Improved ROLO Model Method Combining Lunar Observation Data from Ground-based and Meteorological Satellites

Abstract

The present invention belongs to the field of remote sensor calibration, and provides an improved method for the ROLO model by combining ground-based and meteorological satellite lunar observation data, including obtaining meteorological satellite lunar observation image datasets and ground-based lunar observation image datasets and performing corrections respectively to obtain standard meteorological satellite observed lunar radiance and ground-based observed lunar radiance, and calculating the observed lunar irradiance; solving the geometric parameters of the observation point, the Earth, the Moon, and the Sun at the observation times of the ground-based and meteorological satellite observations, and calculating the lunar irradiance of the ROLO model; using polynomial fitting to fit the relative deviation between the observed lunar irradiance of different bands and different lunar phase angles and the lunar irradiance of the ROLO model to improve the ROLO model. The present invention is not affected by the atmosphere, directly avoiding the errors caused by the atmospheric correction of the ground-based observation data of the ROLO model; it can also correct the phase angle dependence of the ROLO model and improve the prediction accuracy of the ROLO model.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensor calibration, and particularly to an improved method for the ROLO model by combining ground-based and meteorological satellite lunar observation data. Background Art

[0002] Remote sensor calibration is a prerequisite for the quantification of remote sensing products. The accuracy of remote sensor calibration largely determines the reliability of remote sensing data and the depth and breadth of its applications. Among the current on-orbit calibration methods for remote sensors, for satellites equipped with on-board calibrators, calibration is carried out through the on-board calibrator. However, the degradation problem of the on-board calibrator itself may lead to the calibration result being difficult to accurately reflect the actual on-orbit state of the satellite. For satellites without on-board calibrators, calibration is usually carried out through methods such as cross-calibration, pseudo-invariant calibration point calibration, and in-situ calibration. However, these calibration methods rely on the consistency of reference targets with known reflectivity; in addition, these calibration methods are also troubled by severe atmospheric attenuation. Affected by the Earth's atmosphere, many imager channels are difficult or even impossible to calibrate.

[0003] Compared with the above calibration methods, the calibration method using the moon as a reference target has attracted increasing attention due to its significant advantages. The surface of the moon has no atmosphere and is not affected by atmospheric interference; the radiation characteristics of the moon are very stable, and the irradiance changes by no more than 10 -8 ; the moon can provide calibration references for most Earth-orbiting satellites every month; and the moon has suitable response characteristics and is applicable to any Earth-orbiting satellite, which enables the lunar image to collect sufficient signal-to-noise ratio without overexposure. Research shows that the calibration method based on the moon is very effective and has important practical application value.

[0004] For the calibration method based on the moon, its accuracy depends on the accuracy of the lunar radiation model. In order to establish a common calibration reference for spacecraft, the U.S. Geological Survey implemented a lunar ground-based observation named the "Robotic Lunar Observatory (ROLO)" program. Through 8 years of observation, the ROLO lunar irradiance model (abbreviated as the ROLO model) was developed. The ROLO model predicts the equivalent reflectance of the lunar disk in 32 wavelength bands (from 350 nm to 2500 nm) based on various observation geometries. So far, the ROLO model is the most widely used model for predicting lunar reflectance. However, the absolute accuracy of the ROLO model is only 5 - 10%, mainly due to the following factors leading to insufficient absolute accuracy: (1) The ROLO model uses ground-based observation data and cannot completely eliminate the influence of the atmosphere; (2) Errors generated when interpolating the original 32 bands into hyperspectral by introducing the Apollo spectrum; (3) Errors generated by the phase angle dependence of the ROLO model. Summary of the Invention

[0005] In view of this, the present invention aims to provide an improved method for the ROLO model that combines ground-based and meteorological satellite lunar observation data to solve the problem of insufficient absolute accuracy of the ROLO model.

[0006] To achieve the above object, the technical solution of the present invention is realized as follows:

[0007] An improved method for the ROLO model that combines ground-based and meteorological satellite lunar observation data includes the following steps:

[0008] S1: Obtain the meteorological satellite lunar observation image dataset and the ground-based lunar observation image dataset;

[0009] S2: Calibrate the meteorological satellite lunar observation image dataset and the ground-based lunar observation image dataset respectively to obtain the standard meteorological satellite observed lunar radiance and the ground-based observed lunar radiance;

[0010] S3: Integrate and sum the ground-based observed lunar radiance of all pixels within the lunar boundary in the ground-based lunar observation image to obtain the ground-based observed irradiance, and integrate and sum the meteorological satellite observed lunar radiance of all pixels within the lunar boundary in the meteorological satellite lunar observation image to obtain the meteorological satellite observed irradiance. The ground-based observed irradiance and the meteorological satellite observed irradiance are collectively referred to as the observed lunar irradiance;

[0011] S4: Solve the geometric parameters of the observation point, the Earth, the Moon, and the Sun at the ground-based and meteorological satellite observation times, and calculate the lunar irradiance of the ROLO model;

[0012] S5: Use polynomial fitting to fit the relative deviation between the observed lunar irradiance of different bands and different lunar phase angles and the lunar irradiance of the ROLO model to improve the ROLO model.

