleigh scattering interactions; l (L) g (lambda) is the vector radiation contributed by solar flare, L g (λ)＝0；L wc (lambda) is the vector radiation contributed by sea surface white foam, near infrared band L wc (λ)＝0；L w (lambda) is at the bottom of the atmosphere,Vector off-water radiation above sea surface, near infrared band L w (λ) =0; t (λ) and T (λ) represent the atmospheric diffusion and direct transmittance, respectively, of vector radiation above the sea surface; g (a) p ,λ,d i ) Calculating a function for the aerosol scattering-absorption contribution;
s23, for the near infrared band vector radiation field L t (lambda) normalized by the following formula to obtain the simulated aerosol layer height a at each observation angle p Corresponding near infrared band TOA apparent reflectance f (a p )，
ρ t ()＝π·L t ()/F 0 ，
Wherein ρ is f I 、ρ f Q 、ρ f U Apparent reflectance ρ at the top of the atmosphere t The first three values of (lambda), F 0 Is the value of the solar irradiance outside the ground in each wave band.
5. The method of claim 4, wherein step S3 further comprises:
s31, acquiring continuous spectrum data in a near infrared band acquired by a water color sensor on a passive remote sensing satellite;
s32, identifying the apparent reflectivity f of the TOA of the actually measured near infrared band of the target region under different observation angles in the continuous spectrum data, wherein the apparent reflectivity f is = [ rho ] I (i)、ρ Q (i)、ρ U (i)]；
S33, simulating the apparent reflectivity f (a) of the near infrared band TOA p ) Inputting the apparent reflectivity f of the TOA of the near infrared band to the cost function eta in the following formula 2 Is matched by consistency fit,
wherein i is a specific observation wavelength and an observation angle in a near infrared band; ρ f I (i)、ρ f Q (i)、ρ f U (i) For i the observation wavelength and the observation angle, simulating the aerosol layer height a p The apparent reflectivity of the TOA of the corresponding near infrared band; phi I (i)、Φ Q (i)、Φ U (i) A covariance value related to the error in the cost function;
s34, identifying eta 2 If the matching result is within the set range, judging that the matching result is consistent, otherwise, judging that the matching result is inconsistent, and returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Performing consistency fitting matching again until the matching result is consistent, and then performing the current a p The final aerosol layer as the target zone is high output.
6. The method of claim 5, wherein step S3 further comprises:
s35, further calculating a covariance value phi related to the error in the cost function through the following formula I 、Φ Q 、Φ U ，
In phi, phi f Is background noise phi s Phi is shot noise caused by charge discontinuity c For uncertainty of satellite radiation intensity data, Φ p Is of degree of polarizationUncertainty, θ 0 Is the zenith angle of the sun, ρ I 、ρ Q ρU is the apparent reflectivity of the TOA of the actually measured near infrared band;
s36, identifying phi I 、Φ Q 、Φ U Whether the matching results are all in the respective set ranges, if so, judging that the matching results are reliable, otherwise, judging that the matching results are not reliable, and returning to the step S2 to modify the aerosol layer height a of the forward radiation transmission model p Again, consistency fitting and matching are carried out until the matching results are consistent and phi I 、Φ Q 、Φ U All are within the respective setting range, the current a p The final aerosol layer as the target zone is high output.
