Infrared spectrum radiance deduction method, device, equipment, medium and product
By constructing a simplified atmospheric radiative transfer model based on an n-layer equivalent atmosphere and optimizing the solution of equivalent parameters, the problem of balancing computational accuracy and efficiency in existing models is solved, and efficient and reliable spectral radiance extrapolation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIHANG UNIV
- Filing Date
- 2026-01-14
- Publication Date
- 2026-04-10
AI Technical Summary
Existing spectral radiance calculation models are insufficient in balancing computational accuracy and efficiency, and the lack of band data in actual measurements affects the completeness of radiative transfer calculations and atmospheric parameter inversion.
A simplified atmospheric radiative transfer model based on an n-layer equivalent atmosphere is adopted. By optimizing the solution to retrieve the radiance of the reference spectrum in the infrared band, the equivalent path length and equivalent temperature are obtained. The simplified atmospheric radiative transfer model is then used to calculate the radiance of the retrieved infrared spectrum of the target.
It improves computational efficiency, achieves second-level calculations, has clear physical reliability and generalization ability, and the cross-band extrapolation results do not depend on a large amount of training data for specific scenarios, thus improving the completeness and reliability of spectral radiance acquisition.
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Figure CN121837488A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of spectral radiance modeling, and in particular to a method, apparatus, equipment, medium, and product for extrapolating infrared spectral radiance. Background Technology
[0002] Spectral radiance is a fundamental parameter for studying atmospheric radiative transfer characteristics and retrieving atmospheric composition, playing a crucial role in remote sensing. While existing spectral radiance calculation models are relatively mature, a technical bottleneck remains: high-precision models typically have long computation times, while faster models often suffer from insufficient accuracy. Furthermore, in practical measurements, limitations in observation techniques and equipment typically restrict the range of radiance bands that can be acquired, leading to missing data in certain bands and affecting the completeness of radiative transfer calculations and atmospheric parameter inversion.
[0003] The following are some of the atmospheric radiation calculation methods in related technologies: 1. The 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) radiative transfer model. This is an atmospheric radiative transfer model used to simulate the propagation of satellite signals within the solar spectrum. This model calculates the radiance signal received by the satellite by considering various factors such as solar radiation absorption, scattering, and ground reflection. When calculating spectral radiance, 6S treats the atmosphere as multiple homogeneous layers and simulates the propagation process of radiation in the atmosphere based on the optical properties of different atmospheric components (such as aerosols, clouds, temperature, etc.). However, the 6S model is relatively slow due to its high complexity. Its calculation process requires consideration of a large number of physical parameters and multi-layered atmospheric models, becoming particularly cumbersome and time-consuming, especially when dealing with multiple bands or complex atmospheric conditions. The model requires numerous complex input parameters, including atmospheric parameters (such as temperature, humidity, aerosol concentration, cloud distribution) and surface parameters. Furthermore, to accurately simulate the radiative transfer effects at different wavelengths, the model also needs to be specifically processed for different observation geometries (such as satellite viewpoint, observation path, etc.). These factors combined make the 6S model quite complex to set up and implement.
[0004] 2. Vegetation Parameter Estimation Methods Based on Statistical Regression. This type of method aims to predict vegetation physiological parameters that are difficult to obtain directly using remote sensing observation data in known bands. Its core idea is to utilize ground-measured sample data and spectral information from corresponding locations on remote sensing images to construct an empirical mapping relationship from "spectral features" to "target parameters" through a statistical regression model or machine learning algorithm. However, this method is essentially an empirical model that relies on a large amount of ground-sampled data for "training," and its estimation accuracy is severely limited by the number, representativeness, and regionality of the training samples. When the model is applied to areas with different atmospheric conditions, underlying surface environments, or where the observed geometry differs from the training data, its generalization ability and robustness are insufficient, and the estimation accuracy drops significantly. Furthermore, this method lacks clear physical mechanism support, making it a "black box" or "gray box" model with poor physical interpretability.