[0013] Furthermore, in step S1, the process of obtaining the meteorological satellite lunar observation image dataset is as follows:

[0014] Use the STK software to simulate the operating orbit of the meteorological satellite. According to the geometric relationship between the satellite, the Earth, the Sun, and the Moon, calculate the time when the Moon appears in the cold space field of the satellite, and collect the meteorological satellite lunar observation images.

[0015] Furthermore, in step S1, the process of obtaining the ground-based lunar observation image dataset is as follows:

[0016] Use a hyperspectral imager to observe the Moon on the ground to obtain the ground-based lunar observation image; among them, the observation range of the hyperspectral imager is 380 - 1000 nm, and the spectral resolution ≥ 1 nm.

[0017] Further, images with lunar phase angles in the range of [-92°, 92°] and complete lunar regions are selected from the collected meteorological satellite lunar observation data to form a meteorological satellite lunar observation image dataset.

[0018] Further, in step S2, the process of correcting the meteorological satellite lunar observation image dataset to obtain the standard meteorological satellite observed lunar radiance is as follows:

[0019] S21a: Geometrically correct the meteorological satellite observed lunar radiance and correct the meteorological satellite observed lunar radiance to the standard distance;

[0020]

[0021] In the formula, represents the meteorological satellite observed lunar radiance after geometric correction, represents the meteorological satellite observed lunar radiance before geometric correction, represents the distance between the sun and the moon, represents the distance between the meteorological satellite and the moon, represents the average distance between the earth and the moon, and 1 AU represents the average distance between the earth and the sun;

[0022] S22a: Use the effective wavelength to replace the spectral channel;

[0023]

[0024] In the formula, represents the effective wavelength, represents the starting wavelength of the spectral channel, represents the ending wavelength of the spectral channel, represents the spectral response function, represents the lunar soil reflectance reference curve, represents the reference solar spectral irradiance curve, represents the lunar solid angle;

[0025] S23a: First, use the threshold algorithm to extract the rectangular area containing the moon from the meteorological satellite lunar observation image, and then use the Sobel edge detection algorithm to detect the lunar boundary within the extracted rectangular area;

[0026] S24a: Select the average radiance value of the area 15 to 30 pixels wide outside the lunar edge as the night sky noise value, and subtract this night sky noise value from the meteorological satellite lunar observation image;

[0027]

[0028] In the formula, Represents the lunar radiance observed by a meteorological satellite after night sky noise correction, Represents the night sky noise value.

[0029] Furthermore, in step S2, the process of correcting the ground-based lunar observation image dataset to obtain the standard ground-based observed lunar radiance is as follows:

[0030] S21b: Geometrically correct the ground-based observed lunar radiance to the standard distance;

[0031]

[0032] In the formula, Represents the ground-based observed lunar radiance after geometric correction, Represents the ground-based observed lunar radiance before geometric correction, Represents the distance between the sun and the moon, Represents the distance between the ground position and the moon, Represents the average distance between the earth and the moon, and 1 AU represents the average distance between the earth and the sun;

[0033] S22b: Use the LBLRTM model to simulate the atmospheric transmittance and perform atmospheric correction on the ground-based observed lunar radiance according to the atmospheric transmittance;

[0034] ;

[0035] In the formula, Represents the ground-based observed lunar radiance after atmospheric correction, Represents the atmospheric transmittance;

[0036] S23b: First, use the threshold algorithm to extract the rectangular area containing the moon from the ground-based lunar observation image, and then use the Sobel edge detection algorithm to detect the lunar boundary within the extracted rectangular area;

[0037] S24b: Select the pure night sky area of the ground-based lunar observation image that does not include the moon as the night sky noise value, and subtract this night sky noise value from the ground-based lunar observation image;

[0038]

[0039] In the formula, Represents the ground-based observed lunar radiance after night sky noise correction, Represents the night sky noise value.

[0040] Furthermore, in step S3, integrate and sum the ground-based observed lunar radiance of all pixels within the lunar boundary in the ground-based lunar observation image to obtain the ground-based observed irradiance. The calculation formula is as follows:

[0041] ;

[0042] In the formula, represents the ground-based observed irradiance, represents the lunar radiance observed by the meteorological satellite after night sky noise correction, represents the solid angle of the pixel, and respectively represent the number of rows and columns of the meteorological satellite lunar observation image and the ground-based lunar observation image;

[0043] Integrate and sum the lunar radiance observed by the meteorological satellite for all pixels within the lunar boundary in the meteorological satellite lunar observation image to obtain the meteorological satellite observed irradiance. The calculation formula is as follows:

[0044] ;

[0045] In the formula, represents the meteorological satellite observed irradiance, represents the ground-based observed lunar radiance after night sky noise correction.

[0046] Furthermore, step S4 specifically includes the following steps:

[0047] S41: Solve the geometric parameters of the observation point, the Earth, the Moon, and the Sun at the ground-based and meteorological satellite observation times, input the geometric parameters into the ROLO model, and calculate the lunar albedo of the ROLO model corresponding to the ground-based and meteorological satellite observation times; among them, the geometric parameters include the lunar phase angle, the longitude of the Sun in the lunar coordinate system, the longitude of the observation point in the lunar coordinate system, and the latitude of the observation point in the lunar coordinate system;

[0048]

[0049]

[0050] In the formula, represents the lunar albedo of the ROLO model, represents the band, represents the lunar phase angle, represents the longitude of the Sun in the lunar coordinate system, represents the latitude of the observation point in the lunar coordinate system, represents the longitude of the observation point in the lunar coordinate system, , , , , , , , , , , , , respectively represent coefficient terms; the observation points are ground positions or meteorological satellites;

[0051] S42: Calculate the lunar irradiance of the ROLO model according to the lunar albedo of the ROLO model;

[0052]

[0053] In the formula, represents the lunar irradiance of the ROLO model, represents the distance correction factor, represents the solid angle of the moon relative to the earth at the standard distance, represents the solar irradiance.

[0054] Furthermore, in step S5, the calculation formula for the relative deviation between the observed lunar irradiance and the lunar irradiance of the ROLO model is:

[0055]

[0056] In the formula, represents the relative deviation between the observed lunar irradiance and the lunar irradiance of the ROLO model at the k band. The k band selects the common band of the meteorological satellite lunar observation image, the ground-based lunar observation image and the ROLO model, specifically any one of 470nm, 510nm, 640nm, 855nm, 1610nm and 2255nm, represents the observed lunar irradiance at the k band, represents the lunar irradiance of the ROLO model at the k band.

[0057] Furthermore, in step S5, polynomial fitting is used to fit the relative deviation of each k band at different lunar phase angles, correct the phase angle dependence of the ROLO model at different k bands and different lunar phase angles, and realize the improvement of the ROLO model;

[0058] , ;

[0059] ,

[0060]

[0061] ,

[0062] In the formula, and respectively represent the improved ROLO model lunar irradiance under negative lunar phase angle and positive lunar phase angle; and respectively represent the improvement coefficients of the ROLO model under negative lunar phase angle and positive lunar phase angle; represents the solid angle of the moon relative to the earth at the standard distance; represents the solar irradiance; represents the lunar albedo of the ROLO model, represents the distance correction factor; and both represent coefficients and are fixed values.

[0063] Compared with the prior art, the present invention can achieve the following technical effects:

[0064] (1) The lunar data observed by meteorological satellites is not affected by the atmosphere, directly avoiding the errors caused by atmospheric correction of the ROLO model ground-based observation data; the hyperspectral data of ground-based observation has a spectral resolution better than that of ROLO data and can better reflect spectral information. By combining the lunar irradiance observed by meteorological satellites and ground-based observation, the limitations of ROLO data can be broken through, and the advantages of data from two different platforms can be integrated.

[0065] (2) The combination of the lunar irradiance observed by meteorological satellites and ground-based observation can also correct the phase angle dependence of the ROLO model, assign model correction coefficients to different bands and different lunar phase angles of the ROLO model, improve the ROLO model, and improve the prediction accuracy of the ROLO model. The average relative deviation between the improved ROLO model and the observed data is reduced to 0.27% - 0.39%. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a schematic flow chart of the method for improving the ROLO model by combining ground-based and meteorological satellite lunar observation data according to the embodiment of the present invention;

[0067] Figure 2 is a schematic diagram of the relative deviation between the observed lunar irradiance and the lunar irradiance of the ROLO model according to the embodiment of the present invention;

[0068] Figure 3 is a schematic diagram of the relative deviation between the observed lunar irradiance and the lunar irradiance of the improved ROLO model according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0069] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation to the present invention.

