Very-long wave on-satellite spectrum calibration evaluation method and device based on radiation transmission model

Abstract

The invention discloses a very long wave on-satellite spectrum calibration evaluation method and device based on a radiation transmission model, and belongs to the technical field of remote sensing optics. Aiming at a wide-spectrum very-long-wave infrared hyperspectral sensor with the wavelength range covering 12-16 microns, atmospheric absorption characteristics and sensor imaging spectral characteristics in the spectrum range are analyzed, and the spectral calibration precision is evaluated by means of physical model simulation, and the method comprises the following steps: performing analog simulation by using an MODTRAN radiation transmission model; constructing a spectrum drift model; solving a central wavelength offset based on spectrum matching in combination with an iterative optimization algorithm; and uncertainty analysis is carried out on the influence of factors such as estimation errors of the algorithm, a signal-to-noise ratio of a sensor, inter-spectrum response inconsistency and CO2 concentration in the atmosphere on an estimation result. The method is applied to evaluating the spectrum calibration precision by the on-orbit satellite data.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing optics, and in particular relates to a very long wave on-board spectrum calibration evaluation method and device based on a radiation transmission model. Background Art

[0002] Any object at a temperature other than absolute zero will generate thermal radiation information, and its spectral distribution varies with temperature. The thermal radiation energy of objects at normal and low temperatures is mainly concentrated in the infrared band. In recent years, the strategic position of the polar cryosphere has gradually increased, and the demand for polar observations has continued to grow. Due to the special geographical environment and extreme climatic conditions in the polar regions, the temperature of the detection target reaches minus 100 degrees Celsius. According to Planck's law, deep low-temperature detection targets often have very weak radiation energy density, and the peak wavelength of radiation redshifts toward the long-wave direction as the temperature decreases. Very long wavelength infrared (VLWIR) (12-16μm) hyperspectral imaging technology has become a key research method for deep low-temperature target detection. After the satellite is launched into space, during its in-orbit operation, the sensor's spectral performance will be affected by mechanical vibration, space environment rays, sunlight irradiation and other factors and attenuate. The most direct manifestation is the drift of the central wavelength of each channel of the sensor. It is necessary to conduct long-term in-orbit monitoring and spectral calibration of very long-wave satellite-borne infrared sensors.

[0003] At present, the development of very long wave hyperspectral infrared sensors with high spatial resolution, wide spectrum and wide imaging in China is still in its infancy. Although infrared calibration technology is relatively mature, international infrared payloads have not made breakthroughs in calibration technology in the very long wave and extremely low temperature region. Spectral calibration, as a key link in calibration, also needs to be studied urgently. The main on-orbit spectral calibration methods include the on-board standard lamp spectral calibration method and the spectral matching calibration method based on the characteristic peaks of gas absorption. The prerequisite for the on-board standard lamp spectral calibration method is that the required instruments and equipment are carried on board, which puts high demands on the development of payloads. The spectral matching calibration method based on the characteristic peaks of gas absorption is one of the most commonly used methods at present. This method shows superior performance on hyperspectral sensors with higher spectral resolution. However, for hyperspectral sensors whose working spectrum is in the very long wave spectral range and whose spectral resolution is limited, there is still a lack of relevant research on the application effect of this method. Summary of the invention

[0004] In order to solve the defect of the prior art that there is a lack of spectral calibration methods for wide-band very long wave infrared hyperspectral sensors, the present invention provides a very long wave satellite spectral calibration evaluation method and device based on the radiation transmission model. The present invention is aimed at a wide-band very long wave infrared hyperspectral sensor with a wavelength range covering 12-16 μm, analyzes the atmospheric absorption characteristics and sensor imaging spectral characteristics within the spectral range, and evaluates the spectral calibration accuracy by means of physical model simulation.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme:

[0006] A very long wave on-board spectrum calibration and evaluation method based on a radiation transfer model comprises the following steps:

[0007] S1: Use the MODTRAN (MODerate spectral resolution radiative TRANsportmodel) radiation transfer model to simulate the radiance curve in the 12-16μm spectral range;

[0008] S2: Construct a spectral drift model of the very long wave hyperspectral sensor. Given a central wavelength offset, simulate the spectral response function of the very long wave hyperspectral sensor after spectral drift. Convolve the radiance curve obtained in S1 with the spectral response function before and after spectral drift to obtain the reference spectrum and the measured spectrum.