7. The method of claim 5 or 6, wherein the step S3 is performed on the aerosol layer height a of the forward radiation transmission model by the sub-steps of p Inversion modification is performed:
s341, coating the aerosol with a p F (a) obtained by the forward radiation transmission model as the target argument x p ) As the dependent variable f (x), an initial value x of the independent variable is set 0 Radius of trust delta 0 Iterative accuracy epsilon>0；
S342, obtaining the k iteration f (x) at x (k) Function value f at k Gradient of the body k Identifying whether:
‖g k ‖≤ε，
if yes, ending the iteration, otherwise, calculating a Hessian matrix H k And a trust domain model of the Hessian matrix is derived as follows,
wherein, ψ is k (s) def Is a substitution function of f (x), s is the change amount of a target variable, T is a transposition operation, and delta k The radius of the confidence domain for the kth iteration, s.t. represents the boundary;
s343, solving the trust domain model to obtain a change amount optimal solution s of the target variable k ；
S344, according to the optimal solution s k To obtain f (x k +s k )、Ψ k (s) def Further, the ratio ρ of the actual drop amount to the predicted drop amount is obtained by the following formula k ，
S345, identifying the rho k Whether or not ρ is satisfied k >Mu, if so, updating the argument x of the next iteration k+1 ＝x k +Δ k Otherwise, the independent variable of the next iteration is the same as the independent variable of the current iteration, and x k+1 ＝x k ；
S346, identifying the rho k Whether or not ρ is satisfied k >η, if so, update the confidence region radius Δ for the next iteration k+1 ＝2Δ k Otherwise, executing the next step; 0<μ<η<1；
S347. Identifying the above ρ k Whether or not mu is satisfied<ρ k <η, if so, the confidence region radius of the next iteration is the same as the confidence region radius of the current iteration, i.e. Δ k+1 ＝Δ k Otherwise, the confidence region radius Δ for the next iteration is updated according to the following formula k+1 ，
S348, turning to the step S342, continuing the subsequent iteration process until II g k The II is less than or equal to epsilon, the iteration is finished, and the target independent variable x in the last iteration is output and is used as the optimal a p 。
8. The method of claim 4-6, further comprising the step of using the final aerosol layer height of the target region to reflect the turbidity of the water:
s4, enabling the aerosol layer height a of the forward radiation transmission model p Setting the final aerosol layer obtained in the step S3;
s5, inputting components of the aerosol to be detected and optical parameters of each component into the forward radiation transmission model by combining the multi-angle polarization spectrum data to obtain the apparent reflectivity f (a) of the near infrared band TOA p ) ρ in (b) f I 、ρ f Q 、ρ f U ；
S6, according to the rho f I 、ρ f Q 、ρ f U The concentration IPM of inorganic particles in the water body for reflecting the turbidity of the water body is further obtained by the following formula,
lPM＝1.469ln(DoLP-44.498)+5.957，
9. the method for obtaining aerosol layer height based on passive polarized satellite data according to claim 6, wherein in step S35, background noise Φ f ＝7×10 -5 Shot noise Φ caused by charge discontinuity s ＝7×10 -8 Uncertainty Φ of satellite radiation intensity data c Uncertainty of polarization degree Φ=0.03 p ＝0.01。
10. The method of claim 4-6, wherein the marine aerosols consist of 5% water-soluble aerosol particles and 95% marine aerosol particles.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310398990.8A CN116148189B (en) | 2023-04-14 | 2023-04-14 | Aerosol layer height acquisition method based on passive polarized satellite data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310398990.8A CN116148189B (en) | 2023-04-14 | 2023-04-14 | Aerosol layer height acquisition method based on passive polarized satellite data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116148189A CN116148189A (en) | 2023-05-23 |
| CN116148189B true CN116148189B (en) | 2023-07-21 |
Family
ID=86352745
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310398990.8A Active CN116148189B (en) | 2023-04-14 | 2023-04-14 | Aerosol layer height acquisition method based on passive polarized satellite data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116148189B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313564B (en) * | 2023-11-30 | 2024-04-12 | 自然资源部第二海洋研究所 | Method, device and storage medium for inverting ocean-atmosphere optical parameters |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114218786A (en) * | 2021-12-13 | 2022-03-22 | 北京理工大学 | On-orbit polarization radiation characteristic inversion method for non-polarization satellite sensor |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9396528B2 (en) * | 2013-03-15 | 2016-07-19 | Digitalglobe, Inc. | Atmospheric compensation in satellite imagery |
| CN103927455B (en) * | 2014-04-24 | 2017-01-18 | 中国科学院遥感与数字地球研究所 | Land aerosol optical property retrieval method based on Gaofen-1 satellite |