[0070] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0071] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0072] As Figure 1 shown, the improved ROLO model method for combining ground-based and meteorological satellite lunar observation data provided by the embodiments of the present invention includes the following steps:

[0073] S1: Obtain a meteorological satellite lunar observation image dataset and a ground-based lunar observation image dataset.

[0074] The process of obtaining the meteorological satellite lunar observation image dataset is as follows:

[0075] Use the STK software to simulate the operating orbit of the meteorological satellite. According to the geometric relationship between the satellite, the earth, the sun, and the moon, calculate the time when the moon appears in the cold space field of view of the satellite, and collect the meteorological satellite lunar observation images.

[0076] The meteorological satellite includes but is not limited to Himawari-8. In the Satellite Tool Kit 10 (STK 10) software, according to the two-line orbital data (TLE) of the Himawari imager (AHI) carried by Himawari-8, simulate the operating orbit of Himawari-8. According to the geometric relationship between the satellite-earth-sun-moon, calculate the time when the moon appears in the cold space field of view of the satellite, and obtain the lunar imaging time and the corresponding lunar phase angle.

[0077] Screen the meteorological satellite lunar observation images with the lunar phase angle in the range of [-92°, 92°], ensure that the illuminated part of the moon is greater than 50%, and at the same time, it is consistent with the lunar phase angle range of the ROLO model. Retain the meteorological satellite lunar observation images with the complete lunar region, so as to eliminate the lunar images in the following situations:

[0078] (1) Part of the moon is outside the field of view and only part of it is imaged within the field of view;

[0079] (2) Part of the moon is blocked by the earth;

[0080] (3) The moon is cut off by the boundary between the scanning strips; the moon is within a 0.1° circle around the earth;

[0081] (4) The combination of any two or more of the above three situations.

[0082] The process of obtaining the ground-based lunar observation image dataset is as follows:

[0083] The moon is observed using a hyperspectral imager on the ground to obtain ground-based lunar observation images. Among them, the observation range of the hyperspectral imager is 380 - 1000 nm, and the spectral resolution is ≥1 nm.

[0084] When observing the moon using a hyperspectral imager, choose a night that is far from the urban area, has an open view, is clear and cloudless, and has a wind speed less than level 3 (<5.4 m / s) for observation. During the observation process, the atmospheric conditions are grasped in real time through a mobile meteorological station for atmospheric correction using meteorological data.

[0085] S2: Correct the meteorological satellite lunar observation image dataset and the ground-based lunar observation image dataset respectively to obtain the standard meteorological satellite observed lunar radiance and the ground-based observed lunar radiance.

[0086] The process of correcting the meteorological satellite lunar observation image dataset to obtain the standard meteorological satellite observed lunar radiance is as follows:

[0087] S21a: Geometric correction.

[0088] Geometric correction is performed on the meteorological satellite observed lunar radiance to correct the meteorological satellite observed lunar radiance to the standard distance.

[0089]

[0090] In the formula, represents the meteorological satellite observed lunar radiance after geometric correction; represents the meteorological satellite observed lunar radiance before geometric correction; represents the distance between the sun and the moon; represents the distance between the meteorological satellite and the moon; represents the average distance between the earth and the moon, ; 1 AU represents the average distance between the earth and the sun, .

[0091] S22a: Replace the spectral channel with the effective wavelength.

[0092] Use the effective wavelength as a rough approximation of the spectral channel (ensuring that the ratio of the spectral channel bandwidth to the effective wavelength is ≤0.2) so that the spectral data between different platforms can be compared and integrated.

[0093]

[0094] In the formula, represents the effective wavelength; represents the starting wavelength of the spectral channel; represents the ending wavelength of the spectral channel; represents the Spectral Response Function (SRF); represents the lunar soil reflectance reference curve, represents the reference solar spectral irradiance curve, with the unit of ; represents the lunar solid angle, with the unit of .

[0095] S23a: Lunar boundary extraction.

[0096] First, use the threshold algorithm to extract the rectangular area containing the moon from the lunar observation image of the meteorological satellite, and then use the Sobel edge detection algorithm to detect the lunar boundary within the extracted rectangular area.

[0097] Set the connected area with pixel values greater than the threshold as the rough range of the moon, center the moon, and form a rectangular area containing the moon and a part of the surrounding night sky background. Then use the Sobel edge detection algorithm to further refine the lunar boundary.

[0098] S24a: Night sky noise correction.