[0005] Therefore, there is an urgent need for a method that can infer the infrared spectral radiance of a target from the inverted infrared spectral radiance, while taking into account both computational accuracy and efficiency, in order to make up for missing data in the infrared band and improve the completeness and reliability of the acquisition of infrared spectral radiance. Summary of the Invention
[0006] The purpose of this application is to provide a method, apparatus, equipment, medium, and product for extrapolating infrared spectral radiance, which can extrapolate the infrared radiance of a target based on the inverted infrared radiance, while taking into account both computational accuracy and efficiency.
[0007] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for extrapolating infrared spectral radiance, comprising: constructing a simplified atmospheric radiation transfer model based on an n-layer equivalent atmosphere; the n-layer equivalent atmosphere is obtained by simplifying the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere; 3≤n≤10.
[0008] Obtain the inverted infrared reference spectral radiance and the target infrared band.
[0009] The objective function is optimized based on the radiance of the reference spectrum in the inverted infrared band to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared band. The objective function aims to minimize the total absolute error between the radiance of the inverted infrared band and the radiance of the reference spectrum in the inverted infrared band.
[0010] By inputting the target infrared light band and the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band into the simplified atmospheric radiative transfer model, the inverted spectral radiance of the target infrared light band is obtained.
[0011] Secondly, this application provides an infrared spectral radiance extrapolation device, comprising: a model building module for constructing a simplified atmospheric radiation transfer model based on an n-layer equivalent atmosphere; the n-layer equivalent atmosphere is obtained by simplifying the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere; 3≤n≤10.
[0012] The acquisition module is used to acquire the reference spectral radiance of the inverted infrared band and the target infrared band.
[0013] The equivalent atmospheric parameter determination module is used to optimize the objective function based on the inverted infrared light band reference spectral radiance, and obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band; the objective function aims to minimize the total absolute error between the inverted infrared light band inverted spectral radiance and the inverted infrared light band reference spectral radiance.
[0014] The radiance extrapolation module is used to input the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the target infrared light band and the inverted infrared light band into a simplified atmospheric radiative transfer model to obtain the inverted spectral radiance of the target infrared light band.
[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the infrared spectral radiance extrapolation method described above.
[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the infrared spectral radiance extrapolation method described above.
[0017] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the infrared spectral radiance extrapolation method described above.
[0018] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides an infrared spectral radiance extrapolation method, apparatus, equipment, medium, and product. This application simplifies the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere to obtain n equivalent atmospheric layers. Based on the n equivalent atmospheric layers, a simplified atmospheric radiative transfer model is constructed, simplifying tens or hundreds of atmospheric layers into n layers, greatly reducing the computational complexity and improving the computational efficiency to the second level. The objective function is optimized and solved based on the inverted infrared light band reference spectral radiance to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band. The parameters obtained from the optimized solution have clear physical meaning, making... The cross-band extrapolation results do not rely on a large amount of training data for specific scenarios, avoiding the poor generalization ability of "black box" models. While ensuring efficiency, it has stronger physical reliability and generalization ability; it balances computational accuracy and computational efficiency; it optimizes the objective function based on the reference spectral radiance of the inverted infrared band to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared band. The equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the target infrared band are input into a simplified atmospheric radiative transfer model to obtain the inverted spectral radiance of the target infrared band. It can extrapolate the radiance of the target infrared band based on the radiance of the inverted infrared band. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is a flowchart illustrating an infrared spectral radiance extrapolation method provided in an embodiment of this application.
[0021] Figure 2 This is a structural diagram of a simplified atmospheric radiation transfer model based on four equivalent atmospheric layers.
[0022] Figure 3 This is a schematic diagram of the radiation transmission path in a four-layer equivalent atmosphere.
[0023] Figure 4 A comparison diagram and a relative error diagram of the predicted value and the reference value at a tangent height of 51.6 km provided for another embodiment of this application.
[0024] Figure 5 A comparison diagram and a relative error diagram of the predicted value and the reference value at a tangent height of 53.7 km provided for another embodiment of this application.
[0025] Figure 6 A comparison diagram and a relative error diagram of the predicted value and the reference value at a tangent height of 55.8 km provided for another embodiment of this application.
[0026] Figure 7 A comparison diagram and a relative error diagram of the predicted value and the reference value at a tangent height of 57.9 km provided for another embodiment of this application.
[0027] Figure 8 A flowchart for verifying the effectiveness of an infrared spectral radiance extrapolation method provided in another embodiment of this application.
[0028] Figure 9 This is a schematic diagram of the functional modules of an infrared spectral radiance extrapolation device provided in an embodiment of this application.
[0029] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0032] In one exemplary embodiment, such as Figure 1 As shown, an infrared spectral radiance extrapolation method is provided, including: Step 201: Constructing a simplified atmospheric radiative transfer model based on an n-layer equivalent atmosphere; the n-layer equivalent atmosphere is obtained by simplifying the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere; 3≤n≤10.
[0033] Step 202: Obtain the inverted infrared band reference spectral radiance and the target infrared band.
[0034] Step 203: Optimize the objective function based on the retrieved infrared reference spectral radiance to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the retrieved infrared band. The objective function aims to minimize the total absolute error between the retrieved infrared spectral radiance and the retrieved infrared reference spectral radiance. The retrieved infrared spectral radiance includes the spectral radiance corresponding to the wavelength at each point within the retrieved infrared band. The retrieved infrared reference spectral radiance includes the reference spectral radiance corresponding to the wavelength at each point within the retrieved infrared band.
[0035] Step 204: Input the target infrared light band and the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band into the simplified atmospheric radiative transfer model to obtain the inverted spectral radiance of the target infrared light band.
[0036] In practical applications, the target infrared light band and the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band are input into a simplified atmospheric radiative transfer model to obtain the inverted spectral radiance of the target infrared light band. Specifically, any wavelength within the target infrared light band and the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band are input into a simplified atmospheric radiative transfer model to obtain the inverted spectral radiance corresponding to the wavelength.
[0037] In practical applications, the target infrared light band is the long-wave infrared band. The spectral radiance is the radiance of the Earth's limb.
[0038] In practical applications, n equivalent atmospheric layers are n concentric spherical equivalent atmospheric layers with uniformly distributed atmospheric gas composition.
[0039] In another exemplary embodiment of this application, the process of determining the reference spectral radiance of the inverted infrared band is as follows: obtaining the atmospheric conditions, aerosol type, visibility, relative humidity, resolution, cut-off height range, temperature range, and path range of the inverted infrared band and each equivalent atmospheric layer.
[0040] The reference spectral radiance of the inverted infrared band is obtained by calculating the atmospheric conditions, aerosol type, relative humidity, resolution, visibility, cut-off height range, temperature range, and path range of each equivalent atmospheric layer, as well as the input radiance of the inverted infrared band, into the reference model.
[0041] In practical applications, the reference model for radiance calculation is a high-precision radiative transfer model, such as the Line-By-Line Radiative Transfer Model (LBLRTM).
[0042] When n=4, the simplified structure diagram of the atmospheric radiative transfer model is as follows: Figure 2As shown, the radiation transmission paths in the four equivalent atmospheric layers are as follows: Figure 3 As shown, the total spectral radiance received by the sensor The atmospheric radiative transfer model is composed of the superposition of emission and absorption contributions from four equivalent atmospheric layers. In another exemplary embodiment of this application, the simplified atmospheric radiative transfer model is as follows: in, , , ,in, In order to be in Below, the wavelength is The corresponding inversion spectral radiance; For wavelength At that time, the first i The equivalent atmosphere at the equivalent temperature T i Planck radiance below, i =1,2,3,4 represent the four atmospheric layers from the innermost to the outermost layer, T i Let be the equivalent temperature of the i-th equivalent atmospheric layer, in Calvin (K). and Represent the equivalent atmospheric layer i and j at wavelengths respectively. The optical thickness at time, where π represents pi, e is the natural constant, and h is Planck's constant with a value of 6.62607 × 10⁻⁶. −34 , c The speed of light has a value of 2.99792 × 10⁻⁶. 8 m / s, K B is the Boltzmann constant, with a value of 1.38 × 10⁻⁶. −23 J / K, For wavelength The spectral absorption coefficient of the i-th equivalent atmospheric layer, Indicates wavelength as The scattering coefficient of the i-th equivalent atmospheric layer, Let be the equivalent path length of the i-th equivalent atmospheric layer. For wavelength The spectral absorption coefficient of the j-th equivalent atmospheric layer, Indicates wavelength as The scattering coefficient of the j-th equivalent atmospheric layer, Let be the equivalent path length of the j-th equivalent atmospheric layer. Since this method is applicable to the long-wave infrared band, where scattering is very weak, therefore... and Negligible.
[0043] In another exemplary embodiment of this application, the objective function is: ,in, This represents the value that minimizes L. L represents the total absolute error between the retrieved infrared spectral radiance and the retrieved infrared reference spectral radiance. Indicates in The wavelength at the k-th point in the lower inversion infrared band The corresponding inversion spectral radiance, where N represents the total number of wave points in the inversion infrared band. This represents the wavelength at the k-th point within the inverted infrared band. The corresponding reference spectral radiance.
[0044] In another exemplary embodiment of this application, during the solution of the objective function, physical constraints are imposed on the equivalent parameters to ensure the rationality of the solution. The constraints on the objective function include a path length constraint expression and a constraint expression for the model's equivalent temperature. The path length constraint expression is: ,in, For minimum observation distance, This represents the maximum observation distance.
[0045] ,in, R For the Earth's radius, h max Indicates the maximum observation height of the sensor. h tan This is the tangent height.
[0046] The constraint expression for the model's equivalent temperature is as follows: ,in, T min The minimum threshold, T max The maximum threshold is set, and the constraint range is based on the standard atmospheric temperature profile (US Standard Atmosphere, 1976).
[0047] In another exemplary embodiment of this application, the objective function is optimized based on the radiance of the reference spectrum of the inverted infrared band to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared band. Specifically, the Sequential Quadratic Programming (SQP) algorithm is used to optimize the objective function based on the radiance of the reference spectrum of the inverted infrared band to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared band.
[0048] In the simplified atmospheric radiative transfer model, the core equivalent parameters for each equivalent atmospheric layer are the equivalent path length and equivalent temperature. These parameters are obtained by solving a constrained nonlinear optimization problem. This application first selects M wavelengths. As an inversion infrared band, its corresponding reference spectral radiance is obtained. Then, the objective function is solved based on the reference spectral radiance to obtain the equivalent path length and equivalent temperature. Finally, the target infrared band and the obtained equivalent path length and equivalent temperature are input into the simplified atmospheric radiative transfer model to obtain the target infrared band spectral radiance, which is suitable for spectral radiance modeling and the derivation of lateral spectral radiance characteristics.
[0049] Compared with the above methods, this application has the following advantages: (1) The method of this application has a higher computational speed. The atmospheric radiative transfer model used in this application is a simplified atmospheric radiative transfer model, which greatly reduces the computational complexity and improves the computational efficiency. Compared with high-precision radiative transfer models such as 6S, the atmospheric radiative transfer model used in this application simplifies dozens or hundreds of atmospheric layers to n layers, which greatly reduces the computational complexity and improves the computational efficiency to the second level. A good balance between computational efficiency and simulation accuracy is achieved. The bottleneck problem that traditional high-precision radiative transfer models cannot be applied in real time is solved.