[0009] S3: Spectrally match the reference spectrum obtained in S2 with the measured spectrum, estimate the center wavelength offset by combining the iterative optimization algorithm, and compare it with the center wavelength offset given in S2 to evaluate the accuracy;

[0010] S4: Uncertainty analysis is performed based on the estimation error of the algorithm itself, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the impact of the CO2 concentration in the atmosphere on the estimation result;

[0011] S5: Applied to atmospheric infrared sounder satellite data to further evaluate the accuracy of the method.

[0012] Further, the S1 includes:

[0013] In the MODTRAN radiation transfer model, set the sensor parameter information, including observation geometry, observation time, band range, and spectral resolution; set the atmospheric parameter information, including aerosol optical thickness and water vapor content; set the surface characteristic parameters, including surface albedo and surface Lambertian body information; run the MODTRAN radiation transfer model to simulate the pupil radiance of the clear sky ocean surface obtained by the satellite at zenith observation .

[0014] Further, the S2 includes:

[0015] S21: Based on the band range, sampling interval, and half-height width of the target sensor, a Gaussian function is used to simulate the sensor spectral response function ;

[0016] S22: Radiance curve simulated by MODTRAN and the sensor spectral response function Convolution, simulate the spectral curve observed by the sensor as a reference spectrum ;

[0017] S23: Assuming that only the central wavelength shifts during the satellite sensor’s in-orbit operation, and that the shifts of each channel are consistent, a spectral drift model for the hyperspectral sensor is established to simulate the sensor radiance after spectral drift as the measured spectrum. .

[0018] Further, the S3 includes:

[0019] S31: Use the spectrum matching method to compare the measured spectrum with the reference spectrum, and use the spectral angle as the evaluation standard to measure the similarity between the two spectra;

[0020] S32: Shift the central wavelength of the standard spectral response function by δ nm, and then Sampling is performed to obtain the observed spectrum after the central wavelength of the target sensor is shifted by δ nm ,calculate and Spectral angle between

[0021] S33: Constructing cost loss function , using the simplex method, through iterative optimization to find Extreme point; When taking the minimum value and The similarity between the two spectral lines is the highest. The corresponding center wavelength offset This is the estimated center wavelength offset.

[0022] Further, the S4 includes:

[0023] S41: Assuming that the central wavelength of the sensor drifts within the range of the left and right sampling interval widths, different central wavelength offsets are given in step S2 to obtain multiple measured spectra, and the central wavelength offsets are estimated respectively through S3, and the residuals are calculated with the actually given central wavelength offsets to evaluate the uncertainty of the algorithm of S3;

[0024] S42: Assuming that the sensor noise conforms to Gaussian random distribution, the simulated measured spectrum Add random noise to evaluate the uncertainty caused by the sensor signal-to-noise ratio;

[0025] S43: Assuming that the inconsistency of inter-spectral response conforms to Gaussian random distribution and the relative magnitude relationship of the responses between the spectral segments is fixed, the inconsistency of inter-spectral response is added to the simulated sensor measured gray value curve to evaluate the algorithm uncertainty caused by the inconsistency of inter-spectral response;

[0026] S44: Assume that there is a difference between the atmospheric CO2 concentration of the reference spectrum and the atmospheric CO2 concentration under actual observation conditions, and evaluate the uncertainty caused by the difference in CO2 concentration;

[0027] S45: It is considered that the estimation error of the S3 algorithm, the sensor signal-to-noise ratio, the inconsistency of the inter-spectral response, and the influence of CO2 concentration on the algorithm are independent of each other and synthesize the uncertainty.