[0099] Select the average radiation luminance value of the area with a width of 15 to 30 pixels outside the lunar edge as the night sky noise value, and subtract this night sky noise value from the lunar observation image of the meteorological satellite;

[0100]

[0101] In the formula, represents the lunar radiation luminance observed by the meteorological satellite after night sky noise correction, represents the night sky noise value.

[0102] The process of correcting the ground-based lunar observation image dataset to obtain the standard ground-based observed lunar radiation luminance is as follows:

[0103] S21b: Geometric correction.

[0104] Perform geometric correction on the ground-based observed lunar radiation luminance, and correct the ground-based observed lunar radiation luminance to the standard distance;

[0105]

[0106] In the formula, represents the ground-based observed lunar radiation luminance after geometric correction, represents the ground-based observed lunar radiation luminance before geometric correction, represents the distance between the sun and the moon, represents the distance between the ground position and the moon, denotes the average distance between the Earth and the Moon, and 1 AU denotes the average distance between the Earth and the Sun.

[0107] S22b: Atmospheric correction.

[0108] The LBLRTM (Line-By-Line Radiative Transfer Model) is used to simulate the atmospheric transmittance and eliminate the atmospheric effect. Meteorological and atmospheric data from multiple sources are input into the LBLRTM model to simulate the atmospheric transmittance in the range of 380 nm - 1000 nm, and the ground-based observed lunar radiance is corrected for the atmosphere to obtain the ground-based observed lunar radiance after atmospheric correction, that is, the ground-based observed lunar radiance at the top of the atmosphere.

[0109] The meteorological and atmospheric data include air temperature, air pressure, wind speed, water vapor profile, aerosol profile, and carbon dioxide content. The air temperature, air pressure, and wind speed are from the synchronous detection data of mobile meteorological stations, and the water vapor profile, aerosol profile, and carbon dioxide content are from the ERA5 dataset.

[0110] ;

[0111] In the formula, denotes the ground-based observed lunar radiance after atmospheric correction, denotes the atmospheric transmittance.

[0112] S23b: Lunar boundary extraction.

[0113] First, a threshold algorithm is used to extract the rectangular area containing the Moon from the ground-based lunar observation image, and then the Sobel edge detection algorithm is used to detect the lunar boundary within the extracted rectangular area.

[0114] S24b: Night sky noise correction.

[0115] Select the pure night sky area that does not include the Moon in the ground-based lunar observation image as the night sky noise value, and subtract this night sky noise value from the ground-based lunar observation image;

[0116]

[0117] In the formula, denotes the ground-based observed lunar radiance after night sky noise correction, denotes the night sky noise value.

[0118] S3: Integrate and sum up the ground-based observed lunar radiance of all pixels within the lunar boundary in the ground-based lunar observation image to obtain the ground-based observed irradiance, and integrate and sum up the meteorological satellite observed lunar radiance of all pixels within the lunar boundary in the meteorological satellite lunar observation image to obtain the meteorological satellite observed irradiance. The ground-based observed irradiance and the meteorological satellite observed irradiance are collectively referred to as the observed lunar irradiance.

[0119] Integrate and sum up the ground-based observed lunar radiance of all pixels within the lunar boundary in the ground-based lunar observation image to obtain the ground-based observed irradiance. The calculation formula is as follows:

[0120] ;

[0121] In the formula, represents the ground-based observed irradiance, represents the meteorological satellite observed lunar radiance after night sky noise correction, represents the solid angle of the pixel, and represent the number of rows and columns of the meteorological satellite lunar observation image and the ground-based lunar observation image respectively.

[0122] Integrate and sum up the meteorological satellite observed lunar radiance of all pixels within the lunar boundary in the meteorological satellite lunar observation image to obtain the meteorological satellite observed irradiance. The calculation formula is as follows:

[0123] ;

[0124] In the formula, represents the meteorological satellite observed irradiance, represents the ground-based observed lunar radiance after night sky noise correction.

[0125] The ground-based observed irradiance and the meteorological satellite observed irradiance are collectively referred to as the observed lunar irradiance , that is, in the following content, the observed lunar irradiance refers to the meteorological satellite observed irradiance or refers to the ground-based observed irradiance .

[0126] S4: Solve the geometric parameters of the observation point, the Earth, the Moon, and the Sun at the ground-based and meteorological satellite observation times, and calculate the lunar irradiance of the ROLO model.

[0127] Step S4 specifically includes the following steps:

[0128] S41: Calculate the lunar albedo of the ROLO model.