[0050] (2) This application, while ensuring efficiency, has stronger physical reliability and generalization ability. Compared with purely data-driven statistical regression and other band extrapolation methods, the extrapolation process of this application is rooted in a simplified atmospheric radiative transfer model. The equivalent parameters derived by the optimized algorithm have clear physical meaning (equivalent path length, equivalent temperature), so that the results of cross-band extrapolation do not depend on a large amount of training data for a specific scenario, avoiding the problem of poor generalization ability of "black box" models, and showing better adaptability and robustness when facing different atmospheric conditions and observation geometry.
[0051] This application also provides an embodiment to illustrate the effectiveness of the infrared spectral radiance extrapolation method proposed in this application. In this embodiment, the inverted infrared light band is the 7.00μm-7.30μm band, and the target infrared light band is the 9.00μm-10.00μm band. Equivalent parameters are inverted using reference data from the 7.00μm-7.30μm band, and the Earth's lateral radiance in the 9.00μm-10.00μm band is extrapolated. Finally, the extrapolation results are compared and verified with the calculation results of a high-precision model (LBLRTM model). Figure 8 As shown, the specific steps are as follows: Step 1: Establish a simplified atmospheric radiation transfer model.
[0052] For ease of understanding, we will use a four-layer atmospheric sub-layer (equivalent atmospheric layer) as an example. The non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere is simplified into four concentric spherical equivalent atmospheric layers with uniformly distributed atmospheric gas composition. This simplification is based on the following assumptions: First, the atmospheric gas composition is uniformly distributed within the equivalent layer, and its composition ratio does not change over time; second, in the long-wave infrared band, the scattering effect of aerosols and molecules is much weaker than the absorption effect, therefore the model only considers absorption and ignores the scattering coefficient. This yields a simplified atmospheric radiative transfer model.
[0053] Step 2: Input the calculation conditions and atmospheric parameters as shown in Table 1, and input these input data into the radiance calculation reference model (the LBLRTM model is used in this embodiment) to calculate the spectral radiance of the selected band (inverted infrared band).
[0054] Table 1 Parameter Table
[0055] Step 3: Compare the spectral radiance calculated by the simplified atmospheric radiative transfer model (inverted infrared spectral radiance) with the reference atmospheric radiance (radiance calculated by the LBLRTM model). Optimize the algorithm to calculate the equivalent path length of each atmospheric sublayer in the selected band. l 1, l 2, l 3, l 4 and equivalent temperature T 1, T 2, T 3, T 4.
[0056] Using the 7.00μm~7.30μm band reference spectral radiance obtained in step 2 as the target, the SQP optimization algorithm is used to optimize the equivalent parameter set { in the objective function. T 1, T 2, T 3, T 4, l 1, l 2, l 3, l 4) Perform the inversion solution. The optimization process must comply with the aforementioned physical constraints. After the inversion calculation, the optimized equivalent parameters are shown in Table 2.
[0057] Table 2. Optimized Equivalent Parameter Table
[0058] Step 4: Substitute the equivalent parameters into the simplified atmospheric radiative transfer model to calculate the spectral radiance of the target in the infrared band.
[0059] Substitute the optimized equivalent parameters obtained from step 3 into the simplified atmospheric radiative transfer model to calculate the target infrared band inversion spectral radiance (Earth rim radiance). To verify the inference accuracy, the spectral radiance of the target infrared band calculated by the LBLRTM model under the same conditions is used as a reference.
