Emitted long wave radiation acquisition method and system based on infrared hyperspectral data

Abstract

The invention discloses a method and a system for acquiring emitted long-wave radiation based on infrared hyperspectral data. The method comprises the following steps: acquiring infrared hyperspectral channel observation data and observation geometric information of a target satellite; screening all effective channels based on a channel signal-to-noise ratio, and constructing a radiance matrix; performing principal component analysis on the matrix to extract principal components of the first n dimensions; based on the principal component, calculating an initial radiation value through a pre-established emitted long wave radiation inversion model; according to a satellite observation angle, interpolation is carried out on initial radiation values calculated by a plurality of inversion models pre-established based on different observation angle intervals, and finally a high-precision emission long wave radiation value is obtained. Spectral information of all effective channels of the infrared hyperspectrum is fully utilized, errors caused by optical path difference are corrected through observation angle interpolation, and the accuracy and reliability of radiation inversion of emitted long waves are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of satellite remote sensing and atmospheric science, and particularly relates to a method and system for obtaining outgoing longwave radiation based on infrared hyperspectral data. BACKGROUND

[0002] Outgoing longwave radiation (OLR) at the top of the atmosphere is a key physical parameter of the energy balance of the Earth's atmospheric system, and is of great importance to the study of climate change, convection and global energy budget. At present, the main means of obtaining OLR by satellite is divided into two categories: one is direct observation of wide band, such as CERES instrument, which has high product accuracy but limited data spatial and temporal resolution; the other is indirect calculation by using narrow band channel data through inversion model, such as using a small number of channels of AVHRR or HIRS instrument to establish a fitting relationship.

[0003] With the development of infrared hyperspectral technology, methods for using AIRS, CrIS and other instruments to retrieve OLR have emerged. However, the mainstream way of existing hyperspectral retrieval algorithms is usually to select some "equivalent channels", and then use narrow band fitting method to establish a relationship. This method fails to fully utilize the information contained in all effective channels of hyperspectral data, resulting in information loss. At the same time, the difference in optical path caused by different satellite observation angles is one of the main sources of retrieval error, and the existing methods often lack effective correction of this physical effect.

[0004] Therefore, the existing technology has two main defects of failing to fully utilize spectral information and failing to effectively correct the influence of observation geometry, resulting in room for improvement in the accuracy and reliability of OLR retrieval. SUMMARY

[0005] In view of the defects in the prior art, the present application provides a method for obtaining outgoing longwave radiation based on infrared hyperspectral data, comprising the following steps:

[0006] Step S101, obtaining channel observation data of an infrared hyperspectral instrument of a target satellite and corresponding observation geometry information;

[0007] Step S103, filtering the channel observation data based on channel signal-to-noise ratio to determine all effective channels for inversion;

[0008] Step S105, constructing a radiance matrix based on the radiance data of all effective channels;

[0009] Step S107, performing principal component analysis on the radiance matrix to 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 principal components, the initial radiation value is calculated by a pre-established outgoing long-wave radiation inversion model;

[0011] Step S1011, according to the satellite observation angle in the observation geometry information, the initial radiation value calculated based on the pre-established multiple inversion models of different satellite observation angle intervals is interpolated to obtain the final outgoing long-wave radiation value.

[0012] The method for constructing the outgoing long-wave radiation inversion model comprises:

[0013] Obtaining the wide-band outgoing long-wave radiation true value data of the reference satellite;

[0014] The infrared hyperspectral data of the target satellite and the wide-band outgoing long-wave radiation true value data of the reference satellite are spatio-temporally matched to construct a matching data set;

[0015] Based on the matching data set, the true value data and the first n 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 spatio-temporal matching needs to meet: the matching time interval is less than 15 minutes, and the observation point of the target satellite is located in the spatial coverage range of the reference satellite true value data pixel.

[0017] The reference satellite is AQUA satellite, and the wide-band outgoing long-wave radiation true value data is obtained by CERES instrument observation.

