A Fast Cloud Correction Method for Improving the Accuracy of Trace Gases in Hyperspectral Satellite Remote Sensing

Abstract

The present invention discloses a fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing of trace gases. The related methods include: simulating the set physical parameters by using an atmospheric radiation transfer model, constructing a lookup table in combination with the vertical column concentration and reflectivity obtained by the differential absorption spectroscopy method; performing simulation based on the profile temperature characteristics to obtain a correction coefficient, and using the correction coefficient to correct the lookup table; fitting the acquired observed spectral data by the differential absorption spectroscopy method to obtain the vertical column concentration of (O2)2, calculating the continuous reflectivity, and then combining geometric auxiliary data, and performing multi-linear interpolation on the above parameters according to the corrected lookup table to obtain the cloud correction parameters for trace gas inversion. The above solution can efficiently, timely and accurately achieve cloud correction. Using this cloud correction method is easier and more convenient when performing trace gas inversion, and can also improve the inversion accuracy of trace gases to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric remote sensing monitoring, and in particular to a fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing of trace gases. Background Art

[0002] Satellite remote sensing has advantages such as wide spatial coverage and high data consistency (unified retrieval algorithms and spectral sources), and is an important means to comprehensively understand the regional distribution and variation law of atmospheric pollutants. Its development is a huge driving force for the improvement of environmental protection level.

[0003] Clouds have an important and undeniable impact on the spectral retrieval of trace gases in the stratosphere and troposphere. Clouds will affect the final retrieval accuracy by shielding solar short-wave radiation and absorbing terrestrial long-wave radiation. Among them, the shielding effect will lead to an underestimation of the vertical column density of trace gases, and the absorption effect will lead to an increase in the visibility of trace gases above the cloud top and its upper part. Therefore, a cloud correction method is needed to eliminate these effects for the retrieval of trace gases to improve the retrieval accuracy of trace gases. Summary of the Invention

[0004] The purpose of the present invention is to provide a fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing of trace gases, which can efficiently, timely and accurately achieve cloud correction, thereby improving the retrieval accuracy of trace gases.

[0005] The purpose of the present invention is achieved by the following technical solutions:

[0006] A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing of trace gases, comprising:

[0007] Simulating the set physical parameters by using an atmospheric radiative transfer model, and constructing a look-up table including the corresponding relationship between the vertical column concentration and continuous reflectivity of (O2)2 obtained by the differential absorption spectroscopy method and cloud correction parameters; where (O2)2 represents the collision pair generated by the free collision of oxygen molecules in the atmosphere;

[0008] Simulating based on the profile temperature characteristics to obtain a correction coefficient, and correcting the look-up table by using the correction coefficient;

[0009] Fitting the obtained observed spectral data by the differential absorption spectroscopy method to obtain the vertical column concentration and continuous reflectivity of (O2)2, and then combining the geometric auxiliary data with the corrected look-up table to obtain the cloud correction parameters for trace gas retrieval, and realizing the cloud correction of trace gas retrieval by using the cloud correction parameters.

[0010] A fast cloud correction system for improving the accuracy of hyperspectral satellite remote sensing of trace gases, comprising:

[0011] A lookup table construction unit, configured to simulate set physical parameters by using an atmospheric radiative transfer model, and construct a lookup table including the corresponding relationship between the vertical column concentration and the continuous reflectance of (O2)2 and cloud correction parameters by combining the vertical column concentration of (O2)2 and the continuous reflectance obtained by the differential absorption spectroscopy method;

[0012] A lookup table correction unit, configured to perform simulation based on the profile temperature characteristics to obtain a correction coefficient, and correct the lookup table by using the correction coefficient;

[0013] A cloud correction parameter acquisition and cloud correction unit, configured to fit the acquired observed spectral data by using the differential absorption spectroscopy method to obtain the vertical column concentration and the continuous reflectance of (O2)2, and then combine geometric auxiliary data with the corrected lookup table to obtain cloud correction parameters for trace gas inversion, and perform cloud correction for trace gas inversion by using the cloud correction parameters.

[0014] A processing device, comprising: one or more processors; a memory, configured to store one or more programs;

[0015] Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the foregoing method.

[0016] A readable storage medium, storing a computer program, which implements the foregoing method when executed by a processor.

[0017] It can be seen from the technical solutions provided by the present invention described above that an efficient, timely, and accurate cloud correction method has been developed for trace gas optical inversion based on satellite observation hyperspectrum, which can achieve real-time results for satellite spectral data, and at the same time can reduce the cumbersome calculation process, so that the time complexity of the algorithm is greatly reduced. Using this cloud correction method is easier and more convenient for trace gas inversion, and can also improve the inversion accuracy of trace gas to a certain extent. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0019] Figure 1 It is a flowchart of a fast cloud correction method for improving the accuracy of trace gases in hyperspectral satellite remote sensing provided by an embodiment of the present invention;

[0020] Figure 2Schematic diagram of a fast cloud correction system for improving the accuracy of hyperspectral satellite remote sensing trace gases provided by an embodiment of the present invention;

[0021] Figure 3 Schematic diagram of a processing device provided by an embodiment of the present invention. Detailed implementation manners

[0022] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] First, the following explanations are made for the terms that may be used in this article:

[0024] Descriptions with semantic meanings such as "including", "comprising", "containing", "having" or other similar ones should be interpreted as non-exclusive inclusion. For example: including a certain technical feature element (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, processes, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or articles, etc.) should be interpreted as not only including the clearly listed certain technical feature element, but also including other well-known technical feature elements in the art that are not clearly listed.

[0025] Next, a detailed description is given to a fast cloud correction solution for improving the accuracy of hyperspectral satellite remote sensing trace gases provided by the present invention. The content not described in detail in the embodiments of the present invention belongs to the prior art well-known to those of ordinary skill in the art. In the embodiments of the present invention, those not specified in specific conditions are carried out according to the conventional conditions in the art or the conditions recommended by the manufacturer. For the instruments used in the embodiments of the present invention that are not specified in the manufacturer, they are all conventional products that can be obtained through commercial purchase.

[0026] As Figure 1 shown, a fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases mainly includes the following steps:

[0027] Step 1: Use an atmospheric radiative transfer model to simulate the set physical parameters, and combine the vertical column concentration and continuous reflectance of (O2)2 obtained by the differential absorption spectroscopy method to construct a lookup table including the corresponding relationship between the vertical column concentration and continuous reflectance and cloud correction parameters.

[0028] The preferred implementation manner of this step is as follows:

[0029] 1) Use an atmospheric radiative transfer model to simulate the set physical parameters to obtain simulated spectral data.

[0030] In the embodiment of the present invention, physical parameters may include, but are not limited to, cloud correction parameters (effective cloud cover and cloud pressure) and geometric auxiliary data. Geometric auxiliary data may include, but are not limited to, solar zenith angle, observation zenith angle, relative azimuth, surface pressure, surface albedo, etc.

[0031] 2) The simulated spectral data are fitted by differential absorption spectroscopy (DOAS) to obtain the vertical column concentration and continuous reflectivity.

[0032] In the embodiment of the present invention, the reflectivity of each pixel is calculated using the independent pixel approximation method, which divides a spatial pixel into multiple sub-pixels without lateral radiation transmission. The reflectivity of the spatial pixel is the average of the sub-pixel reflectivities, where the reflectivity is calculated using multiple physical parameters. The calculation formula for the reflectivity is expressed as:

[0033] R(λ; SZA, VZA, RAA, CF, CP, SA, SP)

[0034] =CF*R cloud (λ; SZA, VZA, RAA, CA, SP)+(1-CF)*R clear (λ; SZA, VZA, RAA, SA, SP)

[0035] Where λ is the wavelength, SZA, VZA, and RAA are the solar zenith angle, observation zenith angle, and relative azimuth, respectively; CF is the effective cloud cover, CP is the cloud pressure, CA is the cloud albedo, SA is the surface albedo, SP is the surface pressure, R is the top of atmosphere reflectivity, and R clear is the clear sky reflectivity, R cloud is the cloudy reflectivity.

[0036] The fitting process of differential absorption spectroscopy is expressed as:

[0037]

[0038]

[0039] Where, e is a natural constant, R(λ) is the spectral reflectance, R c is the continuous reflectivity at wavelength λ, SCD, The oblique columns represent the concentrations of (O2)2, O3 and NO2, respectively. represent the absorption cross sections of (O2)2, O3 and NO2 respectively, and γ0 and γ1 are the fitting parameters in the DOAS fitting polynomial.