[0060] The comparison results and relative deviations between the predicted values (the spectral radiance in the 9.00μm-10.00μm band obtained by the infrared spectral radiance extrapolation method provided in this application) and the reference values (the spectral radiance in the 9.00μm-10.00μm band calculated under the same conditions using the LBLRTM model) are as follows: Figures 4 to 7 As shown, Figure 4 Part (a) shows the comparison between the predicted and reference values at a tangent height of 51.6 km. Figure 4 Part (b) shows the relative error between the predicted and reference values at a tangent height of 51.6 km. Figure 5 Part (a) shows the comparison between the predicted and reference values at a tangent height of 53.7 km. Figure 5 Part (b) shows the relative error between the predicted and reference values at a tangent height of 53.7 km. Figure 6 Part (a) shows the comparison between the predicted and reference values at a tangent height of 55.8 km. Figure 6 Part (b) shows the relative error between the predicted and reference values at a tangent height of 55.8 km. Figure 7 Part (a) shows the comparison between the predicted and reference values at a tangent height of 57.9 km. Figure 7 Part (b) shows the relative error between the predicted and reference values at a tangent height of 57.9 km. At a tangent height of 51.6 km, the average relative deviation is 22.91%, with the maximum relative deviation occurring at 9.26 μm (37.60%) and the minimum relative deviation at 10.00 μm (15.82%). At a tangent height of 53.7 km, the average relative deviation is 19.83%, with the maximum relative deviation occurring at 9.26 μm (32.87%) and the minimum relative deviation at 10.00 μm (12.24%). At a tangent height of 55.8 km, the average relative deviation is 5.89%, with the maximum relative deviation occurring at 9.26 μm (14.74%) and the minimum relative deviation at 9.61 μm (0.02%). At a tangent height of 57.90 km, the average relative deviation is 6.91%, with the maximum relative deviation occurring at 9.99 μm (17.08%) and the minimum relative deviation at 9.48 μm (0.07%).
[0061] Based on the same inventive concept, this application also provides an infrared spectral radiance extrapolation device for implementing the infrared spectral radiance extrapolation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more infrared spectral radiance extrapolation device embodiments provided below can be found in the limitations of the infrared spectral radiance extrapolation method described above, and will not be repeated here.
[0062] In one exemplary embodiment, such as Figure 9 As shown, an infrared spectral radiance extrapolation device is provided, comprising: a model building module for constructing a simplified atmospheric radiative transfer model based on an n-layer equivalent atmosphere; the n-layer equivalent atmosphere is obtained by simplifying the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere; 3≤n≤10.
[0063] The acquisition module is used to acquire the reference spectral radiance of the inverted infrared band and the target infrared band.
[0064] The equivalent atmospheric parameter determination module is used to optimize the objective function based on the inverted infrared light band reference spectral radiance, and obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band; the objective function aims to minimize the total absolute error between the inverted infrared light band inverted spectral radiance and the inverted infrared light band reference spectral radiance.
[0065] The radiance extrapolation module is used to input the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the target infrared light band and the inverted infrared light band into a simplified atmospheric radiative transfer model to obtain the inverted spectral radiance of the target infrared light band.
[0066] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 10As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores infrared spectral radiance estimation data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements an infrared spectral radiance estimation method.
[0067] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0068] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.
[0069] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.
[0070] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.
[0071] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0072] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0073] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.
[0074] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0075] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for extrapolating infrared spectral radiance, characterized in that, The infrared spectral radiance extrapolation method includes: A simplified atmospheric radiation transfer model is constructed based on an n-layer equivalent atmosphere; the n-layer equivalent atmosphere is obtained by simplifying the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere; 3≤n≤10. Obtain the inverted infrared reference spectral radiance and the target infrared band; The objective function is optimized based on the inverted infrared light band reference spectral radiance to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band; the objective function aims to minimize the total absolute error between the inverted infrared light band inverted spectral radiance and the inverted infrared light band reference spectral radiance. By inputting the target infrared light band and the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band into the simplified atmospheric radiative transfer model, the inverted spectral radiance of the target infrared light band is obtained.