[0018] Before constructing the radiation rate matrix, the radiation rate data of all effective channels is subjected to normalization preprocessing.

[0019] The satellite observation angle interval is divided according to equal angle interval.

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

[0021] The method further comprises: the final outgoing long-wave radiation value is geographically located and equi-latitude and longitude projected according to its latitude and longitude information to generate an outgoing long-wave radiation spatial distribution image.

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

[0023] The application further provides an outgoing long-wave radiation acquisition system based on infrared hyperspectral data for implementing the above method, comprising:

[0024] A data acquisition and preprocessing module is configured to acquire and filter infrared hyperspectral channel observation data and observation geometry information of a target satellite based on channel signal-to-noise ratio.

[0025] A matrix construction and dimension reduction module is configured to construct a radiance matrix based on radiance data of all effective channels and perform principal component analysis to extract the first n principal components.

[0026] A radiance value inversion module is configured to calculate initial radiance values based on the first n principal components and a pre-established outgoing longwave radiation inversion model.

[0027] An observation angle correction module is configured to interpolate the initial radiance values from different inversion models according to satellite observation angles, acquire and output final outgoing longwave radiation values.

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

[0029] Information is more fully utilized. Through principal component analysis technology, the information of all effective channels of infrared hyperspectral is included in the inversion model, avoiding the information loss caused by selecting part of the channels in the traditional method, and more completely representing OLR from a physical point of view.

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

[0031] The reliability is strong. In the present application, channel screening and quality control steps based on signal-to-noise ratio are introduced, and a high-quality sample library is constructed by using space-time matching, ensuring the robustness of the inversion process and results.

[0032] The practical value is high. The present application provides higher-quality and more reliable OLR data products for climate monitoring and weather process research, which has important scientific value and application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0033] The above and other objects, features and advantages of the disclosed example embodiments will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which several embodiments of the disclosure are illustrated by way of example and not limitation, in which like references indicate similar or corresponding parts, wherein:

[0034] Figure 1 is a flowchart of the outgoing longwave radiation acquisition method of FY-3E satellite HIRAS instrument in the embodiment of the present application;

[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); 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 HIRAS instrument on the FY-3E satellite in this embodiment of the invention. 2 (tiles per square meter);

[0037] 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

[0038] 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

[0039] 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.

[0040] 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.

[0041] 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...

[0042] It should be understood that the term "and / or" as used herein merely describes an associated relationship between associated objects, and can represent three relationships, for example, A and / or B can represent three cases of A existing alone, A and B existing simultaneously, and B existing alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0043] Depending on the context, the word "if" as used herein can be interpreted as meaning "when" or "while" or "in response to determining" or "in response to detecting." Similarly, depending on the context, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted as meaning "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)."

[0044] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, so that a product or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such product or device. Without more limitation, the element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the product or device comprising the element.

[0045] The optional embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0046] Embodiment one,

[0047] As Figure 1 shown, the present application discloses an outgoing long-wave radiation acquisition method based on infrared hyperspectral data, comprising the following steps:

[0048] Step S101, acquiring channel observation data of an infrared hyperspectral instrument of a target satellite and corresponding observation geometric information;

[0049] Step S103, screening the channel observation data based on channel signal-to-noise ratio, and determining all effective channels for inversion;

[0050] Step S105, constructing a radiance matrix based on radiance data of all effective channels;

[0051] Step S107, performing principal component analysis on the radiance matrix, and extracting the first n-dimensional principal components, wherein n is the minimum dimension that makes the cumulative contribution rate of the principal components reach a preset threshold;

[0052] Step S109, calculating an initial radiation value based on the first n-dimensional principal components through a pre-established outgoing long-wave radiation inversion model;

[0053] Step S1011, according to the satellite observation angle in the observation geometry information, interpolating the initial radiation value calculated based on the pre-established multiple inversion models of different satellite observation angle intervals, to obtain the final outgoing long-wave radiation value.