[0129] Solve the geometric parameters of the observation point, the Earth, the Moon, and the Sun at the observation times of the ground-based and meteorological satellites, input the geometric parameters into the ROLO model, and calculate the lunar albedo of the ROLO model corresponding to the observation times of the ground-based and meteorological satellites; wherein, the geometric parameters include the lunar phase angle, the longitude of the Sun in the lunar coordinate system, the longitude of the observation point in the lunar coordinate system, and the latitude of the observation point in the lunar coordinate system.

[0130]

[0131]

[0132] In the formula, represents the lunar albedo of the ROLO model, represents the band, represents the lunar phase angle, represents the longitude of the Sun in the lunar coordinate system, represents the latitude of the observation point in the lunar coordinate system, represents the longitude of the observation point in the lunar coordinate system, , , , , , , , , , , , , respectively represent coefficient terms.

[0133] The ground-based observation point refers to the ground position, and the meteorological satellite observation point refers to the meteorological satellite itself.

[0134] The lunar albedo of the ROLO model is actually the multi-spectral lunar albedo of the ROLO model. In order to obtain the high-spectral lunar irradiance of the ROLO model, the average near-side high-spectral reflectivity of the M3 data is used as a reference to replace the Apollo measured reflectivity, and interpolation fitting is performed on the multi-spectral lunar albedo of the ROLO model to obtain the high-spectral lunar albedo of the ROLO model.

[0135] M3 data: The lunar surface reflectivity data in the 430 - 3000 nm band provided by the Moon Mineralogy Mapper (M3).

[0136] The original ROLO model uses the laboratory hyperspectral albedo of Apollo samples to interpolate the ROLO multispectral albedo to obtain the ROLO hyperspectral albedo. However, the spectra of Apollo samples measured in the laboratory may not describe the most authentic characteristics of the lunar surface, resulting in the ROLO model being prone to underestimating the overall radiation intensity of the moon. In the present invention, the average near-side reflection spectrum of M3 is used to adjust the ROLO model. M3 obtained hyperspectral and high-spatial-resolution lunar data in 85 bands, covering more than 95% of the lunar surface. Compared with the laboratory reflectance measurements of Apollo that may be disturbed, the reflection spectrum of M3 data more accurately reflects the true reflectance of the entire moon. Therefore, the average near-side reflection spectrum of M3 data is more suitable for fitting the ROLO hyperspectral albedo.

[0137] S42: Calculate the lunar irradiance of the ROLO model.

[0138] According to the hyperspectral lunar albedo of the ROLO model, the calculation formula for the lunar irradiance of the ROLO model is as follows:

[0139]

[0140] In the formula, represents the lunar irradiance of the ROLO model, represents the distance correction factor, represents the solid angle of the moon relative to the earth at the standard distance, represents the solar irradiance.

[0141] S5: Use polynomial fitting to improve the ROLO model by fitting the relative deviation between the observed lunar irradiance and the lunar irradiance of the ROLO model at different bands and different lunar phase angles.

[0142] The calculation formula for the relative deviation between the observed lunar irradiance and the lunar irradiance of the ROLO model is:

[0143]

[0144] In the formula, represents the relative deviation between the observed lunar irradiance and the lunar irradiance of the ROLO model at the k band. The k band selects the common bands of the meteorological satellite lunar observation image, the ground-based lunar observation image and the ROLO model. The common bands are 470nm, 510nm, 640nm, 855nm, 1610nm and 2255nm. The k band can select any one of these six bands. represents the observed lunar irradiance (ground-based observation irradiance or meteorological satellite observation irradiance) at the k band. represents the lunar irradiance of the ROLO model at the k band.

[0145] As Figure 2 shown, as the lunar phase angle increases, the error of the ROLO model increases accordingly. Near the lunar phase angle of ±92°, the error of the ROLO model reaches 2% - 8%. And this lunar phase angle dependence is related to the wavelength, with the lunar phase angle dependence being the smallest at 640 nm and gradually increasing towards 470 nm and 2255 nm.

[0146] The relative deviation of each k - band at different lunar phase angles is fitted by a polynomial to correct the phase angle dependence of the ROLO model at different k - bands and different lunar phase angles, thereby realizing the improvement of the ROLO model.

[0147] , ;

[0148] ,

[0149]

[0150] ,

[0151] In the formula, and respectively represent the lunar irradiance of the improved ROLO model under negative lunar phase angles and positive lunar phase angles; and respectively represent the improvement coefficients of the ROLO model under negative lunar phase angles and positive lunar phase angles; represents the solid angle of the moon relative to the earth at the standard distance; represents the solar irradiance; represents the distance correction factor; and both represent coefficients and are fixed values (see Table 1 and Table 2).