2. The infrared spectral radiance extrapolation method according to claim 1, characterized in that, The process for determining the radiance of the reference spectrum in the inverted infrared band is as follows: Obtain the atmospheric conditions, aerosol types, visibility, relative humidity, resolution, cut-off height range, temperature range, and path range for the inverted infrared band and each equivalent atmospheric layer; The reference spectral radiance of the inverted infrared band is obtained by inputting the atmospheric conditions, aerosol type, visibility, relative humidity, resolution, cut-off height range, temperature range, and path range of each equivalent atmospheric layer into the radiance calculation reference model.
3. The infrared spectral radiance extrapolation method according to claim 1, characterized in that, When n=4, the simplified atmospheric radiative transfer model is: in, , , ,in, In order to be in Below, wavelength The corresponding inversion spectral radiance; For wavelength At that time, the i-th equivalent atmospheric layer is at the equivalent temperature T i Planck radiance under T i Let be the equivalent temperature of the i-th equivalent atmospheric layer. and Represent the equivalent atmospheric layer i and j at wavelengths respectively. The optical thickness at that time, where π represents pi. e is the natural constant, and h is Planck's constant. c At the speed of light, K B Boltzmann's constant, For wavelength The spectral absorption coefficient of the i-th equivalent atmospheric layer, Indicates wavelength as The scattering coefficient of the i-th equivalent atmospheric layer, Let be the equivalent path length of the i-th equivalent atmospheric layer. For wavelength The spectral absorption coefficient of the j-th equivalent atmospheric layer, Indicates wavelength as The scattering coefficient of the j-th equivalent atmospheric layer, Let be the equivalent path length of the j-th equivalent atmospheric layer.
4. The infrared spectral radiance extrapolation method according to claim 3, characterized in that, The objective function is: ,in, This represents the value that minimizes L. L represents the total absolute error between the retrieved infrared spectral radiance and the retrieved infrared reference spectral radiance. Indicates in The wavelength at the k-th point in the lower inversion infrared band The corresponding inversion spectral radiance, where N represents the total number of wave points in the inversion infrared band. This represents the wavelength at the k-th point within the inverted infrared band. The corresponding reference spectral radiance.
5. The infrared spectral radiance extrapolation method according to claim 4, characterized in that, The constraints of the objective function are: ,in, For minimum observation distance, This is the maximum observation distance; Where R is the Earth's radius. This indicates the maximum observation altitude of the space-based infrared sensor. The vertical distance from the Earth's surface to the point where the line of sight to Earth is tangent to the Earth's atmosphere; ,in, The minimum threshold, This is the maximum threshold.
6. The infrared spectral radiance extrapolation method according to claim 1, characterized in that, The objective function is optimized based on the radiance of the reference spectrum in the inverted infrared band to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared band. Specifically: The SQP algorithm is used to optimize the objective function based on the radiance of the reference spectrum in the inverted infrared band, so as to obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared band.
7. An infrared spectral radiance extrapolation device, characterized in that, The infrared spectral radiance extrapolation device includes: The model building module is used to construct a simplified atmospheric radiation transfer model based on an n-layer equivalent atmosphere; the n-layer equivalent atmosphere is obtained by simplifying the non-uniform atmospheric path from the observation tangent point at the edge of the Earth to the top of the atmosphere; 3≤n≤10; The acquisition module is used to acquire the reference spectral radiance of the inverted infrared band and the target infrared band; The equivalent atmospheric parameter determination module is used to optimize the objective function based on the inverted infrared light band reference spectral radiance, and obtain the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the inverted infrared light band; the objective function aims to minimize the total absolute error between the inverted infrared light band inverted spectral radiance and the inverted infrared light band reference spectral radiance. The radiance extrapolation module is used to input the equivalent path length and equivalent temperature of each equivalent atmospheric layer corresponding to the target infrared light band and the inverted infrared light band into a simplified atmospheric radiative transfer model to obtain the inverted spectral radiance of the target infrared light band.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the infrared spectral radiance extrapolation method according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the infrared spectral radiance extrapolation method as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the infrared spectral radiance extrapolation method as described in any one of claims 1-6.