[0054] Embodiment two,

[0055] The application provides an outgoing long-wave radiation acquisition method based on infrared hyperspectral data, which comprises the following steps:

[0056] Step S101, acquiring channel observation data of an infrared hyperspectral instrument of a target satellite and corresponding observation geometry information;

[0057] Step S103, screening the channel observation data based on channel signal-to-noise ratio, to determine all effective channels for inversion;

[0058] Step S105, constructing a radiation rate matrix based on radiation rate data of all effective channels;

[0059] Step S107, performing principal component analysis on the radiation rate matrix, and extracting the first n-dimensional principal components, wherein n is the minimum dimension that makes the cumulative contribution rate of the principal components reach a preset threshold;

[0060] Step S109, calculating an initial radiation value through a pre-established outgoing long-wave radiation inversion model based on the first n-dimensional principal components;

[0061] Step S1011, according to the satellite observation angle in the observation geometry information, interpolating the initial radiation value calculated based on the pre-established multiple inversion models of different satellite observation angle intervals, to obtain the final outgoing long-wave radiation value.

[0062] In the application, the principal component analysis is used to extract the main radiation features sensitive to OLR in the hyperspectral data, and the cumulative contribution rate threshold of the first n-dimensional principal components is set to 90% to 95%, so that the noise can be effectively suppressed on the premise of ensuring information reservation. Further, the application finds that the first 35-dimensional principal components can stably represent the OLR change in multiple observation angle intervals, and have physical interpretability, for example, the first principal component is dominated by the surface temperature, and the second and third principal components are affected by water vapor and clouds.

[0063] The method for constructing the outgoing long-wave radiation inversion model comprises the following steps:

[0064] Acquiring wide-band outgoing long-wave radiation true value data of a reference satellite;

[0065] Performing space-time matching on the infrared hyperspectral data of the target satellite and the wide-band outgoing long-wave radiation true value data of the reference satellite, to construct a matching data set;

[0066] Based on the matching data set, the true value data is subjected to multiple linear regression with the first n-dimensional principal components after principal component analysis dimension reduction, to obtain the regression coefficients of the inversion model.

[0067] The application adopts multiple linear regression instead of complex models such as neural networks, and the reasons are as follows: 1) principal component has realized feature extraction, and linear relationship is significant; 2) linear model coefficients have physical interpretability, which is convenient for business debugging and verification; 3) in the case of a large amount of hyperspectral data, the linear model has high calculation efficiency and is suitable for real-time processing.

[0068] Wherein, the space-time matching needs to meet: the matching time interval is less than 15 minutes, and the observation point of the target satellite is located in the spatial coverage range of the reference satellite true value data pixel.

[0069] Wherein, the reference satellite is AQUA satellite, and the wide-band outgoing long-wave radiation true value data is obtained by CERES instrument observation.

[0070] Wherein, before constructing the radiance matrix, the radiance data of all effective channels is normalized and pretreated.

[0071] Wherein, the satellite observation angle interval is divided according to equal angle interval.

[0072] Wherein, the interpolation is linear interpolation, and the interpolation weight is determined based on the relative position of the real-time satellite observation angle and the center value of the adjacent satellite observation angle interval.

[0073] Wherein, the method further comprises: geolocating and equi-latitude-longitude projecting the final outgoing long-wave radiation value according to its latitude and longitude information, to generate an outgoing long-wave radiation spatial distribution image.

[0074] Wherein, the target satellite is FY-3 series satellite, and the infrared hyperspectral instrument is HIRAS instrument.

[0075] Embodiment three,

[0076] In an embodiment of the application, the following steps are specifically adopted.

[0077] (1) Selection of observation true value: based on the FY3D satellite HIRAS instrument observation channel radiance data, the real-time data of the outgoing long-wave radiation of the American AQUA satellite CERES instrument is taken as the true value, the matching standard is determined for matching. FY3D and AQUA are both afternoon satellites, and the observation trajectories are close, so the matching probability is high.