[0040] After obtaining the slant column concentration of (O2)2 through the above fitting process, the vertical column concentration is calculated by combining with the corresponding air quality coefficient (determined by conventional techniques), that is, the formula VCD introduced later. (O2)2 .

[0041] In the embodiments of the present invention, (O2)2 is a collision pair generated by the free collision of O2 molecules (oxygen molecules) in the atmosphere. Generally, the form of O2 - O2 is used, and the form of (O2)2 is used to avoid conflicts.

[0042] 3) Determine the node density of the lookup table according to the set data accuracy, and perform interpolation by combining the node density with the obtained vertical column concentration and continuous reflectivity to obtain a lookup table including the corresponding relationship between the vertical column concentration and continuous reflectivity and the cloud correction parameter.

[0043] In the embodiments of the present invention, the vertical column concentration and continuous reflectivity of (O2)2 are used as inputs, and a lookup table of effective cloud amount and cloud pressure is obtained by using the radial basis function interpolation method. The lookup table is an array, and through the index operation of querying, the subsequent calculation speed is accelerated. The nodes in the lookup table refer to the known constructed results, which are the physical quantities corresponding to the simulated spectra calculated by using the DOAS fitting simulation spectra.

[0044] As shown in Table 1, an example of node settings is given. Lambertian clouds are selected, and the lookup table is obtained by interpolation based on the data calculated in the above manner.

[0045]

[0046] Table 1 Example of Lookup Table Node Settings

[0047] In Table 1, (O2)2 VCD represents the vertical column concentration of (O2)2, 10 43 molec 2 cm -5 is the unit of vertical column concentration.

[0048] Step 2: Perform simulation based on the profile temperature characteristics to obtain a correction coefficient, and use the correction coefficient to correct the lookup table.

[0049] Since the vertical column concentration of (O2)2 is closely related to the temperature distribution, in order to correct the influence caused by temperature, a mid-latitude seasonal profile is introduced for simulation, and a correction coefficient γ is used to correct the lookup table in different situations. The correction coefficient γ is calculated by the following formula:

[0050]

[0051] In the formula, the subscripts clear and cloud represent the clear sky and cloudy parts respectively, CF is the effective cloud cover, R is the top-of-atmosphere reflectance at wavelength λ, p represents pressure, TP is the top-of-atmosphere pressure, SP is the surface pressure, m(p, λ) is the altitude-resolution air mass factor, T(p) is the actually observed temperature profile, and T ref (p) is the temperature profile used in simulation when creating the lookup table.

[0052] In the embodiment of the present invention, the vertical column concentration of (O2)2 is converted into the slant column concentration of the reference temperature profile (temperature profile T ref (p) used when creating the lookup table) through the correction factor γ.

[0053] Step 3: Fit the acquired observed spectral data by differential absorption spectroscopy to obtain the vertical column concentration and continuous reflectance of (O2)2, and then combine the geometric auxiliary data with the corrected lookup table to obtain the cloud correction parameters for trace gas inversion, and use the cloud correction parameters to achieve cloud correction for trace gas inversion.

[0054] In this step, the preferred implementation manner of fitting the acquired observed spectral data by differential absorption spectroscopy to obtain the vertical column concentration of (O2)2 is as follows:

[0055] 1) Calculate the air mass coefficient of the corresponding gas (O2)2 according to the physical factors of (O2)2 in the atmosphere (such as vertical distribution, optical path length, reflectance, etc.).

[0056] 2) Calculate the slant column concentration of (O2)2 in the atmosphere by using differential absorption spectroscopy to calculate the acquired spectral data of (O2)2.

[0057] 3) Calculate the vertical column concentration of (O2)2 by using the air mass coefficient of (O2)2 and the slant column concentration of (O2)2 in the atmosphere, which is expressed as:

[0058]

[0059] Among them, VCD (O2)2 is the vertical column concentration of (O2)2, SCD (o2)2 is the slant column concentration of (O2)2 in the atmosphere, and AMF (O2)2 is the air mass coefficient of (O2)2.

[0060] The continuous reflectance is also calculated by differential absorption spectroscopy according to the observed spectral data, and the specific method can refer to the method in Step 1 above.