[0028] Furthermore, the step S43 includes:

[0029] Assuming that there is a linear relationship between the grayscale value and radiance of each spectral band of the sensor, the radiance curve is converted into a grayscale value curve.

[0030] Further, the S5 includes:

[0031] S51: Select the atmospheric infrared detector image, find the satellite cloud product MYD35_L2 of the corresponding time and location, and after image registration, select the clear sky and ocean pixels;

[0032] S52: Spectrally resample the pixel radiance curve and give a central wavelength offset as the measured spectrum in S23 ;

[0033] S53: Estimate the central wavelength offset of each clear sky ocean pixel through S2~S3, and take the average value as the final central wavelength offset;

[0034] S54: Calculate the residual between the estimated center wavelength offset in S53 and the actual center wavelength offset given in S52 to evaluate the accuracy.

[0035] The present invention also provides a very long wave satellite spectral calibration and evaluation device based on a radiation transfer model, comprising the following modules:

[0036] The simulation module uses the MODTRAN radiation transfer model to simulate the radiance curve in the 12-16μm spectrum range;

[0037] The spectrum acquisition module constructs a spectrum drift model of the very long wave hyperspectral sensor, and simulates the spectrum response function of the very long wave hyperspectral sensor after spectrum drift given a central wavelength offset. The radiance curve is convolved with the spectrum response function before and after spectrum drift to obtain the reference spectrum and the measured spectrum.

[0038] The evaluation module performs spectral matching between the reference spectrum and the measured spectrum, estimates the center wavelength offset by combining the iterative optimization algorithm, and compares it with the center wavelength offset given in the spectrum acquisition module to evaluate the accuracy;

[0039] The analysis module performs uncertainty analysis based on the estimation error of the algorithm itself, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the impact of the CO2 concentration in the atmosphere on the estimation result;

[0040] The re-evaluation module is applied to the atmospheric infrared detector measured satellite data to further evaluate the accuracy of the method.

[0041] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the above-mentioned very long wave satellite on-board spectral calibration and evaluation method based on the radiation transfer model are implemented.

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned very long wave satellite on-board spectral calibration evaluation method based on the radiation transfer model.

[0043] The beneficial effects of the present invention are as follows:

[0044] 1. The present invention is a spectral calibration method for a wide-band very long-wave infrared hyperspectral sensor. The present invention studies the spectral calibration of a wide-band very long-wave infrared hyperspectral sensor with a wavelength range covering 12-16 μm and a spectral resolution of the order of 48 nm, and has high accuracy.

[0045] 2. The present invention evaluates the spectral calibration accuracy from multiple angles by means of simulation, using the MODTRAN radiation transfer model for simulation as the basic means, using the spectral matching method, combined with the iterative optimization algorithm to solve the central wavelength offset, and performing uncertainty analysis. Finally, the method is applied to atmospheric infrared detector satellite data and the accuracy is evaluated. The present invention provides important support for the on-orbit spectral calibration accuracy evaluation of very long wave hyperspectral sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a very long wave satellite spectral calibration evaluation method based on a radiation transfer model of the present invention;

[0047] Figure 2 is a schematic diagram of simulating the radiance spectrum of a hyperspectral sensor according to an embodiment of the present invention;

[0048] Figure 3 1 is a graph showing the variation of the spectral angle of the reference spectrum and the measured spectrum with the center wavelength offset in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other. The present invention is further described below in conjunction with the accompanying drawings and specific embodiments, and the illustrative examples and descriptions of the present invention are only used to explain the present invention, but are not used as limitations of the present invention.

[0050] like Figure 1 As shown, the present invention proposes a very long wave satellite spectral calibration evaluation method based on the radiation transfer model, which includes the following steps:

[0051] S1: Use the MODTRAN radiation transfer model to simulate the radiance curve in the 12-16μm spectral range;

[0052] S2: Construct a spectral drift model of the very long wave hyperspectral sensor. Given a central wavelength offset, simulate the spectral response function of the very long wave hyperspectral sensor after spectral drift. Convolve the radiance curve obtained in S1 with the spectral response function before and after spectral drift to obtain the reference spectrum and the measured spectrum.

[0053] S3: Spectrally match the reference spectrum obtained in S2 with the measured spectrum, estimate the center wavelength offset by combining the iterative optimization algorithm, and compare it with the center wavelength offset given in S2 to evaluate the accuracy;

[0054] S4: Uncertainty analysis is performed based on the estimation error of the algorithm itself, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the impact of the CO2 concentration in the atmosphere on the estimation result;

[0055] S5: Applied to atmospheric infrared sounder satellite data to further evaluate the accuracy of the method.

[0056] A further preferred embodiment of the present invention is that step S1 comprises:

[0057] Set the sensor parameter information in the MODTRAN radiation transfer model, including observation geometry, observation time, band range, spectral resolution, etc. Set the atmospheric parameter information, including aerosol optical thickness, water vapor content, etc. Set the surface characteristic parameters, including surface albedo, surface Lambertian body information, etc. Run the MODTRAN radiation transfer model to simulate the pupil radiance of the clear sky ocean surface obtained by the satellite at zenith observation ,like Figure 2 As shown, Indicates wavelength.

[0058] A further preferred embodiment of the present invention is that step S2 comprises the following steps:

[0059] S21: Based on the target sensor’s band range, sampling interval, half-width and other parameter information, a Gaussian function is used to simulate the sensor’s spectral response function. :

[0060] ;

[0061] in, , is the center wavelength, and FWHM is the half-maximum width of the target sensor.

[0062] S22: Radiance curve simulated by MODTRAN and the sensor spectral response function Convolution, simulating the spectral curve observed by the sensor ;

[0063] ;

[0064] S23: Assuming that only the central wavelength shifts during the satellite sensor’s in-orbit operation, and that the shifts of each channel are consistent, a spectral drift model for the hyperspectral sensor is established to simulate the sensor radiance after spectral drift as the measured spectrum. :

[0065] ;

[0066] in, This is the actual offset of the center wavelength.

[0067] A further preferred embodiment of the present invention is that step S3 comprises the following steps:

[0068] S31: Use the spectral matching method to compare the measured spectrum with the reference spectrum, and select the spectral angle (SA) as the evaluation standard to measure the similarity between the two spectra. The smaller the spectral angle, the higher the similarity between the two spectra.

[0069] S32: Shift the central wavelength of the standard spectral response function by δ nm, and then Sampling is performed to obtain the observed spectrum after the central wavelength of the target sensor is shifted by δ nm :

[0070] ;

[0071] Where δ is the central wavelength shift.

[0072] calculate and Spectral angle between:

[0073]

[0074] in, is the number of bands to be calibrated, i represents the i-th band, For the The center wavelength of the band.

[0075] S33: Constructing cost loss function , using the simplex method to find Extreme point. When the minimum value is taken, the similarity between the two spectral lines is the highest, corresponding to This is the estimated center wavelength offset, such as Figure 3 As shown;

[0076] ;

[0077] A further preferred embodiment of the present invention is that step S4 specifically comprises the following steps:

[0078] S41: Assume that the sensor center wavelength drifts within the range of the left and right sampling interval widths, and different center wavelength offsets are given in step S2 , multiple measured spectra are obtained, and the center wavelength offset is estimated by S3 respectively, which is different from the actual given center wavelength offset Calculate the residual to evaluate the uncertainty of the algorithm in step S3 ;

[0079] S42: Assuming that the sensor noise conforms to Gaussian random distribution, the simulated measured spectrum Add random noise , evaluate the uncertainty caused by the sensor signal-to-noise ratio ;

[0080] , which means the mean is 0 and the variance is The Gaussian random distribution function is , Representative Bands, For the The central wavelength of the band, represents the number of sensor bands, SNR is the signal-to-noise ratio, Take the value from.

