An outcoming long-wave radiation acquisition method and system based on infrared hyperspectral data

Abstract

This invention discloses a method and system for acquiring emitted longwave radiation based on infrared hyperspectral data. The method includes: acquiring infrared hyperspectral channel observation data and observation geometry information of a target satellite; filtering all effective channels based on channel signal-to-noise ratio and constructing an radiance matrix; performing principal component analysis on the matrix to extract the first n principal components; calculating initial radiation values ​​based on the principal components using a pre-established emitted longwave radiation inversion model; and interpolating the initial radiation values ​​calculated by multiple pre-established inversion models based on different observation angle intervals according to the satellite observation angle, ultimately obtaining high-precision emitted longwave radiation values. This invention fully utilizes the spectral information of all effective channels in the infrared hyperspectral system and corrects errors introduced by optical path differences through observation angle interpolation, significantly improving the accuracy and reliability of emitted longwave radiation inversion.

Description

Technical Field

[0001] This invention belongs to the field of satellite remote sensing and atmospheric science, and in particular relates to a method and system for acquiring emitted long-wave radiation based on infrared hyperspectral data. Background Technology

[0002] Outgoing longwave radiation (OLR) is a key physical parameter for the energy balance of the Earth's atmospheric system, crucial for studying climate change, convective activity, and the global energy budget. Currently, satellite acquisition of OLR primarily falls into two categories: one is direct observation over a wide band, such as with instruments like CERES, which offer high accuracy but have limited spatiotemporal resolution; the other is indirect calculation using narrow-band channel data through inversion models, such as establishing fitting relationships using a limited number of channels from instruments like AVHRR or HIRS.

[0003] With the development of infrared hyperspectral technology, methods for retrieving OLR using instruments such as AIRS and CrIS have emerged. However, the mainstream approach of existing hyperspectral inversion algorithms typically involves selecting a subset of "equivalent channels" and then establishing relationships using narrow-band fitting. This method fails to fully utilize the information of all effective channels contained in the hyperspectral data, resulting in information loss. Furthermore, the optical path difference caused by varying satellite observation angles is one of the main sources of inversion error, and existing methods often lack effective correction for this physical effect.

[0004] Therefore, existing technologies have two main drawbacks: they fail to fully utilize spectral information and do not effectively correct for the effects of observation geometry. As a result, there is still room for improvement in the accuracy and reliability of OLR inversion. Summary of the Invention

[0005] To address the shortcomings of the existing technology, this invention provides a method for acquiring emitted long-wave radiation based on infrared hyperspectral data, comprising the following steps:

[0006] Step S101: Obtain the channel observation data and corresponding observation geometry information of the infrared hyperspectral instrument of the target satellite;

[0007] Step S103: Filter the channel observation data based on the channel signal-to-noise ratio to determine all valid channels for inversion;

[0008] Step S105: Construct an emissivity matrix based on the emissivity data of all effective channels;

[0009] Step S107: Perform principal component analysis on the emissivity matrix and extract the first n-dimensional principal components, where n is the minimum dimension that makes the cumulative contribution rate of the principal components reach a preset threshold.

[0010] Step S109: Based on the first n-dimensional principal components, the initial radiation value is calculated using a pre-established long-wavelength radiation inversion model;

[0011] Step S1011: Based on the satellite observation angle in the observation geometry information, interpolate the initial radiation values ​​calculated by multiple inversion models pre-established based on different satellite observation angle intervals to obtain the final emitted longwave radiation value.

[0012] The method for constructing the emitted long-wave radiation inversion model includes:

[0013] Obtain true data of wideband emitted longwave radiation from a reference satellite;

[0014] The infrared hyperspectral data of the target satellite and the true value data of the broadband emitted longwave radiation of the reference satellite are spatiotemporally matched to construct a matching dataset.

[0015] Based on the matching dataset, the true data and the first n-dimensional principal components after dimensionality reduction by principal component analysis are subjected to multiple linear regression to obtain the regression coefficients of the inversion model.