[0061] In an embodiment of the present invention, after obtaining the vertical column concentration and continuous reflectance of (O2)2, multivariate linear interpolation is performed on the vertical column concentration of (O2)2, the continuous reflectance, and geometric auxiliary data according to the corrected look-up table to obtain the cloud correction parameter for trace gas inversion.

[0062] In an embodiment of the present invention, the cloud correction parameter refers to the correction parameter for trace gas inversion, that is, the effective cloud amount and cloud pressure. The cloud correction parameter is used for cloud correction in trace gas inversion, that is, using the effective cloud amount and cloud pressure obtained by the present invention to complete cloud correction in trace gas inversion. The process of this part can be implemented with reference to conventional techniques and will not be elaborated in the present invention.

[0063] Another embodiment of the present invention also provides a fast cloud correction system for improving the accuracy of hyperspectral satellite remote sensing of trace gases, which is mainly used to implement the method provided in the foregoing embodiment, as Figure 2 shown. The system mainly includes:

[0064] A look-up table construction unit, configured to simulate the set physical parameters using an atmospheric radiative transfer model, and construct a look-up table including the corresponding relationship between the vertical column concentration and continuous reflectance of (O2)2 and the cloud correction parameter in combination with the vertical column concentration and continuous reflectance of (O2)2 obtained by the differential absorption spectroscopy method;

[0065] A look-up table correction unit, configured to perform simulation based on the profile temperature characteristics to obtain a correction coefficient, and correct the look-up table using the correction coefficient;

[0066] A cloud correction parameter acquisition and cloud correction unit, configured to fit the acquired observed spectral data by the differential absorption spectroscopy method to obtain the vertical column concentration and continuous reflectance of (O2)2, and then combine the geometric auxiliary data with the corrected look-up table to obtain the cloud correction parameter for trace gas inversion, and use the cloud correction parameter to implement cloud correction for trace gas inversion.

[0067] It should be noted that the specific technical details involved in the above system have been introduced in detail in the previous method embodiments, so they will not be elaborated here.

[0068] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above.

[0069] Another embodiment of the present invention also provides a processing device, as Figure 3As shown, it mainly includes: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the foregoing embodiments.

[0070] Further, the processing device further includes at least one input device and at least one output device; in the processing device, the processor, the memory, the input device, and the output device are connected through a bus.

[0071] In the embodiments of the present invention, the specific types of the memory, the input device, and the output device are not limited; for example:

[0072] The input device can be a touch screen, an image acquisition device, a physical button, or a mouse, etc.;

[0073] The output device can be a display terminal;

[0074] The memory can be a Random Access Memory (RAM), or a non-volatile memory, such as a disk memory.

[0075] Another embodiment of the present invention further provides a readable storage medium storing a computer program, which implements the method provided in the foregoing embodiments when the computer program is executed by a processor.

[0076] In the embodiments of the present invention, the readable storage medium as a computer-readable storage medium can be disposed in the foregoing processing device, for example, as the memory in the processing device. In addition, the readable storage medium can also be various media that can store program codes, such as a USB flash drive, a mobile hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disc.

[0077] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases, characterized in that, Including: Simulating the set physical parameters by using an atmospheric radiative transfer model, and constructing a look-up table including the corresponding relationship between the vertical column concentration and continuous reflectance of (O2)2 and cloud correction parameters by combining the vertical column concentration and reflectance of (O2)2 obtained by the differential absorption spectroscopy method; wherein, (O2)2 represents a collision pair generated by the free collision of oxygen molecules in the atmosphere. Simulating based on the profile temperature characteristics to obtain a correction coefficient, and correcting the look-up table by using the correction coefficient. Fitting the obtained observed spectral data by the differential absorption spectroscopy method to obtain the vertical column concentration and continuous reflectance of (O2)2, and then combining the geometric auxiliary data and the corrected look-up table to obtain the cloud correction parameters for trace gas inversion, and realizing the cloud correction for trace gas inversion by using the cloud correction parameters. Wherein, the simulating based on the profile temperature characteristics to obtain a correction coefficient includes: Introducing a mid-latitude seasonal profile for simulation, and using a correction coefficient γ to correct the look-up table in different cases. The correction coefficient γ is calculated by the following formula: where the subscripts clear and cloud represent the clear sky and cloudy parts respectively, CF is the effective cloud cover, R is the top-of-atmosphere reflectance at wavelength λ, p represents pressure, TP is the top-of-atmosphere pressure, SP is the surface pressure, m(p, λ) is the altitude-resolution air mass factor, T(p) is the actually observed temperature profile, and T ref (p) is the temperature profile used in simulation when creating the lookup table.