[0081] Sensor measured spectrum after adding noise for ;

[0082] Each set of experiments was repeated 100 times to avoid the influence of random noise.

[0083] S43: Assuming that the inconsistency of the inter-spectral response conforms to the Gaussian random distribution and the relative magnitude relationship of the responses between the spectral bands is fixed, the simulated measured gray value curve is Add the inconsistency of inter-spectral response to evaluate the algorithm uncertainty caused by the inconsistency of inter-spectral response ;

[0084] Due to the existence of spectral response inconsistency, the actual gray value of the pixel for:

[0085] ;

[0086] in, is the response rate of each spectrum segment. Assuming that the inconsistency of the inter-spectral response conforms to the Gaussian random distribution and the relative size relationship of the response between each spectrum segment is fixed, then:

[0087] ;

[0088] in, The representative mean is The variance is Gaussian random distribution function, where , we can get The inter-spectral response, the inter-spectral pixel inconsistency is within 10%, and the relative size relationship of the inter-spectral response remains unchanged.

[0089] S44: Assume that there is a difference between the atmospheric CO2 concentration in the reference spectrum and the atmospheric CO2 concentration in the actual observation situation, and evaluate the uncertainty caused by the difference in CO2 concentration .

[0090] ;

[0091] in, The atmospheric CO2 concentration under actual observation is The entrance pupil radiance simulated by the MODTRAN radiation transfer model when ;

[0092] S45: It is considered that the algorithm estimation error in S3, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the influence of CO2 concentration on the algorithm are independent of each other, and the combined uncertainty :

[0093] ;

[0094] in, They represent the algorithm estimation error in S3, the sensor signal-to-noise ratio, the inconsistency of the inter-spectral response, and the uncertainty caused by the CO2 concentration to the algorithm.

[0095] A further preferred embodiment of the present invention is that step S43 comprises:

[0096] Assuming that there is a linear relationship between the grayscale value and radiance of each spectral band of the sensor, the radiance curve is converted into a grayscale value curve .

[0097] ;

[0098] in, is the radiance at each band, and is the radiation calibration coefficient of each band, is the grayscale value at each band.

[0099] A further preferred embodiment of the present invention is that step S5 comprises the following steps:

[0100] S51: Select the atmospheric infrared detector image, find the satellite cloud product MYD35_L2 of the corresponding time and location, and after image registration, filter out the clear sky and ocean pixels.

[0101] S52: Clear sky ocean pixel radiance curve Resample the spectrum and give a central wavelength offset As the measured spectrum in step S23 ;

[0102] ;

[0103] S53: Estimate the central wavelength offset of each clear sky ocean pixel and take the average value as the final central wavelength offset ;

[0104] S54: Calculate the residual between the estimated center wavelength offset and the actual center wavelength offset to evaluate accuracy.

[0105] residual= ;

[0106] Figure 2 The embodiment of the present invention simulates the radiance spectrum of the hyperspectral sensor. The horizontal axis is the wavelength in micrometers (μm), and the vertical axis is the radiance in W. cm -2 sr -1 μm -1As the wavelength increases, the radiance decreases due to the strong absorption of CO2, with a small peak near 15μm. Limited by the spectral resolution of the target sensor of 48nm, the sensor cannot capture the clear CO2 absorption characteristic line, and only retains some peaks and troughs.

[0107] Figure 3 is the variation of the reference spectrum and the measured spectrum with the center wavelength offset in the embodiment of the present invention. The horizontal axis is the assumed center wavelength offset of the reference spectrum, in nanometers (nm), and the vertical axis is the spectral angle between the reference spectrum after the assumed center wavelength offset and the measured spectrum, in degrees (°). The actual center wavelength offset of the measured spectrum is 4nm. When the assumed center wavelength offset of the reference spectrum is nm, the spectral angle between the two spectral lines is the smallest, and the central wavelength offset estimated by the method of the present invention is nm.