[0152] Table 1 Coefficients in the improved ROLO model Values of

[0153]

[0154] Table 2 Coefficients in the improved ROLO model Values of

[0155]

[0156] The present invention uses a random sampling method to divide the observed lunar irradiance into a modeling data set and a validation data set. The modeling data set is used to fit the relative deviation of each k-band at different lunar phase angles, correct the phase angle dependence of the ROLO model at different k-bands and different lunar phase angles, and improve the ROLO model. The validation data set is used to verify the relative deviation between the observed lunar irradiance and the lunar irradiance of the improved ROLO model.

[0157] As Figure 3 shown, near the lunar phase angle of ±92°, the error of the improved ROLO model is reduced to 0.27% - 0.39%, and the relative deviations at different lunar phase angles are evenly distributed around 0, indicating that the prediction accuracy of the improved ROLO model is significantly improved and the error of the phase angle dependence is reduced.

[0158] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the disclosure of the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and no limitation is imposed herein.

[0159] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for improving the ROLO model by combining ground-based and meteorological satellite lunar observation data, characterized in that: The steps include: S1: Acquire meteorological satellite lunar observation image datasets and ground-based lunar observation image datasets; S2: Correct the meteorological satellite lunar observation image dataset and the ground-based lunar observation image dataset respectively to obtain the standard meteorological satellite observation lunar radiance and ground-based observation lunar radiance; S3: integrating and summing the ground-based observed lunar radiance of all pixels within the lunar boundary in the ground-based lunar observation image to obtain the ground-based observed irradiance, and integrating and summing the meteorological satellite observed lunar radiance of all pixels within the lunar boundary in the meteorological satellite lunar observation image to obtain the meteorological satellite observed irradiance. The ground-based observed irradiance and the meteorological satellite observed irradiance are collectively referred to as the observed lunar irradiance. S4: Solve the geometric parameters of the observation point, the Earth, the Moon, and the Sun at the time of ground-based and meteorological satellite observations, and calculate the lunar irradiance of the ROLO model; S5: The ROLO model is improved by using polynomial fitting to find the relative deviation between the observed lunar irradiance in different bands and different lunar phases and the ROLO model lunar irradiance.

2. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1 is characterized in that: In step S1, the process of obtaining the meteorological satellite lunar observation image dataset is as follows: Use STK software to simulate the orbit of meteorological satellites, calculate the time when the moon appears in the satellite's cold sky field of view based on the geometric relationship between the satellite, the earth, the sun, and the moon, and collect meteorological satellite lunar observation images.

3. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1 is characterized in that: In step S1, the process of obtaining the ground-based lunar observation image dataset is as follows: A hyperspectral imager is used on the ground to observe the moon and obtain ground-based lunar observation images; the observation range of the hyperspectral imager is 380nm-1000nm, and the spectral resolution is ≥1nm.

4. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 2 is characterized in that: Images with a complete lunar region and a lunar phase angle in the range of [-92°, 92°] were screened from the collected meteorological satellite lunar observation data to form a meteorological satellite lunar observation image dataset.

5. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1 is characterized in that: In step S2, the meteorological satellite lunar observation image data set is corrected to obtain the standard meteorological satellite lunar radiance brightness as follows: S21a: geometric correction of the lunar radiance observed by meteorological satellites, correcting the lunar radiance observed by meteorological satellites to the standard distance; In the formula, represents the lunar radiance observed by meteorological satellites after geometric correction, It represents the lunar radiance observed by meteorological satellites before geometric correction. represents the distance between the sun and the moon, represents the distance between the meteorological satellite and the moon, represents the average distance between the Earth and the Moon, and 1 AU represents the average distance between the Earth and the Sun; S22a: Use effective wavelengths instead of spectral channels; In the formula, represents the effective wavelength, represents the starting wavelength of the spectral channel, represents the end wavelength of the spectral channel, represents the spectral response function, represents the lunar soil reflectivity reference curve, represents the reference solar spectrum irradiance curve, represents the lunar solid angle; S23a: First, a threshold algorithm is used to extract a rectangular area containing the moon from the meteorological satellite lunar observation image, and then a Sobel edge detection algorithm is used to detect the lunar boundary in the extracted rectangular area; S24a: Select the average radiance value of the area from the 15th pixel to the 30th pixel width outside the edge of the moon as the night sky noise value, and subtract the night sky noise value from the meteorological satellite lunar observation image; In the formula, represents the lunar radiance observed by meteorological satellites after correction for night sky noise, Represents the night sky noise value.

6. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1 is characterized in that: In step S2, the ground-based lunar observation image dataset is corrected to obtain the standard ground-based lunar radiance as follows: S21b: geometric correction of the lunar radiance observed by ground-based observations, and correction of the lunar radiance observed by ground-based observations to the standard distance; In the formula, represents the ground-based lunar radiance after geometric correction, represents the ground-based lunar radiance before geometric correction, represents the distance between the sun and the moon, represents the distance between the ground position and the moon, represents the average distance between the Earth and the Moon, and 1 AU represents the average distance between the Earth and the Sun; S22b: Use the LBLRTM model to simulate atmospheric transmittance and perform atmospheric correction on the ground-based lunar radiance observations based on the atmospheric transmittance. ； In the formula, represents the ground-based observed lunar radiance after atmospheric correction, represents the atmospheric transmittance; S23b: firstly, a threshold algorithm is used to extract a rectangular region containing the moon from the ground-based lunar observation image, and then a Sobel edge detection algorithm is used to detect the lunar boundary in the extracted rectangular region; S24b: selecting a pure night sky area of ​​the ground-based lunar observation image that does not include the moon as a night sky noise value, and subtracting the night sky noise value from the ground-based lunar observation image; In the formula, represents the ground-based lunar radiance after night sky noise correction, Represents the night sky noise value.

7. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1 is characterized in that: In step S3, the ground-based lunar observation radiance of all pixels within the lunar boundary in the ground-based lunar observation image is integrated and summed to obtain the ground-based observation irradiance. The calculation formula is as follows: ； In the formula, represents the ground-based observation irradiance, represents the lunar radiance observed by meteorological satellites after correction for night sky noise, represents the solid angle of the pixel, and Respectively represent the number of rows and columns of meteorological satellite lunar observation images and ground-based lunar observation images; The meteorological satellite observed lunar radiation brightness of all pixels within the lunar boundary in the meteorological satellite lunar observation image is integrated and summed to obtain the meteorological satellite observed irradiance. The calculation formula is as follows: ； In the formula, represents the irradiance observed by meteorological satellite, Represents the ground-based lunar radiance after correction for night sky noise.

8. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1 is characterized in that: Step S4 specifically includes the following steps: S41: solving geometric parameters of the observation point, the earth, the moon, and the sun at the ground-based and meteorological satellite observation times, inputting the geometric parameters into the ROLO model, and calculating the lunar albedo of the ROLO model corresponding to the ground-based and meteorological satellite observation times; wherein the geometric parameters include the moon phase angle, the longitude of the sun in the lunar coordinate system, the longitude of the observation point in the lunar coordinate system, and the latitude of the observation point in the lunar coordinate system; In the formula, represents the lunar albedo of the ROLO model, Indicates the band, Indicates the moon phase angle, represents the longitude of the sun in the lunar coordinate system, represents the latitude of the observation point in the lunar coordinate system, represents the longitude of the observation point in the lunar coordinate system, , , , , , , , , , , , , Respectively represent coefficient terms; observation points are ground locations or meteorological satellites; S42: Calculate the lunar irradiance of the ROLO model according to the lunar albedo of the ROLO model; In the formula, represents the lunar irradiance of the ROLO model, represents the distance correction factor, It represents the solid angle of the moon relative to the earth at the standard distance. Represents solar radiation illuminance.

9. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 1, characterized in that: In step S5, the calculation formula for the relative deviation between the observed lunar irradiance and the ROLO model lunar irradiance is: In the formula, It represents the relative deviation between the observed lunar irradiance at the k-band and the lunar irradiance of the ROLO model. The k-band selects the common band of the meteorological satellite lunar observation image, the ground-based lunar observation image and the ROLO model, specifically any one of the bands of 470nm, 510nm, 640nm, 855nm, 1610nm and 2255nm. represents the observed lunar irradiance at the k-band, represents the ROLO model lunar irradiance at the k-band.

10. The ROLO model improvement method combining ground-based and meteorological satellite lunar observation data according to claim 9 is characterized in that: In step S5, a polynomial is used to fit the relative deviation of each k-band at different moon phase angles, and the phase angle dependence of the ROLO model at different k-bands and different moon phase angles is corrected to improve the ROLO model; ， ； ， ， In the formula, and They represent the lunar irradiance of the improved ROLO model under negative and positive lunar phase angles, respectively; and They represent the improvement coefficients of the ROLO model under negative and positive moon phase angles, respectively; It represents the solid angle of the moon relative to the earth at standard distance; represents the solar radiation intensity; represents the lunar albedo of the ROLO model, represents the distance correction factor; and Both represent coefficients and are fixed values.

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