[0016] The spatiotemporal matching must meet the following conditions: the matching time interval is less than 15 minutes, and the observation point of the target satellite is located within the spatial coverage area of ​​the reference satellite's true value data pixels.

[0017] The reference satellite is the AQUA satellite, and the true data of the wideband emitted longwave radiation were obtained by the CERES instrument.

[0018] Before constructing the emissivity matrix, the emissivity data of all effective channels are preprocessed by normalization.

[0019] The satellite observation angle intervals are divided into equal angular intervals.

[0020] The interpolation is a linear interpolation, and the interpolation weight is determined based on the relative position of the center value of the real-time satellite observation angle and the adjacent satellite observation angle interval.

[0021] The method further includes: geographically locating and projecting the final emitted longwave radiation value according to its latitude and longitude information to generate an emitted longwave radiation spatial distribution image.

[0022] The target satellite is the FY-3 series satellite, and the infrared hyperspectral instrument is the HIRAS instrument.

[0023] This invention also proposes a system for acquiring emitted long-wave radiation based on infrared hyperspectral data to implement the above method, comprising:

[0024] The data acquisition and preprocessing module is used to acquire and filter infrared hyperspectral channel observation data and observation geometric information of the target satellite based on the channel signal-to-noise ratio;

[0025] The matrix construction and dimensionality reduction module is used to construct an emissivity matrix based on the emissivity data of all effective channels and perform principal component analysis to extract the first n principal components.

[0026] The radiation value inversion module is used to calculate the initial radiation value based on the first n-dimensional principal components and the pre-established emitted long-wave radiation inversion model;

[0027] The observation angle correction module is used to interpolate the initial radiation values ​​from different inversion models based on the satellite observation angle, and to obtain and output the final emitted longwave radiation value.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] Information utilization is more comprehensive. By using principal component analysis, information from all effective channels of the infrared hyperspectral spectrum is incorporated into the inversion model, avoiding information loss caused by selecting only some channels in traditional methods, and providing a more complete physical characterization of OLR.

[0030] The accuracy is significantly improved. By establishing an inversion model for each satellite observation angle interval and combining it with linear interpolation, the optical path difference error caused by different observation angles is effectively corrected. Experiments show that, using the method of this invention, the root mean square error of OLR inversion by the FY-3E satellite HIRAS instrument can be reduced to approximately 10.05 W / m², which is better than the accuracy of approximately 11.95 W / m² of the multispectral instrument MERSI.

[0031] High reliability. This invention introduces channel screening and quality control steps based on signal-to-noise ratio, and utilizes spatiotemporal matching to construct a high-quality sample library, ensuring the robustness of the inversion process and results.

[0032] High practical value. This invention provides higher quality and more reliable OLR data products for climate monitoring and weather process research, and has significant scientific value and application prospects. Attached Figure Description

[0033] The above and other objects, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0034] Figure 1 This is a flowchart of the method for obtaining long-wave radiation emitted by the HIRAS instrument on the FY-3E satellite in this embodiment of the invention;

[0035] Figure 2 This is a global spatial distribution map of the number of FY-3D HIRAS observations matched with the US CERES OLR in this embodiment of the invention;

[0036] Figure 3(a) is a satellite orbital observation diagram of the global spatial distribution of daily average radio longwave radiation data calculated using this method, obtained by the HIRAS instrument on the FY-3E satellite in this embodiment of the invention (unit: W / m). 2 (tiles per square meter);

[0037] Figure 3(b) is a satellite descent observation map (unit: W / m) of the global spatial distribution of daily average radio longwave radiation data calculated using the method observed by the HIRAS instrument of the FY-3E satellite in this embodiment of the invention. 2 (tiles per square meter);

[0038] Figure 4(a) is a frequency histogram of the difference between the daily OLR value calculated by the HIRAS instrument and the true CERES value in an embodiment of the present invention; and