[0108] The present invention also provides a very long wave satellite spectral calibration and evaluation device based on a radiation transfer model, comprising the following modules:

[0109] The simulation module uses the MODTRAN radiation transfer model to simulate the radiance curve in the 12-16μm spectrum range;

[0110] The spectrum acquisition module constructs a spectrum drift model of the very long wave hyperspectral sensor, and simulates the spectrum response function of the very long wave hyperspectral sensor after spectrum drift given a central wavelength offset. The radiance curve is convolved with the spectrum response function before and after spectrum drift to obtain the reference spectrum and the measured spectrum.

[0111] The evaluation module performs spectral matching between the reference spectrum and the measured spectrum, estimates the center wavelength offset by combining the iterative optimization algorithm, and compares it with the center wavelength offset given in the spectrum acquisition module to evaluate the accuracy;

[0112] The analysis module performs uncertainty analysis based on the estimation error of the algorithm itself, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the impact of the CO2 concentration in the atmosphere on the estimation result;

[0113] The re-evaluation module is applied to the atmospheric infrared detector measured satellite data to further evaluate the accuracy of the method.

[0114] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the above-mentioned very long wave satellite on-board spectral calibration and evaluation method based on the radiation transfer model are implemented.

[0115] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned very long wave satellite on-board spectral calibration evaluation method based on the radiation transfer model.

[0116] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0117] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0121] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A very long wave satellite spectral calibration and evaluation method based on a radiation transfer model, characterized in that: The following steps are involved: S1: Use the MODTRAN radiation transfer model to simulate the radiance curve in the 12-16μm spectral range; S2: Construct a spectral drift model of the very long wave hyperspectral sensor. Given a central wavelength offset, simulate the spectral response function of the very long wave hyperspectral sensor after spectral drift. Convolve the radiance curve obtained in S1 with the spectral response function before and after spectral drift to obtain the reference spectrum and the measured spectrum. S3: Spectrally match the reference spectrum obtained in S2 with the measured spectrum, estimate the center wavelength offset by combining the iterative optimization algorithm, and compare it with the center wavelength offset given in S2 to evaluate the accuracy; S4: Uncertainty analysis is performed based on the estimation error of the algorithm itself, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the impact of the CO2 concentration in the atmosphere on the estimation result; S5: Applied to atmospheric infrared sounder satellite data to further evaluate the accuracy of the method.

2. The very long wave satellite spectral calibration and evaluation method based on the radiation transfer model according to claim 1 is characterized in that: The S1 includes: In the MODTRAN radiation transfer model, set the sensor parameter information, including observation geometry, observation time, band range, and spectral resolution; set the atmospheric parameter information, including aerosol optical thickness and water vapor content; set the surface characteristic parameters, including surface albedo and surface Lambertian body information; run the MODTRAN radiation transfer model to simulate the pupil radiance of the clear sky ocean surface obtained by the satellite at zenith observation .

3. The very long wave satellite spectral calibration and evaluation method based on the radiation transfer model according to claim 1 is characterized in that: The S2 includes: S21: Based on the band range, sampling interval, and half-height width of the target sensor, a Gaussian function is used to simulate the sensor spectral response function ; S22: Radiance curve simulated by MODTRAN and the sensor spectral response function Convolution, simulate the spectral curve observed by the sensor as a reference spectrum ; S23: Assuming that only the central wavelength shifts during the satellite sensor’s in-orbit operation, and that the shifts of each channel are consistent, a spectral drift model for the hyperspectral sensor is established to simulate the sensor radiance after spectral drift as the measured spectrum. .

4. The very long wave satellite spectral calibration and evaluation method based on the radiation transfer model according to claim 1 is characterized in that: The S3 includes: S31: Use the spectrum matching method to compare the measured spectrum with the reference spectrum, and use the spectral angle as the evaluation standard to measure the similarity between the two spectra; S32: Shift the central wavelength of the standard spectral response function by δ nm, and then Sampling is performed to obtain the observed spectrum after the central wavelength of the target sensor is shifted by δ nm ,calculate and Spectral angle between S33: Constructing cost loss function , using the simplex method, through iterative optimization to find Extreme point; When taking the minimum value and The similarity between the two spectral lines is the highest. The corresponding center wavelength offset This is the estimated center wavelength offset.