2. A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases according to claim 1, characterized in that, The constructing a look-up table including the corresponding relationship between the vertical column concentration and continuous reflectance of (O2)2 and cloud correction parameters by simulating the set physical parameters by using an atmospheric radiative transfer model and combining the vertical column concentration and reflectance of (O2)2 obtained by the differential absorption spectroscopy method includes: Simulating the set physical parameters by using an atmospheric radiative transfer model to obtain simulated spectral data. Fitting the simulated spectral data by the differential absorption spectroscopy method to obtain the vertical column concentration and reflectance of (O2)2. Determining the node density of the look-up table according to the set data accuracy, and performing interpolation by combining the node density with the obtained vertical column concentration and reflectance of (O2)2 to obtain a look-up table including the corresponding relationship between the vertical column concentration and continuous reflectance of (O2)2 and cloud correction parameters; wherein, the nodes are the constructed known results.

3. A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases according to claim 2, characterized in that, The fitting process of the differential absorption spectroscopy method is expressed as: R c = γ0 + γ1λ In the formula, e is the natural constant, R(λ) is the spectral reflectance, and R c is the continuous reflectance at wavelength λ. SCD and respectively represent the slant column concentrations of (O2)2, O3, and NO2. respectively represent the absorption cross-sections of (O2)2, O3, and NO2. Both γ0 and γ1 are fitting parameters.

4. A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases according to claim 1, characterized in that, The obtaining the vertical column concentration of (O2)2 by fitting the obtained observed spectral data by the differential absorption spectroscopy method includes: Calculating the air quality coefficient of the corresponding gas (O2)2 according to the physical factors of (O2)2 in the atmosphere. Calculating the slant column concentration of (O2)2 in the atmosphere by using the differential absorption spectroscopy method to calculate the obtained spectral data of (O2)2. Calculating the vertical column concentration of (O2)2 by using the air quality coefficient of (O2)2 and the slant column concentration of (O2)2 in the atmosphere, which is expressed as: Among them, VCD (O2)2 is the vertical column concentration of (O2)2, SCD (O2)2 is the slant column concentration of (O2)2 in the atmosphere, AMF (O2)2 is the air mass factor of (O2)2.

5. A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases according to claim 1, characterized in that, The obtaining the vertical column concentration and continuous reflectance of (O2)2, and then combining the geometric auxiliary data and the corrected look-up table to obtain the cloud correction parameters for trace gas inversion includes: Performing multi-linear interpolation on the vertical column concentration, continuous reflectance of (O2)2 and geometric auxiliary data according to the corrected look-up table to obtain the cloud correction parameters for trace gas inversion.

6. A fast cloud correction method for improving the accuracy of hyperspectral satellite remote sensing trace gases according to claim 1, characterized in that, The physical parameters include: cloud correction parameters and geometric auxiliary data; the cloud correction parameters include: effective cloud amount and cloud pressure.

7. A rapid cloud correction system for improving the accuracy of hyperspectral satellite remote sensing of trace gases, characterized in that, A system for implementing the method according to any one of claims 1 to 6, the system includes: A lookup table construction unit, configured to simulate set physical parameters by using an atmospheric radiative transfer model, and construct a lookup table including the corresponding relationship between the vertical column concentration and the continuous reflectivity of (O2)2, and cloud correction parameters, in combination with the vertical column concentration of (O2)2 and the continuous reflectivity obtained by the differential absorption spectroscopy method; A lookup table correction unit, configured to perform simulation based on the profile temperature characteristics to obtain a correction coefficient, and correct the lookup table by using the correction coefficient; A cloud correction parameter acquisition and cloud correction unit, configured to fit the acquired observed spectral data by using the differential absorption spectroscopy method to obtain the vertical column concentration and the continuous reflectivity of (O2)2, and then combine geometric auxiliary data with the corrected lookup table to obtain cloud correction parameters for trace gas inversion, and implement cloud correction for trace gas inversion by using the cloud correction parameters.

8. A processing device, characterized in that, Comprising: One or more processors; A memory, configured to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A readable storage medium storing a computer program, characterized in that, When a computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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