[0039] Figure 4(b) is a frequency histogram of the difference between the daily OLR value calculated by the MERSI instrument and the true CERES value in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0041] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0042] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0043] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0044] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0045] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0046] The optional embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0047] Example 1

[0048] like Figure 1 As shown, this invention discloses a method for obtaining emitted long-wave radiation based on infrared hyperspectral data, comprising the following steps:

[0049] Step S101: Obtain the channel observation data and corresponding observation geometry information of the infrared hyperspectral instrument of the target satellite;

[0050] Step S103: Filter the channel observation data based on the channel signal-to-noise ratio to determine all valid channels for inversion;

[0051] Step S105: Construct an emissivity matrix based on the emissivity data of all effective channels;

[0052] Step S107: Perform principal component analysis on the emissivity matrix and extract the first n-dimensional principal components, where n is the minimum dimension that makes the cumulative contribution rate of the principal components reach a preset threshold.

[0053] Step S109: Based on the first n-dimensional principal components, the initial radiation value is calculated using a pre-established long-wavelength radiation inversion model;

[0054] Step S1011: Based on the satellite observation angle in the observation geometry information, interpolate the initial radiation values ​​calculated by multiple inversion models pre-established based on different satellite observation angle intervals to obtain the final emitted longwave radiation value.

[0055] Example 2

[0056] The present invention proposes a method for obtaining emitted long-wave radiation based on infrared hyperspectral data, comprising the following steps:

[0057] Step S101: Obtain the channel observation data and corresponding observation geometry information of the infrared hyperspectral instrument of the target satellite;

[0058] Step S103: Filter the channel observation data based on the channel signal-to-noise ratio to determine all valid channels for inversion;

[0059] Step S105: Construct an emissivity matrix based on the emissivity data of all effective channels;

[0060] Step S107: Perform principal component analysis on the emissivity matrix and extract the first n-dimensional principal components, where n is the minimum dimension that makes the cumulative contribution rate of the principal components reach a preset threshold.

[0061] Step S109: Based on the first n-dimensional principal components, the initial radiation value is calculated using a pre-established long-wavelength radiation inversion model;

[0062] Step S1011: Based on the satellite observation angle in the observation geometry information, interpolate the initial radiation values ​​calculated by multiple inversion models pre-established based on different satellite observation angle intervals to obtain the final emitted longwave radiation value.

[0063] In this invention, principal component analysis is used to extract the main radiative features sensitive to OLR in hyperspectral data. The cumulative contribution rate threshold of the first n principal components is set to 90%~95%, effectively suppressing noise while ensuring information preservation. Furthermore, this invention found that the first 35 principal components can stably characterize OLR changes across multiple observation angle intervals, possessing physical interpretability. For example, the first principal component corresponds to the dominance of surface temperature, while the second and third principal components correspond to the influence of water vapor and clouds.

[0064] The method for constructing the emitted long-wave radiation inversion model includes:

[0065] Obtain true data of wideband emitted longwave radiation from a reference satellite;

[0066] The infrared hyperspectral data of the target satellite and the true value data of the broadband emitted longwave radiation of the reference satellite are spatiotemporally matched to construct a matching dataset.

[0067] Based on the matching dataset, the true data and the first n-dimensional principal components after dimensionality reduction by principal component analysis are subjected to multiple linear regression to obtain the regression coefficients of the inversion model.

[0068] This invention uses multiple linear regression instead of complex models such as neural networks because: 1) the principal components have already achieved feature extraction and the linear relationship is significant; 2) the coefficients of the linear model have physical interpretability, which is convenient for business debugging and verification; 3) when the amount of hyperspectral data is extremely large, the linear model has high computational efficiency and is suitable for real-time processing.

[0069] The spatiotemporal matching must meet the following conditions: the matching time interval is less than 15 minutes, and the observation point of the target satellite is located within the spatial coverage area of ​​the reference satellite's true value data pixels.

[0070] The reference satellite is the AQUA satellite, and the true data of the wideband emitted longwave radiation were obtained by the CERES instrument.

[0071] Before constructing the emissivity matrix, the emissivity data of all effective channels are preprocessed by normalization.

[0072] The satellite observation angle intervals are divided into equal angular intervals.

[0073] The interpolation is a linear interpolation, and the interpolation weight is determined based on the relative position of the center value of the real-time satellite observation angle and the adjacent satellite observation angle interval.

[0074] The method further includes: geographically locating and projecting the final emitted longwave radiation value according to its latitude and longitude information to generate an emitted longwave radiation spatial distribution image.

[0075] The target satellite is the FY-3 series satellite, and the infrared hyperspectral instrument is the HIRAS instrument.

[0076] Example 3

[0077] In one embodiment of the present invention, the following steps are specifically adopted.

[0078] (1) Selection of the true value of observation: Based on the radiance data of the HIRAS instrument observation channel of FY3D satellite, the real-time data of the long-wave radiation emitted by the CERES instrument of AQUA satellite of the United States is used as the true value to determine the matching standard for matching. FY3D and AQUA are both afternoon satellites with similar observation trajectories, so the matching probability is high.

[0079] (2) Matching process between the channel radiance of the HIRAS instrument and the emitted longwave radiation of the CERES instrument: The matching principles include a spatial resolution of 33 km, a time interval of less than 15 minutes, and the selection of samples from one day of each month in 2019. The above matching pairs are arranged in the following order: radiance of all HIRAS channels, year, month, day, hour, minute, satellite observation angle, longitude, latitude, elevation, surface type, true value, etc.

[0080] (3) Establish inversion coefficients through principal component analysis:

[0081] Based on the satellite observation angle, the HIRAS channel radiance matrix is ​​constructed using equation (1), eigenvalue decomposition is performed, and its eigenvector P is calculated.

[0082] (1)

[0083] in, As given by equation (2), N is the number of channels selected. Let E be the eigenvalue of the matrix and E be the covariance matrix of the HIRAS instrument channel radiance vector matrix.

[0084] (2)

[0085] The first 35 eigenvectors that can characterize all channel information are selected and subjected to multivariate linear regression with the long-wave radiation emitted by the CERES instrument to establish the HIRAS OLR inversion coefficients.

[0086] (4) Calculation of long-wave radiation emitted by HIRAS instrument:

[0087] Input the real-time L1 data and real-time satellite observation angle of the FY3E HIRAS instrument, determine the two sets of coefficients for the satellite observation angle interval, and calculate the emitted long-wave radiation values ​​of the two HIRAS instruments using equation (3).

[0088] (3)

[0089] In the formula, b0 and It is the regression coefficient established by the observation angle in step (3).

[0090] Linear interpolation of the emitted longwave radiation is performed at the position of the observation angle interval by the real-time satellite observation angle to obtain the final inversion value of the emitted longwave radiation of the HIRAS instrument.

[0091] Example 4

[0092] This invention also proposes a system for acquiring emitted long-wave radiation based on infrared hyperspectral data to implement the above method, comprising:

[0093] The data acquisition and preprocessing module is used to acquire and filter infrared hyperspectral channel observation data and observation geometric information of the target satellite based on the channel signal-to-noise ratio;

[0094] The matrix construction and dimensionality reduction module is used to construct an emissivity matrix based on the emissivity data of all effective channels and perform principal component analysis to extract the first n principal components.

[0095] The radiation value inversion module is used to calculate the initial radiation value based on the first n-dimensional principal components and the pre-established emitted long-wave radiation inversion model;

[0096] The observation angle correction module is used to interpolate the initial radiation values ​​from different inversion models based on the satellite observation angle, and to obtain and output the final emitted longwave radiation value.

[0097] Example 5

[0098] This method is implemented using the Hyperspectral Infrared Atmospheric Sounder (HIRAS) on Fengyun-3 (FY-3)E and FY-3D satellites as the target instrument and the broadband OLR product of the CERES instrument on the AQUA satellite as the true value.

[0099] Data preparation and matching:

[0100] The radiance data of the L1 channel of the FY-3D satellite HIRAS instrument for a total of 12 days (1 day per month) throughout 2019 were selected.

[0101] Obtain the instantaneous OLR product from the CERES instrument on the AQUA satellite during the same period as the true value.

[0102] Spatiotemporal matching was performed: spatially, ensuring that the HIRAS observation point was located within the CERES pixels (approximately 33km resolution); temporally, ensuring that the observation time difference between the two was less than 15 minutes. Ultimately, over 50,000 effective matched sample pairs were obtained, covering the entire globe and encompassing different seasons and diurnal cycles (e.g., Figure 2 (As shown).

[0103] Inversion model construction:

[0104] For the matched HIRAS data, first filter out channels with low signal-to-noise ratios according to the calibration noise table, and retain all valid channels.

[0105] The samples were grouped according to the satellite zenith angle (e.g., at 5° intervals).

[0106] For each sample group, construct a radiometric matrix L (rows represent the number of samples, columns represent the number of effective channels), and follow the formula... Normalization is performed, where μ is the mean and σ is the standard deviation.

[0107] Experiments have shown that the inversion error is minimized when the observation angle interval is divided into 5° intervals. If the interval is too large (e.g., 10°), the correction is insufficient; if it is too small (e.g., 1°), there are insufficient samples, leading to model instability. Linear interpolation is superior to nonlinear methods in this scenario because it introduces the least error when the observation angle changes smoothly, and it has high computational efficiency, making it suitable for operational use.

[0108] Principal component analysis (PCA) was performed on the normalized matrix to calculate its covariance matrix and perform eigenvalue decomposition to extract eigenvalues ​​and eigenvectors. The top 35 eigenvectors P with a cumulative contribution rate of over 90% were selected.

[0109] Score these 35 principal components A model was established by performing multiple linear regression with the true values ​​of CERES and OLR: The regression coefficients b0, b1, ..., b of the observed angle interval are obtained. 35 .

[0110] Real-time data acquisition:

[0111] Acquire real-time L1 observation data, satellite observation angle, latitude and longitude information from the HIRAS instrument on the FY-3E satellite.

[0112] Perform data quality control and effective channel screening.

[0113] OLR value calculation and output:

[0114] Based on the real-time satellite observation angle θ, find two adjacent predefined observation angle intervals (such as θ1 and θ2).

[0115] Using the inversion model coefficients corresponding to these two intervals respectively, two initial OLR values, OLR1 and OLR2, are calculated.

[0116] Using linear interpolation formula Calculate the final OLR inversion value.

[0117] Finally, the inverted values ​​are geolocated based on the observed latitude and longitude information and projected onto a 1°×1° global grid to generate OLR spatial distribution products (as shown in Figures 3(a) and 3(b)).

[0118] This method was applied to FY-3E HIRAS data, and the inversion results were compared with the true CERES values. As shown in Figure 4(a), the difference histogram is concentrated near zero, with a root mean square error of 10.05 W / m². In contrast, the OLR retrieved by the MERSI multispectral instrument on FY-3E using a traditional method has a root mean square error of 11.95 W / m² (Figure 4(b)). The results show that the method of this invention significantly improves the accuracy of OLR inversion. Compared with existing hyperspectral inversion methods (such as AIRS's OLR products), this invention achieves an approximately 8% accuracy improvement on FY-3E HIRAS data and remains stable under different underlying surface conditions, including ocean, land, and polar regions. A t-test showed no significant deviation between the error distribution and the true CERES values ​​(p>0.05), demonstrating its operational potential.

[0119] Example 6

[0120] This disclosure provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0121] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0122] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0123] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (AN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0125] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0126] The preferred embodiments of the present invention have been described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope summarized by the appended claims.

Claims

1. A method for acquiring emitted long-wave radiation based on infrared hyperspectral data, characterized in that, Includes the following steps: Step S101: Obtain the channel observation data and corresponding observation geometry information of the infrared hyperspectral instrument of the target satellite; Step S103: Filter the channel observation data based on the channel signal-to-noise ratio to determine all valid channels for inversion; Step S105: Construct an emissivity matrix based on the emissivity data of all effective channels; Step S107: Perform principal component analysis on the emissivity matrix and extract the first n-dimensional principal components, where n is the minimum dimension that makes the cumulative contribution rate of the principal components reach a preset threshold. Step S109: Based on the first n-dimensional principal components, the initial radiation value is calculated using a pre-established long-wavelength radiation inversion model; Step S1011: Based on the satellite observation angle in the observation geometry information, interpolate the initial radiation values ​​calculated by multiple inversion models pre-established based on different satellite observation angle intervals to obtain the final emitted longwave radiation value. The method for constructing the emitted long-wave radiation inversion model includes: Obtain true data of wideband emitted longwave radiation from a reference satellite; The infrared hyperspectral data of the target satellite and the true value data of the broadband emitted longwave radiation of the reference satellite are spatiotemporally matched to construct a matching dataset. Based on the matching dataset, the true data and the first n-dimensional principal components after dimensionality reduction by principal component analysis are subjected to multiple linear regression to obtain the regression coefficients of the inversion model.

2. The method as described in claim 1, characterized in that, The spatiotemporal matching must meet the following conditions: the matching time interval is less than 15 minutes, and the observation point of the target satellite is located within the spatial coverage area of ​​the reference satellite's true value data pixels.

3. The method as described in claim 1, characterized in that, The reference satellite is the AQUA satellite, and the true data of the wideband emitted longwave radiation were obtained by the CERES instrument.

4. The method as described in claim 1, characterized in that, Before constructing the emissivity matrix, the emissivity data of all effective channels are preprocessed by normalization.

5. The method as described in claim 1, characterized in that, The satellite observation angle intervals are divided into equal angular intervals.

6. The method as described in claim 1, characterized in that, The interpolation is linear interpolation, and the interpolation weight is determined based on the relative position of the center value of the interval between the real-time satellite observation angle and the adjacent satellite observation angle.

7. The method as described in claim 1, characterized in that, The method further includes: geolocating and projecting the final emitted longwave radiation value according to its latitude and longitude information to generate an emitted longwave radiation spatial distribution image.

8. The method as described in claim 1, characterized in that, The target satellite is the FY-3 series satellite, and the infrared hyperspectral instrument is a HIRAS instrument.

9. A system for acquiring emitted long-wave radiation based on infrared hyperspectral data, used to implement the method as described in any one of claims 1 to 8, characterized in that, include: The data acquisition and preprocessing module is used to acquire and filter infrared hyperspectral channel observation data and observation geometric information of the target satellite based on the channel signal-to-noise ratio; The matrix construction and dimensionality reduction module is used to construct an emissivity matrix based on the emissivity data of all effective channels and perform principal component analysis to extract the first n principal components. The radiation value inversion module is used to calculate the initial radiation value based on the first n-dimensional principal components and the pre-established emitted long-wave radiation inversion model; The observation angle correction module is used to interpolate the initial radiation values ​​from different inversion models based on the satellite observation angle, and to obtain and output the final emitted longwave radiation value. The method for constructing the emitted long-wave radiation inversion model includes: Obtain true data of wideband emitted longwave radiation from a reference satellite; The infrared hyperspectral data of the target satellite and the true value data of the broadband emitted longwave radiation of the reference satellite are spatiotemporally matched to construct a matching dataset. Based on the matching dataset, the true data and the first n-dimensional principal components after dimensionality reduction by principal component analysis are subjected to multiple linear regression to obtain the regression coefficients of the inversion model.

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