[0078] (2) The process of matching the channel radiance of HIRAS instrument with the outgoing longwave radiation of CERES instrument: the matching principles include spatial resolution of 33km, time interval less than 15 minutes, and sample selection of 1 day per month in 12 months in 2019. The above matching pairs of data include: radiance of all HIRAS channels, year, month, day, hour, minute, satellite observation angle, longitude, latitude, elevation, land surface type, true value, etc.

[0079] (3) Establishing inversion coefficients by principal component analysis:

[0080] By satellite observation angle, the radiance matrix of HIRAS channels is constructed by using formula (1), feature decomposition is carried out, and the eigenvector P is calculated.

[0081] (1)

[0082] Wherein, Given by formula (2), N is the number of selected channels, is the eigenvalue of the matrix, and E is the covariance matrix of the HIRAS instrument channel radiance vector matrix.

[0083] (2)

[0084] The first 35-dimensional eigenvectors which can represent the information of all channels are selected, and multiple linear regression is carried out with the outgoing longwave radiation of CERES instrument, so as to establish the HIRAS OLR inversion coefficient.

[0085] (4) Calculation of outgoing longwave radiation value of HIRAS instrument:

[0086] Input real-time L1 data and real-time satellite observation angle of FY3E HIRAS instrument, determine two sets of coefficients of satellite observation angle interval, and calculate two sets of outgoing longwave radiation values of HIRAS instrument by formula (3).

[0087] (3)

[0088] In the formula, b0 and are the regression coefficients established in step (3).

[0089] According to the linear interpolation of outgoing longwave radiation in the position of observation angle interval according to the real-time satellite observation angle, the final HIRAS instrument outgoing longwave radiation inversion value is obtained.

[0090] Example four,

[0091] The application further provides an outgoing longwave radiation acquisition system based on infrared hyperspectral data, which realizes the above method, and comprises:

[0092] A data acquisition and preprocessing module is configured to acquire and filter infrared hyperspectral channel observation data and observation geometry information of a target satellite based on channel signal-to-noise ratio.

[0093] A matrix construction and dimension reduction module is configured to construct a radiance matrix based on radiance data of all effective channels, and perform principal component analysis to extract the first n-dimensional principal components.

[0094] A radiance value inversion module is configured to calculate initial radiance values based on the first n-dimensional principal components and a pre-established outgoing longwave radiation inversion model.

[0095] An observation angle correction module is configured to interpolate the initial radiance values from different inversion models according to satellite observation angles, to obtain and output final outgoing longwave radiation values.

[0096] Embodiment five,

[0097] The method is implemented by taking the hyperspectral infrared atmospheric sounder (HIRAS) on the FY-3E and D satellites as the target instrument, and taking the CERES instrument wide-band OLR product on the AQUA satellite as the true value.

[0098] Data preparation and matching:

[0099] The FY-3D HIRAS instrument L1 channel radiance data for 12 days (one day per month) in 2019 are selected.

[0100] The OLR instantaneous product of the CERES instrument on the AQUA satellite during the same period is obtained as the true value.

[0101] Temporal and spatial matching is performed: spatially, it is ensured that the HIRAS observation point is located within the CERES pixel (about 33 km resolution); temporally, it is ensured that the observation time difference between the two is less than 15 minutes. Finally, more than 50,000 groups of effective matching sample pairs covering the globe, different seasons and day cycles are obtained (as shown in Figure 2 ).

[0102] Inversion model construction:

[0103] For the matched HIRAS data, first, filter out channels with too low signal-to-noise ratio according to the calibration noise table, and retain all effective channels.

[0104] Group the samples according to the satellite zenith angle (for example, at intervals of 5°).

[0105] For each group of samples, construct a radiance matrix L (rows are sample numbers, columns are effective channel numbers), and perform normalization according to the formula , where μ is the mean and σ is the standard deviation.

[0106] It is verified by experiments that the inversion error is the smallest when the observation angle interval is divided into 5°; if the interval is too large (such as 10°), the correction is insufficient, and if the interval is too small (such as 1°), the sample is insufficient, resulting in unstable model. Linear interpolation is better than nonlinear method in this scenario because it introduces the smallest error when the observation angle changes smoothly, and has high computational efficiency, and is suitable for business operation.

[0107] The normalized matrix is subjected to principal component analysis (PCA), the covariance matrix is calculated and subjected to eigenvalue decomposition, and the eigenvalues and eigenvectors are extracted. The first 35-dimensional eigenvectors P with a cumulative contribution rate of more than 90% are selected.

[0108] The 35-dimensional principal component scores are subjected to multivariate linear regression with the CERES OLR true value to establish a model: , and the regression coefficients b0, b1,..., b 35 are obtained for the observation angle interval.

[0109] Real-time data acquisition:

[0110] Real-time L1 observation data, satellite observation angle, latitude and longitude, etc. of the HIRAS instrument of FY-3E satellite are acquired.

[0111] Data quality control and effective channel screening are performed.

[0112] OLR value calculation and output:

[0113] According to the real-time satellite observation angle θ, find its adjacent two predefined observation angle intervals (such as θ1 and θ2).

[0114] Use the inversion model coefficients corresponding to the two intervals respectively to calculate two initial OLR values OLR1 and OLR2.

[0115] Use the linear interpolation formula to calculate the final OLR inversion value.

[0116] Finally, the inversion value is geographically located according to the observation latitude and longitude information, projected onto a 1°×1° global grid, and an OLR spatial distribution product is generated (as shown in FIGS. 3(a) and 3(b)).

[0117] The method is applied to FY-3E HIRAS data, and the inversion result is compared with the CERES true value. As shown in Fig. 4 (a), the difference histogram is concentrated near zero, and the root mean square error is 10.05 W / m2. As a comparison, the OLR inverted by the traditional method of the multi-spectral instrument MERSI on the FY-3E satellite in the same period has a root mean square error of 11.95 W / m2 (Fig. 4 (b)). The results show that the method significantly improves the accuracy of OLR inversion. Compared with existing hyperspectral inversion methods (such as the OLR product of AIRS), the method achieves an accuracy improvement of about 8% on FY-3E HIRAS data, and is stable under different underlying surface conditions such as oceans, lands and polar regions. Through t-test, the error distribution has no significant deviation (p>0.05) from the CERES true value, and has business promotion value.

[0118] Embodiment six,

[0119] The embodiment of the present disclosure provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the method steps of the above embodiments.

[0120] Note that the computer readable medium described above can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In the disclosure, the computer readable signal medium can include a computer readable program code propagated on or through a carrier wave in a baseband or passed on a carrier, in which the computer readable program code can be embodied. Such a propagated computer readable signal medium can take many forms, including but not limited to, electro-magnetic, optical or any suitable combination thereof. The computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code embodied on a computer readable medium can be transmitted using any appropriate medium, including but not limited to, wire, cable, wireless, R.F., infrared or any suitable combination of the foregoing.

[0121] The computer readable medium described above can be included in the electronic device described above; alternatively, the computer readable medium can exist as a separate entity in which the electronic device is incorporated.

[0122] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0123] The computer program product of the present disclosure can be a computer program embodied on a non-transitory computer readable medium. When the program runs on a computer, the flowchart and / or block diagram in the flowchart and / or block diagram can be implemented.

[0124] The units described in the embodiments of the present disclosure can be implemented by software or by hardware. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0125] The above describes the preferred embodiments of the present disclosure, which aims to make the spirit of the present disclosure clearer and easier to understand, and is not intended to limit the present disclosure. Any modifications, replacements, improvements made within the spirit and principle of the present disclosure shall be included in the protection scope of the appended claims of the present disclosure.

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.

2. The method as described in claim 1, characterized in that, The method for constructing the emitted longwave 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.

3. The method as described in claim 2, 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.

4. The method as described in claim 2, 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.

5. 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.

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

7. 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.

8. 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.

9. 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.

10. 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 9, 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.

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