5. The very long wave satellite spectral calibration and evaluation method based on the radiation transfer model according to claim 1 is characterized in that: The S4 includes: S41: Assuming that the central wavelength of the sensor drifts within the range of the left and right sampling interval widths, different central wavelength offsets are given in step S2 to obtain multiple measured spectra, and the central wavelength offsets are estimated respectively through S3, and the residuals are calculated with the actually given central wavelength offsets to evaluate the uncertainty of the algorithm of S3; S42: Assuming that the sensor noise conforms to Gaussian random distribution, the simulated measured spectrum Add random noise to evaluate the uncertainty caused by the sensor signal-to-noise ratio; S43: Assuming that the inconsistency of inter-spectral response conforms to Gaussian random distribution and the relative magnitude relationship of the responses between the spectral segments is fixed, the inconsistency of inter-spectral response is added to the simulated sensor measured gray value curve to evaluate the algorithm uncertainty caused by the inconsistency of inter-spectral response; S44: Assume that there is a difference between the atmospheric CO2 concentration of the reference spectrum and the atmospheric CO2 concentration under actual observation conditions, and evaluate the uncertainty caused by the difference in CO2 concentration; S45: It is considered that the estimation error of the S3 algorithm, the sensor signal-to-noise ratio, the inconsistency of the inter-spectral response, and the influence of CO2 concentration on the algorithm are independent of each other and synthesize the uncertainty.

6. The very long wave satellite spectral calibration and evaluation method based on the radiation transfer model according to claim 5 is characterized in that: The step S43 comprises: Assuming that there is a linear relationship between the grayscale value and radiance of each spectral band of the sensor, the radiance curve is converted into a grayscale value curve.

7. The very long wave satellite spectral calibration and evaluation method based on the radiation transfer model according to claim 1 is characterized in that: The S5 includes: S51: Select the atmospheric infrared detector image, find the satellite cloud product MYD35_L2 of the corresponding time and location, and after image registration, select the clear sky and ocean pixels; S52: Spectrally resample the pixel radiance curve and give a central wavelength offset as the measured spectrum in S23 ; S53: Estimate the central wavelength offset of each clear sky ocean pixel through S2~S3, and take the average value as the final central wavelength offset; S54: Calculate the residual between the center wavelength offset estimated in S53 and the actual center wavelength offset given in S52 to evaluate the accuracy.

8. A very long wave satellite spectral calibration and evaluation device based on a radiation transfer model, characterized in that: Includes the following modules: The simulation module uses the MODTRAN radiation transfer model to simulate the radiance curve in the 12-16μm spectrum range; The spectrum acquisition module constructs a spectrum drift model of the very long wave hyperspectral sensor, and simulates the spectrum response function of the very long wave hyperspectral sensor after spectrum drift given a central wavelength offset. The radiance curve is convolved with the spectrum response function before and after spectrum drift to obtain the reference spectrum and the measured spectrum. The evaluation module performs spectral matching between the reference spectrum and the measured spectrum, estimates the center wavelength offset by combining the iterative optimization algorithm, and compares it with the center wavelength offset given in the spectrum acquisition module to evaluate the accuracy; The analysis module performs uncertainty analysis based on the estimation error of the algorithm itself, the sensor signal-to-noise ratio, the inconsistency of the spectrum response, and the impact of the CO2 concentration in the atmosphere on the estimation result; The re-evaluation module is applied to the atmospheric infrared detector measured satellite data to further evaluate the accuracy of the method.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the very long wave satellite spectral calibration and evaluation method based on the radiation transfer model are implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the very long wave satellite spectral calibration and evaluation method based on the radiation transfer model as described in any one of claims 1 to 7 are implemented.

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