| CN104483663B (en) * | 2014-12-25 | 2017-01-04 | 武汉大学 | A kind of target in hyperspectral remotely sensed image Atmospheric Correction method and system |
| KR101609740B1 (en) * | 2015-08-24 | 2016-04-08 | 대한민국 | Vertical distribution calculation method of Aerosol mass concentration |
| CN107389617A (en) * | 2017-06-27 | 2017-11-24 | 环境保护部卫星环境应用中心 | The inversion method and equipment of aerosol optical depth based on No. four satellites of high score |
| RU2674560C1 (en) * | 2017-12-28 | 2018-12-11 | Евгений Сергеевич Зубко | Atmosphere optical characteristics measuring method |
| CN109974665B (en) * | 2019-04-04 | 2021-05-07 | 东北师范大学 | Aerosol remote sensing inversion method and system for short-wave infrared data lack |
| CN110411927B (en) * | 2019-08-02 | 2020-12-04 | 中国科学院遥感与数字地球研究所 | Atmospheric fine particle AOD and earth surface polarization reflectivity collaborative inversion method |
| CN111753439B (en) * | 2020-07-09 | 2023-07-21 | 中国科学院空天信息创新研究院 | Aerosol optical thickness inversion method of multi-angle polarized satellite sensor |
| CN114624731A (en) * | 2022-03-11 | 2022-06-14 | 桂林电子科技大学 | Inversion method for optical thickness of aerosol above cloud layer based on polarization remote sensing data |
| CN114544452B (en) * | 2022-04-25 | 2022-07-26 | 自然资源部第二海洋研究所 | Multi-angle polarized water color remote sensor satellite atmosphere correction method |
| CN115630256B (en) * | 2022-12-07 | 2023-07-07 | 自然资源部第二海洋研究所 | Multi-angle polarized water color satellite atmospheric correction method based on dark pixel assumption |
-
2023
- 2023-04-14 CN CN202310398990.8A patent/CN116148189B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114218786A (en) * | 2021-12-13 | 2022-03-22 | 北京理工大学 | On-orbit polarization radiation characteristic inversion method for non-polarization satellite sensor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116148189A (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116148189B (en) | Aerosol layer height acquisition method based on passive polarized satellite data | |
| Sromovsky et al. | Saturn’s Great Storm of 2010–2011: Evidence for ammonia and water ices from analysis of VIMS spectra | |
| Pan et al. | An improved spectral optimization algorithm for atmospheric correction over turbid coastal waters: A case study from the Changjiang (Yangtze) estuary and the adjacent coast | |
| CN114544452B (en) | Multi-angle polarized water color remote sensor satellite atmosphere correction method | |
| Shi et al. | First level 1 product results of the greenhouse gas monitoring instrument on the GaoFen-5 satellite | |
| CN110388986B (en) | Land surface temperature inversion method based on TASI data | |
| CN102707336A (en) | Novel method of using A-Train series satellite data for synergetic inversion of cloud phase states and cloud parameters | |
| Monaldo et al. | Daylight imagery of ocean surface waves for wave spectra | |
| CN114624731A (en) | Inversion method for optical thickness of aerosol above cloud layer based on polarization remote sensing data | |
| CN114581791A (en) | Inversion method and system for atmospheric water vapor content based on MODIS data | |
| Gordon | A preliminary assessment of the Nimbus-7 CZCS atmospheric correction algorithm in a horizontally inhomogeneous atmosphere | |
| Xi et al. | Simulated retrievals for the remote sensing of CO 2, CH 4, CO, and H 2 O from geostationary orbit | |
| CN111707622A (en) | Method for measuring and calculating vertical distribution and transport flux of atmospheric water vapor based on foundation MAX-DOAS | |
| CN114295585B (en) | Multi-view-field ocean laser radar data regularization inversion method based on analytical model | |
| Skorik et al. | Regularized Newton type method for retrieval of heavy water in atmosphere by IR spectra of the solar light transmission | |
| CN111241991A (en) | Water quality classification method based on remote sensing image | |
| CN111650128B (en) | High-resolution atmospheric aerosol inversion method based on surface reflectivity library | |
| CN113155740B (en) | Calibration reference field BRDF characteristic analysis method and system | |
| O'Brien et al. | Testing the polarization model for TANSO-FTS on GOSAT against clear-sky observations of sun glint over the ocean | |
| CN105259145B (en) | A kind of while remote sensing islands and reefs underwater topography and the method for atural object | |
| Zhao et al. | Polarization imaging in atmospheric environment based on polarized reflectance retrieval | |
| Zheng et al. | McPrA-A new gas profile inversion algorithm for MAX-DOAS and apply to 50 m vertical resolution | |
| CN109738372A (en) | A kind of Airborne Hyperspectral rock mine detection multi-data processing method | |
| CN107389595B (en) | One kind being based on the average weighted water vapor retrieval method of MODIS triple channel | |
| Cho et al. | Measurement enhancement of TDLAS based on variable weighted cross-correlation tomography for the simultaneous reconstruction of 2D temperature and concentration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |